Returns the fitted coefficients as a one-column sparse matrix of class
"dgCMatrix" (from the Matrix package), mirroring the output of
coef() for glmnet. The first row is the intercept, labeled
"(Intercept)" (value 0 when no intercept was fitted); the remaining
rows are the predictors. Row names are taken from the column names of X
when available (matrix colnames(X) or data-frame column names) and
otherwise default to V1, ..., Vp. Zero coefficients are stored implicitly
and printed as ".", which keeps the display compact in high dimensions.
Usage
# S3 method for class 'pic'
coef(object, standardized = FALSE, ...)Value
A sparse (p + 1) by 1 matrix of class "dgCMatrix", with row
names c("(Intercept)", <variables>) and column name "coefficient".
Use as.numeric() to obtain a plain numeric vector, or
which(coef(fit) != 0) to list the selected entries.
Details
Internally the model is fitted on a standardized design matrix, so the
raw coefficients live on the standardized scale. By default this method
rescales them back to the original scale of X — the values to
plug into the un-standardized design for prediction — via
$$beta\_orig = beta / s \quad intercept\_orig = intercept - sum(m * beta\_orig)$$
where m and s are the column mean and standard deviation. Pass
standardized = TRUE to skip the rescaling and return the raw fit
values (identical to fit$beta).