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## function: summary cv.grpregOverlap, need revise
# ------------------------------------------------------------------------------
summary.cv.grpregOverlap <- function(object, ...) {
obj.new <- object
if (object$fit$family != 'cox') {
class(obj.new) <- 'cv.grpreg'
} else {
class(obj.new) <- 'cv.grpsurv'
}
res <- summary(obj.new, ...)
nvars.latent <- predict(object$fit, type="nvars", latent = T)
res$nvars.latent <- nvars.latent
d.latent <- dim(object$fit$beta.latent)
if (length(d.latent)==3) {
p.latent <- d.latent[2] - 1
} else {
p.latent <- d.latent[1] - 1
}
res$p.latent <- p.latent
class(res) <- c('summary.cv.grpregOverlap', 'summary.cv.grpreg')
res
}
# -------------------------------------------------------------------------------
## function: print summary.cv.grpregOverlap
# -------------------------------------------------------------------------------
print.summary.cv.grpregOverlap <- function(x, digits, ...) {
if (missing(digits)) {
digits <- c(2, 4, 2, 2, 3)
} else {
digits <- rep(digits, length.out=5)
}
cat("---------------------------------------------------------------\n")
cat("Note: Overlapping-group selection via penalized regression: \n")
cat(" 'p.latent' is the number of latent variables!\n")
cat("---------------------------------------------------------------\n")
if (length(x$d)==3) {
cat(x$penalty, "-penalized multivariate ", x$model,
" regression with m=", x$d[1], ", n=", x$n/x$d[1], ", p=", x$p,
" (p.latent=", x$p.latent, ")", "\n", sep="")
} else {
cat(x$penalty, "-penalized ", x$model, " regression with n=", x$n,
", p=", x$p, " (p.latent=", x$p.latent, ")", "\n", sep="")
}
cat("At minimum cross-validation error (lambda=", formatC(x$lambda[x$min], digits[2], format="f"), "):\n", sep="")
cat("---------------------------------------------------------------\n")
cat(" Nonzero coefficients: ", x$nvars[x$min], "\n", sep="")
cat(" Nonzero latent coefficients: ", x$nvars.latent[x$min], "\n", sep="")
cat(" Nonzero groups: ", x$ngroups[x$min], "\n", sep="")
cat(" Cross-validation error of ",
formatC(min(x$cve), digits[1], format="f"), "\n", sep="")
cat(" Maximum R-squared: ",
formatC(max(x$r.squared), digits[3], format="f"), "\n", sep="")
cat(" Maximum signal-to-noise ratio: ",
formatC(max(x$snr), digits[4], format="f"), "\n", sep="")
if (x$model == "logistic") {
cat(" Prediction error at lambda.min: ",
formatC(x$pe[x$min], digits[5], format="f"), "\n", sep="")
}
if (x$model == "linear") {
cat(" Scale estimate (sigma) at lambda.min: ",
formatC(sqrt(x$cve[x$min]), digits[5], format="f"), "\n", sep="")
}
}
# -------------------------------------------------------------------------------
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