cutpoint (version 1.0.0)

cp_splines_plot: Plot penalized smoothing splines from cpobj object

Description

Create penalized smoothing splines plot with different degrees of freedom and shows the cutpoints of the dichotomised variable.

Usage

cp_splines_plot(cpobj, show_splines = TRUE, adj_splines = TRUE)

Value

Plots penalized smoothing splines and shows the cutpoints.

Arguments

cpobj

list, contains variables for pspline plot:

• nb_of_cp (number of cutpoints)

• cp (contain one or two cutpoint/s)

• dp (digits for plot)

• cpvarname (name of the variable for that the cutpoints are estimated)

• cpdata a data frame, contains the following variables: a variable that is dichotomized, time (follow-up time), event (status indicator), covariates (a vector with the names of the covariates and/or factors))

show_splines

logical, if TRUE, The plot shows splines with different degrees of freedom. This may help determine whether misspecification or overfitting occurs.

adj_splines

logical, if TRUE, the splines are adjusted for the covariates. Default is TRUE.

See Also

cp_est() for main function of the package, cp_value_plot() for Value plots and Index plots

Examples

cpvar <- rnorm(100, mean = 100, sd = 10)
time <- seq(1, 100, 1)
event <- rbinom(100, 1, 0.5)
datf <- data.frame(time, event, cpvar)
plot_splines_list <- list(cpdata = datf, nb_of_cp = 1, cp = 95, dp = 2,
    cpvarname = "Biomarker")
cp_splines_plot(plot_splines_list)