anproc_file: Analysis Procedure File

Description Arguments Details Important See Also

Description

The analysis procedure file is used to first split the dataset according to the provided values in the 'split dataset' section, and then, in the 'statistics' section (starting with do.pca), to tell the system which statistics to apply resp. what models to calculate on those datasets. It also contains specific and general plotting options that are used by the plot function. Arguments used to control the split-process, the behaviour of statistics / calculations / specific plotting options and the general plotting options start with a certain prefix:

By providing any of the arguments of the analysis procedure file to the function getap, also when using it inside the function gdmm, you can override the values in the file with the provided values. See examples at gdmm.

Arguments

spl.var

NULL or character vector. If NULL, no splitting of the dataset will be performed. Provide a character vector with the column names of class variables to split the dataset along these variables.

spl.wl

NULL or character vector. If NULL, all in the dataset available wavelengths will be used. Provide a character vector in the format "wlFrom-to-wlTo" (e.g. c("1000-to-2000", "1300-to-1600", ...)) to use all previously defined splits in these wavelengths.

dpt.pre

Character vector, which of the available modules of data pre-treatments to apply AFTER a (possible) split by variable spl.var and wavelength spl.w., and BEFORE a (possible) splitting of the dataset according to the provided split-variables below (csAvg, noise, exOut). Leave at NULL for no data pre-treatment. Possible values are 'sgol', 'snv', 'msc', 'emsc', 'osc', 'deTr', 'gsd'. Add additional parameters to some of the single strings via the separator '@'. For further information and examples see dpt_modules.

spl.do.csAvg

Logical. If all the consecutive scans of a single sample should be reduced, i.e. averaged into a single spectrum.

spl.csAvg.raw

Logical. If, should the consecutive scans of a single sample be reduced, an other dataset containing every single consecutive scan should be kept as well as well.

spl.do.noise

Logical. If artifical noise should be added to the dataset.

spl.noise.raw

If, should the noise-test be performed, the raw data will be used as well in addition to the noise-data.

spl.do.exOut

Logical. If exclusion of outliers should be performed.

spl.exOut.raw

Logical. If, should exclusion of outliers be performed, the raw original data should be used as well. If set to TRUE, outliers will be flagged in the dataset in any case.

spl.exOut.var

Character vector. The variables that should be used for the grouping defining the scope for outlier detection. The name of the resulting column consists of the class variable prefix (as defined in the settings.r file in p_ClassVarPref), the general designator for an outlier-column (as defined in the settings.r file in p_outlierCol) followed by an underscore '_', and each of the provided variables (without the class variable prefix) separated by a '.' dot. For example, if the provided variables are C_Group and C_Time, the column containing the outlier-flags might be called C_outlier_Group.Time.

dpt.post

Character vector, which of the available modules of data pre-treatments to apply AFTER (possibly) splitting the dataset. Leave at NULL for no additional data treatment. Possible values are 'sgol', 'snv', 'msc', 'emsc', 'osc', 'deTr', 'gsd'. Add additional parameters to some of the single strings via the separator '@'. For examples and further information see dpt_modules.

do.pca

Logical. If used in getap, if a PCA should be calculated with a given dataset.

do.pca

Logical. If used in a plotting function, if PCA score / loading plots should be plotted.

pca.colorBy

NULL or character vector. Which class-variables should be used for coloring the PCA score plot. Set to NULL for using all available class variables for coloring.

pca.elci

'def' or numeric length one. The confidence interval for the ellipse to be drawn around groups in score plots. Leave at 'def' to read in the default from the settings.r file; provide a numeric length one (e.g. 0.95); or set to NULL for not drawing ellipses at all.

pca.elcolorBy

Character vector or NULL. The variables to use for plotting additional confidence intervall ellipses. Set to NULL for *not* drawing additional CI-ellipses. Provide one variable (gets recycled) or a vector with equal length as pca.colorBy to have the additional CI-ellipses along these variables.

pca.what

Character length one. What element of the PCA analysis to plot. Possible values are 'both', 'scores', 'loadings'.

pca.sc

Numeric length 2. Two PCs to be plotted against each other in the score plots.

pca.sc.pairs

Numeric vector of length >=2, indicating what PCs to plot in the score pairs plot. Set to NULL for *not* plotting the pairs plot.

pca.lo

Numeric vector of length >=2, indicating what PCs to plot in the loadingplot.

do.sim

Logical. If used in getap, if SIMCA models of the dataset should be calculated.

sim.vars

NULL or character vector. Which variables should be used to group the data. Set to NULL for using all available class-variables, or provide a character vector with the column names of class variables to group the data along those for calculating SIMCA models.

sim.K

Numeric length one. The number of components used for calculating the SIMCA models. In mode 'robust' leave at '0' for automatic detection of optimal number of components. [It is a capital 'K' in the argument.]

do.sim

Logical. If used in a plotting function, if analysis of SIMCA models should be plotted.

do.pls

Logical. If used in getap, if PLSR models should be calculated with a given dataset.

pls.regOn

NULL or character vector. Which variables should be used to regress on. Set to NULL for using all numerical variables to regress on, or provide a character vector with the column names of numerical variables to use those for regression in the PLSR.

pls.ncomp

NULL or integer length one. The number of components used in PLSR. Set to NULL for automatic detection, or provide an integer to use this number of components in the PLSR.

pls.valid

Character. Which crossvalidation to use. Possible values are:

  • "def" Read in the default value from settings.r (parameter plsr_calc_typeOfCrossvalid)

  • A numeric length one for this n-fold crossvalidation. The default is to always exclude resp. include consecutive scans together.

  • A valid name of a class variable for performing a crossvalidation based on the grouping defined by this variable. For a class variable containing e.g. four different levels, a 4-fold crossvalidation with always all members of one group being excluded is performed. This is overruling any grouping that would result from the consecutive scans, please see below.

  • "LOO" for a leave-one-out crossvalidation

If a vector with the same length as the vector in pls.regOn is provided, each element of pls.valid is used for crossvalidating the corresponding element in pls.regOn. Any of the above mentioned input types can be mixed, so the input could be e.g. pls.valid <- c("C_FooBar", 10, "C_BarFoo", 10). The corresponding pls.regOn input for this would then be e.g. pls.regOn <- c("Y_FooBar", "Y_FooBar", "Y_BarFoo", "Y_BarFoo"). Please note that via the parameter plsr_calc_CV_consecsTogether in the settings file you can select if for crossvalidation the consecutive scans (i.e. the scans with the same sample number) should always be excluded or included together. The default is to always exclude resp. include the consecutive scans of a single sample together.

pls.exOut

Logical. If a plsr-specific box-plot based outlier-detection algorithm should be used on the data of a first plsr model to determine the outliers that then will be excluded in the final plsr model. Possible values are:

  • "def" Read in the default value from settings.r (parameter plsr_calc_excludePlsrOutliers)

  • TRUE for excluding plsr specific outliers

  • FALSE for not performing the plsr specific outlier exclusion

If a vector with the same length as the vector in pls.regOn is provided, each element of pls.exOut is used to perform the corresponding outlier-detection (or not) for each element in pls.regOn.

do.pls

Logical. If used in a plotting function, if analysis from PLSR models should be plotted.

pls.colorBy

NULL or character. What class-variable should be used for coloring in the RMSEC and RMSECV plots. Set to NULL for no coloring, or provide a character length one with a single column name of a class variable that should be used for coloring.

pls.what

What types of plsr analysis to plot. Possible values are 'both', 'errors', 'regression'.

pls.rdp

Logical (TRUE or FALSE). If errors in the error plots should be given in RDP or not.

do.aqg

Logical. If used in getap, if Aquagrams should be calculated with a given dataset.

aqg.vars

NULL or character vector. Which class variables should be used for grouping the data for the Aquagram. Provide a character vector with the column names of one or more class variables for grouping data and generate an Aquagram for every one of them.

aqg.nrCorr

Character or Logical. If the number of observations in each spectral pattern should be corrected (if necessary by random sampling) so that all the spectral pattern are calculated out from the same number of observations. If left at the default "def", the default value from the settings will be used. Provide "TRUE" or "FALSE" to switch number correction manually on or off.

aqg.spectra

Logical or Character. If left at "FALSE" (the default) no additional spectra are calculated / prepared for plotting. Other possible values are one or more of:

  • "raw" for the raw spectra

  • "avg" for the averaged spectra of the data represented in the aquagram;

  • "subtr" for subtractions in the averaged spectra (see "minus" below)

  • "all" for all of the aforementioned

aqg.minus

Character length one, character vector or NULL. Which of the levels present in each of the class-variables provided in aqg.vars should be used for subtractions – the average of this 'minus' gets subtracted from all the other averages. aqg.minus is used for the subtractions in the raw spectra as well as for the subtractions within the Aquagram, should you choose any of the -diff modes. If a vector with the same length as the vector in aqg.vars is provided, each element of aqg.minus is used to perform the corresponding subtraction for each element in aqg.vars. If a character length one is provided and the input in aqg.vars is longer than one, the single value in aqg.minus gets recycled and is used in each element in aqg.vars for subtractions.

aqg.mod

Character. What mode, what kind of Aquagram should be calculated? Possible values are: 'classic', 'classic-diff', 'sfc', 'sfc-diff', 'aucs', 'aucs-diff', 'aucs.tn', 'aucs.tn-diff', 'aucs.tn.dce', 'aucs.tn.dce-diff', 'aucs.dce', 'aucs.dce-diff', and 'def' for reading in the default from settings.r. Please see calc_aqg_modes for an explanation of the different modes.

aqg.TCalib

Character, numeric or NULL. The default (leave at 'def') can be set in the settings. If 'NULL' the complete temperature range of the calibration data is used for calibration. Provide a numeric length two [c(x1, x2)] for manually determining the calibration range. Provide a character '[email protected]', with 'x' being the plus and minus delta in temperature from the temperature of the experiment for having a calibration range from Texp-x to Texp+x. The 'Factory' default is '[email protected]'. Applies to all modes except the 'classic' and 'sfc' modes. If, in any of the modes showing percentages, the numbers on the Aquagram are below 0 or above 100, then the calibration range has to be extended. To record your own temperature calibration spectra, please see genTempCalibExp.

aqg.Texp

Numeric length one. The temperature at which the spectra were taken. The default (leave at 'def') can be set in the settings. Please see also genTempCalibExp.

aqg.bootCI

Logical. If confidence intervalls for the selected wavelengths should be calculated within each group (using bootstrap). Leave at 'def' for getting the default from the settings.

aqg.R

Character or numeric. Given aqg.bootCI = TRUE, how many bootstrap replicates should be performed? Leave at 'def' for choossing the default from the settings, where the factory-default is "[email protected]" for for 3 x nrow(samples). By manually providing a character in the form of '[email protected]' where x is any number, you can set the factor with which the number of rows get multiplicated, the result of this multiplication is then used for the number of bootstrap replicates. By providing a length one numeric you can directly set the number of bootstrap replicates.

aqg.smoothN

Only used in the 'classic' and 'sfc' modes. Numeric length 1. Must be odd. Smoothing points for the Sav. Golay smoothing that is applied before making the calculations. Change to NULL or anything not-numeric to switch off smoothing.

aqg.selWls

Only used in the 'classic' and 'sfc' modes. Numerical vector. If provided and in the mode "classic", classic-diff", "sfc" and "sfc-diff" these numbers will be used to determine the coordinates of the aquagram. Leave at 'def' to use the defaults from the settings file.

aqg.msc

Only used in the 'classic' and 'sfc' modes. Logical. If MSC should be performed.

aqg.reference

Only used in the 'classic' and 'sfc' modes. An optional numerical vector (loadings, etc..) used for MSC.

do.aqg

Logical. If used in a plotting function, if Aquagrams should be plotted.

aqg.fsa

'Fix scale for Aquagram'. Logical, numeric or Character. If left at the default logical FALSE, every single aquagram will be plotted in its own, independent scale. If a numeric vector length two is provided, all the aquagrams to be plotted (normal AND bootstrapped ones) will be in the provided range, no independently scaled aquagrams will be plotted. If character, the following values are possible:

  • "both": both independently scaled AND automatically calculated fix-scaled aquagrams will be plotted

  • "only": only the automatically calculated fix-scaled aquagrams will be plotted. (normal AND bootstrap)

aqg.fss

'Fix scale for subtraction spectra'. Logical, numeric or character. If left at the default logical FALSE', every single subtraction-spectra plot will be plotted in its own, independendent scale. If a numeric vector length two is provided, all the subtraction-spectra to be plotted (if 'plotSpectra' contains 'subtr', and 'minus' contains a valid value) will be in the provided range, no independently scaled subtraction-spectra will be plotted. If character, the following values are possible:

  • "both": both independently scaled AND automatically calculated fix-scaled spectra will be plotted

  • "only": only the automatically calulated fix-scaled subtraction spectra will be plotted

aqg.ccol

Custom Color - NULL, Numeric or Character vector. Custom colors for drawing the lines in the aquagram. Length must exactly match the number of groups to be plotted in the aquagram. If not, the default coloring from the dataset is used. This can be used when plotting aquagrams with different numbers of groups: only this group that matches the number of provided custom colors is colored differently. Especially useful when you have more than 8 lines to be plotted – custom-color similar groups in similar colors.

aqg.clt

Character or Integer vector. Custom line type for plotting the lines in the Aquagram. If left at the default 'def', the vector provided in the settings.r file is taken (and recycled). If an integer vector is provided, this is used (and recycled) as line-types in the Aquagram.

aqg.pplot

Logical or character 'def'. If, should spectra be plotted, an additional plot with picked peaks should be added. If left at the default value 'def', the default from the settings.r file is used.

aqg.plines

Logical, numeric or character 'def'. If set to FALSE, no additional lines, if set to TRUE all the additional lines will be plotted. If an integer vector [2..5] is provided, one or more of the additional lines get plotted. See adLinesToVector for details. If left at the default value 'def', the default from the settings.r file (parameter aqg_AdLines) is used.

aqg.discr

Logical or character 'def'. If set to TRUE, negative (resp. positive) peaks can be only found in peak-heights below (resp. above) zero.

do.da

Logical. If used in getap, if classification via discriminant analysis (lda, qda, fda, MclustDA) should be performed in the given dataset.

da.type

Character vector. The type of discriminant analysis (DA) to perform; possible values (one or more) are: 'lda', 'qda', 'fda', 'mclustda':

  • lda Linear DA using lda.

  • qda Quadratic DA using qda.

  • fda Flexible DA using fda.

  • mclustda DA based on Gaussian finite mixture modeling using MclustDA.

da.classOn

Character vector. One or more class variables to define the grouping used for classification.

da.testCV

Logical, if the errors of the test-data should be crossvalidated. If set to true, CV and testing is repeated in alternating datasets. See below.

da.percTest

Numeric length one. The percentage of the dataset that should be set aside for testing the models; these data are never seen during training and crossvalidation.

da.cvBootCutoff

The minimum number of observations (W) that should be in the smallest subgroup (as defined by the classification grouping variable) *AFTER* the split into da.valid crossvalidation segments (below). If W is equal or higher than da.cvBootCutoff, the crossvalidation is done via splitting the training data in da.valid (see below) segments, otherwise the crossvalidation is done via bootstrap resampling, with the number of bootstrap iterations resulting from the multiplication of the number of observations in this smallest subgroup (as defined by the classification grouping variable) in *all* of the training data with da.cvBootFactor. To never perform the CV of the training data via bootstrap, set the parameter cl_gen_neverBootstrapForCV in the settings.r file to TRUE. An example: With da.cvBootCutoff set to 15 and a 8-fold crossvalidation da.valid <- 8, the required minimum number of observations in the smallest subgroup *after* the split in 8 segments would be 15, and in all the training data to perform the desired 8-fold CV would be (8x15=) 120, in what case then 8 times 15 observations will form the test data to be projected into models made from (120-15=) 105 observations. If there would be less than 120 observations, lets say for example, only 100 observations in the smallest group as defined by the classification grouping variable, bootstrap resampling with da.cvBootFactor * 100 iterations would be performed. In this example, if we would also be satisfied with a 5-fold crossvalidation, then we would have enough data: 100 / 5 = 20, and with the da.cvBootCutoff value being 15, the 5-fold crossvalidation would be performed.

da.cvBootFactor

The factor used to multiply the number of observations within the smallest subgroup defined by the classification grouping variable with, resulting in the number of iterations of a possible bootstrap crossvalidation of the trainign data – see .cvBootCutoff.

da.valid

The number of segments the training data should be divided into in case of a "traditional" crossvalidation of the training data; see above.

da.pcaRed

Logical, if variable reduction via PCA should be applied; if TRUE, the subsequent classifications are performed on the PCA scores, see da.pcaNComp below.

da.pcaNComp

Character or integer vector. Provide the character "max" to use the maximum number of components (i.e. the number of observations minus 1), or an integer vector specifying the components resp. their scores to be used for DA.

reserved

– No plotting parameter yet defined –

do.rnf

Logical. If used in getap, if classification via randomForest should be performed in the given dataset.

rnf.classOn

Character vector. One or more class variables to define the grouping used for classification.

rnf.testCV

Logical, if the errors of the test-data should be crossvalidated. If set to true, CV and testing is repeated in alternating datasets. See below.

rnf.percTest

Numeric length one. The percentage of the dataset that should be set aside for testing the models; these data are never seen during training and crossvalidation.

rnf.cvBootCutoff

The minimum number of observations (W) that should be in the smallest subgroup (as defined by the classification grouping variable) *AFTER* the split into rnf.valid crossvalidation segments (below). If W is equal or higher than rnf.cvBootCutoff, the crossvalidation is done via splitting the training data in rnf.valid (see below) segments, otherwise the crossvalidation is done via bootstrap resampling, with the number of bootstrap iterations resulting from the multiplication of the number of observations in this smallest subgroup (as defined by the classification grouping variable) in *all* of the training data with rnf.cvBootFactor. To never perform the CV of the training data via bootstrap, set the parameter cl_gen_neverBootstrapForCV in the settings.r file to TRUE. An example: With rnf.cvBootCutoff set to 15 and a 8-fold crossvalidation rnf.valid <- 8, the required minimum number of observations in the smallest subgroup *after* the split in 8 segments would be 15, and in all the training data to perform the desired 8-fold CV would be (8x15=) 120, in what case then 8 times 15 observations will form the test data to be projected into models made from (120-15=) 105 observations. If there would be less than 120 observations, lets say for example, only 100 observations in the smallest group as defined by the classification grouping variable, bootstrap resampling with rnf.cvBootFactor * 100 iterations would be performed. In this example, if we would also be satisfied with a 5-fold crossvalidation, then we would have enough data: 100 / 5 = 20, and with the rnf.cvBootCutoff value being 15, the 5-fold crossvalidation would be performed.

rnf.cvBootFactor

The factor used to multiply the number of observations within the smallest subgroup defined by the classification grouping variable with, resulting in the number of iterations of a possible bootstrap crossvalidation of the trainign data – see .cvBootCutoff.

rnf.valid

The number of segments the training data should be divided into in case of a "traditional" crossvalidation of the training data; see above.

rnf.pcaRed

Logical, if variable reduction via PCA should be applied; if TRUE, the subsequent classifications are performed on the PCA scores, see rnf.pcaNComp below.

rnf.pcaNComp

Character or integer vector. Provide the character "max" to use the maximum number of components (i.e. the number of observations minus 1), or an integer vector specifying the components resp. their scores to be used for random forest classification.

reserved

– No plotting parameter yet defined –

do.svm

Logical. If used in getap, if classification via svm should be performed in the given dataset.

svm.classOn

Character vector. One or more class variables to define the grouping used for classification.

svm.testCV

Logical, if the errors of the test-data should be crossvalidated. If set to true, CV and testing is repeated in alternating datasets. See below.

svm.percTest

Numeric length one. The percentage of the dataset that should be set aside for testing the models; these data are never seen during training and crossvalidation.

svm.cvBootCutoff

The minimum number of observations (W) that should be in the smallest subgroup (as defined by the classification grouping variable) *AFTER* the split into svm.valid crossvalidation segments (below). If W is equal or higher than svm.cvBootCutoff, the crossvalidation is done via splitting the training data in svm.valid (see below) segments, otherwise the crossvalidation is done via bootstrap resampling, with the number of bootstrap iterations resulting from the multiplication of the number of observations in this smallest subgroup (as defined by the classification grouping variable) in *all* of the training data with svm.cvBootFactor. To never perform the CV of the training data via bootstrap, set the parameter cl_gen_neverBootstrapForCV in the settings.r file to TRUE. An example: With svm.cvBootCutoff set to 15 and a 8-fold crossvalidation svm.valid <- 8, the required minimum number of observations in the smallest subgroup *after* the split in 8 segments would be 15, and in all the training data to perform the desired 8-fold CV would be (8x15=) 120, in what case then 8 times 15 observations will form the test data to be projected into models made from (120-15=) 105 observations. If there would be less than 120 observations, lets say for example, only 100 observations in the smallest group as defined by the classification grouping variable, bootstrap resampling with svm.cvBootFactor * 100 iterations would be performed. In this example, if we would also be satisfied with a 5-fold crossvalidation, then we would have enough data: 100 / 5 = 20, and with the svm.cvBootCutoff value being 15, the 5-fold crossvalidation would be performed.

svm.cvBootFactor

The factor used to multiply the number of observations within the smallest subgroup defined by the classification grouping variable with, resulting in the number of iterations of a possible bootstrap crossvalidation of the trainign data – see .cvBootCutoff.

svm.valid

The number of segments the training data should be divided into in case of a "traditional" crossvalidation of the training data; see above.

svm.pcaRed

Logical, if variable reduction via PCA should be applied; if TRUE, the subsequent classifications are performed on the PCA scores, see svm.pcaNComp below.

svm.pcaNComp

Character or integer vector. Provide the character "max" to use the maximum number of components (i.e. the number of observations minus 1), or an integer vector specifying the components resp. their scores to be used for SVM classification.

reserved

– No plotting parameter yet defined –

do.nnet

Logical. If used in getap, if classification via artificial neural networks (nnet) should be performed in the given dataset.

nnet.classOn

Character vector. One or more class variables to define the grouping used for classification.

nnet.testCV

Logical, if the errors of the test-data should be crossvalidated. If set to true, CV and testing is repeated in alternating datasets. See below.

nnet.percTest

Numeric length one. The percentage of the dataset that should be set aside for testing the models; these data are never seen during training and crossvalidation.

nnet.cvBootCutoff

The minimum number of observations (W) that should be in the smallest subgroup (as defined by the classification grouping variable) *AFTER* the split into nnet.valid crossvalidation segments (below). If W is equal or higher than nnet.cvBootCutoff, the crossvalidation is done via splitting the training data in nnet.valid (see below) segments, otherwise the crossvalidation is done via bootstrap resampling, with the number of bootstrap iterations resulting from the multiplication of the number of observations in this smallest subgroup (as defined by the classification grouping variable) in *all* of the training data with nnet.cvBootFactor. To never perform the CV of the training data via bootstrap, set the parameter cl_gen_neverBootstrapForCV in the settings.r file to TRUE. An example: With nnet.cvBootCutoff set to 15 and a 8-fold crossvalidation nnet.valid <- 8, the required minimum number of observations in the smallest subgroup *after* the split in 8 segments would be 15, and in all the training data to perform the desired 8-fold CV would be (8x15=) 120, in what case then 8 times 15 observations will form the test data to be projected into models made from (120-15=) 105 observations. If there would be less than 120 observations, lets say for example, only 100 observations in the smallest group as defined by the classification grouping variable, bootstrap resampling with nnet.cvBootFactor * 100 iterations would be performed. In this example, if we would also be satisfied with a 5-fold crossvalidation, then we would have enough data: 100 / 5 = 20, and with the nnet.cvBootCutoff value being 15, the 5-fold crossvalidation would be performed.

nnet.cvBootFactor

The factor used to multiply the number of observations within the smallest subgroup defined by the classification grouping variable with, resulting in the number of iterations of a possible bootstrap crossvalidation of the trainign data – see .cvBootCutoff.

nnet.valid

The number of segments the training data should be divided into in case of a "traditional" crossvalidation of the training data; see above.

nnet.pcaRed

Logical, if variable reduction via PCA should be applied; if TRUE, the subsequent classifications are performed on the PCA scores, see nnet.pcaNComp below.

nnet.pcaNComp

Character or integer vector. Provide the character "max" to use the maximum number of components (i.e. the number of observations minus 1), or an integer vector specifying the components resp. their scores to be used for nnet classification.

reserved

– No plotting parameter yet defined –

pg.where

Character length one. If left at the default 'def', the value from the settings.r file is read in (parameter gen_plot_pgWhereDefault). For plotting to PDFs provide "pdf", for plotting to graphics device provide anything but "pdf".

pg.main

Character length one. The additional text on the title of each single plot.

pg.sub

Character length one. The additional text on the subtitle of each single plot.

pg.fns

Character length one. The additional text in the filename of the pdf.

Details

The default name for the analysis procedure file can be set in settings.r. Any other .r file can be loaded by providing a valid .r filename to the appropriate argument, e.g. in the function getap. By providing any of the arguments of the analysis procedure file to the function getap also when using it inside the function gdmm or to any of the plot functions, you can override the values in the file with the provided values. See examples at gdmm and plot.

Important

As the AUC-mods of the Aquagram compare the actual data to your previously recoreded temperature calibration data (see genTempCalibExp and tempCalib_procedures), the application of some data-treatment functions (see e.g. do_gapDer) can lead to unexpected and distorted results in the Aquagram.

See Also

getap, gdmm

Other fileDocs: metadata_file, settings_file


bpollner/aquap2 documentation built on Jan. 30, 2019, 9:08 a.m.