probs_pred: Plot of the predictive model

In sMSROC: Assessment of Diagnostic and Prognostic Markers

Plot of the predictive model

Description

This function plots the predicted probabilities for each marker value computed through the predictive model together, with 95% pointwise confidence intervals.

Usage

probs_pred(sMS, var, nboots, parallel, ncpus)

Arguments

sMS	object of class sMS returned from function sMSROC.
var	parameter indicating whether 95% pointwise confidence intervals for the predictive model will be plotted (value "T") or not (value "F"). The default value is "F".
nboots	number of bootstrap samples to be generated for computing the pointwise confidence intervals. The default value is 500.
parallel	parameter indicating whether parallel computing will be performed (value "T") or not (value "F"). The default is “F”.
ncpus	number of CPUS to be used in the case of carrying out parallel computing. The default value is 1 and the maximum is 2.

Details

The function plots the probability function estimation of the predictive model versus the biomaker. It also computes and plots 95% pointwise confidence intervals on the same graphic when the var parameter is set to "T".

The variance of the probability estimates, obtained by the predictive model, is computed via bootstrap with nboots samples.

Value

A list with these components:

plot	object of class ggplot (graphical output).
thres	ordered biomarker values (x-axis coordinates).
probs	predicted probabilities (y-axis coordinates).
sd.probs	estimates of the standard deviation of the predicted probabilities.

See Also

pred_model_binout, pred_model_timerc and pred_model_timeic

Examples

data(ktfs)
DT <- ktfs
roc <- sMSROC(marker = DT$score,
              status = DT$failure,
              observed.time = DT$time,
              time = 5,
              meth = "S")
probs <- probs_pred(sMS = roc)
probs$plot

sMSROC documentation built on May 29, 2024, 1:43 a.m.