get_predictions

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 named list of the values to use for the predictor terms.
Variables omitted from this list will have the closest observed value to
the median for continuous variables, or the reference level for factors.

cond

Logical: whether or not to return the confidence interval or
standard error around the estimates.

A number to scale the standard error. Defaults to 1.96, resulting
in 95% confidence intervals. For 99% confidence intervals use a value of
2.58.

Logical: whether or not to remove random effects.
Default is TRUE. Alternatively a string (or vector of strings) with the
name of the random effect(s) to remove.

rm.ranef

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

print.summary

Get model predictions for specific conditions.

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_predictions: Get model predictions for specific conditions.

Description

Usage

Arguments

Value

See Also

Examples