Nothing
summary.cv.grpreg <- function(object, ...) {
S <- pmax(object$null.dev - object$cve, 0)
if (!inherits(object, 'cv.grpsurv') && object$fit$family=="gaussian") {
rsq <- pmin(pmax(1 - object$cve/object$null.dev, 0), 1)
} else {
rsq <- pmin(pmax(1 - exp(object$cve-object$null.dev), 0), 1)
}
snr <- S/object$cve
nvars <- predict(object$fit, type="nvars")
ngroups <- predict(object$fit, type="ngroups")
if (inherits(object, 'cv.grpsurv')) {
model <- 'Cox'
} else {
model <- switch(object$fit$family, gaussian="linear", binomial="logistic", poisson="Poisson")
}
d <- dim(object$fit$beta)
if (length(d)==3) {
p <- d[2] - 1
} else {
if (model == 'Cox') {
p <- d[1]
} else {
p <- d[1] - 1
}
}
val <- list(penalty=object$fit$penalty,
model=model,
n=object$fit$n,
p=p,
min=object$min,
lambda=object$lambda,
cve=object$cve,
r.squared=rsq,
snr=snr,
nvars=nvars,
ngroups=ngroups,
d=d)
if (!inherits(object, 'cv.grpsurv') && object$fit$family=="gaussian") val$sigma <- sqrt(object$cve)
if (!inherits(object, 'cv.grpsurv') && object$fit$family=="binomial") val$pe <- object$pe
structure(val, class="summary.cv.grpreg")
}
print.summary.cv.grpreg <- function(x, digits, ...) {
digits <- if (missing(digits)) digits <- c(2, 4, 2, 2, 3) else rep(digits, length.out=5)
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$p, "\n", sep="")
} else {
cat(x$penalty, "-penalized ", x$model, " regression with n=", x$n, ", p=", x$p, "\n", sep="")
}
cat("At minimum cross-validation error (lambda=", formatC(x$lambda[x$min], digits[2], format="f"), "):\n", sep="")
cat("-------------------------------------------------\n")
cat(" Nonzero coefficients: ", x$nvars[x$min], "\n", sep="")
cat(" Nonzero groups: ", x$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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