aux.biom {BioMark}R Documentation

Auxiliary functions in the biomarker package


These functions return coefficient sizes for a variety of modelling methods. Not to be called directly by the user - use function get.biom for that.


pcr.coef(X, Y, ncomp, scale.p, ...)
pcr.stab(X, Y, ncomp, scale.p,
           segments = NULL, variables = NULL, ...)
pls.coef(X, Y, ncomp, scale.p, ...)
pls.stab(X, Y, ncomp, scale.p,
           segments = NULL, variables = NULL, ...)
vip.coef(X, Y, ncomp, scale.p, ...)
vip.stab(X, Y, ncomp, scale.p,
         segments = NULL, variables = NULL, ...)
lasso.coef(X, Y, scale.p,
           lasso.opt = biom.options()$lasso,...)
lasso.stab(X, Y, scale.p,
           segments = NULL, variables = NULL, ...)
shrinkt.coef(X, Y, scale.p, ...)
shrinkt.stab(X, Y, scale.p,
             segments = NULL, variables = NULL, ...)
studentt.coef(X, Y, scale.p, ...)
studentt.stab(X, Y, scale.p,
              segments = NULL, variables = NULL, ...)
pval.pcr(X, Y, ncomp, scale.p, npermut)
pval.plsvip(X, Y, ncomp, scale.p, npermut, smethod)



Data matrix. Usually the number of columns (variables) is (much) larger than the number of rows (samples).


Class indication. Either a factor, or a numeric vector.


Number of latent variables to use in PCR and PLS (VIP) modelling. In function get.biom this may be a vector; in all other functions it should be one number. Default: 2.


Scaling. This is performed individually in every crossvalidation iteration, and can have a profound effect on the results. Default: "none". Other possible choices: "auto" for autoscaling, "pareto" for pareto scaling, "log" and "sqrt" for log and square root scaling, respectively.


matrix where each column indicates a set of samples to be left out of the analysis.


indices of variables to be used in the analysis.


optional arguments to the glmnet function, in the form of a list.


Further arguments for modelling functions. Often used to catch unused arguments.


Number of permutations to use in the calculation of the p values.


Either "both", "pls", or "vip" - indicates what coefficients to convert to p values. Both are derived from PLS models so it is much more efficient to calculate them together.


The functions ending in coef return t-statistics or model coefficients for all variables. The functions ending in stab return these statistics in a matrix, one column per segment. The functions starting with pval convert model coefficients or VIP statistics into p values, using permutation resampling.


Ron Wehrens

See Also

get.biom, glmnet, scalefun

[Package BioMark version 0.4.5 Index]