Description Usage Arguments Value Author(s) See Also
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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)
|
X |
Data matrix. Usually the number of columns (variables) is (much) larger than the number of rows (samples). |
Y |
Class indication. Either a factor, or a numeric vector. |
ncomp |
Number of latent variables to use in PCR and PLS (VIP)
modelling. In function |
scale.p |
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. |
segments |
matrix where each column indicates a set of samples to be left out of the analysis. |
variables |
indices of variables to be used in the analysis. |
lasso.opt |
optional arguments to the |
... |
Further arguments for modelling functions. Often used to catch unused arguments. |
npermut |
Number of permutations to use in the calculation of the p values. |
smethod |
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
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