Various data analysis functions are available for Spectra objects. These
can be categorized into functions that either return a Spectra object
(with the manipulated data) and functions that directly return the
result from the calculation. For the former category, the data manipulations
are cached in the result object's processing queue and only exectuted
on-the-fly when the respective data gets extracted from the Spectra (see
section The processing queue for more information).
For the second category, the calculations are directly executed and the result, usually one value per spectrum, returned. Generally, to reduce memory demand, a chunk-wise processing of the data is performed.
Usage
applyProcessing(
object,
f = processingChunkFactor(object),
BPPARAM = bpparam(),
...
)
processingLog(x)
scalePeaks(x, by = sum, msLevel. = uniqueMsLevels(x))
# S4 method for class 'Spectra'
addProcessing(object, FUN, ..., spectraVariables = character())
# S4 method for class 'Spectra'
bin(
x,
binSize = 1L,
breaks = NULL,
msLevel. = uniqueMsLevels(x),
FUN = sum,
zero.rm = TRUE
)
# S4 method for class 'Spectra'
containsMz(
object,
mz = numeric(),
tolerance = 0,
ppm = 20,
which = c("any", "all"),
BPPARAM = bpparam()
)
# S4 method for class 'Spectra'
containsNeutralLoss(
object,
neutralLoss = 0,
tolerance = 0,
ppm = 20,
BPPARAM = bpparam()
)
# S4 method for class 'Spectra'
entropy(object, normalized = TRUE)
# S4 method for class 'ANY'
entropy(object, ...)
# S4 method for class 'Spectra'
pickPeaks(
object,
halfWindowSize = 2L,
method = c("MAD", "SuperSmoother"),
snr = 0,
k = 0L,
descending = FALSE,
threshold = 0,
msLevel. = uniqueMsLevels(object),
...
)
# S4 method for class 'Spectra'
replaceIntensitiesBelow(
object,
threshold = min,
value = 0,
msLevel. = uniqueMsLevels(object)
)
# S4 method for class 'Spectra'
reset(object, ...)
# S4 method for class 'Spectra'
smooth(
x,
halfWindowSize = 2L,
method = c("MovingAverage", "WeightedMovingAverage", "SavitzkyGolay"),
msLevel. = uniqueMsLevels(x),
...
)
# S4 method for class 'Spectra'
spectrapply(
object,
FUN,
...,
chunkSize = integer(),
f = factor(),
BPPARAM = SerialParam()
)Arguments
- object
A
Spectraobject.- f
For
spectrapply()andapplyProcessing():factordefining howobjectshould be splitted for eventual parallel processing. Defaults tofactor()forspectrapply()hence the object is not splitted while it defaults tof = processingChunkSize(object)forapplyProcessing()splitting thus the object by default into chunks depending onprocessingChunkSize().- BPPARAM
Parallel setup configuration. See
bpparam()for more information. This is passed directly to thebackendInitialize()method of the MsBackend. See alsoprocessingChunkSize()for additional information on parallel processing.- ...
Additional arguments passed to internal and downstream functions.
- x
A
Spectra.- by
For
scalePeaks(): function to calculate a singlenumericfrom intensity values of a spectrum by which all intensities (of that spectrum) should be divided by. The defaultby = sumwill divide intensities of each spectrum by the sum of intensities of that spectrum.- msLevel.
integerdefining the MS level(s) of the spectra to which the function should be applied (defaults to all MS levels ofobject.- FUN
For
addProcessing(): function to be applied to the peak matrix of each spectrum inobject. Forbin(): function to aggregate intensity values of peaks falling into the same bin. Defaults toFUN = sumthus summing up intensities. Forspectrapply()andchunkapply(): function to be applied to each individual or each chunk ofSpectra.- spectraVariables
For
addProcessing():characterwith additional spectra variables that should be passed along to the function defined withFUN. See function description for details.- binSize
For
bin():numeric(1)defining the size for the m/z bins. Defaults tobinSize = 1.- breaks
For
bin():numericdefining the m/z breakpoints between bins.- zero.rm
For
bin():logical(1)indicating whether to remove bins with zero intensity. Defaults toTRUE, meaning the function will discard bins created with an intensity of 0 to enhance memory efficiency.- mz
For
containsMz():numericwith the m/z value(s) of the mass peaks to check.- tolerance
For
containsMz()andneutralLoss():numeric(1)allowing to define a constant maximal accepted difference between m/z values for peaks to be matched.- ppm
For
containsMz()andneutralLoss():numeric(1)defining a relative, m/z-dependent, maximal accepted difference between m/z values for peaks to be matched.- which
For
containsMz(): either"any"or"all"defining whether any (the default) or all providedmzhave to be present in the spectrum.- neutralLoss
for
containsNeutralLoss():numeric(1)defining the value which should be subtracted from the spectrum's precursor m/z.- normalized
for
entropy():logical(1)whether the normalized entropy should be calculated (default). See alsonentropy()for details.- halfWindowSize
For
pickPeaks():integer(1), used in the identification of the mass peaks: a local maximum has to be the maximum in the window from(i - halfWindowSize):(i + halfWindowSize). Forsmooth():integer(1), used in the smoothing algorithm, the window reaches from(i - halfWindowSize):(i + halfWindowSize).- method
For
pickPeaks():character(1), the noise estimators that should be used, currently the the Median Absolute Deviation (method = "MAD") and Friedman's Super Smoother (method = "SuperSmoother") are supported. Forsmooth():character(1), the smoothing function that should be used, currently, the Moving-Average- (method = "MovingAverage"), Weighted-Moving-Average- (method = "WeightedMovingAverage"), Savitzky-Golay-Smoothing (method = "SavitzkyGolay") are supported.- snr
For
pickPeaks():double(1)defining the Signal-to-Noise-Ratio. The intensity of a local maximum has to be higher thansnr * noiseto be considered as peak.- k
For
pickPeaks():integer(1), number of values left and right of the peak that should be considered in the weighted mean calculation.- descending
For
pickPeaks():logical, ifTRUEjust values betwee the nearest valleys around the peak centroids are used.- threshold
For
pickPeaks(): anumeric(1)defining the proportion of the maximal peak intensity. Only values above the threshold are used for the weighted mean calculation. ForreplaceIntensitiesBelow(): anumeric(1)defining the threshold or afunctionto calculate the threshold for each spectrum on its intensity values. Defaults tothreshold = min.- value
For
replaceIntensitiesBelow():numeric(1)defining the value with which intensities should be replaced with.- chunkSize
For
spectrapply(): size of the chunks into which theSpectrashould be split. This parameter overrides parametersfandBPPARAM.
Data analysis methods returning a Spectra
The methods listed here return a Spectra object as a result.
addProcessing(): adds an arbitrary function that should be applied to the peaks matrix of every spectrum inobject. The function (can be passed with parameterFUN) is expected to take a peaks matrix as input and to return a peaks matrix. A peaks matrix is a numeric matrix with two columns, the first containing the m/z values of the peaks and the second the corresponding intensities. The function has to have...in its definition. Additional arguments can be passed with.... With parameterspectraVariablesit is possible to define additional spectra variables fromobjectthat should be passed to the functionFUN. These will be passed by their name (e.g. specifyingspectraVariables = "precursorMz"will pass the spectra's precursor m/z as a parameter namedprecursorMzto the function. The only exception is the spectra's MS level, these will be passed to the function as a parameter calledspectrumMsLevel(i.e. withspectraVariables = "msLevel"the MS levels of each spectrum will be submitted to the function as a parameter calledspectrumMsLevel). Examples are provided in the package vignette.bin(): aggregates individual spectra into discrete (m/z) bins. Binning is performed only on spectra of the specified MS level(s) (parametermsLevel, by default all MS levels ofx). The bins can be defined with parameterbreakswhich by default are equally sized bins, with size being defined by parameterbinSize, from the minimal to the maximal m/z of all spectra (of MS levelmsLevel) withinx. The same bins are used for all spectra inx. All intensity values for peaks falling into the same bin are aggregated using the function provided with parameterFUN(defaults toFUN = sum, i.e. all intensities are summed up). Note that the binning operation is applied to the peak data on-the-fly upon data access and it is possible to revert the operation with thereset()function (see description ofreset()below).countIdentifications: counts the number of identifications each scan has led to. SeecountIdentifications()for more details.pickPeaks(): picks peaks on individual spectra using a moving window-based approach (window size =2 * halfWindowSize). For noisy spectra there are currently two different noise estimators available, the Median Absolute Deviation (method = "MAD") and Friedman's Super Smoother (method = "SuperSmoother"), as implemented in theMsCoreUtils::noise(). The method supports also to optionally refine the m/z value of the identified centroids by considering data points that belong (most likely) to the same mass peak. Therefore the m/z value is calculated as an intensity weighted average of the m/z values within the peak region. The peak region is defined as the m/z values (and their respective intensities) of the2 * kclosest signals to the centroid or the closest valleys (descending = TRUE) in the2 * kregion. For the latter thekhas to be chosen general larger. SeeMsCoreUtils::refineCentroids()for details. If the ratio of the signal to the highest intensity of the peak is belowthresholdit will be ignored for the weighted average.replaceIntensitiesBelow(): replaces intensities below a specified threshold with the providedvalue. Parameterthresholdcan be either a single numeric value or a function which is applied to all non-NAintensities of each spectrum to determine a threshold value for each spectrum. The default isthreshold = minwhich replaces all values which are <= the minimum intensity in a spectrum withvalue(the default forvalueis0). Note that the function specified withthresholdis expected to have a parameterna.rmsincena.rm = TRUEwill be passed to the function. If the spectrum is in profile mode, ranges of successive non-0 peaks <=thresholdare set to 0. ParametermsLevel.allows to apply this to only spectra of certain MS level(s).scalePeaks(): scales intensities of peaks within each spectrum depending on parameterby. Withby = sum(the default) peak intensities are divided by the sum of peak intensities within each spectrum. The sum of intensities is thus 1 for each spectrum after scaling. ParametermsLevel.allows to apply the scaling of spectra of a certain MS level. By default (msLevel. = uniqueMsLevels(x)) intensities for all spectra will be scaled.smooth(): smooths individual spectra using a moving window-based approach (window size =2 * halfWindowSize). Currently, the Moving-Average- (method = "MovingAverage"), Weighted-Moving-Average- (method = "WeightedMovingAverage"), weights depending on the distance of the center and calculated1/2^(-halfWindowSize:halfWindowSize)) and Savitzky-Golay-Smoothing (method = "SavitzkyGolay") are supported. For details how to choose the correcthalfWindowSizeplease seeMsCoreUtils::smooth().
Data analysis methods returning the result from the calculation
The functions listed in this section return immediately the result from the calculation. To reduce memory demand (and allow parallel processing) the calculations a chunk-wise processing is generally performed.
chunkapply(): apply an arbitrary function to chunks of spectra. Seechunkapply()for details and examples.containsMz(): checks for each of the spectra whether they contain mass peaks with an m/z equal tomz(given acceptable difference as defined by parameterstoleranceandppm- seecommon()for details). Parameterwhichallows to define whether any (which = "any", the default) or all (which = "all") of themzhave to match. The function returnsNAifmzis of length 0 or isNA.containsNeutralLoss(): checks for each spectrum inobjectif it has a peak with an m/z value equal to its precursor m/z -neutralLoss(given acceptable difference as defined by parameterstoleranceandppm). ReturnsNAfor MS1 spectra (or spectra without a precursor m/z).entropy(): calculates the entropy of each spectra based on the metrics suggested by Li et al. (https://doi.org/10.1038/s41592-021-01331-z). See alsonentropy()in the MsCoreUtils package for details.estimatePrecursorIntensity(): defines the precursor intensities for MS2 spectra using the intensity of the matching MS1 peak from the closest MS1 spectrum (i.e. the last MS1 spectrum measured before the respective MS2 spectrum). Withmethod = "interpolation"it is also possible to calculate the precursor intensity based on an interpolation of intensity values (and retention times) of the matching MS1 peaks from the previous and next MS1 spectrum. SeeestimatePrecursorIntensity()for examples and more details.estimatePrecursorMz(): for DDA data: allows to estimate a fragment spectra's precursor m/z based on the reported precursor m/z and the data from the previous MS1 spectrum. SeeestimatePrecursorMz()for details.neutralLoss(): calculates neutral loss spectra for fragment spectra. SeeneutralLoss()for detailed documentation.spectrapply(): applies a given function to each individual spectrum or sets of aSpectraobject. By default, theSpectrais split into individual spectra (i.e.Spectraof length 1) and the functionFUNis applied to each of them. An alternative splitting can be defined with parameterf. Parameters forFUNcan be passed using.... The returned result and its order depend on the functionFUNand howobjectis split (hence onf, if provided). Parallel processing is supported and can be configured with parameterBPPARAM, is however only suggested for computational intenseFUN. As an alternative to the (eventual parallel) processing of the fullSpectra,spectrapply()supports also a chunk-wise processing. For this, parameterchunkSizeneeds to be specified.objectis then split into chunks of sizechunkSizewhich are then (stepwise) processed byFUN. This guarantees a lower memory demand (especially for on-disk backends) since only the data for one chunk needs to be loaded into memory in each iteration. Note that by specifyingchunkSize, parametersfandBPPARAMwill be ignored. See alsochunkapply()above or examples below for details on chunk-wise processing.
The processing queue
Operations that modify mass peak data, i.e. the m/z and intensity values of
a Spectra are generally not applied immediately to the data but are
cached within the object's processing queue. These operations are then
applied to the data only upon request, for example when m/z and/or
intensity values are extracted. This lazy execution guarantees that the
same functionality can be applied to any Spectra object, regardless of
the type of backend that is used. Thus, data manipulation operations can
also be applied to data that is read only. As a side effect, this enables
also to undo operations using the reset() function.
Functions related to the processing queue are:
applyProcessing(): forSpectraobjects that use a writeable backend only: apply all steps from the lazy processing queue to the peak data and write it back to the data storage. Parameterfallows to specify howobjectshould be split for parallel processing. This should either be equal to thedataStorage, orf = rep(1, length(object))to disable parallel processing alltogether. Other partitionings might result in errors (especially if aMsBackendHdf5Peaksbackend is used).processingLog(): returns acharactervector with the processing log messages.reset(): restores the data to its original state (as much as possible): removes any processing steps from the lazy processing queue and callsreset()on the backend which, depending on the backend, can also undo e.g. data filtering operations. Note that areset*(call afterapplyProcessing()will not have any effect. See examples below for more information.
See also
compareSpectra()for calculation of spectra similarity scores.processingChunkSize()for information on parallel and chunk-wise data processing.Spectra for a general description of the
Spectraobject.
Author
Sebastian Gibb, Johannes Rainer, Laurent Gatto, Philippine Louail, Nir Shahaf, Mar Garcia-Aloy
Examples
## Load a `Spectra` object with LC-MS/MS data.
fl <- system.file("TripleTOF-SWATH", "PestMix1_DDA.mzML",
package = "msdata")
sps_dda <- Spectra(fl)
sps_dda
#> MSn data (Spectra) with 7602 spectra in a MsBackendMzR backend:
#> msLevel rtime scanIndex
#> <integer> <numeric> <integer>
#> 1 1 0.231 1
#> 2 1 0.351 2
#> 3 1 0.471 3
#> 4 1 0.591 4
#> 5 1 0.711 5
#> ... ... ... ...
#> 7598 1 899.491 7598
#> 7599 1 899.613 7599
#> 7600 1 899.747 7600
#> 7601 1 899.872 7601
#> 7602 1 899.993 7602
#> ... 33 more variables/columns.
#>
#> file(s):
#> PestMix1_DDA.mzML
## -------- FUNCTIONS RETURNING A SPECTRA --------
## Replace peak intensities below 40 with a value of 1
sps_mod <- replaceIntensitiesBelow(sps_dda, threshold = 20, value = 1)
sps_mod
#> MSn data (Spectra) with 7602 spectra in a MsBackendMzR backend:
#> msLevel rtime scanIndex
#> <integer> <numeric> <integer>
#> 1 1 0.231 1
#> 2 1 0.351 2
#> 3 1 0.471 3
#> 4 1 0.591 4
#> 5 1 0.711 5
#> ... ... ... ...
#> 7598 1 899.491 7598
#> 7599 1 899.613 7599
#> 7600 1 899.747 7600
#> 7601 1 899.872 7601
#> 7602 1 899.993 7602
#> ... 33 more variables/columns.
#>
#> file(s):
#> PestMix1_DDA.mzML
#> Lazy evaluation queue: 1 processing step(s)
#> Processing:
#> Signal <= 20 in MS level(s) 1, 2 set to 0 [Fri Oct 25 07:13:02 2024]
## Get the intensities of the first spectrum before and after the
## operation
intensity(sps_dda[1])
#> NumericList of length 1
#> [[1]] 0.0307632219046354 0.163443520665169 ... 0.507792055606842
intensity(sps_mod[1])
#> NumericList of length 1
#> [[1]] 1 1 1 1 1 1 88.7230834960938 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1 1 1
## Remove all peaks with an intensity below 5.
sps_mod <- filterIntensity(sps_dda, intensity = c(5, Inf))
intensity(sps_mod)
#> NumericList of length 7602
#> [[1]] 88.7230834960938 6.28782653808594
#> [[2]] 90.9452285766602 6.51183843612671
#> [[3]] 117.253837585449 6.71664762496948
#> [[4]] 75.9008331298828 8.15607166290283
#> [[5]] 63.7168960571289 8.26729297637939 6.08404684066772
#> [[6]] 81.0469970703125 6.34799957275391
#> [[7]] 68.0150375366211 7.7239465713501 5.05049753189087
#> [[8]] 84.4253540039062 7.3393931388855
#> [[9]] intensity=111.353569030762
#> [[10]] 84.0783767700195 8.68693542480469 6.3865818977356
#> ...
#> <7592 more elements>
## In addition it is possible to pass a function to `filterIntensity()`: in
## the example below we want to keep only peaks that have an intensity which
## is larger than one third of the maximal peak intensity in that spectrum.
keep_peaks <- function(x, prop = 3) {
x > max(x, na.rm = TRUE) / prop
}
sps_mod <- filterIntensity(sps_dda, intensity = keep_peaks)
intensity(sps_mod)
#> NumericList of length 7602
#> [[1]] intensity=88.7230834960938
#> [[2]] intensity=90.9452285766602
#> [[3]] intensity=117.253837585449
#> [[4]] intensity=75.9008331298828
#> [[5]] intensity=63.7168960571289
#> [[6]] intensity=81.0469970703125
#> [[7]] intensity=68.0150375366211
#> [[8]] intensity=84.4253540039062
#> [[9]] intensity=111.353569030762
#> [[10]] intensity=84.0783767700195
#> ...
#> <7592 more elements>
## We can also change the proportion by simply passing the `prop` parameter
## to the function. To keep only peaks that have an intensity which is
## larger than half of the maximum intensity:
sps_mod <- filterIntensity(sps_dda, intensity = keep_peaks, prop = 2)
intensity(sps_mod)
#> NumericList of length 7602
#> [[1]] intensity=88.7230834960938
#> [[2]] intensity=90.9452285766602
#> [[3]] intensity=117.253837585449
#> [[4]] intensity=75.9008331298828
#> [[5]] intensity=63.7168960571289
#> [[6]] intensity=81.0469970703125
#> [[7]] intensity=68.0150375366211
#> [[8]] intensity=84.4253540039062
#> [[9]] intensity=111.353569030762
#> [[10]] intensity=84.0783767700195
#> ...
#> <7592 more elements>
## With the `scalePeaks()` function we can alternatively scale the
## intensities of mass peaks per spectrum to relative intensities. This
## is specifically useful for fragment (MS2) spectra. We below thus
## scale the intensities per spectrum by the total sum of intensities
## (such that the sum of all intensities per spectrum is 1).
## Below we scale the intensities of all MS2 spectra in our data set.
sps_mod <- scalePeaks(sps_dda, msLevel = 2L)
## MS1 spectra were not affected
sps_mod |>
filterMsLevel(1L) |>
intensity()
#> NumericList of length 4627
#> [[1]] 0.0307632219046354 0.163443520665169 ... 0.507792055606842
#> [[2]] 0.124385602772236 0.306980639696121 ... 0.752154946327209
#> [[3]] 0.140656530857086 0.194816112518311 ... 0.455461025238037
#> [[4]] 0.0389336571097374 0.357547700405121 ... 0.478326231241226
#> [[5]] 0.124386593699455 0.054143700748682 ... 0.251276850700378
#> [[6]] 0.0940475389361382 0.247442871332169 ... 0.10762557387352
#> [[7]] 0.0940475389361382 0.17366424202919 ... 0.355754435062408
#> [[8]] 0.0389340370893478 0.116887390613556 ... 0.40066459774971
#> [[9]] 0.0307626128196716 0.0626986622810364 ... 0.359330594539642
#> [[10]] 0.217585012316704 0.333028763532639 ... 0.297511428594589
#> ...
#> <4617 more elements>
## Intensities of MS2 spectra were scaled
sps_mod |>
filterMsLevel(2L) |>
intensity()
#> NumericList of length 2975
#> [[1]] 0.237546288845328 0.478541149367473 0.283912561787199
#> [[2]] 0.137308998224213 0.0223434223564616 0.840347579419325
#> [[3]] 0.406266967176935 0.53879813438082 0.0549348984422444
#> [[4]] 0.280229322504475 0.381273738198204 0.338496939297321
#> [[5]] intensity=1
#> [[6]] 0.104432137389865 0.0285431704350093 ... 0.0402209771615151
#> [[7]] 0.0386159813711874 0.346130168120392 ... 0.228058722675167
#> [[8]] numeric(0)
#> [[9]] 0.210018635385678 0.216171000981623 ... 0.0462104568765319
#> [[10]] 0.0555197043853142 0.722227534151142 0.166680504527357 0.0555722569361873
#> ...
#> <2965 more elements>
## Since data manipulation operations are by default not directly applied to
## the data but only cached in the internal processing queue, it is also
## possible to remove these data manipulations with the `reset()` function:
tmp <- reset(sps_mod)
tmp
#> MSn data (Spectra) with 7602 spectra in a MsBackendMzR backend:
#> msLevel rtime scanIndex
#> <integer> <numeric> <integer>
#> 1 1 0.231 1
#> 2 1 0.351 2
#> 3 1 0.471 3
#> 4 1 0.591 4
#> 5 1 0.711 5
#> ... ... ... ...
#> 7598 1 899.491 7598
#> 7599 1 899.613 7599
#> 7600 1 899.747 7600
#> 7601 1 899.872 7601
#> 7602 1 899.993 7602
#> ... 33 more variables/columns.
#>
#> file(s):
#> PestMix1_DDA.mzML
#> Processing:
#> Scale peak intensities in spectra of MS level(s) 2. [Fri Oct 25 07:13:05 2024]
#> Reset object. [Fri Oct 25 07:13:06 2024]
lengths(sps_dda) |> head()
#> [1] 223 211 227 210 220 228
lengths(sps_mod) |> head()
#> [1] 223 211 227 210 220 228
lengths(tmp) |> head()
#> [1] 223 211 227 210 220 228
## Data manipulation operations cached in the processing queue can also be
## applied to the mass peaks data with the `applyProcessing()` function, if
## the `Spectra` uses a backend that supports that (i.e. allows replacing
## the mass peaks data). Below we first change the backend to a
## `MsBackendMemory()` and then use the `applyProcessing()` to modify the
## mass peaks data
sps_dda <- setBackend(sps_dda, MsBackendMemory())
sps_mod <- filterIntensity(sps_dda, intensity = c(5, Inf))
sps_mod <- applyProcessing(sps_mod)
sps_mod
#> MSn data (Spectra) with 7602 spectra in a MsBackendMemory backend:
#> msLevel rtime scanIndex
#> <integer> <numeric> <integer>
#> 1 1 0.231 1
#> 2 1 0.351 2
#> 3 1 0.471 3
#> 4 1 0.591 4
#> 5 1 0.711 5
#> ... ... ... ...
#> 7598 1 899.491 7598
#> 7599 1 899.613 7599
#> 7600 1 899.747 7600
#> 7601 1 899.872 7601
#> 7602 1 899.993 7602
#> ... 33 more variables/columns.
#> Processing:
#> Switch backend from MsBackendMzR to MsBackendMemory [Fri Oct 25 07:13:09 2024]
#> Remove peaks with intensities outside [5, Inf] in spectra of MS level(s) 1, 2. [Fri Oct 25 07:13:09 2024]
#> Applied processing queue with 1 steps [Fri Oct 25 07:13:09 2024]
## While we can't *undo* this filtering operation now using the `reset()`
## function, accessing the data would now be faster, because the operation
## does no longer to be applied to the original data before returning to the
## user.
## -------- FUNCTIONS RETURNING THE RESULT --------
## With the `spectrapply()` function it is possible to apply an
## arbitrary function to each spectrum in a Spectra.
## In the example below we calculate the mean intensity for each spectrum
## in a subset of the sciex_im data. Note that we can access all variables
## of each individual spectrum either with the `$` operator or the
## corresponding method.
res <- spectrapply(sps_dda[1:20], FUN = function(x) mean(x$intensity[[1]]))
head(res)
#> $`1`
#> [1] 0.9623952
#>
#> $`2`
#> [1] 1.016938
#>
#> $`3`
#> [1] 1.056198
#>
#> $`4`
#> [1] 0.9000712
#>
#> $`5`
#> [1] 0.8756414
#>
#> $`6`
#> [1] 0.9105883
#>
## As an alternative, applying a function `FUN` to a `Spectra` can be
## performed *chunk-wise*. The advantage of this is, that only the data for
## one chunk at a time needs to be loaded into memory reducing the memory
## demand. This type of processing can be performed by specifying the size
## of the chunks (i.e. number of spectra per chunk) with the `chunkSize`
## parameter
spectrapply(sps_dda[1:20], lengths, chunkSize = 5L)
#> [1] 223 211 227 210 220 228 201 215 214 211 208 217 219 190 201 195 196 208 233
#> [20] 224
## Precursor intensity estimation. Some manufacturers don't report the
## precursor intensity for MS2 spectra:
sps_dda |>
filterMsLevel(2L) |>
precursorIntensity()
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [556] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [630] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [667] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [704] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [778] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [815] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [926] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [963] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1000] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1037] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1074] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1111] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1148] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1222] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1259] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1296] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1333] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1370] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1407] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1444] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1518] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1555] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1592] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1629] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1666] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1703] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1740] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1814] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1851] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1888] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1962] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1999] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2036] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2073] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2110] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2147] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2184] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2258] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2295] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2332] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2406] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2443] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2480] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2517] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2554] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2591] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2628] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2702] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2739] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2776] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2813] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2850] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2887] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2924] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2961] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## This intensity can however be estimated from the previously measured
## MS1 scan with the `estimatePrecursorIntensity()` function:
pi <- estimatePrecursorIntensity(sps_dda)
## This function returned the result as a `numeric` vector with one
## value per spectrum:
pi
#> [1] NA NA NA NA NA
#> [6] NA NA NA NA NA
#> [11] NA NA NA NA NA
#> [16] NA NA NA NA NA
#> [21] NA NA NA NA NA
#> [26] NA NA NA NA NA
#> [31] NA NA NA NA NA
#> [36] NA NA NA NA NA
#> [41] NA NA NA NA NA
#> [46] NA NA NA NA NA
#> [51] NA NA NA NA NA
#> [56] NA NA 0.2562894 NA NA
#> [61] NA NA NA NA NA
#> [66] NA NA NA NA NA
#> [71] NA NA NA NA NA
#> [76] NA NA NA NA NA
#> [81] NA NA NA NA NA
#> [86] NA NA NA NA NA
#> [91] NA NA NA NA NA
#> [96] NA NA NA NA NA
#> [101] NA NA NA NA NA
#> [106] 0.6533012 NA NA NA NA
#> [111] NA NA NA NA NA
#> [116] NA NA NA NA NA
#> [121] NA NA NA NA NA
#> [126] NA NA NA NA NA
#> [131] NA NA NA NA NA
#> [136] NA NA NA NA NA
#> [141] NA NA NA NA NA
#> [146] NA NA NA NA NA
#> [151] NA NA NA NA NA
#> [156] NA NA NA NA NA
#> [161] NA NA NA NA NA
#> [166] NA NA NA NA NA
#> [171] NA NA NA NA NA
#> [176] NA NA NA NA NA
#> [181] NA NA NA NA NA
#> [186] 0.9115737 NA NA NA NA
#> [191] NA NA NA NA NA
#> [196] NA NA NA NA NA
#> [201] 2.0881963 NA NA NA NA
#> [206] NA NA NA NA NA
#> [211] NA NA NA NA NA
#> [216] NA NA NA 0.4161463 NA
#> [221] NA NA NA NA NA
#> [226] NA NA NA NA NA
#> [231] NA NA NA NA NA
#> [236] NA NA NA NA NA
#> [241] NA 1.6393325 NA NA NA
#> [246] NA NA NA NA NA
#> [251] NA NA NA NA NA
#> [256] NA NA NA NA NA
#> [261] NA 1.9403350 0.2138104 NA NA
#> [266] NA NA NA NA NA
#> [271] NA NA NA NA NA
#> [276] NA 2.2869656 NA NA NA
#> [281] NA NA NA NA NA
#> [286] NA NA NA NA 0.3581794
#> [291] NA NA NA NA NA
#> [296] NA NA 1.2965702 NA NA
#> [301] NA NA NA NA NA
#> [306] NA NA NA NA NA
#> [311] NA NA NA NA NA
#> [316] NA NA NA NA NA
#> [321] NA NA NA NA NA
#> [326] NA 7.1932635 NA 1.7952728 NA
#> [331] NA NA 1.7991002 4.6187558 NA
#> [336] 2.3082116 NA NA 2.1572299 7.6883049
#> [341] NA NA NA NA NA
#> [346] NA NA NA NA NA
#> [351] NA NA NA NA 0.6671617
#> [356] NA NA NA NA NA
#> [361] NA NA NA NA NA
#> [366] NA NA NA 1.3682123 1.7227559
#> [371] NA NA 0.8182324 NA NA
#> [376] NA 1.3030148 NA NA NA
#> [381] NA NA NA NA NA
#> [386] 0.4186687 NA NA NA NA
#> [391] NA NA NA NA NA
#> [396] NA NA NA NA NA
#> [401] NA NA NA NA NA
#> [406] NA NA NA NA NA
#> [411] NA NA NA NA NA
#> [416] NA NA NA NA NA
#> [421] 0.4893252 NA NA NA NA
#> [426] NA NA NA NA NA
#> [431] NA NA NA NA NA
#> [436] NA NA NA NA NA
#> [441] NA NA NA NA NA
#> [446] NA NA NA NA NA
#> [451] NA NA NA NA NA
#> [456] NA NA NA NA NA
#> [461] NA 0.6470237 NA NA NA
#> [466] NA NA NA NA NA
#> [471] NA NA NA NA NA
#> [476] NA NA NA NA NA
#> [481] NA NA NA NA NA
#> [486] NA NA NA NA NA
#> [491] NA NA NA NA NA
#> [496] NA NA NA NA NA
#> [501] NA NA NA NA NA
#> [506] NA NA NA NA NA
#> [511] NA NA NA NA NA
#> [516] NA NA NA NA NA
#> [521] NA NA NA NA NA
#> [526] NA NA NA NA NA
#> [531] NA NA NA NA NA
#> [536] NA NA NA NA NA
#> [541] NA NA NA NA NA
#> [546] NA NA NA 1.2660308 NA
#> [551] NA NA NA NA NA
#> [556] NA NA NA NA 0.3448766
#> [561] NA NA NA NA NA
#> [566] NA NA NA NA NA
#> [571] NA NA NA NA NA
#> [576] NA NA NA NA NA
#> [581] NA NA NA NA NA
#> [586] NA NA NA NA NA
#> [591] NA NA NA NA NA
#> [596] NA NA NA NA NA
#> [601] NA NA NA NA NA
#> [606] 1.0501561 NA NA NA 1.0818608
#> [611] NA 0.1523102 NA 1.6358998 NA
#> [616] NA 1.0994979 NA 2.0477426 0.9073558
#> [621] NA NA 0.5244763 NA 2.2316427
#> [626] NA 2.6152663 NA NA NA
#> [631] 1.7466167 1.7643924 NA 1.0328368 2.0097375
#> [636] NA NA NA NA NA
#> [641] NA NA NA NA NA
#> [646] NA NA NA NA NA
#> [651] NA NA NA NA NA
#> [656] 0.3149102 NA NA NA NA
#> [661] NA NA NA NA NA
#> [666] NA NA NA NA NA
#> [671] NA 0.5953222 NA NA NA
#> [676] 0.8350294 NA 1.0704968 NA NA
#> [681] NA NA NA 1.3077880 NA
#> [686] NA NA 1.5747164 NA NA
#> [691] NA NA NA NA NA
#> [696] NA NA NA NA NA
#> [701] NA NA NA NA NA
#> [706] NA 0.3147899 NA NA NA
#> [711] NA NA NA NA NA
#> [716] NA NA NA NA NA
#> [721] NA 0.4084390 NA NA NA
#> [726] NA NA NA NA NA
#> [731] NA NA NA NA NA
#> [736] NA NA NA NA 0.4539037
#> [741] NA 0.9779218 NA 2.4162381 NA
#> [746] 6.3833795 NA 2.8862731 1.2910393 10.6581869
#> [751] NA 1.4951602 3.7959058 2.8722730 NA
#> [756] 2.4529448 5.3233647 3.5772564 4.4303422 NA
#> [761] 2.9447591 2.1176453 5.0967083 3.3420610 NA
#> [766] 2.4258153 0.8650878 7.3779054 6.8362355 NA
#> [771] 2.1940181 0.7469152 2.0079441 6.3011317 10.4023485
#> [776] 3.3065796 NA 2.3813672 1.3428380 4.0137057
#> [781] 1.4098256 2.4288177 1.3962808 7.4319925 NA
#> [786] 20.5047512 0.9047846 1.3858606 2.8306339 3.7321482
#> [791] 2.8828564 5.5602937 NA 115.1193542 2.0473588
#> [796] 4.9510341 NA 118.5648346 2.0066054 2.9266124
#> [801] 2.4156189 4.2713437 NA NA NA
#> [806] NA NA NA 0.2551613 NA
#> [811] 2.6899714 5.8423100 11.0782356 NA NA
#> [816] 0.8772338 NA 2.3091309 NA NA
#> [821] NA NA NA NA NA
#> [826] NA NA 3.4256361 NA NA
#> [831] NA NA 3.7716985 NA NA
#> [836] NA NA NA NA NA
#> [841] NA NA NA NA NA
#> [846] NA NA NA NA NA
#> [851] NA NA NA NA NA
#> [856] NA NA NA NA NA
#> [861] NA NA NA NA NA
#> [866] NA NA NA 1.5463245 NA
#> [871] NA NA NA NA NA
#> [876] NA NA NA 1.8332181 NA
#> [881] NA NA NA NA NA
#> [886] NA NA NA NA NA
#> [891] NA NA NA NA NA
#> [896] NA NA NA NA NA
#> [901] NA NA NA NA NA
#> [906] NA NA NA NA NA
#> [911] 1.1230040 3.6158812 NA NA NA
#> [916] NA NA NA 1.1808953 NA
#> [921] NA NA NA NA NA
#> [926] 1.2916937 NA NA NA NA
#> [931] NA NA NA NA NA
#> [936] NA 3.6537952 NA NA NA
#> [941] NA NA NA NA NA
#> [946] 1.9253193 NA NA 6.6556292 NA
#> [951] NA NA NA NA NA
#> [956] NA NA NA NA NA
#> [961] NA NA NA NA NA
#> [966] NA NA NA NA NA
#> [971] NA NA NA NA NA
#> [976] NA NA NA NA NA
#> [981] NA 2.8096755 0.4722714 NA NA
#> [986] NA 1.5738993 NA 3.1981614 NA
#> [991] 2.3436296 NA 1.1402670 NA 4.2488189
#> [996] 5.5579276 1.3360999 NA 1.4311922 165.9235382
#> [1001] 1.0471303 2.2803576 2.3772638 1.4516939 2.6042576
#> [1006] NA 1.4513292 110.1937103 1.6985731 NA
#> [1011] 7.4143949 2.9037418 4.3727541 NA 2.5551207
#> [1016] 3.4643288 3.0462031 5.5572038 5.8211880 3.4240117
#> [1021] 3.9230976 NA 326.0313110 2.8209546 4.4502177
#> [1026] 45.2657852 2.6507335 2.4535000 5.7406173 5.1763091
#> [1031] NA 1.5400567 1.0795214 1.5230681 3.4662752
#> [1036] 4.3179688 NA 0.7036181 NA NA
#> [1041] NA 3.9843678 NA 3.7730997 NA
#> [1046] NA NA 0.8382843 NA NA
#> [1051] NA NA 0.5566598 NA NA
#> [1056] NA NA NA NA NA
#> [1061] NA 0.8787179 NA NA 0.4405660
#> [1066] NA NA NA NA NA
#> [1071] NA NA NA NA NA
#> [1076] NA NA NA NA NA
#> [1081] NA NA NA NA NA
#> [1086] NA NA 2.2968674 NA 0.4199017
#> [1091] NA NA NA NA NA
#> [1096] NA NA NA NA NA
#> [1101] NA NA NA NA NA
#> [1106] NA NA NA NA NA
#> [1111] NA NA NA NA NA
#> [1116] NA NA NA 0.9166892 NA
#> [1121] NA NA NA 3.8255417 NA
#> [1126] NA NA NA NA NA
#> [1131] NA NA NA NA NA
#> [1136] NA NA NA NA NA
#> [1141] NA NA NA NA NA
#> [1146] NA NA NA NA NA
#> [1151] NA 0.5752081 NA NA NA
#> [1156] NA NA NA NA NA
#> [1161] NA NA NA NA NA
#> [1166] NA NA NA NA NA
#> [1171] NA NA NA NA NA
#> [1176] NA NA NA NA NA
#> [1181] NA NA NA NA NA
#> [1186] NA NA NA 0.5180702 NA
#> [1191] NA NA NA NA NA
#> [1196] NA NA NA NA NA
#> [1201] NA NA NA NA 1.5857564
#> [1206] NA NA 1.6556677 NA NA
#> [1211] NA NA NA NA NA
#> [1216] NA NA NA NA NA
#> [1221] NA NA NA NA 0.5855740
#> [1226] 1.0657172 NA NA NA NA
#> [1231] NA NA NA NA NA
#> [1236] 0.4307829 NA NA NA NA
#> [1241] NA NA NA NA NA
#> [1246] NA NA NA NA NA
#> [1251] 1.3963100 NA NA NA NA
#> [1256] NA NA NA NA NA
#> [1261] NA NA NA NA NA
#> [1266] NA NA NA NA NA
#> [1271] NA NA NA NA 2.4678311
#> [1276] NA NA NA NA NA
#> [1281] NA NA NA NA 0.9768236
#> [1286] NA NA 1.0707787 NA NA
#> [1291] NA NA NA NA NA
#> [1296] 0.8175089 1.4743868 NA NA NA
#> [1301] NA NA NA NA NA
#> [1306] NA NA NA NA NA
#> [1311] NA 5.0737371 0.5766439 NA NA
#> [1316] NA NA NA NA NA
#> [1321] NA NA NA NA NA
#> [1326] NA NA NA NA NA
#> [1331] NA NA NA NA NA
#> [1336] NA NA NA NA NA
#> [1341] NA NA NA NA NA
#> [1346] NA 1.4214748 NA NA NA
#> [1351] NA 0.2014372 2.2585330 NA NA
#> [1356] 0.7659990 NA NA NA NA
#> [1361] 0.6345024 NA 1.1232179 1.2145635 NA
#> [1366] NA NA NA NA NA
#> [1371] NA NA NA NA NA
#> [1376] NA NA NA NA NA
#> [1381] NA 10.4214382 NA NA NA
#> [1386] NA NA 1.5072360 NA NA
#> [1391] NA 1.6478479 NA NA NA
#> [1396] NA NA 2.0655625 NA NA
#> [1401] NA NA NA NA NA
#> [1406] NA NA NA NA NA
#> [1411] NA NA NA 0.5699065 NA
#> [1416] NA NA NA NA NA
#> [1421] NA NA NA NA 1.0214655
#> [1426] NA 0.6936538 NA NA NA
#> [1431] NA NA NA NA NA
#> [1436] NA NA NA NA NA
#> [1441] NA NA NA NA NA
#> [1446] NA NA NA NA NA
#> [1451] NA NA NA NA NA
#> [1456] NA NA NA NA 2.5300288
#> [1461] NA NA NA NA NA
#> [1466] NA NA NA NA NA
#> [1471] 0.5699503 NA NA NA 0.4178057
#> [1476] NA NA NA NA NA
#> [1481] NA NA NA NA NA
#> [1486] NA 2.3085105 7.4867916 NA NA
#> [1491] NA NA NA NA NA
#> [1496] NA 2.4417286 NA NA NA
#> [1501] NA 3.0711961 2.6359253 1.0029373 NA
#> [1506] NA NA NA 0.3156207 3.3271339
#> [1511] NA NA NA 0.7859326 0.9116367
#> [1516] NA NA 2.2751508 NA 3.6905310
#> [1521] 1.7462349 NA NA NA NA
#> [1526] NA NA NA 0.6127930 NA
#> [1531] 0.6677385 NA NA NA 0.3307993
#> [1536] NA NA NA NA NA
#> [1541] NA NA NA NA NA
#> [1546] NA 2.4790754 NA NA NA
#> [1551] NA NA NA NA NA
#> [1556] NA NA NA NA NA
#> [1561] NA NA NA NA NA
#> [1566] NA NA NA NA 0.9691072
#> [1571] NA NA NA NA NA
#> [1576] NA NA NA NA NA
#> [1581] NA NA NA NA NA
#> [1586] NA NA 3.0772917 NA NA
#> [1591] NA 0.3885723 NA 0.3215049 1.6329483
#> [1596] 4.4057989 NA NA NA 0.4121964
#> [1601] NA NA NA 0.8756598 NA
#> [1606] 1.1511103 NA NA NA NA
#> [1611] NA NA NA NA NA
#> [1616] NA NA NA 1.2199212 2.9056976
#> [1621] NA NA NA NA NA
#> [1626] NA NA NA NA 0.9633790
#> [1631] NA 1.1713226 2.6179757 NA NA
#> [1636] NA NA 7.3096509 NA 4.6009355
#> [1641] NA NA NA NA 0.6218465
#> [1646] NA 4.1894031 NA 10.0505095 NA
#> [1651] 12.7552290 10.4093504 NA NA NA
#> [1656] NA NA NA NA NA
#> [1661] NA NA NA 2.2360206 NA
#> [1666] NA NA NA NA NA
#> [1671] NA 1.2496341 NA NA NA
#> [1676] NA NA NA NA NA
#> [1681] NA NA NA NA NA
#> [1686] NA 0.4214600 NA NA NA
#> [1691] NA NA NA NA NA
#> [1696] NA NA NA NA NA
#> [1701] NA 1.4234974 NA 5.9685678 NA
#> [1706] 1.4870299 6.5259218 NA 10.9112473 NA
#> [1711] NA NA NA NA NA
#> [1716] NA NA NA NA NA
#> [1721] 3.0024679 NA NA NA NA
#> [1726] NA NA NA NA NA
#> [1731] NA NA NA NA NA
#> [1736] NA NA 5.5636888 NA NA
#> [1741] NA NA 1.0834279 NA NA
#> [1746] NA NA NA NA NA
#> [1751] NA NA 6.3247514 NA NA
#> [1756] NA 8.4820938 NA 1.0724249 NA
#> [1761] 1.7462807 NA 0.2578186 2.8363187 NA
#> [1766] 2.7652164 NA 9.1981554 NA 2.7739875
#> [1771] 27.5907974 NA 3.4431446 NA NA
#> [1776] 7.6219230 NA 7.2448454 0.9365459 NA
#> [1781] NA NA NA NA NA
#> [1786] 1.4254460 NA NA 1.9484344 NA
#> [1791] 25.4685478 3.3151047 NA 5.4201798 NA
#> [1796] NA 1.0591918 NA NA NA
#> [1801] NA NA NA NA 2.2389860
#> [1806] NA NA 7.2797980 NA 177.9112701
#> [1811] NA 712.0562744 NA 1.9180223 1.0850360
#> [1816] NA 15.4796858 2.2603140 1.6407841 3.5157232
#> [1821] 1.3970622 2.8673556 4.1254120 NA 2.8693426
#> [1826] 4.2749281 0.8203884 2.7943869 NA 3.3467057
#> [1831] 3.0753074 2.7253366 NA 2.1626289 5.0227842
#> [1836] 2.3628030 NA 1.2205384 0.8150464 NA
#> [1841] 3.2100937 4247.9296875 NA NA NA
#> [1846] 4877.1162109 NA 1.7616867 1.8174063 0.7508621
#> [1851] NA 3.5258093 2.7256949 NA 2.5574954
#> [1856] NA 1.5129316 NA 0.6094913 2.6934810
#> [1861] NA 0.9192376 NA 1.3823618 NA
#> [1866] 111.0667496 0.6147639 1.4611453 NA 0.7056657
#> [1871] 0.4673733 0.5605560 1.7962961 0.7591861 0.6475862
#> [1876] NA NA NA 1.0416414 13.4244280
#> [1881] 0.8334653 1.4189600 0.8297019 2.9611456 1.5615076
#> [1886] NA NA NA 1.4051919 NA
#> [1891] NA 0.9027803 NA 3.6805460 NA
#> [1896] NA NA NA 145.7117004 NA
#> [1901] NA NA NA NA NA
#> [1906] NA NA NA NA NA
#> [1911] NA NA NA NA NA
#> [1916] NA NA NA NA NA
#> [1921] NA 0.3470792 NA 1.5938562 1.4932191
#> [1926] NA NA NA NA NA
#> [1931] NA NA NA NA 4.3753552
#> [1936] NA 0.6481672 2.1519599 NA NA
#> [1941] NA NA NA 1.0128362 NA
#> [1946] 0.6915093 NA 1.9947214 NA NA
#> [1951] NA NA NA NA NA
#> [1956] NA NA NA 0.2430045 NA
#> [1961] 2.9182062 0.6795040 1.6664841 2.3586757 NA
#> [1966] NA 0.8883650 NA NA 3.2617228
#> [1971] NA 2.8392348 NA NA NA
#> [1976] 1.6086547 NA NA NA NA
#> [1981] NA 0.6026787 NA NA NA
#> [1986] NA NA NA NA NA
#> [1991] 1.2004064 NA NA NA NA
#> [1996] NA 1.3173006 3.6571672 NA NA
#> [2001] 2.2624974 NA 0.9398944 3.5710230 NA
#> [2006] 1.0233446 NA NA NA 1.3720112
#> [2011] 1.0055754 0.5655854 1.5054272 NA 1.0397023
#> [2016] 3.3238027 NA NA NA 2.1077371
#> [2021] NA 2.7032313 NA 3.8104489 NA
#> [2026] 2.8180630 7.8234811 NA 4.9062996 NA
#> [2031] NA NA 1.3160198 NA NA
#> [2036] 1.0076615 NA 1.0950712 NA NA
#> [2041] 3.9908450 NA 4.2435541 NA 0.7174511
#> [2046] 5.2825084 NA NA NA NA
#> [2051] NA NA NA NA NA
#> [2056] NA NA NA NA NA
#> [2061] NA NA NA NA NA
#> [2066] 0.8802158 NA 0.3888731 NA NA
#> [2071] NA NA NA NA NA
#> [2076] 0.7251250 NA NA NA NA
#> [2081] NA NA 1.0856016 NA NA
#> [2086] NA NA NA 0.5659533 NA
#> [2091] NA NA NA NA NA
#> [2096] NA NA NA NA NA
#> [2101] NA NA NA 0.4917859 NA
#> [2106] NA NA NA NA 0.4981284
#> [2111] 0.9569623 NA 0.7542380 NA NA
#> [2116] NA NA NA 1.5303959 NA
#> [2121] NA NA NA NA NA
#> [2126] 2.5028656 NA NA NA NA
#> [2131] 1.2001288 NA NA NA NA
#> [2136] NA NA NA NA NA
#> [2141] NA NA NA NA NA
#> [2146] NA NA NA 1.7258205 NA
#> [2151] 0.5163053 NA NA NA NA
#> [2156] NA NA NA 1.1474305 NA
#> [2161] NA NA 2.1486368 NA 2.4524887
#> [2166] 3.0907106 3.9163256 NA 6.1858454 6.0148554
#> [2171] NA 3.3022101 6.8535738 NA 0.9091500
#> [2176] 5.3752904 NA NA NA 2.6620142
#> [2181] 14.2303267 NA 0.3268394 3.2703023 3.7256567
#> [2186] 3.4891803 NA NA NA 6.1556778
#> [2191] NA NA NA NA NA
#> [2196] NA NA NA NA NA
#> [2201] NA NA 1.2317547 NA NA
#> [2206] NA 3.0012579 NA 6.3596826 NA
#> [2211] 0.6037928 0.5698133 NA 3.7267346 NA
#> [2216] 10.2576380 0.8706866 4.9864445 4.3741260 NA
#> [2221] NA NA NA 1.0901091 10.9061537
#> [2226] 4.1417003 0.5189514 8.2503548 2.2269182 NA
#> [2231] NA NA NA NA NA
#> [2236] NA NA NA NA NA
#> [2241] NA NA NA NA NA
#> [2246] 0.3962348 0.3956501 NA NA NA
#> [2251] NA NA NA 2.1887872 NA
#> [2256] NA NA NA NA NA
#> [2261] NA NA NA NA NA
#> [2266] NA NA 1.7824746 NA 3.7205944
#> [2271] NA 3.6917150 NA NA NA
#> [2276] 0.7435969 NA 9.6342754 24.0429783 1.5591800
#> [2281] NA NA NA NA 1.9394968
#> [2286] NA 3.0097637 NA 5.7848148 9.7665796
#> [2291] NA NA 2.0749505 NA NA
#> [2296] NA 0.4870613 NA 2.6161067 0.6431220
#> [2301] NA NA NA NA NA
#> [2306] 1.2651863 NA NA NA NA
#> [2311] 3.6852174 NA 1.1431226 21.9405651 NA
#> [2316] NA NA 1.2046832 1.8209703 NA
#> [2321] 0.5066832 0.9095992 9.7609291 NA 3.3460293
#> [2326] NA 0.4121834 NA NA 0.9202868
#> [2331] NA 0.6617500 NA NA NA
#> [2336] NA NA NA NA NA
#> [2341] NA NA NA NA NA
#> [2346] NA NA NA NA NA
#> [2351] NA NA NA 2.0171058 1.1644038
#> [2356] NA NA 2.6082158 NA NA
#> [2361] 3.6380553 NA NA 0.8339515 NA
#> [2366] 1.4020016 7.2250385 NA 0.7268345 0.7294782
#> [2371] NA NA 0.9507068 NA 2.5481384
#> [2376] NA NA NA 3.6101770 NA
#> [2381] 0.6119207 5.6888528 NA 16.1643982 NA
#> [2386] 2.6750910 NA 4.8989611 NA 0.4897856
#> [2391] 15.6936617 NA 3.9424517 NA NA
#> [2396] NA NA 1.3334647 NA 0.4453856
#> [2401] 3.3320382 NA NA 7.2846007 NA
#> [2406] 3.1082804 NA 3.1209931 NA NA
#> [2411] 9.3145094 NA NA 0.8143211 NA
#> [2416] 1.1926577 NA 0.6434940 NA NA
#> [2421] NA NA NA NA NA
#> [2426] NA NA NA NA NA
#> [2431] NA NA NA NA NA
#> [2436] NA NA 0.6482291 2.3913691 2.1481240
#> [2441] 2.7833135 NA 4.5657282 5.3265414 NA
#> [2446] 10.3909426 15.4557104 NA 0.6454072 43.3171730
#> [2451] NA 1.4262301 NA 2.5555761 1.7280343
#> [2456] NA 2.7765193 1.3546977 1.9755590 1.4631903
#> [2461] 1.5031183 1.5941623 1.5892071 NA 5.8746567
#> [2466] 2.2622027 2.5123510 5.3907886 5.1953430 3.1958568
#> [2471] 3.0968442 4.0582609 NA 3.2192631 2.5363874
#> [2476] 4.4969444 5.3325839 2.3695500 3.0062342 5.6627531
#> [2481] 6.4923205 NA 4.7920384 4.5700383 4.0006895
#> [2486] 9.5566549 5.8871794 7.4422898 5.2004848 9.2628069
#> [2491] NA 784.9196167 4.6816382 4.1257901 6.6807156
#> [2496] 4.1821823 6.8080244 7.7087774 12.4988031 NA
#> [2501] 1.4474308 985.7752686 5.5145364 2.7746949 2.3561726
#> [2506] 3.7145381 6.2821894 10.4331989 NA 1.7673082
#> [2511] NA NA 3.1912456 NA 7.3891306
#> [2516] 1.3537637 NA 0.2704858 4.0082321 8.5943022
#> [2521] NA 1.3064193 3.6371551 NA NA
#> [2526] 2.5390623 NA NA NA NA
#> [2531] NA NA NA NA 0.7411902
#> [2536] 0.5046070 NA NA NA NA
#> [2541] 0.5680771 NA 1.2300223 2.8467999 2.6166818
#> [2546] NA NA 0.5497193 NA 16.0667572
#> [2551] 1.9547974 NA 0.9511589 15.4262342 NA
#> [2556] 2.4667881 NA 1.0057802 NA 2.2017863
#> [2561] 3.7578349 5.2645087 NA 0.7512842 2.5041356
#> [2566] 1.8741575 11.7041502 NA 22.0120201 NA
#> [2571] 3.6391535 2.4130566 NA NA 1.3888769
#> [2576] 1.4703723 NA NA 1.5291082 NA
#> [2581] 2.3573880 NA NA NA NA
#> [2586] 0.6399383 1.6180218 NA 2.6052721 NA
#> [2591] 1237.6204834 19.8997154 NA 39.6545715 4069.6533203
#> [2596] 3.8779757 2.9248793 14.3836384 NA 38.9334831
#> [2601] 4267.4248047 6.9302726 5.0617962 4.1785703 28.3963814
#> [2606] NA 6.4566789 3.4296451 NA 3.7816880
#> [2611] NA NA NA NA NA
#> [2616] NA 11.0247078 27.2765732 NA NA
#> [2621] NA NA NA NA NA
#> [2626] NA 2.2371943 NA 0.9170940 0.7460240
#> [2631] NA 0.8210835 1.6487689 9.5727634 NA
#> [2636] 1.6570802 NA 0.8196399 0.8946962 13.3170853
#> [2641] NA 28.8410645 NA 1.4570224 0.2454604
#> [2646] 0.6586674 NA 1.0929065 1.2879230 NA
#> [2651] 2.9674864 0.5844296 NA 11.5823431 NA
#> [2656] 2.6197197 0.6617230 1.0072753 NA 1.0431374
#> [2661] 1.7980380 1.8940541 4.3480368 2.6686113 NA
#> [2666] NA 2.4794784 4.1561966 NA 1.2986863
#> [2671] 1.2777241 3.0134482 NA 101.8303833 3.4352007
#> [2676] 3.0277224 13.1016092 NA 0.5244251 1.7064619
#> [2681] 0.3486639 50.7429390 1.7649055 NA 2.7503626
#> [2686] NA 0.3800137 3.3221421 3.1534879 NA
#> [2691] 15.4715948 1.4704005 1.8855512 4.0389700 1.0823137
#> [2696] 2.6767426 2.1071370 3.6332088 NA 0.6826217
#> [2701] 1.0185049 3.3197520 3.8973808 6.3822713 5.1280479
#> [2706] 8.5763950 3.2864881 NA 1.3272406 3.2673051
#> [2711] 5.7568297 8.3966360 16.3681469 7.2476711 5.4942021
#> [2716] 2.1211190 NA 1.5519290 3.6947889 3.6529276
#> [2721] 7.1617284 23.2306347 20.4122696 17.6928806 5.1081491
#> [2726] NA 117.3710403 2.7161827 48.0717468 18.4067097
#> [2731] 3.7467299 3.6180439 15.2693739 NA 4.7089896
#> [2736] 2.5892634 9.7226076 139.4568787 7.4229794 11.8008423
#> [2741] 44.5208054 10.5249119 NA 5.9375591 20.7310448
#> [2746] 216.7257538 12.0509796 32.5807915 140.1287079 18.1983509
#> [2751] 7.9473476 NA 4.0145078 NA 5.9600363
#> [2756] 4.9861374 40.1272926 16.1208572 13.0407543 6.8589644
#> [2761] NA 4.9722199 11.0004854 7.2423544 6.4803615
#> [2766] 14.1402922 23.9029579 15.6984034 17.9236164 NA
#> [2771] 3.2453372 9.5185261 8.6210403 3.0081079 3.5888493
#> [2776] 6.0433631 7.4204693 11.3490496 NA 3.6138747
#> [2781] 1.7851294 4.3751335 38.7952423 25.8243275 20.6631317
#> [2786] NA 2.3311818 701.8855591 333.9076538 5.9194384
#> [2791] 5.2374392 50.5938759 6.6050563 29.6740894 NA
#> [2796] 6.8099937 729.9282227 4.7258272 377.9533081 3.7225027
#> [2801] 5.8937206 8.3230190 8.7469826 NA 1.2891947
#> [2806] 2.5859647 75.5915375 4.7863827 45.3149376 2.4862499
#> [2811] 6.5945859 6.9634075 NA 35.3314667 3.0827610
#> [2816] 86.5234299 1061.5833740 3.1925797 39.1028519 5.1561656
#> [2821] 727.2340088 NA 4.6954513 4.7806416 2.0299914
#> [2826] 67.0260162 NA NA 8.1010742 1.6631573
#> [2831] 1.8530455 79.0636978 30.3669281 NA 0.8927791
#> [2836] 10.3849897 1.0268044 NA 3.1952302 0.7755960
#> [2841] 5.7425203 7.2843447 NA 1.2209314 1.8280462
#> [2846] 7.8098340 NA NA NA 3.8438480
#> [2851] 1.6488374 NA 1.9727218 21.7567501 0.7352579
#> [2856] NA 1.9201657 52.0057869 NA 127.3143768
#> [2861] 5.6821346 NA 2.5211284 2.3330369 2.1601825
#> [2866] 9.7243500 9.3593960 NA 2.9505100 5.4880805
#> [2871] 8.6688786 2.6024387 NA 11.9668989 2.1561337
#> [2876] 12.3195581 1.2745084 1.9595144 5.3821392 19.5642281
#> [2881] 16.0015182 NA 3.0178616 4.6744413 21.4054432
#> [2886] 2.3722150 17.8877811 7.6304569 36.1747169 4.2541361
#> [2891] NA 33.9568787 6.3856707 2.6096203 13.1905775
#> [2896] 2.4566786 47.8784180 6.1847539 NA 46.1483879
#> [2901] 3.2702918 3.5770297 12.7957163 5.9115996 4.5292888
#> [2906] 5.1561074 NA 4.0170932 3.9871593 5.1863651
#> [2911] 1.8988326 7.9102783 NA 5.5774412 2934.2749023
#> [2916] 1.7749847 8.5469313 16.3584347 10.1700211 NA
#> [2921] 0.2661873 6.4565039 8.4741077 NA NA
#> [2926] NA 0.1989040 0.4645469 1.7402502 29.9008598
#> [2931] 0.5417653 NA 0.7619571 2.3052864 23.7764740
#> [2936] 3.5269954 NA 6.3716865 NA 4.5834875
#> [2941] 0.6944337 9.3756256 NA 0.4391756 0.4102839
#> [2946] NA 0.4088447 4.4651365 NA NA
#> [2951] 2.3560781 0.8629871 1.7296497 NA 2.3054824
#> [2956] 0.4738956 0.8453057 0.6674856 2.9770677 0.9882508
#> [2961] 2.5485382 1.3252913 NA 0.9046306 3.3730309
#> [2966] NA 1.3325157 0.8287238 3.2393219 2.8408599
#> [2971] 1.1969875 NA 4.9852405 16.4274426 0.7676155
#> [2976] 5.0130310 NA 2.2948136 NA 2.0398157
#> [2981] 1.0926311 NA 1.6752559 2.0661981 0.7237425
#> [2986] 4.8128242 11.1487827 NA 0.8632420 1.8443307
#> [2991] NA 0.4144078 1.5147480 2.7527251 NA
#> [2996] 13.0752268 NA 1.8903236 7.9921408 1.5463461
#> [3001] NA 0.8109860 1.0667026 1.1346698 NA
#> [3006] 3.6560605 NA 3.3797889 NA 3.5629609
#> [3011] 3.4544880 4.5592079 NA 16.6013031 NA
#> [3016] 4.6765566 1.6968081 1.1385404 NA 1.4192655
#> [3021] 2.8486712 2.6313102 NA 0.6307487 2.3814776
#> [3026] 2.7711914 0.7061450 NA 4.9312925 1.1609619
#> [3031] 6.7711430 NA 2.8384895 NA 3.6513107
#> [3036] NA 3.9373784 5.0244951 9.3638000 NA
#> [3041] 17.6887131 NA NA NA 8.8094635
#> [3046] NA NA 4.6279211 NA 1.6137590
#> [3051] NA NA NA 0.6182525 0.8786079
#> [3056] 15.0547085 24.5552330 7.9792995 NA 64.7950058
#> [3061] 97.0138016 9.9027309 0.5985516 NA 1.5779028
#> [3066] 11.9724445 NA 1.9544525 NA 163.6211090
#> [3071] NA 2.1614091 8.1083727 NA 2.6397953
#> [3076] 3.5538256 7.1930056 NA 0.6664237 442.5400391
#> [3081] 3.0430346 6.4073930 5.0266309 5.8590345 19.6998043
#> [3086] NA 4.3898072 1.1305788 13.5925970 7.2308283
#> [3091] NA 1.1136869 1.3520640 1.6164834 1.0973755
#> [3096] 2.0894964 9.5560360 NA NA 8.9295197
#> [3101] 779.4788818 2.5705523 809.9169312 3.0098879 94.3604507
#> [3106] 8.8758574 NA 3.4457500 4.9686089 1.5195554
#> [3111] 2.1578839 4.8023272 2.1327522 NA 0.3200580
#> [3116] 12.4226055 12.1050863 1096.2231445 1066.9301758 129.4904938
#> [3121] 4.5963664 3.0736189 NA 8.0031509 3.2705626
#> [3126] NA 0.3757358 15.2121391 2.8834486 15.9752741
#> [3131] 1346.7353516 2.6217887 1228.9621582 5.0895567 NA
#> [3136] 2.1218104 184.3674316 4.2164469 2.8267379 6.7104831
#> [3141] 3.8288674 3.0664554 4.4310689 NA 2268.6291504
#> [3146] 1836.1269531 2.0746028 2.2918937 10.7542877 51.3735046
#> [3151] 6.6448908 6.1085730 NA 1.4560728 2.8452797
#> [3156] 3.7901433 2.0456872 1.9614782 5.3406096 6.5233908
#> [3161] 5.8153443 NA 4.9779243 5.2980742 1.9569403
#> [3166] NA 3.0366473 NA 0.8441148 0.7282568
#> [3171] 0.6270677 1.9153599 2.5157802 2.5211849 NA
#> [3176] 1.3664742 16.0325813 NA 1.9972137 1.1040767
#> [3181] NA 0.9441139 NA NA 0.9234766
#> [3186] NA 2.2210290 0.8783525 2.1501570 NA
#> [3191] 8.6357822 NA 1.6162349 NA NA
#> [3196] 1.9563223 1.2261102 5.9679408 NA 2.4582469
#> [3201] 3.3074541 NA 0.6635520 NA 3.4940929
#> [3206] NA NA 2.6760423 NA 1.0429727
#> [3211] 0.5086066 1.0268028 3.8812792 NA 3.5886321
#> [3216] NA 3.9809752 7.7894588 NA NA
#> [3221] 0.8405986 NA 1.1336366 NA NA
#> [3226] 2.8971629 NA NA NA NA
#> [3231] NA NA NA 0.7070799 NA
#> [3236] NA NA NA NA 0.7151046
#> [3241] NA 0.5353748 1.3802744 NA 4.1309824
#> [3246] 4.8025975 NA 8.7120161 2.2102430 10.5090933
#> [3251] 10.4218655 NA 22.3232746 NA 36.9990120
#> [3256] NA 2.9090240 2.3153019 76.6485138 2.9318345
#> [3261] NA 1.6010859 5.8318458 2.6627228 6.4075303
#> [3266] 2.0914986 11.3002634 NA 1.0663972 9.0038490
#> [3271] 7.7891731 1.4418688 4.8872490 13.7815018 7.2834721
#> [3276] NA 0.6722869 12.7788944 14.1886673 6.0767541
#> [3281] 17.2682152 NA 5.8392038 3.2879653 25.1995487
#> [3286] 27.0342846 11.1438723 38.4615974 10.6467648 20.4387722
#> [3291] NA 3.0112209 1.9950336 10.1919136 1.5909361
#> [3296] 4.7708578 1141.7640381 9.8121815 22.5490570 NA
#> [3301] 3.3931487 9.8001604 4.6790090 1271.7685547 2.8506472
#> [3306] 1103.0401611 17.5955009 5.1776738 NA 2.9927020
#> [3311] 2.1627703 3.1698050 NA 13.8667717 1411.4106445
#> [3316] 1272.9426270 21.3413105 NA 0.8185354 2.8377655
#> [3321] 2.0303440 21.7762794 NA 1345.3233643 1350.6333008
#> [3326] 6.6278219 29.8857651 NA 52.9047775 1294.8477783
#> [3331] 12.9526482 9.6068335 17.7135353 NA NA
#> [3336] NA 2.6911139 3.7828991 NA 0.7059131
#> [3341] 5.9516506 4.0300317 2.7630870 5.6403260 NA
#> [3346] 7.4636693 2.0741365 5.1799192 6.4390368 9.8243055
#> [3351] NA 1.5092793 4.3210769 NA 1.7446249
#> [3356] 3.4029963 2283.2792969 3.9234891 6.9994988 4.1006012
#> [3361] NA 0.6282611 1.8591487 4.4599166 2.1398880
#> [3366] 2337.1853027 4.3932910 9.6464186 12.7156582 NA
#> [3371] 0.9086848 9.3874264 3.2540348 2646.2402344 6.6778555
#> [3376] 3.9801006 21.6677666 NA 1.3727722 2.5189867
#> [3381] 7.1042595 10.0052004 1.5813689 23.2121410 2.8682461
#> [3386] NA 1.3422523 0.7672858 0.6866859 3.6084216
#> [3391] 18.3351460 NA 0.8394666 1.9887234 1.5026515
#> [3396] 1.3683980 5.7951837 9.8654814 2.1221330 NA
#> [3401] 2.1047983 3.0744431 1.9053514 2.6970651 2.0816550
#> [3406] 1.9742609 9.6418734 7.6057978 NA 3.4838214
#> [3411] 8.8084707 6.6040335 3.2611005 9.0701380 3.0636346
#> [3416] 1.7033354 12.7065725 NA 1.9277512 11.4872437
#> [3421] 12.6639729 1.8488846 4.4552321 4.2770810 9.8547640
#> [3426] 3.8485291 NA 2.1161973 2.0724041 6.0630097
#> [3431] 2.5432432 29.9757633 3.4243822 1.5343714 7.8658175
#> [3436] NA 1.2879468 6.8999977 6.3626080 268.5536194
#> [3441] 5.7748380 NA 5.2410469 2.0550699 NA
#> [3446] 0.4435597 5.4935684 0.9196581 75.9311295 3.3142908
#> [3451] 80.4429626 5.9293828 2.2810714 NA 2.8269784
#> [3456] 20.1030636 NA 1.1432312 3.6678035 NA
#> [3461] 1.0070125 3.5375540 2.9908266 22.7290974 NA
#> [3466] 2.3943624 0.9211383 1.9665295 NA 0.8259042
#> [3471] 0.4845653 25.3855438 3.8617392 NA 6.6030455
#> [3476] 18.8038082 NA 0.5806934 3.3330538 101.8569031
#> [3481] NA 14.4165783 NA 1.7161655 7.3081284
#> [3486] NA 2.1098645 0.8144141 1.3382024 1.4885535
#> [3491] NA 0.8770674 0.6791099 NA 1.6695246
#> [3496] NA 1.2673935 NA 0.6891360 1.2439822
#> [3501] 5.2793922 NA 1.2573010 NA 3.7120969
#> [3506] 4.9950824 11.5787373 1.9612808 NA 10.3544693
#> [3511] 6.0420938 18.6085644 NA 1.5667670 1.2138346
#> [3516] 30.0845642 19.6141224 3.2639980 NA 3.6546156
#> [3521] 29.6098766 2.8610103 6.0040512 1.8776323 3.8169975
#> [3526] NA 4.7191100 1.0195367 3.8605018 8.1590776
#> [3531] 2.8668685 12.0980616 5.0243416 7.0915346 NA
#> [3536] 2.7560163 6.1319265 5.5243936 46.6120872 24.7890339
#> [3541] 6.4685655 9.1421661 10.3797007 NA 10.7507782
#> [3546] 7.7589669 12.2742815 4.6532531 5.1379547 23.7994213
#> [3551] 29.3476200 4.0036497 NA 7.5182543 11.9706621
#> [3556] 63.0551071 5.1469116 8.4730129 72.0657578 10.1063595
#> [3561] 65.6397705 NA 4.1467509 13.0881023 27.2654285
#> [3566] 96.3133926 108.4684830 15.9769907 16.9430561 14.3318129
#> [3571] NA 4.6791883 126.8739243 6.6298981 18.2740936
#> [3576] 28.1178570 8.4018116 24.9042740 22.9308643 NA
#> [3581] 5.7845116 2804.5610352 6.1323233 7.0216298 34.0006905
#> [3586] 11.0745640 23.1465626 26.1845646 NA 15.7390766
#> [3591] 6.2733459 11.7022743 4.5109916 3.0380044 72.1676025
#> [3596] 18.0594807 16.0637493 NA 8.0370464 176.6657867
#> [3601] 2181.4362793 9.6984138 3978.9870605 8.6989155 11.3172474
#> [3606] 39.0961494 NA 2588.9499512 3505.7983398 144.8855133
#> [3611] 5.1680942 30.6885090 6.6054115 5.6432614 2.7389083
#> [3616] NA 53.9481773 62.0971260 2972.6391602 161.4343109
#> [3621] 35.4546394 11.2379074 13.3652372 15.0986557 NA
#> [3626] 20.7111149 9.2977657 425.2767639 216.2778320 7.9142838
#> [3631] 5.0309882 NA 1.0399815 1.0769179 4.5379925
#> [3636] 3.6899538 2.2575519 17.9379234 NA 22.7302589
#> [3641] 6.5185528 19.6637669 NA 0.2854209 7.1664414
#> [3646] 9.3295479 NA 1.1119181 1.1998634 44.4294777
#> [3651] 6.0005445 1.2984159 NA 0.4181677 1.3962787
#> [3656] NA 0.8486760 0.6826237 1.3289268 41.6967010
#> [3661] 1.6696991 NA 0.8142897 0.3997326 64.2423019
#> [3666] NA 4.2105532 8.1320543 0.8433636 21.3994884
#> [3671] 0.6499675 1.6173933 2.2819724 79.2927475 NA
#> [3676] 2.7942066 0.6122881 5.6340408 2.3877366 2.5358839
#> [3681] NA 1.6963638 2.8207321 1.6094570 3.0496490
#> [3686] NA 5.2695556 0.7497861 1.1942160 NA
#> [3691] 2.8949137 1.8337013 NA 7.7278204 3.0212984
#> [3696] NA 4.6463079 1.1313710 NA 3.0398493
#> [3701] NA NA NA 1.1938435 NA
#> [3706] 0.5559963 3.7946312 NA NA 0.7459199
#> [3711] 2.6556878 4.8397417 NA NA NA
#> [3716] 2.1508582 4.1808691 NA 1.3044889 3.6563311
#> [3721] NA 5.6848607 4.3057156 4.3253880 NA
#> [3726] 44.0154037 6.8031969 NA 1.0149941 16.5446339
#> [3731] NA 50.2134972 1.4178553 5.2461462 NA
#> [3736] 6.6964250 NA 1.3668361 7.0547361 15.4979038
#> [3741] NA 2.5466137 12.0476017 16.9358387 4.2765665
#> [3746] NA 3.0282452 2.0438735 3.0750351 30.2993698
#> [3751] 4.1311669 10.5620518 NA 6.3157725 2.0541127
#> [3756] 6.4616790 52.7001801 6.5350013 2.7538323 5.5697684
#> [3761] 7.0785255 NA 0.7346154 4.6684666 2.3665750
#> [3766] 2.1643167 13.1310358 11.3682957 NA 1.0712550
#> [3771] 12.9957743 1371.8287354 1.3735292 4.6520715 20.4687824
#> [3776] 14.5587559 NA 1770.5562744 2.6400170 15.1324883
#> [3781] 1.9400562 4.7408748 2.0517797 32.1368103 NA
#> [3786] 0.9072015 1766.3382568 11.0576754 2.3649356 7.1545792
#> [3791] 5.3592334 29.8745804 NA 99.2443008 1.9519557
#> [3796] 3.5576828 5.4862013 19.1255341 NA 1.0205218
#> [3801] 74.6288681 2.6257737 3.8454990 0.9508846 15.9569130
#> [3806] NA 3.9706428 NA 6.0614076 NA
#> [3811] 0.9358777 0.4645265 1.3660873 2.9063818 6.6953201
#> [3816] NA 1.7190092 0.9622721 1.7018661 11.3596716
#> [3821] NA NA 3.0443096 NA 0.6346511
#> [3826] NA NA NA 10.0340395 0.8095649
#> [3831] 0.9709995 NA 3.2444031 NA NA
#> [3836] 0.5028589 2.7934396 6.8828177 NA 0.5587204
#> [3841] 1.6042945 1.3587892 NA 0.8313419 2.4558291
#> [3846] 1.7974735 NA 3.6726115 5.6037540 6.7877126
#> [3851] NA 3.6458204 0.8579472 8.0946331 12.1906815
#> [3856] 1.5101539 6.0351577 11.0641689 NA 17.6339569
#> [3861] 3.6026669 16.1998940 2.8316128 NA 4.9478583
#> [3866] 27.5424156 7.0986490 3.9904847 NA 4.6039705
#> [3871] 3.3738575 12.1051941 11.3188763 14.8368187 9.5498314
#> [3876] NA 1.9773377 13.8263979 23.9170589 13.4375019
#> [3881] 9.8440132 NA 11.2628098 2.5026691 22.5849972
#> [3886] 29.0189171 18.8118534 NA 22.0679207 4.0647068
#> [3891] 2.0535538 1.5048116 3.3824587 2.3311884 32.8195572
#> [3896] NA 37.0816650 7.0685787 5.5009451 5.8517952
#> [3901] 4.9319654 3.4151902 4.2116370 8.8043442 NA
#> [3906] 1099.1087646 5.3256183 5.2614517 480.0812378 5.3612590
#> [3911] 12.7172546 6.3167968 14.1994934 NA 59.6927795
#> [3916] 964.6279297 1400.3564453 535.6882324 23.6954079 14.5343561
#> [3921] 2.9595783 17.3649712 NA 1.0824900 1221.8245850
#> [3926] 147.4770660 683.7954712 6.1305389 4.9751444 15.9264107
#> [3931] 18.4570618 NA 1083.5487061 191.7899017 875.8353271
#> [3936] 577.2622681 71.0305328 8.1075430 5.3227873 11.6976309
#> [3941] NA 0.4594952 1.4792782 1.6332765 4.2618947
#> [3946] 6.4441838 2.9269114 130.0458374 5.1534815 NA
#> [3951] 3.5288918 2.2328265 7.3872643 35.0871201 164.8385925
#> [3956] 41.0983658 NA 3.6965251 3.6046982 34.9871025
#> [3961] 54.6165199 NA 3.3092432 6.2893038 54.9078522
#> [3966] NA 4.6468277 5.5542288 52.0769882 NA
#> [3971] 7.5430121 11.7523575 32.7870789 NA 4.3187008
#> [3976] 22.1579170 5.4699397 1.8834021 NA 9.8157177
#> [3981] 3.3649621 10.4119244 12.9897251 NA 3.0194745
#> [3986] 20.3988075 9.6839437 1.9245056 27.7328892 4.6774187
#> [3991] 2.8958423 NA 5.1254649 25.3332367 15.3362989
#> [3996] 5.6253791 9.0580263 3.3217881 16.1069527 5.7460155
#> [4001] NA 9.0998583 6.4395881 27.5900745 11.9862118
#> [4006] 10.4788322 4.7713022 7.4465909 21.6194534 NA
#> [4011] 3.4900868 11.9859657 7.1533313 53.6382294 22.4019108
#> [4016] 3.4466097 11.8166838 31.7925739 NA 7.1903763
#> [4021] 22.3436050 21.0166111 11.6625166 9.6446323 11.8060122
#> [4026] 10.7140808 20.5072823 NA 5.8385706 12.4566240
#> [4031] 27.2418690 1374.2124023 5.0976796 8.5298624 8.9547720
#> [4036] 14.1582394 NA 3.6345949 8.9698286 746.4771729
#> [4041] 16.0491295 14.2799292 12.4969416 16.1063213 17.6739025
#> [4046] NA 13.8779955 5.8102989 4.7912970 8.5829220
#> [4051] 7.5092740 16.8982792 8.7955656 27.1313457 NA
#> [4056] 10.5999660 102.9700699 5.2469325 78.4538498 23.4321194
#> [4061] 13.5085144 28.0264759 42.0644264 NA 3.0340507
#> [4066] 16.6190434 1717.7260742 6.9299235 10.5656214 27.8841705
#> [4071] 17.4288807 47.0360756 NA 5.2313685 49.5542450
#> [4076] 4.6091428 7.5845995 5.1649594 5.6527944 6.7112393
#> [4081] 7.1952438 NA 2.8361075 936.4321899 10.8392658
#> [4086] 300.9952393 3.7918711 5.8666444 9.0892420 7.9013219
#> [4091] NA 5.9726405 6.4315825 6.7033081 174.0914154
#> [4096] 9.4583406 13.8090487 25.1833706 10.3936863 NA
#> [4101] 11.1641397 3.6089337 4.3211293 4.5236745 5.9249415
#> [4106] 19.1762638 44.9207954 1162.3734131 NA 9.1217899
#> [4111] 6.2500715 14.8576088 9.4355440 27.3175831 75.4738541
#> [4116] 1927.7430420 10.6061239 NA 2.5857298 5.2106519
#> [4121] 16.0033913 13.5474854 30.9249420 2882.4428711 37.9162636
#> [4126] 75.5530014 NA 6.0951519 4.3891191 10.3061371
#> [4131] 6.6429935 39.4176941 10.0774698 83.8411789 21.2222996
#> [4136] NA NA 0.6372588 3.5606251 2.5927734
#> [4141] 3.2620273 2.3674390 1.8626410 6.6079321 NA
#> [4146] 2.4800649 6.8728633 2.8025930 10.0381966 4.8456726
#> [4151] 89.1146164 4.3337107 3.8712926 NA 42.9793968
#> [4156] 2.5299530 7.4297533 4.6089058 NA 3.5378845
#> [4161] NA 0.1817949 1.1290787 1.5784779 1.1125787
#> [4166] NA NA NA NA 2.0033450
#> [4171] NA NA NA NA 1.0376083
#> [4176] 1.9011122 2.3181555 NA NA NA
#> [4181] 1.6446155 NA NA NA 0.9089839
#> [4186] 0.4841611 NA 0.6897894 0.7658206 0.6156495
#> [4191] 1.4828655 2.3340645 1.1662629 0.9815852 2.3534944
#> [4196] NA NA NA 2.1499813 0.7019742
#> [4201] 0.3516256 3.2850077 5.8242555 NA NA
#> [4206] 1.3007295 1.5015216 0.8093400 3.2225409 2.6426129
#> [4211] NA 2.3901305 2.9790335 2.9894137 5.2419791
#> [4216] 4.8620076 NA 0.3997991 3.0470288 11.9397249
#> [4221] 3.6320026 8.3058691 1.3834330 NA 4.1316576
#> [4226] 3.7130520 3.9005911 16.1633472 3.6801958 12.0339565
#> [4231] NA 1.1500748 1.2219627 0.5826390 3.3800447
#> [4236] 5.2994637 NA 2.6451149 NA 2.7287869
#> [4241] 3.1930273 NA 1.8436399 2.9893756 NA
#> [4246] 4.9069676 NA 5.8526864 NA 1.9956532
#> [4251] 5.7515755 NA 9.3381653 NA 3.7572572
#> [4256] NA NA NA NA NA
#> [4261] NA NA NA NA NA
#> [4266] NA 0.4758977 NA NA NA
#> [4271] NA 0.5950958 NA 1.2651656 0.5604061
#> [4276] NA NA NA NA NA
#> [4281] NA NA NA NA 1.5873302
#> [4286] NA NA 1.7309831 NA 2.2026825
#> [4291] NA NA NA NA NA
#> [4296] NA NA NA NA NA
#> [4301] NA 0.8321332 NA NA NA
#> [4306] NA NA NA 3.1331923 NA
#> [4311] NA NA 1.8596388 NA 4.0986276
#> [4316] NA 1.4462790 5.0948448 NA 3.3446779
#> [4321] NA 14.7490797 4.2720022 2.3573306 12.4373245
#> [4326] 5.4349809 NA 5.5168352 7.1895194 NA
#> [4331] 1.3353804 31.1978798 6.0550547 5.3862381 NA
#> [4336] 2.3742425 1.6283454 NA NA NA
#> [4341] 3.8663564 NA 2.5557170 NA NA
#> [4346] 3.9340308 NA 4.8355684 NA NA
#> [4351] NA NA 0.8258027 NA NA
#> [4356] 1.6850734 NA NA 8.2018566 NA
#> [4361] 2.8547313 NA 2.7901099 NA 1.2237481
#> [4366] NA 1.2317826 5.9977107 NA 2.4985216
#> [4371] 5.4254308 10.2619724 3.6585343 NA 0.7505842
#> [4376] 1.0290449 1.4102111 31.6295795 6.0977502 3.3116033
#> [4381] NA 6.6416101 13.4169693 NA 2.0772371
#> [4386] 2.5722609 1.4239205 1.1951129 19.3541183 6.6528106
#> [4391] 7.6534238 NA 3.3320429 3.9984677 2.1092513
#> [4396] 8.5107756 2.7920604 28.8576584 1.6512991 2.0430546
#> [4401] NA 6.1738434 2.2872250 3.3046799 4.1366568
#> [4406] 2.0184140 3.4700344 2.1820960 9.8032455 NA
#> [4411] 9.3485680 7.2345314 20.3594875 7.5089312 1627.1451416
#> [4416] 5.3730831 8.5319214 12.6467562 NA 6.4055276
#> [4421] 5.4178729 3.8174756 60.1280174 6.4091296 14.2986145
#> [4426] 13.2007456 17.6533527 NA 4.6876645 2218.6071777
#> [4431] 5.9873571 5.4104090 13.5144920 4.3813696 8.5980825
#> [4436] 19.0352879 NA 1989.9606934 6.5922198 8.3580027
#> [4441] 5.7576084 341.0995483 3.3168538 9.5687895 9.9157639
#> [4446] NA 8.6866083 366.3923645 6.9480758 10.2027311
#> [4451] 7.9508238 12.0425510 5.7334862 5.7009211 NA
#> [4456] 9.3929729 4.2744284 16.8116989 3.3318050 61.5876274
#> [4461] 3.6692767 4.6599665 NA 16.0338726 43.3646393
#> [4466] NA 0.4666836 1.1231649 3.3837605 5.3292937
#> [4471] 3.6405241 9.7523746 NA 0.9140767 0.7435523
#> [4476] 0.2535407 5.4856982 15.5203247 NA NA
#> [4481] 3.2470930 2.7420650 NA 0.6101449 1.0404911
#> [4486] 2.7917345 6.6811185 2.0745556 NA 1.2540616
#> [4491] 0.3098164 1.6586421 9.5700045 NA 0.7319268
#> [4496] 12.7017021 NA NA 0.6637144 NA
#> [4501] 1.5885706 0.8827127 NA 2.1940100 3.7667232
#> [4506] NA 2.0905075 NA 0.6379106 2.9787626
#> [4511] NA NA NA 0.4874143 NA
#> [4516] NA 0.3836070 NA NA 0.5107485
#> [4521] NA NA NA 1.1823709 1.2667042
#> [4526] NA 19.2034588 1.6550628 1.1661954 0.8966293
#> [4531] NA 3.2779715 NA 11.0150242 NA
#> [4536] 1.6130922 26.5314026 2.5117977 2.3132839 2.2919271
#> [4541] NA 3.1338563 1.5114740 NA 2.0381725
#> [4546] 5.2738991 2.9814677 3.5173035 NA 2.9813018
#> [4551] 1.8998176 NA 7.4101906 5.3717108 4.2854128
#> [4556] NA 0.2399864 6.4908400 1.6760609 4.9045634
#> [4561] 3.0678418 NA 2.1384492 9.3216219 10.4645767
#> [4566] NA 0.3764410 1.2218126 1.2995534 9.7801199
#> [4571] 2.0047073 26.3789768 NA 1.0760976 0.9736537
#> [4576] 2.0357609 1576.8568115 1.4469386 3.0481942 1.6708410
#> [4581] NA 0.9496741 2.6938272 1.8960530 2.1988547
#> [4586] 1.2718045 1.2236259 NA 1.4919758 4.3979073
#> [4591] 71.3828812 2.0391872 2.2703087 2.6103971 1.4898348
#> [4596] NA 1.5168892 0.8818803 1.8848048 0.6037043
#> [4601] 1.8731611 7.1765389 1.9562593 NA 11.7375755
#> [4606] 1.6933267 1.5986614 5.0060325 5.2257977 NA
#> [4611] 4.1671195 4.4332304 6.8677626 NA 2.4653215
#> [4616] 6.3500257 10.9838686 6.4111762 9.4890432 4.7053976
#> [4621] 1.8102279 1.4213001 NA 1.6444297 1.9174886
#> [4626] 0.4131429 1.5709261 3.1760740 1.0655589 919.2457886
#> [4631] 1.5643872 NA 1.7901465 1.7823997 6.9229293
#> [4636] 2.2213833 NA 0.8164910 NA 6.7707281
#> [4641] 1034.1815186 2.8209743 0.6380021 NA 1.1908475
#> [4646] 2.2225995 NA NA NA 1.8188674
#> [4651] NA NA 2.1192210 NA 1.1434188
#> [4656] NA 1.2050375 NA NA NA
#> [4661] NA 1.7906495 NA NA 3.5892394
#> [4666] NA NA NA NA NA
#> [4671] NA NA NA 0.7533023 NA
#> [4676] NA NA NA NA NA
#> [4681] NA 1.4840194 2.2305763 NA NA
#> [4686] NA NA NA NA NA
#> [4691] NA NA NA NA NA
#> [4696] NA NA NA NA NA
#> [4701] NA NA NA NA NA
#> [4706] NA NA NA NA NA
#> [4711] NA NA NA NA NA
#> [4716] NA NA NA NA NA
#> [4721] NA 2.4143374 NA NA NA
#> [4726] NA NA NA NA NA
#> [4731] NA NA NA 1.9114826 NA
#> [4736] 1.0137789 NA NA 4.2369156 NA
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#> [6986] 0.6335756 NA NA NA NA
#> [6991] NA NA NA NA NA
#> [6996] NA NA NA NA 0.6562991
#> [7001] NA NA NA 3.2722898 NA
#> [7006] NA NA NA NA NA
#> [7011] 2.3044112 NA NA NA NA
#> [7016] NA 1.4757828 NA NA NA
#> [7021] NA NA NA NA NA
#> [7026] NA NA NA NA 8.4148054
#> [7031] NA 0.3076566 1.2632453 0.8317384 6.0139632
#> [7036] 2.7857358 NA 1.4718702 NA NA
#> [7041] NA 2.8902125 NA NA NA
#> [7046] 3.4636676 NA NA NA NA
#> [7051] NA NA NA NA NA
#> [7056] 2.9858587 NA NA 1.1537194 6.6131492
#> [7061] NA NA NA NA NA
#> [7066] 0.2267639 0.5474497 0.4946014 NA NA
#> [7071] NA 1.2936714 NA NA NA
#> [7076] NA NA NA NA NA
#> [7081] NA NA NA NA NA
#> [7086] NA NA NA NA NA
#> [7091] NA NA NA NA NA
#> [7096] NA NA NA NA NA
#> [7101] NA NA NA NA 1.6368855
#> [7106] 0.9594902 1.4098917 NA NA NA
#> [7111] NA NA 0.7878828 NA NA
#> [7116] NA NA NA 0.6673341 NA
#> [7121] NA NA NA 3.1816411 NA
#> [7126] NA NA NA NA NA
#> [7131] NA NA 1.2840652 NA 2.6560736
#> [7136] 19.9554462 NA NA NA NA
#> [7141] NA NA NA NA NA
#> [7146] NA NA NA 1.0239617 NA
#> [7151] 1.8550906 2.3586714 NA NA NA
#> [7156] NA NA NA NA NA
#> [7161] NA NA NA NA NA
#> [7166] NA NA NA 0.4221451 NA
#> [7171] NA NA NA NA NA
#> [7176] NA NA NA NA NA
#> [7181] NA NA NA NA NA
#> [7186] NA NA NA 2.8507216 3.4885705
#> [7191] 7.3413062 NA NA NA NA
#> [7196] NA NA NA NA NA
#> [7201] NA NA 35.7378654 NA 1.4843854
#> [7206] 0.8229836 NA 1.9679027 NA NA
#> [7211] NA NA NA NA NA
#> [7216] NA NA NA NA NA
#> [7221] NA NA NA NA NA
#> [7226] NA NA NA NA NA
#> [7231] 0.9418428 NA 2.5392017 NA NA
#> [7236] NA NA NA NA NA
#> [7241] NA NA NA NA NA
#> [7246] NA NA NA NA NA
#> [7251] NA NA NA NA NA
#> [7256] 0.9196062 NA NA NA 4.2101269
#> [7261] NA NA NA 0.8723121 NA
#> [7266] 1.1009536 NA 2.2867596 5.5058069 NA
#> [7271] NA NA NA NA NA
#> [7276] NA NA 1.2713206 NA NA
#> [7281] NA NA NA NA NA
#> [7286] NA 2.4941492 NA NA NA
#> [7291] NA NA NA NA NA
#> [7296] NA NA 1.5185130 2.7117639 NA
#> [7301] NA NA NA NA NA
#> [7306] NA NA NA NA NA
#> [7311] NA NA NA NA NA
#> [7316] NA 4.5919437 5.1406364 NA 2.8025935
#> [7321] 5.8709002 13.7791338 14.5640020 4.8297606 7.8658409
#> [7326] 6.8229294 27.7137413 NA 5.3379407 14.4617805
#> [7331] 4.4964433 16.1921463 10.2128115 4.2068844 10.3527794
#> [7336] 30.8382893 NA 6.8301725 9.5161114 46.6889000
#> [7341] 5.9609809 13.2284927 7.8532238 36.7056198 8.2213917
#> [7346] NA 7.4940300 11.8976650 13.4008360 19.2198009
#> [7351] 2.2085142 16.2607841 27.6975002 84.5502777 NA
#> [7356] 8.9375477 17.9089260 15.0652647 9.7659874 20.4718914
#> [7361] 10.5261488 102.4522476 17.9931507 NA 11.4199753
#> [7366] 19.1676826 17.0927010 24.3487606 6.6103988 8.1302404
#> [7371] 9.6394157 30.0782166 NA 10.6189022 3.9972832
#> [7376] 13.7391968 28.9560966 6.7734051 8.2172832 10.3078299
#> [7381] 20.1655922 NA 18.3504696 2.8396056 27.5377102
#> [7386] 4.3330150 4.9756093 8.2070942 2.5828452 7.6423254
#> [7391] NA 7.4956326 3.3020027 8.2716713 10.5372095
#> [7396] 5.9630933 5.2237558 9.0436430 7.9005804 NA
#> [7401] 4.6265149 4.5348096 14.0217648 11.7508678 5.1471868
#> [7406] 11.1270819 7.9933724 12.3714352 NA NA
#> [7411] 9.6415024 1.0363822 2.1618273 2.2038519 16.1064682
#> [7416] 6.1964874 5.3220701 NA 1.6039859 98.4771194
#> [7421] 4.0282149 2.6477625 16.8007278 3.5877471 2.6565950
#> [7426] 5.3537140 NA 39.1409950 3.9032741 51.0578613
#> [7431] 14.7823324 NA 23.7130947 41.8512573 27.0040703
#> [7436] 8.8542757 40.3777084 36.5384369 6.2642756 10.2587223
#> [7441] NA 25.8907166 44.1283989 16.7547779 1.5664260
#> [7446] 36.2730598 45.0348854 5.4926982 9.8852091 NA
#> [7451] 38.9618721 45.9797630 NA 3.3837142 NA
#> [7456] 1.5297805 2.0968738 NA 4.4807029 NA
#> [7461] NA NA 0.6716859 NA 4.6078916
#> [7466] 2.3965619 0.9893416 NA NA 1.2310627
#> [7471] NA 5.1445694 1.9888287 NA 0.6328241
#> [7476] 0.5137627 NA 5.9770460 0.9208446 NA
#> [7481] NA 1.4403317 NA NA NA
#> [7486] 4.3081975 0.9803630 0.8477652 0.4548255 11.3158970
#> [7491] 1.3294666 NA NA NA 1.6702332
#> [7496] NA 4.7679958 0.8518687 1.8222345 2.7682319
#> [7501] NA NA NA NA NA
#> [7506] NA NA NA NA NA
#> [7511] NA NA NA 4.0749702 NA
#> [7516] 0.4489682 NA NA NA NA
#> [7521] NA NA NA NA NA
#> [7526] NA NA NA NA NA
#> [7531] NA NA NA 0.6329405 1.6010697
#> [7536] NA NA NA NA NA
#> [7541] NA NA NA NA NA
#> [7546] NA NA NA NA NA
#> [7551] 0.1622699 0.8471114 NA 0.4407594 NA
#> [7556] NA NA NA NA NA
#> [7561] NA NA NA NA NA
#> [7566] 1.1682943 NA 1.4405993 NA 2.0458810
#> [7571] 6.8692994 NA NA NA 2.7137775
#> [7576] NA 1.1100407 NA NA NA
#> [7581] NA NA NA NA NA
#> [7586] NA NA NA NA NA
#> [7591] 0.3899231 NA NA NA NA
#> [7596] NA NA NA NA NA
#> [7601] NA NA
## We can replace the precursor intensity values of the originating
## object:
sps_dda$precursorIntensity <- pi
sps_dda |>
filterMsLevel(2L) |>
precursorIntensity()
#> [1] 0.2562894 0.6533012 NA 0.9115737 2.0881963
#> [6] 0.4161463 1.6393325 1.9403350 0.2138104 2.2869656
#> [11] 0.3581794 1.2965702 7.1932635 1.7952728 1.7991002
#> [16] 4.6187558 2.3082116 2.1572299 7.6883049 0.6671617
#> [21] 1.3682123 1.7227559 0.8182324 1.3030148 0.4186687
#> [26] 0.4893252 NA NA 0.6470237 NA
#> [31] 1.2660308 0.3448766 1.0501561 1.0818608 0.1523102
#> [36] 1.6358998 1.0994979 2.0477426 0.9073558 0.5244763
#> [41] 2.2316427 2.6152663 1.7466167 1.7643924 1.0328368
#> [46] 2.0097375 0.3149102 0.5953222 0.8350294 1.0704968
#> [51] 1.3077880 1.5747164 0.3147899 0.4084390 0.4539037
#> [56] 0.9779218 2.4162381 6.3833795 2.8862731 1.2910393
#> [61] 10.6581869 1.4951602 3.7959058 2.8722730 2.4529448
#> [66] 5.3233647 3.5772564 4.4303422 2.9447591 2.1176453
#> [71] 5.0967083 3.3420610 2.4258153 0.8650878 7.3779054
#> [76] 6.8362355 2.1940181 0.7469152 2.0079441 6.3011317
#> [81] 10.4023485 3.3065796 2.3813672 1.3428380 4.0137057
#> [86] 1.4098256 2.4288177 1.3962808 7.4319925 20.5047512
#> [91] 0.9047846 1.3858606 2.8306339 3.7321482 2.8828564
#> [96] 5.5602937 115.1193542 2.0473588 4.9510341 118.5648346
#> [101] 2.0066054 2.9266124 2.4156189 4.2713437 NA
#> [106] 0.2551613 2.6899714 5.8423100 11.0782356 0.8772338
#> [111] 2.3091309 3.4256361 3.7716985 1.5463245 1.8332181
#> [116] 1.1230040 3.6158812 1.1808953 1.2916937 3.6537952
#> [121] 1.9253193 6.6556292 2.8096755 0.4722714 1.5738993
#> [126] 3.1981614 2.3436296 1.1402670 4.2488189 5.5579276
#> [131] 1.3360999 1.4311922 165.9235382 1.0471303 2.2803576
#> [136] 2.3772638 1.4516939 2.6042576 1.4513292 110.1937103
#> [141] 1.6985731 7.4143949 2.9037418 4.3727541 2.5551207
#> [146] 3.4643288 3.0462031 5.5572038 5.8211880 3.4240117
#> [151] 3.9230976 326.0313110 2.8209546 4.4502177 45.2657852
#> [156] 2.6507335 2.4535000 5.7406173 5.1763091 1.5400567
#> [161] 1.0795214 1.5230681 3.4662752 4.3179688 0.7036181
#> [166] 3.9843678 3.7730997 0.8382843 0.5566598 0.8787179
#> [171] 0.4405660 2.2968674 0.4199017 NA 0.9166892
#> [176] 3.8255417 0.5752081 0.5180702 1.5857564 1.6556677
#> [181] 0.5855740 1.0657172 0.4307829 1.3963100 2.4678311
#> [186] 0.9768236 1.0707787 0.8175089 1.4743868 NA
#> [191] NA 5.0737371 0.5766439 1.4214748 0.2014372
#> [196] 2.2585330 0.7659990 0.6345024 1.1232179 1.2145635
#> [201] 10.4214382 1.5072360 1.6478479 2.0655625 0.5699065
#> [206] 1.0214655 0.6936538 2.5300288 0.5699503 0.4178057
#> [211] 2.3085105 7.4867916 2.4417286 3.0711961 2.6359253
#> [216] 1.0029373 0.3156207 3.3271339 0.7859326 0.9116367
#> [221] 2.2751508 3.6905310 1.7462349 0.6127930 0.6677385
#> [226] 0.3307993 2.4790754 0.9691072 3.0772917 0.3885723
#> [231] 0.3215049 1.6329483 4.4057989 0.4121964 0.8756598
#> [236] 1.1511103 1.2199212 2.9056976 0.9633790 1.1713226
#> [241] 2.6179757 7.3096509 4.6009355 0.6218465 4.1894031
#> [246] 10.0505095 12.7552290 10.4093504 2.2360206 1.2496341
#> [251] 0.4214600 1.4234974 5.9685678 1.4870299 6.5259218
#> [256] 10.9112473 3.0024679 5.5636888 1.0834279 6.3247514
#> [261] 8.4820938 1.0724249 1.7462807 0.2578186 2.8363187
#> [266] 2.7652164 9.1981554 2.7739875 27.5907974 3.4431446
#> [271] 7.6219230 7.2448454 0.9365459 1.4254460 1.9484344
#> [276] 25.4685478 3.3151047 5.4201798 1.0591918 NA
#> [281] 2.2389860 7.2797980 177.9112701 712.0562744 1.9180223
#> [286] 1.0850360 15.4796858 2.2603140 1.6407841 3.5157232
#> [291] 1.3970622 2.8673556 4.1254120 2.8693426 4.2749281
#> [296] 0.8203884 2.7943869 3.3467057 3.0753074 2.7253366
#> [301] 2.1626289 5.0227842 2.3628030 1.2205384 0.8150464
#> [306] 3.2100937 4247.9296875 NA 4877.1162109 1.7616867
#> [311] 1.8174063 0.7508621 3.5258093 2.7256949 2.5574954
#> [316] 1.5129316 0.6094913 2.6934810 0.9192376 1.3823618
#> [321] 111.0667496 0.6147639 1.4611453 0.7056657 0.4673733
#> [326] 0.5605560 1.7962961 0.7591861 0.6475862 1.0416414
#> [331] 13.4244280 0.8334653 1.4189600 0.8297019 2.9611456
#> [336] 1.5615076 NA 1.4051919 NA 0.9027803
#> [341] NA 3.6805460 145.7117004 0.3470792 1.5938562
#> [346] 1.4932191 4.3753552 0.6481672 2.1519599 1.0128362
#> [351] 0.6915093 1.9947214 NA 0.2430045 2.9182062
#> [356] 0.6795040 1.6664841 2.3586757 0.8883650 3.2617228
#> [361] 2.8392348 1.6086547 0.6026787 1.2004064 1.3173006
#> [366] 3.6571672 2.2624974 0.9398944 3.5710230 1.0233446
#> [371] 1.3720112 1.0055754 0.5655854 1.5054272 1.0397023
#> [376] 3.3238027 2.1077371 2.7032313 3.8104489 2.8180630
#> [381] 7.8234811 4.9062996 1.3160198 1.0076615 1.0950712
#> [386] 3.9908450 4.2435541 0.7174511 5.2825084 0.8802158
#> [391] 0.3888731 0.7251250 1.0856016 0.5659533 0.4917859
#> [396] 0.4981284 0.9569623 0.7542380 1.5303959 2.5028656
#> [401] 1.2001288 1.7258205 0.5163053 1.1474305 2.1486368
#> [406] 2.4524887 3.0907106 3.9163256 6.1858454 6.0148554
#> [411] 3.3022101 6.8535738 0.9091500 5.3752904 2.6620142
#> [416] 14.2303267 0.3268394 3.2703023 3.7256567 3.4891803
#> [421] 6.1556778 1.2317547 3.0012579 6.3596826 0.6037928
#> [426] 0.5698133 3.7267346 10.2576380 0.8706866 4.9864445
#> [431] 4.3741260 1.0901091 10.9061537 4.1417003 0.5189514
#> [436] 8.2503548 2.2269182 NA 0.3962348 0.3956501
#> [441] 2.1887872 1.7824746 3.7205944 3.6917150 0.7435969
#> [446] 9.6342754 24.0429783 1.5591800 1.9394968 3.0097637
#> [451] 5.7848148 9.7665796 2.0749505 0.4870613 2.6161067
#> [456] 0.6431220 1.2651863 3.6852174 1.1431226 21.9405651
#> [461] 1.2046832 1.8209703 0.5066832 0.9095992 9.7609291
#> [466] 3.3460293 0.4121834 0.9202868 0.6617500 2.0171058
#> [471] 1.1644038 2.6082158 3.6380553 0.8339515 1.4020016
#> [476] 7.2250385 0.7268345 0.7294782 0.9507068 2.5481384
#> [481] 3.6101770 0.6119207 5.6888528 16.1643982 2.6750910
#> [486] 4.8989611 0.4897856 15.6936617 3.9424517 1.3334647
#> [491] 0.4453856 3.3320382 7.2846007 3.1082804 3.1209931
#> [496] 9.3145094 0.8143211 1.1926577 0.6434940 0.6482291
#> [501] 2.3913691 2.1481240 2.7833135 4.5657282 5.3265414
#> [506] 10.3909426 15.4557104 0.6454072 43.3171730 1.4262301
#> [511] 2.5555761 1.7280343 2.7765193 1.3546977 1.9755590
#> [516] 1.4631903 1.5031183 1.5941623 1.5892071 5.8746567
#> [521] 2.2622027 2.5123510 5.3907886 5.1953430 3.1958568
#> [526] 3.0968442 4.0582609 3.2192631 2.5363874 4.4969444
#> [531] 5.3325839 2.3695500 3.0062342 5.6627531 6.4923205
#> [536] 4.7920384 4.5700383 4.0006895 9.5566549 5.8871794
#> [541] 7.4422898 5.2004848 9.2628069 784.9196167 4.6816382
#> [546] 4.1257901 6.6807156 4.1821823 6.8080244 7.7087774
#> [551] 12.4988031 1.4474308 985.7752686 5.5145364 2.7746949
#> [556] 2.3561726 3.7145381 6.2821894 10.4331989 1.7673082
#> [561] 3.1912456 7.3891306 1.3537637 0.2704858 4.0082321
#> [566] 8.5943022 1.3064193 3.6371551 2.5390623 NA
#> [571] 0.7411902 0.5046070 0.5680771 1.2300223 2.8467999
#> [576] 2.6166818 0.5497193 16.0667572 1.9547974 0.9511589
#> [581] 15.4262342 2.4667881 1.0057802 2.2017863 3.7578349
#> [586] 5.2645087 0.7512842 2.5041356 1.8741575 11.7041502
#> [591] 22.0120201 3.6391535 2.4130566 NA 1.3888769
#> [596] 1.4703723 1.5291082 2.3573880 0.6399383 1.6180218
#> [601] 2.6052721 1237.6204834 19.8997154 39.6545715 4069.6533203
#> [606] 3.8779757 2.9248793 14.3836384 38.9334831 4267.4248047
#> [611] 6.9302726 5.0617962 4.1785703 28.3963814 6.4566789
#> [616] 3.4296451 3.7816880 11.0247078 27.2765732 2.2371943
#> [621] 0.9170940 0.7460240 0.8210835 1.6487689 9.5727634
#> [626] 1.6570802 NA 0.8196399 0.8946962 13.3170853
#> [631] 28.8410645 1.4570224 0.2454604 0.6586674 1.0929065
#> [636] 1.2879230 NA 2.9674864 0.5844296 11.5823431
#> [641] NA 2.6197197 0.6617230 1.0072753 1.0431374
#> [646] 1.7980380 1.8940541 4.3480368 2.6686113 NA
#> [651] 2.4794784 4.1561966 1.2986863 1.2777241 3.0134482
#> [656] 101.8303833 3.4352007 3.0277224 13.1016092 0.5244251
#> [661] 1.7064619 0.3486639 50.7429390 1.7649055 2.7503626
#> [666] 0.3800137 3.3221421 3.1534879 15.4715948 1.4704005
#> [671] 1.8855512 4.0389700 1.0823137 2.6767426 2.1071370
#> [676] 3.6332088 0.6826217 1.0185049 3.3197520 3.8973808
#> [681] 6.3822713 5.1280479 8.5763950 3.2864881 1.3272406
#> [686] 3.2673051 5.7568297 8.3966360 16.3681469 7.2476711
#> [691] 5.4942021 2.1211190 1.5519290 3.6947889 3.6529276
#> [696] 7.1617284 23.2306347 20.4122696 17.6928806 5.1081491
#> [701] 117.3710403 2.7161827 48.0717468 18.4067097 3.7467299
#> [706] 3.6180439 15.2693739 4.7089896 2.5892634 9.7226076
#> [711] 139.4568787 7.4229794 11.8008423 44.5208054 10.5249119
#> [716] 5.9375591 20.7310448 216.7257538 12.0509796 32.5807915
#> [721] 140.1287079 18.1983509 7.9473476 4.0145078 NA
#> [726] 5.9600363 4.9861374 40.1272926 16.1208572 13.0407543
#> [731] 6.8589644 4.9722199 11.0004854 7.2423544 6.4803615
#> [736] 14.1402922 23.9029579 15.6984034 17.9236164 3.2453372
#> [741] 9.5185261 8.6210403 3.0081079 3.5888493 6.0433631
#> [746] 7.4204693 11.3490496 3.6138747 1.7851294 4.3751335
#> [751] 38.7952423 25.8243275 20.6631317 2.3311818 701.8855591
#> [756] 333.9076538 5.9194384 5.2374392 50.5938759 6.6050563
#> [761] 29.6740894 6.8099937 729.9282227 4.7258272 377.9533081
#> [766] 3.7225027 5.8937206 8.3230190 8.7469826 1.2891947
#> [771] 2.5859647 75.5915375 4.7863827 45.3149376 2.4862499
#> [776] 6.5945859 6.9634075 35.3314667 3.0827610 86.5234299
#> [781] 1061.5833740 3.1925797 39.1028519 5.1561656 727.2340088
#> [786] 4.6954513 4.7806416 2.0299914 67.0260162 NA
#> [791] 8.1010742 1.6631573 1.8530455 79.0636978 30.3669281
#> [796] 0.8927791 10.3849897 1.0268044 3.1952302 0.7755960
#> [801] 5.7425203 7.2843447 1.2209314 1.8280462 7.8098340
#> [806] 3.8438480 1.6488374 1.9727218 21.7567501 0.7352579
#> [811] 1.9201657 52.0057869 127.3143768 5.6821346 2.5211284
#> [816] 2.3330369 2.1601825 9.7243500 9.3593960 2.9505100
#> [821] 5.4880805 8.6688786 2.6024387 11.9668989 2.1561337
#> [826] 12.3195581 1.2745084 1.9595144 5.3821392 19.5642281
#> [831] 16.0015182 3.0178616 4.6744413 21.4054432 2.3722150
#> [836] 17.8877811 7.6304569 36.1747169 4.2541361 33.9568787
#> [841] 6.3856707 2.6096203 13.1905775 2.4566786 47.8784180
#> [846] 6.1847539 46.1483879 3.2702918 3.5770297 12.7957163
#> [851] 5.9115996 4.5292888 5.1561074 4.0170932 3.9871593
#> [856] 5.1863651 1.8988326 7.9102783 5.5774412 2934.2749023
#> [861] 1.7749847 8.5469313 16.3584347 10.1700211 0.2661873
#> [866] 6.4565039 8.4741077 0.1989040 0.4645469 1.7402502
#> [871] 29.9008598 0.5417653 0.7619571 2.3052864 23.7764740
#> [876] 3.5269954 6.3716865 4.5834875 0.6944337 9.3756256
#> [881] 0.4391756 0.4102839 NA 0.4088447 4.4651365
#> [886] 2.3560781 0.8629871 1.7296497 2.3054824 0.4738956
#> [891] 0.8453057 0.6674856 2.9770677 0.9882508 2.5485382
#> [896] 1.3252913 0.9046306 3.3730309 1.3325157 0.8287238
#> [901] 3.2393219 2.8408599 1.1969875 4.9852405 16.4274426
#> [906] 0.7676155 5.0130310 2.2948136 2.0398157 1.0926311
#> [911] 1.6752559 2.0661981 0.7237425 4.8128242 11.1487827
#> [916] 0.8632420 1.8443307 0.4144078 1.5147480 2.7527251
#> [921] 13.0752268 1.8903236 7.9921408 1.5463461 0.8109860
#> [926] 1.0667026 1.1346698 3.6560605 3.3797889 3.5629609
#> [931] 3.4544880 4.5592079 16.6013031 4.6765566 1.6968081
#> [936] 1.1385404 1.4192655 2.8486712 2.6313102 0.6307487
#> [941] 2.3814776 2.7711914 0.7061450 4.9312925 1.1609619
#> [946] 6.7711430 2.8384895 3.6513107 3.9373784 5.0244951
#> [951] 9.3638000 17.6887131 8.8094635 4.6279211 1.6137590
#> [956] 0.6182525 0.8786079 15.0547085 24.5552330 7.9792995
#> [961] 64.7950058 97.0138016 9.9027309 0.5985516 1.5779028
#> [966] 11.9724445 1.9544525 163.6211090 2.1614091 8.1083727
#> [971] 2.6397953 3.5538256 7.1930056 0.6664237 442.5400391
#> [976] 3.0430346 6.4073930 5.0266309 5.8590345 19.6998043
#> [981] 4.3898072 1.1305788 13.5925970 7.2308283 1.1136869
#> [986] 1.3520640 1.6164834 1.0973755 2.0894964 9.5560360
#> [991] NA 8.9295197 779.4788818 2.5705523 809.9169312
#> [996] 3.0098879 94.3604507 8.8758574 3.4457500 4.9686089
#> [1001] 1.5195554 2.1578839 4.8023272 2.1327522 0.3200580
#> [1006] 12.4226055 12.1050863 1096.2231445 1066.9301758 129.4904938
#> [1011] 4.5963664 3.0736189 8.0031509 3.2705626 0.3757358
#> [1016] 15.2121391 2.8834486 15.9752741 1346.7353516 2.6217887
#> [1021] 1228.9621582 5.0895567 2.1218104 184.3674316 4.2164469
#> [1026] 2.8267379 6.7104831 3.8288674 3.0664554 4.4310689
#> [1031] 2268.6291504 1836.1269531 2.0746028 2.2918937 10.7542877
#> [1036] 51.3735046 6.6448908 6.1085730 1.4560728 2.8452797
#> [1041] 3.7901433 2.0456872 1.9614782 5.3406096 6.5233908
#> [1046] 5.8153443 4.9779243 5.2980742 1.9569403 3.0366473
#> [1051] 0.8441148 0.7282568 0.6270677 1.9153599 2.5157802
#> [1056] 2.5211849 1.3664742 16.0325813 1.9972137 1.1040767
#> [1061] 0.9441139 0.9234766 2.2210290 0.8783525 2.1501570
#> [1066] 8.6357822 NA 1.6162349 1.9563223 1.2261102
#> [1071] 5.9679408 2.4582469 3.3074541 0.6635520 3.4940929
#> [1076] 2.6760423 1.0429727 0.5086066 1.0268028 3.8812792
#> [1081] 3.5886321 3.9809752 7.7894588 0.8405986 1.1336366
#> [1086] 2.8971629 0.7070799 0.7151046 0.5353748 1.3802744
#> [1091] 4.1309824 4.8025975 8.7120161 2.2102430 10.5090933
#> [1096] 10.4218655 22.3232746 36.9990120 2.9090240 2.3153019
#> [1101] 76.6485138 2.9318345 1.6010859 5.8318458 2.6627228
#> [1106] 6.4075303 2.0914986 11.3002634 1.0663972 9.0038490
#> [1111] 7.7891731 1.4418688 4.8872490 13.7815018 7.2834721
#> [1116] 0.6722869 12.7788944 14.1886673 6.0767541 17.2682152
#> [1121] 5.8392038 3.2879653 25.1995487 27.0342846 11.1438723
#> [1126] 38.4615974 10.6467648 20.4387722 3.0112209 1.9950336
#> [1131] 10.1919136 1.5909361 4.7708578 1141.7640381 9.8121815
#> [1136] 22.5490570 3.3931487 9.8001604 4.6790090 1271.7685547
#> [1141] 2.8506472 1103.0401611 17.5955009 5.1776738 2.9927020
#> [1146] 2.1627703 3.1698050 NA 13.8667717 1411.4106445
#> [1151] 1272.9426270 21.3413105 0.8185354 2.8377655 2.0303440
#> [1156] 21.7762794 1345.3233643 1350.6333008 6.6278219 29.8857651
#> [1161] 52.9047775 1294.8477783 12.9526482 9.6068335 17.7135353
#> [1166] NA 2.6911139 3.7828991 0.7059131 5.9516506
#> [1171] 4.0300317 2.7630870 5.6403260 7.4636693 2.0741365
#> [1176] 5.1799192 6.4390368 9.8243055 1.5092793 4.3210769
#> [1181] 1.7446249 3.4029963 2283.2792969 3.9234891 6.9994988
#> [1186] 4.1006012 0.6282611 1.8591487 4.4599166 2.1398880
#> [1191] 2337.1853027 4.3932910 9.6464186 12.7156582 0.9086848
#> [1196] 9.3874264 3.2540348 2646.2402344 6.6778555 3.9801006
#> [1201] 21.6677666 1.3727722 2.5189867 7.1042595 10.0052004
#> [1206] 1.5813689 23.2121410 2.8682461 1.3422523 0.7672858
#> [1211] 0.6866859 3.6084216 18.3351460 0.8394666 1.9887234
#> [1216] 1.5026515 1.3683980 5.7951837 9.8654814 2.1221330
#> [1221] 2.1047983 3.0744431 1.9053514 2.6970651 2.0816550
#> [1226] 1.9742609 9.6418734 7.6057978 3.4838214 8.8084707
#> [1231] 6.6040335 3.2611005 9.0701380 3.0636346 1.7033354
#> [1236] 12.7065725 1.9277512 11.4872437 12.6639729 1.8488846
#> [1241] 4.4552321 4.2770810 9.8547640 3.8485291 2.1161973
#> [1246] 2.0724041 6.0630097 2.5432432 29.9757633 3.4243822
#> [1251] 1.5343714 7.8658175 1.2879468 6.8999977 6.3626080
#> [1256] 268.5536194 5.7748380 NA 5.2410469 2.0550699
#> [1261] 0.4435597 5.4935684 0.9196581 75.9311295 3.3142908
#> [1266] 80.4429626 5.9293828 2.2810714 2.8269784 20.1030636
#> [1271] 1.1432312 3.6678035 1.0070125 3.5375540 2.9908266
#> [1276] 22.7290974 2.3943624 0.9211383 1.9665295 0.8259042
#> [1281] 0.4845653 25.3855438 3.8617392 6.6030455 18.8038082
#> [1286] NA 0.5806934 3.3330538 101.8569031 14.4165783
#> [1291] 1.7161655 7.3081284 2.1098645 0.8144141 1.3382024
#> [1296] 1.4885535 0.8770674 0.6791099 1.6695246 1.2673935
#> [1301] 0.6891360 1.2439822 5.2793922 1.2573010 NA
#> [1306] 3.7120969 4.9950824 11.5787373 1.9612808 10.3544693
#> [1311] 6.0420938 18.6085644 1.5667670 1.2138346 30.0845642
#> [1316] 19.6141224 3.2639980 3.6546156 29.6098766 2.8610103
#> [1321] 6.0040512 1.8776323 3.8169975 4.7191100 1.0195367
#> [1326] 3.8605018 8.1590776 2.8668685 12.0980616 5.0243416
#> [1331] 7.0915346 2.7560163 6.1319265 5.5243936 46.6120872
#> [1336] 24.7890339 6.4685655 9.1421661 10.3797007 10.7507782
#> [1341] 7.7589669 12.2742815 4.6532531 5.1379547 23.7994213
#> [1346] 29.3476200 4.0036497 7.5182543 11.9706621 63.0551071
#> [1351] 5.1469116 8.4730129 72.0657578 10.1063595 65.6397705
#> [1356] 4.1467509 13.0881023 27.2654285 96.3133926 108.4684830
#> [1361] 15.9769907 16.9430561 14.3318129 4.6791883 126.8739243
#> [1366] 6.6298981 18.2740936 28.1178570 8.4018116 24.9042740
#> [1371] 22.9308643 5.7845116 2804.5610352 6.1323233 7.0216298
#> [1376] 34.0006905 11.0745640 23.1465626 26.1845646 15.7390766
#> [1381] 6.2733459 11.7022743 4.5109916 3.0380044 72.1676025
#> [1386] 18.0594807 16.0637493 8.0370464 176.6657867 2181.4362793
#> [1391] 9.6984138 3978.9870605 8.6989155 11.3172474 39.0961494
#> [1396] 2588.9499512 3505.7983398 144.8855133 5.1680942 30.6885090
#> [1401] 6.6054115 5.6432614 2.7389083 53.9481773 62.0971260
#> [1406] 2972.6391602 161.4343109 35.4546394 11.2379074 13.3652372
#> [1411] 15.0986557 20.7111149 9.2977657 425.2767639 216.2778320
#> [1416] 7.9142838 5.0309882 1.0399815 1.0769179 4.5379925
#> [1421] 3.6899538 2.2575519 17.9379234 22.7302589 6.5185528
#> [1426] 19.6637669 0.2854209 7.1664414 9.3295479 1.1119181
#> [1431] 1.1998634 44.4294777 6.0005445 1.2984159 0.4181677
#> [1436] 1.3962787 0.8486760 0.6826237 1.3289268 41.6967010
#> [1441] 1.6696991 0.8142897 0.3997326 64.2423019 4.2105532
#> [1446] 8.1320543 0.8433636 21.3994884 0.6499675 1.6173933
#> [1451] 2.2819724 79.2927475 2.7942066 0.6122881 5.6340408
#> [1456] 2.3877366 2.5358839 1.6963638 2.8207321 1.6094570
#> [1461] 3.0496490 5.2695556 0.7497861 1.1942160 2.8949137
#> [1466] 1.8337013 7.7278204 3.0212984 4.6463079 1.1313710
#> [1471] 3.0398493 1.1938435 0.5559963 3.7946312 0.7459199
#> [1476] 2.6556878 4.8397417 2.1508582 4.1808691 1.3044889
#> [1481] 3.6563311 5.6848607 4.3057156 4.3253880 44.0154037
#> [1486] 6.8031969 1.0149941 16.5446339 50.2134972 1.4178553
#> [1491] 5.2461462 6.6964250 1.3668361 7.0547361 15.4979038
#> [1496] 2.5466137 12.0476017 16.9358387 4.2765665 3.0282452
#> [1501] 2.0438735 3.0750351 30.2993698 4.1311669 10.5620518
#> [1506] 6.3157725 2.0541127 6.4616790 52.7001801 6.5350013
#> [1511] 2.7538323 5.5697684 7.0785255 0.7346154 4.6684666
#> [1516] 2.3665750 2.1643167 13.1310358 11.3682957 1.0712550
#> [1521] 12.9957743 1371.8287354 1.3735292 4.6520715 20.4687824
#> [1526] 14.5587559 1770.5562744 2.6400170 15.1324883 1.9400562
#> [1531] 4.7408748 2.0517797 32.1368103 0.9072015 1766.3382568
#> [1536] 11.0576754 2.3649356 7.1545792 5.3592334 29.8745804
#> [1541] 99.2443008 1.9519557 3.5576828 5.4862013 19.1255341
#> [1546] 1.0205218 74.6288681 2.6257737 3.8454990 0.9508846
#> [1551] 15.9569130 3.9706428 6.0614076 0.9358777 0.4645265
#> [1556] 1.3660873 2.9063818 6.6953201 1.7190092 0.9622721
#> [1561] 1.7018661 11.3596716 3.0443096 0.6346511 10.0340395
#> [1566] 0.8095649 0.9709995 3.2444031 0.5028589 2.7934396
#> [1571] 6.8828177 0.5587204 1.6042945 1.3587892 0.8313419
#> [1576] 2.4558291 1.7974735 3.6726115 5.6037540 6.7877126
#> [1581] 3.6458204 0.8579472 8.0946331 12.1906815 1.5101539
#> [1586] 6.0351577 11.0641689 17.6339569 3.6026669 16.1998940
#> [1591] 2.8316128 4.9478583 27.5424156 7.0986490 3.9904847
#> [1596] 4.6039705 3.3738575 12.1051941 11.3188763 14.8368187
#> [1601] 9.5498314 1.9773377 13.8263979 23.9170589 13.4375019
#> [1606] 9.8440132 11.2628098 2.5026691 22.5849972 29.0189171
#> [1611] 18.8118534 22.0679207 4.0647068 2.0535538 1.5048116
#> [1616] 3.3824587 2.3311884 32.8195572 37.0816650 7.0685787
#> [1621] 5.5009451 5.8517952 4.9319654 3.4151902 4.2116370
#> [1626] 8.8043442 1099.1087646 5.3256183 5.2614517 480.0812378
#> [1631] 5.3612590 12.7172546 6.3167968 14.1994934 59.6927795
#> [1636] 964.6279297 1400.3564453 535.6882324 23.6954079 14.5343561
#> [1641] 2.9595783 17.3649712 1.0824900 1221.8245850 147.4770660
#> [1646] 683.7954712 6.1305389 4.9751444 15.9264107 18.4570618
#> [1651] 1083.5487061 191.7899017 875.8353271 577.2622681 71.0305328
#> [1656] 8.1075430 5.3227873 11.6976309 0.4594952 1.4792782
#> [1661] 1.6332765 4.2618947 6.4441838 2.9269114 130.0458374
#> [1666] 5.1534815 3.5288918 2.2328265 7.3872643 35.0871201
#> [1671] 164.8385925 41.0983658 3.6965251 3.6046982 34.9871025
#> [1676] 54.6165199 3.3092432 6.2893038 54.9078522 4.6468277
#> [1681] 5.5542288 52.0769882 7.5430121 11.7523575 32.7870789
#> [1686] 4.3187008 22.1579170 5.4699397 1.8834021 9.8157177
#> [1691] 3.3649621 10.4119244 12.9897251 3.0194745 20.3988075
#> [1696] 9.6839437 1.9245056 27.7328892 4.6774187 2.8958423
#> [1701] 5.1254649 25.3332367 15.3362989 5.6253791 9.0580263
#> [1706] 3.3217881 16.1069527 5.7460155 9.0998583 6.4395881
#> [1711] 27.5900745 11.9862118 10.4788322 4.7713022 7.4465909
#> [1716] 21.6194534 3.4900868 11.9859657 7.1533313 53.6382294
#> [1721] 22.4019108 3.4466097 11.8166838 31.7925739 7.1903763
#> [1726] 22.3436050 21.0166111 11.6625166 9.6446323 11.8060122
#> [1731] 10.7140808 20.5072823 5.8385706 12.4566240 27.2418690
#> [1736] 1374.2124023 5.0976796 8.5298624 8.9547720 14.1582394
#> [1741] 3.6345949 8.9698286 746.4771729 16.0491295 14.2799292
#> [1746] 12.4969416 16.1063213 17.6739025 13.8779955 5.8102989
#> [1751] 4.7912970 8.5829220 7.5092740 16.8982792 8.7955656
#> [1756] 27.1313457 10.5999660 102.9700699 5.2469325 78.4538498
#> [1761] 23.4321194 13.5085144 28.0264759 42.0644264 3.0340507
#> [1766] 16.6190434 1717.7260742 6.9299235 10.5656214 27.8841705
#> [1771] 17.4288807 47.0360756 5.2313685 49.5542450 4.6091428
#> [1776] 7.5845995 5.1649594 5.6527944 6.7112393 7.1952438
#> [1781] 2.8361075 936.4321899 10.8392658 300.9952393 3.7918711
#> [1786] 5.8666444 9.0892420 7.9013219 5.9726405 6.4315825
#> [1791] 6.7033081 174.0914154 9.4583406 13.8090487 25.1833706
#> [1796] 10.3936863 11.1641397 3.6089337 4.3211293 4.5236745
#> [1801] 5.9249415 19.1762638 44.9207954 1162.3734131 9.1217899
#> [1806] 6.2500715 14.8576088 9.4355440 27.3175831 75.4738541
#> [1811] 1927.7430420 10.6061239 2.5857298 5.2106519 16.0033913
#> [1816] 13.5474854 30.9249420 2882.4428711 37.9162636 75.5530014
#> [1821] 6.0951519 4.3891191 10.3061371 6.6429935 39.4176941
#> [1826] 10.0774698 83.8411789 21.2222996 NA 0.6372588
#> [1831] 3.5606251 2.5927734 3.2620273 2.3674390 1.8626410
#> [1836] 6.6079321 2.4800649 6.8728633 2.8025930 10.0381966
#> [1841] 4.8456726 89.1146164 4.3337107 3.8712926 42.9793968
#> [1846] 2.5299530 7.4297533 4.6089058 3.5378845 0.1817949
#> [1851] 1.1290787 1.5784779 1.1125787 2.0033450 NA
#> [1856] 1.0376083 1.9011122 2.3181555 1.6446155 0.9089839
#> [1861] 0.4841611 0.6897894 0.7658206 0.6156495 1.4828655
#> [1866] 2.3340645 1.1662629 0.9815852 2.3534944 2.1499813
#> [1871] 0.7019742 0.3516256 3.2850077 5.8242555 NA
#> [1876] 1.3007295 1.5015216 0.8093400 3.2225409 2.6426129
#> [1881] 2.3901305 2.9790335 2.9894137 5.2419791 4.8620076
#> [1886] 0.3997991 3.0470288 11.9397249 3.6320026 8.3058691
#> [1891] 1.3834330 4.1316576 3.7130520 3.9005911 16.1633472
#> [1896] 3.6801958 12.0339565 1.1500748 1.2219627 0.5826390
#> [1901] 3.3800447 5.2994637 2.6451149 2.7287869 3.1930273
#> [1906] 1.8436399 2.9893756 4.9069676 5.8526864 1.9956532
#> [1911] 5.7515755 9.3381653 3.7572572 0.4758977 0.5950958
#> [1916] 1.2651656 0.5604061 1.5873302 1.7309831 2.2026825
#> [1921] NA 0.8321332 3.1331923 1.8596388 4.0986276
#> [1926] 1.4462790 5.0948448 3.3446779 14.7490797 4.2720022
#> [1931] 2.3573306 12.4373245 5.4349809 5.5168352 7.1895194
#> [1936] 1.3353804 31.1978798 6.0550547 5.3862381 2.3742425
#> [1941] 1.6283454 3.8663564 2.5557170 3.9340308 4.8355684
#> [1946] 0.8258027 NA 1.6850734 8.2018566 2.8547313
#> [1951] 2.7901099 1.2237481 1.2317826 5.9977107 2.4985216
#> [1956] 5.4254308 10.2619724 3.6585343 0.7505842 1.0290449
#> [1961] 1.4102111 31.6295795 6.0977502 3.3116033 6.6416101
#> [1966] 13.4169693 2.0772371 2.5722609 1.4239205 1.1951129
#> [1971] 19.3541183 6.6528106 7.6534238 3.3320429 3.9984677
#> [1976] 2.1092513 8.5107756 2.7920604 28.8576584 1.6512991
#> [1981] 2.0430546 6.1738434 2.2872250 3.3046799 4.1366568
#> [1986] 2.0184140 3.4700344 2.1820960 9.8032455 9.3485680
#> [1991] 7.2345314 20.3594875 7.5089312 1627.1451416 5.3730831
#> [1996] 8.5319214 12.6467562 6.4055276 5.4178729 3.8174756
#> [2001] 60.1280174 6.4091296 14.2986145 13.2007456 17.6533527
#> [2006] 4.6876645 2218.6071777 5.9873571 5.4104090 13.5144920
#> [2011] 4.3813696 8.5980825 19.0352879 1989.9606934 6.5922198
#> [2016] 8.3580027 5.7576084 341.0995483 3.3168538 9.5687895
#> [2021] 9.9157639 8.6866083 366.3923645 6.9480758 10.2027311
#> [2026] 7.9508238 12.0425510 5.7334862 5.7009211 9.3929729
#> [2031] 4.2744284 16.8116989 3.3318050 61.5876274 3.6692767
#> [2036] 4.6599665 16.0338726 43.3646393 0.4666836 1.1231649
#> [2041] 3.3837605 5.3292937 3.6405241 9.7523746 0.9140767
#> [2046] 0.7435523 0.2535407 5.4856982 15.5203247 3.2470930
#> [2051] 2.7420650 0.6101449 1.0404911 2.7917345 6.6811185
#> [2056] 2.0745556 1.2540616 0.3098164 1.6586421 9.5700045
#> [2061] 0.7319268 12.7017021 0.6637144 1.5885706 0.8827127
#> [2066] 2.1940100 3.7667232 2.0905075 0.6379106 2.9787626
#> [2071] 0.4874143 0.3836070 0.5107485 1.1823709 1.2667042
#> [2076] 19.2034588 1.6550628 1.1661954 0.8966293 3.2779715
#> [2081] 11.0150242 1.6130922 26.5314026 2.5117977 2.3132839
#> [2086] 2.2919271 3.1338563 1.5114740 2.0381725 5.2738991
#> [2091] 2.9814677 3.5173035 2.9813018 1.8998176 7.4101906
#> [2096] 5.3717108 4.2854128 0.2399864 6.4908400 1.6760609
#> [2101] 4.9045634 3.0678418 2.1384492 9.3216219 10.4645767
#> [2106] 0.3764410 1.2218126 1.2995534 9.7801199 2.0047073
#> [2111] 26.3789768 1.0760976 0.9736537 2.0357609 1576.8568115
#> [2116] 1.4469386 3.0481942 1.6708410 0.9496741 2.6938272
#> [2121] 1.8960530 2.1988547 1.2718045 1.2236259 1.4919758
#> [2126] 4.3979073 71.3828812 2.0391872 2.2703087 2.6103971
#> [2131] 1.4898348 1.5168892 0.8818803 1.8848048 0.6037043
#> [2136] 1.8731611 7.1765389 1.9562593 11.7375755 1.6933267
#> [2141] 1.5986614 5.0060325 5.2257977 4.1671195 4.4332304
#> [2146] 6.8677626 2.4653215 6.3500257 10.9838686 6.4111762
#> [2151] 9.4890432 4.7053976 1.8102279 1.4213001 1.6444297
#> [2156] 1.9174886 0.4131429 1.5709261 3.1760740 1.0655589
#> [2161] 919.2457886 1.5643872 1.7901465 1.7823997 6.9229293
#> [2166] 2.2213833 0.8164910 6.7707281 1034.1815186 2.8209743
#> [2171] 0.6380021 1.1908475 2.2225995 NA 1.8188674
#> [2176] 2.1192210 1.1434188 1.2050375 1.7906495 3.5892394
#> [2181] 0.7533023 NA 1.4840194 2.2305763 2.4143374
#> [2186] 1.9114826 1.0137789 4.2369156 2.1006327 2.7787836
#> [2191] 0.9601986 8.1295986 7.2806950 1.1032010 2.3385823
#> [2196] 1.1522059 1.4659723 8.5681458 1.8708251 9.6687212
#> [2201] 5.7849174 NA 2.4449232 1.6775528 4.9673247
#> [2206] 1.8457032 11.6521568 5.6321864 0.9540480 12.1773853
#> [2211] 0.4559720 0.7230437 1.5481060 1.7013626 1.9925474
#> [2216] 4.0501847 1.5732721 2.9336040 1.2153755 2.3473213
#> [2221] 1.6044481 3.8102901 5.9565816 3.5146675 2.6926224
#> [2226] 4.9956584 7.3882804 31.3160343 1.3993325 6.2274776
#> [2231] 2.9698203 3.2010090 1.3510920 5.6216455 3.3691561
#> [2236] 2.7835443 0.4399868 2.5057735 5.0695376 0.6980689
#> [2241] 1.8963152 8.0071716 9.3238049 2.4393957 4.9562755
#> [2246] 2.4916747 1.4161593 1.7179910 2.8897645 2.1166553
#> [2251] 2.5877254 12.7337999 1.2360386 2.1893265 1.5300977
#> [2256] 3.0833626 8.4302454 4.9040179 10.9480925 2.1738741
#> [2261] 3.3114023 6.0667453 NA 0.6554648 3.5936801
#> [2266] 1.5739572 5.1975174 3.9125762 9.3439398 7.9241571
#> [2271] 3.9933949 4.6318932 8.3959627 0.7565725 2.3423707
#> [2276] 15.7967987 12.7200508 3.1725900 11.7995720 0.9761860
#> [2281] 1.1349560 1.9718618 4.9297872 7.0475721 2.8053823
#> [2286] 34.1454964 156.3358002 2.1250823 4.3354549 64.4767151
#> [2291] 278.9322815 0.7963252 3.8422236 61.7775192 190.2516785
#> [2296] 1.4124620 1.3479694 1.6552913 4.4471312 329.0337830
#> [2301] 2.3342044 2.1571195 0.9733863 2.4092717 4.8433948
#> [2306] 2.3092387 360.7164307 3.0873225 1.7288386 2.3073480
#> [2311] 0.5996385 5.3622589 1420.7687988 7.9223099 5.6030631
#> [2316] 0.6277632 0.8355366 0.4711815 1.3496840 0.8089792
#> [2321] 5.8526330 1.8503476 0.6572140 305.6041870 1.4397534
#> [2326] 0.8994330 277.0521240 2.2045338 NA NA
#> [2331] 0.5602361 1.2226131 1.3568290 1.7129589 2.0047822
#> [2336] 1.7247094 6.7786064 2.6411271 1.9010485 1.7608452
#> [2341] 4.4309907 3.1676409 3.3830304 4.6565275 3.3298314
#> [2346] 1.5452514 3.5828693 1.7718617 0.8061830 1.2403909
#> [2351] 4.6554375 1.6074916 3.6399906 1.1538744 4.6306715
#> [2356] 22.4013309 4.1293302 6.8009396 32.1985893 9.7335100
#> [2361] 0.5418288 0.5632182 2.9177530 2.1166260 6.6263289
#> [2366] 2.1903484 6.9556012 3.3954957 3.9068611 1.8365928
#> [2371] 3.1866224 6.6193805 5.9870872 2.3148339 31.7243080
#> [2376] 1.7895136 3.5860963 1.8591824 77.3616867 3.4268165
#> [2381] 0.5249496 1.5734435 3.8141046 2.7002668 1096.7601318
#> [2386] 1496.8631592 3.2043941 1.8505754 587.6149292 1741.0694580
#> [2391] 885.2774048 23.7601643 594.6196899 0.4923916 29.5300331
#> [2396] 1.3024459 NA 0.7680741 0.8888112 2.1178827
#> [2401] 1.4857029 1.1276029 1.3496284 2.1555452 2.1827545
#> [2406] 1.2788655 1.2924441 0.9934639 5.3240347 1.0079803
#> [2411] 1.3528888 10.8723679 1.9321351 2.5913424 2.0170100
#> [2416] 2.6124203 25.0653572 0.7242321 2.9123745 1.8755200
#> [2421] 5.4409308 1.6049031 4.8650875 3.9305305 2.3677256
#> [2426] 2.1769772 6.9278378 0.9471752 11.2096748 8.1439409
#> [2431] 7.7992425 8.1538477 7.7033796 15.1453743 31.8052731
#> [2436] 18.0042858 1.2677478 5.5277863 2.1457639 3.4404881
#> [2441] 6.6042099 4.3178186 10.6584396 4.6253743 4.4684582
#> [2446] 9.8839989 0.4913557 4.2288108 2.3422270 220.2428894
#> [2451] 76.3024292 2.4397397 0.7708665 0.5308517 153.5867767
#> [2456] 0.7601256 5.8568654 3.3581190 1.1768376 1.9733516
#> [2461] 8.9577475 1.6991717 0.6544989 0.8063468 0.5237735
#> [2466] 1.2291242 0.4108943 11.5966806 1.5359516 0.5914992
#> [2471] 1.5501552 0.4674177 1.4722780 10.3540297 12.8971653
#> [2476] 76.4729843 3.3384745 8.8360243 0.8269219 0.8899398
#> [2481] 6.1822929 1.4949089 6.1136923 45.9732246 1.8630898
#> [2486] 0.4607324 2.4314377 NA 0.3267280 7.7629838
#> [2491] 0.9005343 6.4958630 0.6801585 1.1879852 13.1560583
#> [2496] 2.3879337 NA 0.7733446 1.1475980 3.0229218
#> [2501] 14.0746317 0.5247985 1.2050325 9.2619877 0.7634576
#> [2506] 1.0241749 1.0909539 5.3293109 2.7063551 3.4095821
#> [2511] 0.6897098 1.0274373 0.8178927 1.5089374 0.7430726
#> [2516] 1.1275156 21.9422588 1.3811653 3.5942285 1.6271605
#> [2521] 2.1611414 0.8879982 0.8763292 1.7225560 13.2923336
#> [2526] 3.3895564 21.7352753 0.4632308 1.5177442 1.9604896
#> [2531] 5.8416729 0.6393901 2.9566762 0.3774208 1.6599923
#> [2536] 3.0378304 2.7158394 0.6954392 1.9243698 1.4208319
#> [2541] 0.3644979 0.9764859 1.7312529 1.9927244 8.3358564
#> [2546] 0.3344689 1.5944563 1.4439087 2.7181752 0.9094949
#> [2551] 1.2030648 2.7429266 6.6508756 2.8126271 3.6208489
#> [2556] 6.5295534 0.3424659 0.5115961 0.5035965 1.0872059
#> [2561] 1.5785052 0.2461324 1.9896445 0.8427312 0.7660883
#> [2566] 2.3450923 2.4805903 0.8671566 0.6639645 0.5334827
#> [2571] 0.4702013 3.1127882 64.2761841 5.7723532 0.9449326
#> [2576] 9.8559513 1.5376849 0.6502208 0.8867813 0.9406697
#> [2581] 2.6839008 3.2349586 26.2348919 2.3463531 0.7232664
#> [2586] 0.5971866 2.7867749 1.2694967 5.0291767 1.4869373
#> [2591] 1.9182748 0.8268527 4.4961567 0.4737805 2.8711464
#> [2596] 3.6332841 1.5385498 4.7165794 1.2112534 2.5973957
#> [2601] 4.9447703 0.9545295 1.4072028 1.8630619 1.4024746
#> [2606] 0.5282810 1.8168713 4.9301190 0.1499837 0.7057289
#> [2611] 1.6758749 1.9225767 0.6456749 1.0736265 0.6631852
#> [2616] 2.0254166 0.8686433 2.9635856 2.8306408 0.6591658
#> [2621] 1.8536581 0.7700182 0.6434809 3.0102704 0.6942499
#> [2626] 2.7054408 6.5840912 0.4648814 2.2047083 0.8025916
#> [2631] 0.9868059 0.9812470 1.6068337 1.6466388 0.6577185
#> [2636] 3.3152702 0.3456087 1.4819642 1.6930362 0.6172115
#> [2641] 4.8450933 10.5450792 48.2902794 0.6500265 0.6208660
#> [2646] 0.6039580 0.9597225 0.3537683 0.8526208 0.2884314
#> [2651] 0.8374825 1.8498940 2.6492484 0.8956540 1.2795352
#> [2656] 3.1287794 2.0273194 3.7718840 1.8026490 0.7220110
#> [2661] 0.7175305 4.3183942 1.4981960 0.2836370 0.8527241
#> [2666] 3.5981882 0.3067524 1.3901409 2.7463164 2.1976302
#> [2671] 0.2309106 0.2099868 0.8203602 1.9904572 4.0084724
#> [2676] 0.5368133 0.4482878 1.4325606 2.3086529 0.4549935
#> [2681] 3.1426587 1.3987031 0.4663780 2.3534548 NA
#> [2686] 3.8060784 0.4556686 1.3394214 1.1004189 2.4211915
#> [2691] 3.3820996 31.4883842 0.7637222 2.0601268 0.7897440
#> [2696] 1.1024669 1.4127746 0.7815835 0.3922873 2.3492901
#> [2701] 1.7412961 1.0913932 2.1832561 5.6013675 0.5410951
#> [2706] NA 1.5191215 8.5703373 1.4032021 2.8650708
#> [2711] 1.6290002 0.6252948 2.6510727 5.6316738 0.6707575
#> [2716] 1.2557933 2.8557181 6.8578634 0.6140221 4.2484360
#> [2721] 0.6619285 1.2980875 3.2068131 1.3282340 1.5175761
#> [2726] 2.3767309 0.8093471 0.6040459 0.6218860 1.4518211
#> [2731] 1.7191668 1.9510572 0.3967183 1.0216656 3.4807718
#> [2736] 12.6569872 1.2336719 11.7144327 0.6784623 1.0983104
#> [2741] 0.8703533 0.9070129 2.6424198 1.1471686 0.9019348
#> [2746] 2.5049481 11.4490557 0.5566154 1.9753987 2.3320930
#> [2751] 3.7091351 2.4257834 0.7362632 0.8419358 3.9386895
#> [2756] 0.9128963 1.6723500 1.3298150 0.6335756 0.6562991
#> [2761] 3.2722898 2.3044112 1.4757828 8.4148054 0.3076566
#> [2766] 1.2632453 0.8317384 6.0139632 2.7857358 1.4718702
#> [2771] 2.8902125 3.4636676 2.9858587 1.1537194 6.6131492
#> [2776] 0.2267639 0.5474497 0.4946014 1.2936714 1.6368855
#> [2781] 0.9594902 1.4098917 0.7878828 0.6673341 3.1816411
#> [2786] 1.2840652 2.6560736 19.9554462 1.0239617 1.8550906
#> [2791] 2.3586714 0.4221451 2.8507216 3.4885705 7.3413062
#> [2796] 35.7378654 1.4843854 0.8229836 1.9679027 0.9418428
#> [2801] 2.5392017 0.9196062 4.2101269 NA 0.8723121
#> [2806] 1.1009536 2.2867596 5.5058069 1.2713206 2.4941492
#> [2811] 1.5185130 2.7117639 4.5919437 5.1406364 2.8025935
#> [2816] 5.8709002 13.7791338 14.5640020 4.8297606 7.8658409
#> [2821] 6.8229294 27.7137413 5.3379407 14.4617805 4.4964433
#> [2826] 16.1921463 10.2128115 4.2068844 10.3527794 30.8382893
#> [2831] 6.8301725 9.5161114 46.6889000 5.9609809 13.2284927
#> [2836] 7.8532238 36.7056198 8.2213917 7.4940300 11.8976650
#> [2841] 13.4008360 19.2198009 2.2085142 16.2607841 27.6975002
#> [2846] 84.5502777 8.9375477 17.9089260 15.0652647 9.7659874
#> [2851] 20.4718914 10.5261488 102.4522476 17.9931507 11.4199753
#> [2856] 19.1676826 17.0927010 24.3487606 6.6103988 8.1302404
#> [2861] 9.6394157 30.0782166 10.6189022 3.9972832 13.7391968
#> [2866] 28.9560966 6.7734051 8.2172832 10.3078299 20.1655922
#> [2871] 18.3504696 2.8396056 27.5377102 4.3330150 4.9756093
#> [2876] 8.2070942 2.5828452 7.6423254 7.4956326 3.3020027
#> [2881] 8.2716713 10.5372095 5.9630933 5.2237558 9.0436430
#> [2886] 7.9005804 4.6265149 4.5348096 14.0217648 11.7508678
#> [2891] 5.1471868 11.1270819 7.9933724 12.3714352 NA
#> [2896] 9.6415024 1.0363822 2.1618273 2.2038519 16.1064682
#> [2901] 6.1964874 5.3220701 1.6039859 98.4771194 4.0282149
#> [2906] 2.6477625 16.8007278 3.5877471 2.6565950 5.3537140
#> [2911] 39.1409950 3.9032741 51.0578613 14.7823324 23.7130947
#> [2916] 41.8512573 27.0040703 8.8542757 40.3777084 36.5384369
#> [2921] 6.2642756 10.2587223 25.8907166 44.1283989 16.7547779
#> [2926] 1.5664260 36.2730598 45.0348854 5.4926982 9.8852091
#> [2931] 38.9618721 45.9797630 NA 3.3837142 1.5297805
#> [2936] 2.0968738 4.4807029 0.6716859 4.6078916 2.3965619
#> [2941] 0.9893416 1.2310627 5.1445694 1.9888287 0.6328241
#> [2946] 0.5137627 5.9770460 0.9208446 1.4403317 4.3081975
#> [2951] 0.9803630 0.8477652 0.4548255 11.3158970 1.3294666
#> [2956] 1.6702332 4.7679958 0.8518687 1.8222345 2.7682319
#> [2961] 4.0749702 0.4489682 NA 0.6329405 1.6010697
#> [2966] 0.1622699 0.8471114 0.4407594 1.1682943 1.4405993
#> [2971] 2.0458810 6.8692994 2.7137775 1.1100407 0.3899231