distreg.vis (version 1.7.5)

preds: Predict parameters of a distreg models' target distribution

Description

This function takes a fitted model and a dataframe with explanatory variables and a column for the intercept to compute predicted parameters for the specified distribution. Without worrying about class-specific function arguments, preds() offers a consistent way of obtaining predictions based on specific covariate combinations.

Usage

preds(model, newdata = NULL, what = "mean", vary_by = NULL)

Value

A data.frame with one column for every distributional parameter and a row for every covariate combination that should be predicted.

Arguments

model

A fitted distributional regression model object. Check supported classes at distreg_checker.

newdata

A data.frame with explanatory variables as columns, and rows with the combinations you want to do predictions for. Furthermore, whether or not to include the intercept has to be specified via a logical variable intercept. If omitted, the average of the explanatory variables is used (see set_mean).

what

One of "mean" or "samples". The default for bamlss models is "samples", while the default for gamlss models is "mean". This argument changes how the mean of the parameter is calculated. See details for details.

vary_by

Variable name in character form over which to vary the mean/reference values of explanatory variables. It is passed to set_mean. See that documentation for further details.

Examples

# Generating data
data_fam <- model_fam_data(fam_name = "BE")

# Fit model
library("gamlss")
beta_model <- gamlss(BE ~ norm2 + binomial1,
  data = data_fam, family = BE())

# Get 3 predictions
ndata <- data_fam[sample(1:nrow(data_fam), 3), c("binomial1", "norm2")]
preds(model = beta_model, newdata = ndata)

# If newdata argument is omitted preds uses the means of the explanatory variables
preds(model = beta_model, newdata = NULL) # this gives the same results as ...
preds(model = beta_model, newdata = set_mean(model_data(beta_model))) # ...this