gamlss2

Extracting Fitted or Predicted Parameters or Terms from gamlss2 Models

Description

Methods for gamlss2 model objects for extracting fitted (in-sample) or predicted (out-of-sample) parameters, terms, etc.

Usage

## S3 method for class 'gamlss2'
predict(object, model = NULL, newdata = NULL,
  type = c("parameter", "link", "response", "terms"), terms = NULL,
  se.fit = FALSE, drop = TRUE, exclude = NULL, ...)

Arguments

object model object of class gamlss2.

model character. Which model part(s) should be predicted? Can be one or more of “mu”, “sigma”, etc. By default all model parts are included.

newdata data.frame. Optionally, a new data frame in which to look for variables with which to predict. If omitted, the original observations are used.

type character. Which type of prediction should be computed? Can be the full additive predictor(s) (“link”, before applying the link function(s)), the corresponding parameter (“parameter”, after applying the link function(s)), the individual terms of the additive predictor(s) (“terms”), or the corresponding mean of the response distribution (“response”).

terms character. Which of the terms in the additive predictor(s) should be included? By default all terms are included.

se.fit logical. Should standard errors for the predictions be included? Standard errors are computed by simulating from the approximate multivariate normal distribution of the maximum likelihood estimates. The number of simulations is controlled by the argument R, which defaults to R = 200, and can be passed via ….

drop logical. Should the predictions be simplified to a vector if possible (TRUE) or always returned as a data.frame (FALSE)?

exclude character. Any terms named in this vector will be set to zero.

… currently only used for catching what as an alias for model.

Details

Predictions for gamlss2 model objects are obtained in the following steps: First, the original data is extracted or some newdata is set up. Second, all of the terms in the additive predictors of all model parameters (“mu”, “sigma”, …) are computed. Third, the full additive predictor(s) are obtained by adding up all individual terms. Fourth, the parameter(s) are obtained from the additive predictor(s) by applying the inverse link function(s). In a final step, the mean of the associated probability distribution can be computed.

See also prodist.gamlss2 for setting up a full distributions3 object from which moments, probabilities, quantiles, or random numbers can be obtained.

Value

If drop = FALSE a data.frame. If drop = TRUE (the default), the data.frame might be simplified to a numeric vector, if possible.

Examples

library("gamlss2")

## fit heteroscedastic normal GAMLSS model
## stopping distance (ft) explained by speed (mph)
data("cars", package = "datasets")
m <- gamlss2(dist ~ s(speed) | s(speed), data = cars, family = NO)

GAMLSS-RS iteration  1: Global Deviance = 407.3647 eps = 0.125474     
GAMLSS-RS iteration  2: Global Deviance = 406.1879 eps = 0.002888     
GAMLSS-RS iteration  3: Global Deviance = 406.1521 eps = 0.000088     
GAMLSS-RS iteration  4: Global Deviance = 406.1491 eps = 0.000007

## new data for predictions
nd <- data.frame(speed = c(10, 20, 30))

## default: additive predictors (on link scale) for all model parameters
predict(m, newdata = nd)

        mu    sigma
1 23.28681 10.10793
2 58.70678 18.59707
3 94.12851 34.20698

## mean of the response distribution
predict(m, newdata = nd, type = "response")

       1        2        3 
23.28681 58.70678 94.12851

## model parameter(s)
predict(m, newdata = nd)

        mu    sigma
1 23.28681 10.10793
2 58.70678 18.59707
3 94.12851 34.20698

predict(m, newdata = nd, model = "sigma")

       1        2        3 
10.10793 18.59707 34.20698

predict(m, newdata = nd, model = "sigma", drop = FALSE)

## individual terms in additive predictor(s)
predict(m, newdata = nd, type = "terms", model = "sigma")

  (Intercept)   s(speed)
1    2.642556 -0.3292360
2    2.642556  0.2804473
3    2.642556  0.8898731

predict(m, newdata = nd, type = "terms", model = "sigma", terms = "s(speed)")

    s(speed)
1 -0.3292360
2  0.2804473
3  0.8898731

## standard errors
predict(m, newdata = nd, se.fit = TRUE, R = 200)

    mu.fit     mu.se    sigma.fit     sigma.se
1 23.28681  5.100319 1.111684e+01 5.087797e+00
2 58.70678 12.475172 2.382022e+01 1.763546e+01
3 94.12851 87.872462 8.784568e+06 1.204297e+08