R/partialDependence.R
partialDependence_growth.Rd
Compute the partial dependence of a predictor, or set of predictors, on the predicted trajectory of a latent growth model.
partialDependence_growth(
x,
data,
reference.var,
support = 20,
points = NULL,
mc = NULL,
FUN = "median",
times = NULL,
parameters = NULL,
...
)
An object for which a method exists
Optional data.frame
that was used to train the
model.
Character vector, referring to the (independent) reference variable or variables for which partial dependence is calculated. Providing two (or more) variables allows for probing interactions, but note that this is computationally expensive.
Integer. Number of grid points for interpolating the
reference.var
. Alternatively, use points
for one or more
variables named in reference.var
.
Named list, with elements corresponding to reference.var
. Use this argument to provide specific points for which to obtain marginal
dependence values; for example, the mean and +/- 1SD of reference.var
.
Integer. If mc
is not NULL
, the function will sample
mc
number of rows from data
with replacement, to estimate
marginal dependency using Monte Carlo integration. This is less
computationally expensive.
Character string with function used to integrate predictions
across all elements of x
.
Numeric matrix, representing the factor loadings of a latent
growth model, with columns equal to the number of growth parameters
,
and rows equal to the number of measurement occasions.
Character vector of the names of the growth parameters;
defaults to NULL
, which assumes that the growth parameters are the
only parameters and are in the correct order.
Extra arguments passed to FUN
.