ImpGamlssFit: GAMLSS imputation fit

In ImputeRobust: Robust Multiple Imputation with Generalized Additive Models for Location Scale and Shape

Description

This function takes a data set to fit a gamlss model and another to predict the expected parameters values. It returns a function that will generate a vector of random observations for the predicted parameters. The amount of random observations is the number of units on the dataset used to get such predictions.

Usage

ImpGamlssFit(data, new.data, family, n.ind.par, gam.mod,
  mod.planb = list(type = "pb", par = list(degree = 1, order = 1)),
  n.par.planb = n.ind.par, lin.terms = NULL, n.cyc = 5, bf.cyc = 5,
  cyc = 5, forceNormal = FALSE, trace = FALSE, ...)

Arguments

data	Completely observed data frame to be used to fit a gamlss model estimate.
new.data	Data frame used to predict the parameter values for some given right side x-values on the gamlss model.
family	Family to be used for the response variable on the GAMLSS estimation.
n.ind.par	Number of individual parameters to be fitted. Currently it only allows one or two because of stability issues for more parameters.
gam.mod	list with the parameters of the GAMLSS imputation model.
mod.planb	list with the parameters of the alternative GAMLSS imputation model.
n.par.planb	number of individual parameters in the alternative model.
lin.terms	Character vector specifying which (if any) predictor variables should enter the model linearly.
n.cyc	number of cycles of the gamlss algorithm
bf.cyc	number of cycles in the backfitting algorithm
cyc	number of cycles of the fitting algorithm
forceNormal	Flag that if set to 'TRUE' will use a normal family for the gamlss estimation as a last resource.
trace	whether to print at each iteration (TRUE) or not (FALSE)
...	extra arguments for the control of the gamlss fitting function

Value

Returns a method to generate random samples for the fitted gamlss model using "new.data" as covariates.

ImputeRobust documentation built on May 2, 2019, 3:27 a.m.