jglmm_tidiers: Tidying methods for jglmm models

In mikabr/jglmm: Generalized Mixed-Effects Models in Julia

Tidying methods for jglmm models

Description

These methods tidy the coefficients and fitted values from 'jglmm' objects.

Usage

## S3 method for class 'jglmm'
glance(x, ...)

## S3 method for class 'jglmm'
tidy(x, ...)

## S3 method for class 'jglmm'
augment(x, ...)

Arguments

x	An object of class 'jglmm', as returned by 'jglmm()'.
...	Optional additional arguments, currently none are used.

Value

'glance' returns a data frame with one row and the columns:

'tidy' returns a data frame with one row for each estimated effect. It contains the columns:

effect	"fixed" for fixed effects, "ran_pars" for random effect parameters
group	the group within which the random effect is being estimated (NA for fixed effects)
param	parameter being estimated (beta for fixed effects, sd or cor for random effect parameters)
term	term being estimated
estimate	estimated coefficient
std.error	standard error
statistic	z-statistic (NA for modes)
p.value	p-value computed from z-statistic (NA for modes)

'augment' returns one row for each original observation, with these columns added:

.fitted	predicted values
.resid	residuals

Examples

## Not run: 
jglmm_setup()
cbpp <- dplyr::mutate(lme4::cbpp, prop = incidence / size)
gm <- jglmm(prop ~ period + (1 | herd), data = cbpp, family = "binomial",
            weights = cbpp$size)
tidy(gm)
augment(gm)
glance(gm)

## End(Not run)

mikabr/jglmm documentation built on Nov. 18, 2022, 1:32 a.m.