Calibrate PLS-DA model
plsda.cal(x, c, ncomp, center, scale, cv, method, light, alpha, coeffs.ci,
coeffs.alpha, info, exclcols = NULL, exclrows = NULL, ncomp.selcrit,
classname = NULL)matrix with predictors.
vector with reference class values.
maximum number of components to calculate.
logical, center or not predictors and response values.
logical, scale (standardize) or not predictors and response values.
number of segments for cross-validation (if cv = 1, full cross-validation will be used).
method for calculating PLS model.
run normal or light (faster) version of PLS without calculationg some performance statistics.
significance level for calculating statistical limits for residuals.
method to calculate p-values and confidence intervals for regression coefficients (so far only
jack-knifing is availavle: ='jk').
significance level for calculating confidence intervals for regression coefficients.
short text with information about the model.
columns of x to be excluded from calculations (numbers, names or vector with logical values)
rows to be excluded from calculations (numbers, names or vector with logical values)
criterion for selecting optimal number of components ('min' for first local minimum of
name of class in case of one-class PLS-DA model