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Refit a mixeff model with a new response

Source: R/simulate.R, R/simulate_glmm.R
refit.Rd

refit() fits the same model to a new response, keeping every other setting of the original fit, like lme4::refit(). For an mm_lmm it calls lmm() with the stored model frame, REML setting and prior weights; for an mm_glmm it calls glmm() with the stored family (negative-binomial fits re-estimate theta when the original did), prior weights, offset, method, nAGQ and control settings (verbosity silenced).

Usage

refit(object, newresp, ...)

# Default S3 method
refit(object, newresp, ...)

# S3 method for class 'mm_lmm'
refit(object, newresp, ...)

# S3 method for class 'mm_glmm'
refit(object, newresp, ...)

Arguments

object

A fitted mm_lmm or mm_glmm.

newresp

The new response (see Details).

...

control = to override the stored control settings.

Value

A new fit of the same class as object.

Details

newresp may be a numeric vector, or a one-column data frame such as the output of simulate() with nsim = 1. For binomial GLMMs it may also be a two-column (successes, failures) matrix (the shape simulate() returns for a cbind() response; the trial counts become the new prior weights), a two-level factor, or a logical vector.

As in lme4, newresp is on the scale of the model's response: for a transformed response such as log(y) ~ ... it replaces log(y), and the refit's formula names it .mm_refit_response.

Examples

set.seed(1)
df <- data.frame(
  y = rpois(60, 3), x = rnorm(60),
  g = factor(rep(seq_len(10), each = 6))
)
fit <- glmm(y ~ x + (1 | g), df, family = poisson(),
            control = mm_control(verbose = -1))
ystar <- simulate(fit, nsim = 1, seed = 2)
fixef(refit(fit, ystar))
#> (Intercept)           x 
#>  1.06657255  0.08442761