Calibrates (builds) a PCA model for given data and parameters
pca.cal(x, ncomp, center, scale, method, exclcols = NULL,
exclrows = NULL, cv, rand, lim.type, alpha, gamma, info)matrix with data values
number of principal components to calculate
logical, do mean centering or not
logical, do standardization or not
algorithm for compiting PC space (only 'svd' and 'nipals' are supported so far)
columns 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)
number of segments for random cross-validation (1 for full cross-validation).
vector with parameters for randomized PCA methods (if NULL, conventional PCA is used instead)
which method to use for calculation of critical limits for residuals (see details for pca)
significance level for calculating critical limits for T2 and Q residuals.
significance level for calculating outlier limits for T2 and Q residuals.
a short text line with model description.
an object with calibrated PCA model