predict.maxlogL: Predict Method for `maxlogL` Fits

Description

This function computes predictions and optionally the estimated standard errors of those predictions from a model fitted with maxlogLreg.

Usage

# S3 method for maxlogL
predict(
  object,
  parameter = NULL,
  newdata = NULL,
  type = c("link", "response", "terms"),
  se.fit = FALSE,
  terms = NULL,
  ...
)

Value

If se.fit = FALSE, a vector of predictions is returned. For type = "terms", a matrix with a column per term and an attribute "constant" is returned.

If se.fit = TRUE, a list with the following components is obtained:

fit: Predictions.
se.fit: Estimated standard errors.

Arguments

object: an object of maxlogL class generated by maxlogLreg function.
parameter: a character which specifies the parameter to predict.
newdata: a data frame with covariates with which to predict. It is an optional argument, if omitted, the fitted linear predictors or the (distribution) parameter predictions are used.
type: a character with the type of prediction required. The default (type = "link") is on the scale of the linear predictors; the alternative type = "response" is on the scale of the distribution parameter.
se.fit: logical switch indicating if standard errors of predictions are required.
terms: A character vector that specifies which terms are required if type = "terms". All terms are returned by default.
...: further arguments passed to or from other methods.

Author

Jaime Mosquera Gutiérrez, jmosquerag@unal.edu.co

Details

This predict method computes predictions for values of any distribution parameter in link or original scale.

Examples

Run this code

library(EstimationTools)

#--------------------------------------------------------------------------------
# Example 1: Predictions from a model using a simulated normal distribution
n <- 1000
x <- runif(n = n, -5, 6)
y <- rnorm(n = n, mean = -2 + 3 * x, sd = exp(1 + 0.3* x))
norm_data <- data.frame(y = y, x = x)

# It does not matter the order of distribution parameters
formulas <- list(sd.fo = ~ x, mean.fo = ~ x)

norm_mod <- maxlogLreg(formulas, y_dist = y ~ dnorm, data = norm_data,
                       link = list(over = "sd", fun = "log_link"))
predict(norm_mod)


#--------------------------------------------------------------------------------
# Example 2: Predictions using new values for covariates
predict(norm_mod, newdata = data.frame(x=0:6))


#--------------------------------------------------------------------------------
# Example 3: Predictions for another parameter
predict(norm_mod, newdata = data.frame(x=0:6), param = "sd",
       type = "response")

#--------------------------------------------------------------------------------
# Example 4: Model terms
predict(norm_mod, param = "sd", type = "terms")


#--------------------------------------------------------------------------------

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