predict: Model Prediction

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Model Prediction

Description

Predict outcomes with a fitted model.

Usage

## S3 method for class 'MLModelFit'
predict(
  object,
  newdata = NULL,
  times = numeric(),
  type = c("response", "default", "numeric", "prob"),
  cutoff = MachineShop::settings("cutoff"),
  distr = character(),
  method = character(),
  verbose = FALSE,
  ...
)

## S4 method for signature 'MLModelFit'
predict(object, ...)

Arguments

object	model fit result.
newdata	optional data frame with which to obtain predictions. If not specified, the training data will be used by default.
times	numeric vector of follow-up times at which to predict survival events/probabilities or NULL for predicted survival means.
type	specifies prediction on the original outcome ("response"), numeric ("numeric"), or probability ("prob") scale; or model-specific default predictions ("default").
cutoff	numeric (0, 1) threshold above which binary factor probabilities are classified as events and below which survival probabilities are classified.
distr	character string specifying distributional approximations to estimated survival curves. Possible values are "empirical", "exponential", "rayleigh", or "weibull"; with defaults of "empirical" for predicted survival events/probabilities and "weibull" for predicted survival means.
method	character string specifying the empirical method of estimating baseline survival curves for Cox proportional hazards-based models. Choices are "breslow" or "efron" (default).
verbose	logical indicating whether to display printed output generated by some model-specific predict functions to aid in monitoring progress and diagnosing errors.
...	arguments passed from the S4 to the S3 method.

See Also

confusion, performance, metrics

Examples

## Requires prior installation of suggested package gbm to run

## Survival response example
library(survival)

gbm_fit <- fit(Surv(time, status) ~ ., data = veteran, model = GBMModel)
predict(gbm_fit, newdata = veteran, times = c(90, 180, 360), type = "prob")

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.