cutpoint (version 1.0.0)

cp_value_plot: Plot AIC and LRT-statistics values from cpobj object

Description

Create a plot of AIC or Likelihood ratio test statistic values for the estimation procedure. If there are two cutpoints, a Contour-plot and an Index-plot can be generated.

Usage

cp_value_plot(
  cpobj,
  plotvalues = "AIC",
  dp.plot = 2,
  show_limit = TRUE,
  plottype2cp = "contour"
)

Value

Plots the AIC- or LRT-values, derived from the estimation procedure.

Arguments

cpobj

list, contains a vector of AIC values (AIC_values) and Likelihood ratio test statistic values (LRT_values) of the estimating procedure

plotvalues

character, either AIC or LRT. Either the AIC or LRT values are displayed. Default is AIC.

dp.plot

numeric, digits for the AIC values and LRT values. Default is 2.

show_limit

logical, if TRUE the minimum AIC value is shown in the plot if plotvalues = "AIC", and the maximum LRT value is shown if plotvalues = "LRT"

plottype2cp

character, either contour or index. Default is contour. This option is available only when searching for two cutpoints. Index plots and contour plots can be selected. Index plots display all AIC or LRT values from the estimation process as a scatter plot. Contour plots are shown in the RStudio viewer and illustrate the two potential cutpoints along with the corresponding AIC or LRT values. Index plots that do not show extreme values suggest that there may not be any actual cutpoints in the data. Contour plots provide an opportunity to explore whether there might be other potential cutpoints with similar AIC or LRT values. The smaller the bandwidth (minimum group size per group), the more precise and meaningful the contour plots can be interpreted.

See Also

cp_est() for main function of the package, cp_splines_plot() for penalized spline plots

Examples

# Example 1
# Plot AIC-values and potential cutpoints of the estimation process

# Create AIC values:
AIC_values <- c(1950:1910, 1910:1920, 1920:1880, 1880:1920)
AIC_values <- round(AIC_values + rnorm(length(AIC_values),
                   mean = 0, sd = 5), digits = 2)

# Create a cutpoint variable:
cpvariable_values <- matrix(NA, nrow = length(AIC_values), ncol = 2)
cpvariable_values[ ,1] <- c(1:length(AIC_values))

# Create a cutpoint object (cpobj):
cpobj <- list(AIC_values        = AIC_values,
              nb_of_cp          = 1,
              cpvariable_values = cpvariable_values,
              cpvarname         = "Cutpoint variable"
              )

cp_value_plot(cpobj, plotvalues = "AIC", dp.plot = 2, show_limit = TRUE)

# \donttest{
# Example 2
# Splines plot based on data1
# The data set data1 is included in this package
cpobj <- cp_est(
  cpvarname    = "biomarker",
  covariates   = c("covariate_1", "covariate_2"),
  data         = data1,
  nb_of_cp     = 2,
  plot_splines = TRUE,
)
# Example 3
# Contour plot based on data1
# The data set data1 is included in this package
cpobj <- cp_est(
   cpvarname    = "biomarker",
   covariates   = c("covariate_1", "covariate_2"),
   data         = data1,
   nb_of_cp     = 2,
   plot_splines = FALSE,
)
cp_value_plot(cpobj, plotvalues = "AIC", plottype2cp = "contour")
# }