# extra_fams: Family functions for Student's-t, Beta, Zero-Inflated and... In GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

## Description

Specifies the information required to fit a Beta, zero-inflated and hurdle Poisson, zero-inflated and hurdle Negative Binomial, a hurdle normal and a hurdle Beta mixed-effects model, using `mixed_model()`.

## Usage

 ```1 2 3 4 5 6 7 8``` ```students.t(df = stop("'df' must be specified"), link = "identity") beta.fam() zi.poisson() zi.negative.binomial() hurdle.poisson() hurdle.negative.binomial() hurdle.lognormal() hurdle.beta.fam() ```

## Arguments

 `link` name of the link function. `df` the degrees of freedom of the Student's t distribution.

## Note

Currently only the log-link is implemented for the Poisson and negative binomial models, the logit link for the beta and hurdle beta models and the identity link for the log-normal model.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34``` ```# simulate some data from a negative binomial model set.seed(102) dd <- expand.grid(f1 = factor(1:3), f2 = LETTERS[1:2], g = 1:30, rep = 1:15, KEEP.OUT.ATTRS = FALSE) mu <- 5*(-4 + with(dd, as.integer(f1) + 4 * as.numeric(f2))) dd\$y <- rnbinom(nrow(dd), mu = mu, size = 0.5) # Fit a zero-inflated Poisson model, with only fixed effects in the # zero-inflated part fm1 <- mixed_model(fixed = y ~ f1 * f2, random = ~ 1 | g, data = dd, family = zi.poisson(), zi_fixed = ~ 1) summary(fm1) # We extend the previous model allowing also for a random intercept in the # zero-inflated part fm2 <- mixed_model(fixed = y ~ f1 * f2, random = ~ 1 | g, data = dd, family = zi.poisson(), zi_fixed = ~ 1, zi_random = ~ 1 | g) # We do a likelihood ratio test between the two models anova(fm1, fm2) ############################################################################# ############################################################################# # The same as above but with a negative binomial model gm1 <- mixed_model(fixed = y ~ f1 * f2, random = ~ 1 | g, data = dd, family = zi.negative.binomial(), zi_fixed = ~ 1) summary(gm1) # We do a likelihood ratio test between the Poisson and negative binomial models anova(fm1, gm1) ```

GLMMadaptive documentation built on May 2, 2019, 2:51 p.m.