# family_fun: Evaluate GAMLSS family functions from gamlss object In TiStat/Imputegamlss: Imputing censored covariates with distributional regression - gamlss

## Description

Function that, given a "gamlss" object, evaluates the distribution-specific functions under predicted parameters of the provided dataframe "predictdata".
The distribution-specific functions are density, cumulative distribution function, quantile function and random generation for the given family of the gamlss object.
CAUTION: Exactly ONE of the arguments x, q, p, n MUST be specfied!
Also make sure that n is a multiple of nrow(predictdata)!

## Usage

 ```1 2``` ```family_fun(object, func = c("d", "p", "q", "r"), fitdata, predictdata, p = NULL, q = NULL, x = NULL, n = NULL, ...) ```

## Arguments

 `object` gamlss fit object `func` character. "d", "p", "q", "r" for either density, distribution function, quantile or random data generation. `fitdata` dataframe. Data used as input. `predictdata` dataframe. Containing the observations for which the parameters are predicted. `p` scalar numeric. Probability value if quantile function used. `x, q` scalar numeric. Quantile value if density or probability function used respectively. `n` scalar numeric. Number of observations if random generator function used. `...` argumenst to be passed to the called distributional function.

## Value

Depending on the choice of func, the respective vector of (d)density-, (p)probability- , (q)quantile- or (r)random values is returned.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# Simulating a dataset ld <- simulateData(n= 300, param.formula = list(mu = ~exp(x1) + x2 + x3, sigma = ~sin(x2)), name = 'x1', subset = ~ (x2 < 0.3 & x3 < 0.4), prob = 0.8, damage =c(0.3, 0.9), family = 'NO', correlation = NULL)\$defected # Fitting a gamlss model lmodel <- gamlss(formula = y ~ . -indicator, data=ld) nl <- length(ld\$x1[ld\$indicator==1]) lpredict.df <- data.frame(x1 = runif(n = nl), x2 = runif(n = nl), x3 = runif(n = nl), indicator = 1) family_fun(lmodel, func = 'r',ld, lpredict.df, n = nrow(lpredict.df)) ```

TiStat/Imputegamlss documentation built on May 20, 2019, 9:25 a.m.