get_random

A gam object, produced by <code><a href="/link/gam?package=itsadug&version=0.8&to=mgcv" rd-options="mgcv" data-mini-rdoc="mgcv::gam">gam</a></code> or
<code><a href="/link/bam?package=itsadug&version=0.8&to=mgcv" rd-options="mgcv" data-mini-rdoc="mgcv::bam">bam</a></code>.

model

A string or function description to apply to the random effects
estimates. When NULL (default), the estimates for the random effects are
returned.

A named list of the values to restrict the estimates for the
random predictor terms. When NULL (default) all levels are returned.

cond

Number of data points estimated for each random smooth.

n.grid

Logical: whether or not to print a summary of the
values selected for each predictor. Defaults to TRUE.

print.summary

Get model predictions for the random effects.

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package mgcv (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

get_random: Get model predictions for the random effects.

Description

Usage

Arguments

Value

See Also

Examples