predict.nn.time: Prediction from a Survival Neural Network

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Prediction from a Survival Neural Network

Description

Predict the survival of new observations based on a neural network estimated by using nn.time.

Usage

## S3 method for class 'nn.time'
predict(object, ..., newdata, newtimes)

Arguments

object	An object returned by the function nn.time.
...	Further arguments passed.
newdata	An optional data frame containing covariate values at which to produce predicted values. There must be a column for every covariate included in cov.quanti and cov.quali included in the training sample. The default value isNULL, the predicted values are computed for the subjects of the training sample.
newtimes	The times at which to produce predicted values. The default value isNULL, the predicted values are computed for the observed times in the training data frame.

Value

times	A vector of numeric values with the times of the predictions.
predictions	A matrix with the predictions of survivals of each subject (lines) for each observed times (columns).

Author(s)

Yohann Foucher <[e-mail]>

Camille Sabathe <[e-mail]>

Examples

# On a linux machine

# Install Python 

# sudo apt install python3-pip

# Install Python Miniconda for CPU computaion on a linux machine

# Download the file from https://docs.conda.io/en/latest/miniconda.html#linux-installers

  # chmod +x Miniconda3-latest-Linux-x86_64.sh 
  # ./Miniconda3-latest-Linux-x86_64.sh 
  # conda create --name torch python=3.8
  # conda activate torch
  # conda install pytorch torchvision torchaudio cpuonly -c pytorch
  # conda install -c conda-forge pycox

# export PATH="/home/username/.local/bin:$PATH"

# remotes::install_github("RaphaelS1/survivalmodels")
# install_pycox(method = "auto",  conda = "auto",  pip = TRUE,  install_torch = TRUE )
# install_keras(method = "auto",  conda = "auto",  pip = TRUE,  install_tensorflow = TRUE)

# use_condaenv(condaenv = "~/miniconda3/envs/torch", conda = "auto", required = FALSE)

# data(dataDIVAT2)

# The estimation of the training model

# model<-nn.time(times="times", failures="failures", data=dataDIVAT2,
#  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), n.nodes=10, decay=0,
#  batch.size=128, epochs=10)

# Predicted survival from the validation sample

# pred <- predict(model,
#  newdata=data.frame(age=c(52,52), hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

# plot(y=pred$predictions[1,], x=pred$times, xlab="Time (years)", ylab="Predicted survival",
#     col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

# lines(y=pred$predictions[2,], x=pred$times, col=2, type="l", lty=1, lwd=2)

# legend("bottomright", col=c(1,2), lty=1, lwd=2, c("Subject #1", "Subject #2"))

RISCA documentation built on March 31, 2023, 11:06 p.m.