tpptrFitSplines {TPP}R Documentation

Perform spline fitting

Description

Fit natural splines to all proteins in a dataset.

Usage

tpptrFitSplines(data, factorsH1, factorsH0 = character(0),
  splineDF = 3:7, computeAUC = NULL, returnModels = TRUE,
  nCores = "max")

Arguments

data

the data to be fitted

factorsH1

which factors should be included in the alternative model?

factorsH0

which factors should be included in the null model?

splineDF

degrees of freedom for natural spline fitting.

computeAUC

DEPRECATED

returnModels

should the linear models be returned in a column of the result table? Activation increases memory requirements.

nCores

either a numerical value given the desired number of CPUs, or 'max' to automatically assign the maximum possible number (default).

Argument splineDF specifies the degrees of freedom for natural spline fitting. As a single numeric value, it is directly passed on to the splineDF argument of splines::ns. Experience shows that splineDF = 4 yields good results for TPP data sets with 10 temperature points. It is also possible to provide a numeric vector. In this case, splines are fitted for each entry and the optimal value is chosen per protein using Akaike's Information criterion.

Value

A table containing the fitted models per protein

See Also

ns, AICc

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, 
               normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
normData_longTable <- tpptrTidyUpESets(normData_eSets)
hdacSubset <- subset(normData_longTable, grepl("HDAC", uniqueID))
hdacSplineFits <- tpptrFitSplines(data = hdacSubset, 
                                  factorsH1 = c("condition"), 
                                  nCores = 1)
[Package TPP version 3.15.3 Index]