GraphsROC: ROC curves

In AlzbetaG/Metabol: The statistical analysis of metabolomic data

Description

Display ROC curves with confidence intervals for every variable.

Usage

GraphsROC(data, name, groupnames, tsf = "clr", QCs = FALSE)

Arguments

data	Data table with variables (metabolites) in columns. Samples in rows are sorted according to specific groups.
name	A character string or expression indicating a name of data set. It occurs in names of every output.
groupnames	A character vector defining specific groups in data. Every string must be specific for each group and they must not overlap.
tsf	Data transformation must be defined by "clr" (default), "log", "log10", "PQN", "lnPQN", "pareto" or "none". See Details.
QCs	logical. If FALSE (default) quality control samples (QCs) are automatically distinguished and skipped.

Details

Data transformation: with "clr" clr trasformation is used (see References), with "log" natural logarithm is used, with "log10" decadic logarithm is used, with "pareto" data are only scaled by Pareto scaling, with "PQN" probabilistic quotient normalization is done, with "lnPQN" natural logarithm of PQN transformed data is done, with "none" no tranformation is done.

ROC curves can be used only for comparison of two groups. If there is more groups in data, all possible combinations of pairs are evaluated.

Value

ROC curves with confidence intervals of area under the curve (AUC).

Excel sheet with summary of AUCs.

References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). p. 416.

Examples

data=metabol
name="Metabolomics"    #name of the project
groupnames=c("Con","Pat","QC")
GraphsROC(data,name,groupnames)

AlzbetaG/Metabol documentation built on May 31, 2019, 12:39 a.m.