In this version, the structure of the primary function is rewritten. The function treats factor and numeric base terms with separate side functions leaving room for more distinguished actions on the models with the two types of terms.
stats_cp = "ci"
works for factor base-term models.- Allow to output confidence intervals of the difference between the minimum and maximum values of the conditioning variable when
plot = FALSE
. prePro
can be used on multilevel models
- Update
interplot.plot
for the latest version ofggplot
- Adding new statistics for testing the statistical significance of the conditional effect (by
stats_cp
). - Adding an argument to modify the plot caption (by
txt_caption
).
- Adding example of models based on multiple imputations.
- Modifying the vignette to include new functions.
- interplot no longer sets the random seed; to ensure complete reproducibility, users must now set their own seeds using
set.seed()
before calling interplot functions. - Adds facet_labs argument, an optional character vector of facet labels to be used when plotting an interaction with a factor variable.
- Showing the confidence intervals between the conditional effects at the minimum and maximum values of the conditioning variable.
- Avoiding the warning caused by the
class(m) == "polr"
.
- Adding an argument to adjust CIs to control the false discovery rate.
- Adding an argument to produce conditional predicted probabilities at given values.
- Adding a brief review of the methodology of interaction.
- Adding an example to show how to control for the false discovery rate.
- Adding an example to illustrate plotting conditional predicted propbabilities.
- Adding an argument to adjust the CIs.
- Fixing the error in plotting
lmer
projects.
- Take the
steps
argument back in case of special design requirement of the plot. - Fixed an error in presenting the histogram on categorical conditioning variables.
- Improving the histogram presentation: all the bars for categorical variables are centered.
Updated the vignette including instructions of how to change the aesthetics of the plot and how to use histogram function.
Updated the vignette including instructions of how to change the aesthetics of the plot and how to use histogram function.
- The aesthetics can be modified through built-in arguments or the ggplot
geom_
functions. - A histogram can be superimposed into the plot.
Adding the function to plot interactions based on factor variables.
Fit ggplot2
2.0.0
Fixed the quadratic error (#16)
Adding the function to plot interactions based on factor variables.
Fit ggplot2
2.0.0
Fix the bug for nonlinear multilevel models with multiply imputed data (gmlmmi).
Fix the error to run mlm and mlmmi.