View source: R/pcoxtimeplots.R
plot.pcoxtimecv | R Documentation |
Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the optimal lambdas. Also, plots the solution path as a function of optimal lambdas (or randomly picked fold, if refit = FALSE
) or l1
-norm.
## S3 method for class 'pcoxtimecv' plot( x, ..., type = c("cve", "fit"), xvar = c("lambda", "l1"), show_nzero = FALSE, seed = 1234, geom = c("point", "line"), g.size = 0.2, g.col = "red", bar.col = g.col, scales = "free_x", show_min_cve = TRUE )
x |
fitted |
... |
for future implementations |
type |
which plot to return. |
xvar |
only if |
show_nzero |
logical. Whether to show number of nonzero coefficients on the plot. Default is |
seed |
random number generator. Important if |
geom |
geom ("point" or "line") for partial likelihood |
g.size |
size specification for points/lines |
g.col |
colour specification for points/lines |
bar.col |
colour specification for error bars |
scales |
should scales be "fixed", "free", "free_x" or "free_y"? |
show_min_cve |
whether or not to show the alpha which gives minimum cross-validation error. Ignored if a single |
To plot solution path corresponding to optimal alpha and lambda, set refit = TRUE
in pcoxtimecv
. The plot is a ggplot
object, hence can be be customized further.
a ggplot
object.
library(ggplot2) # Time-varying covariates ## Not run: data(heart, package="survival") # Using a vector of alphas = (0.8, 1) cv1 <- pcoxtimecv(Surv(start, stop, event) ~ age + year + surgery + transplant , data = heart , alphas = c(0.8, 1) , refit = TRUE , lamfract = 0.6 , seed = 1234 ) # Plot cross-validation curves plot(cv1, type = "cve") # Plot plot(cv1, type = "fit") ## End(Not run)
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