R/make_predictions.R
make_predictions.Rd
This is an alternate interface to the underlying tools that
make up effect_plot()
as well as interact_plot
and cat_plot
from
the interactions
package.
make_predictions
creates the data to be plotted and adds information
to the original data to make it more amenable for plotting with the
predicted data.
make_predictions(model, ...) # S3 method for default make_predictions(model, pred, pred.values = NULL, at = NULL, data = NULL, center = TRUE, interval = TRUE, int.type = c("confidence", "prediction"), int.width = 0.95, outcome.scale = "response", robust = FALSE, cluster = NULL, vcov = NULL, set.offset = NULL, new_data = NULL, return.orig.data = FALSE, partial.residuals = FALSE, ...)
model  The model (e.g., 

...  Ignored. 
pred  The name of the focal predictor as a string. This is the variable for which, if you are plotting, you'd likely have along the xaxis (with the dependent variable as the yaxis). 
pred.values  The values of 
at  If you want to manually set the values of other variables in the model, do so by providing a named list where the names are the variables and the list values are vectors of the values. This can be useful especially when you are exploring interactions or other conditional predictions. 
data  Optional, default is NULL. You may provide the data used to
fit the model. This can be a better way to get mean values for centering
and can be crucial for models with variable transformations in the formula
(e.g., 
center  Set numeric covariates to their mean? Default is TRUE. You
may also just provide a vector of names (as strings) of covariates to
center. Note that for 
interval  Logical. If 
int.type  Type of interval to plot. Options are "confidence" or "prediction". Default is confidence interval. 
int.width  How large should the interval be, relative to the standard error? The default, .95, corresponds to roughly 1.96 standard errors and a .05 alpha level for values outside the range. In other words, for a confidence interval, .95 is analogous to a 95% confidence interval. 
outcome.scale  For nonlinear models (i.e., GLMs), should the outcome
variable be plotted on the link scale (e.g., log odds for logit models) or
the original scale (e.g., predicted probabilities for logit models)? The
default is 
robust  Should robust standard errors be used to find confidence
intervals for supported models? Default is FALSE, but you should specify
the type of sandwich standard errors if you'd like to use them (i.e.,

cluster  For clustered standard errors, provide the column name of the cluster variable in the input data frame (as a string). Alternately, provide a vector of clusters. 
vcov  Optional. You may supply the variancecovariance matrix of the coefficients yourself. This is useful if you are using some method for robust standard error calculation not supported by the sandwich package. 
set.offset  For models with an offset (e.g., Poisson models), sets an offset for the predicted values. All predicted values will have the same offset. By default, this is set to 1, which makes the predicted values a proportion. See details for more about offset support. 
new_data  If you would prefer to generate your own hypothetical
(or not hypothetical) data rather than have the function make a call to

return.orig.data  Instead of returning a just the predicted data frame, should the original data be returned as well? If so, then a list will be return with both the predicted data (as the first element) and the original data (as the second element). Default is FALSE. 
partial.residuals  If 