Looking at the bundled data
OrmPlot has educ_data bundled. educ_data is
used in this vignette to show the functionality of the package. To
familiarize with the data:
#show first 6 rows
head(educ_data)
| 2 |
Urban |
Girl |
Unskilled manual |
4 |
0.6328 |
-0.1497 |
-0.5076 |
-9.224 |
1940 |
| 2 |
Urban |
Girl |
Non-manual |
2 |
0.6386 |
0.9394 |
-0.2304 |
-9.224 |
1940 |
| 3 |
Rural |
Girl |
Unskilled manual |
6 |
0.03449 |
-0.4342 |
-0.3903 |
-8.224 |
1941 |
| 2 |
Rural |
Girl |
Skilled manual |
3 |
0.591 |
0.44 |
0.02453 |
-8.224 |
1941 |
| 1 |
Rural |
Girl |
Unskilled manual |
1 |
1.559 |
-0.2515 |
1.6 |
-8.224 |
1941 |
| 2 |
Rural |
Girl |
Unskilled manual |
3 |
0.03757 |
0.08076 |
-0.4793 |
-7.224 |
1942 |
#show variable explanation
help(educ_data)
Setting up rms datadist
The rms package requires that a datadist object is set
up properly. According to datadist documentation:
q.effect is a set of two quantiles for computing the range
of continuous variables to use in estimating regression effects.
Defaults are c(.25,.75), which yields inter-quartile-range
odds ratios
#q.effect determines for what range the odds ratios are given on plots
dd <- datadist(educ_data, q.effect = c(0.5, 0.75))
#set it also to options
options(datadist="dd")
Plotting the model predictions with CI
The main advantage of using ormPlot is that you get plots with
confidence intervals shown on the plot.
The simplest way is to predict for only one value. Plotting returns a
customizable ggplot object.
plot(orm_model, cran_rzs)
For more complex models specify facet column and rows.
plot(orm_model, cran_rzs, Rural, sex)
You can easily set custom labels.
p<-plot(orm_model, cran_rzs, Rural, sex,
xlab = "Cranial volume (residuals to age an birth date)",
facet_labels = list(Rural = c("Urban", "Rural"),sex=c("Male","Female")))
colors <- c("#4a9878", "#0a191e", "#d8b65c")
educ_names <- c("Primary", "Secondary", "Tertiary")
# further modifing like any other ggplot
final_plot<-p + labs(color = "Education", fill = "Education") +
scale_color_manual(values = colors, labels = educ_names) +
scale_fill_manual(values = colors, labels = educ_names)
final_plot
Save like any ggplot graph.
ggsave("educ_cran.svg",final_plot, height = 8 ,width = 8)
Plotting the model summary
The easiest way is to just plot the summary object
forestplot(summary(orm_model))
If this does not look nice enough you can also get
ggplot2 objects to customize to your needs. The best way to
get customizable plots is to specify
return_ggplots=TRUE
# you can use also use plot instead of forestplot
plots<-forestplot(summary(orm_model), return_ggplots=T)
plots[[1]]
These can be joined using the join_ggplots command. You can edit the
plots as any ggplot plot
p1 <- plots[[1]] +
scale_x_discrete(labels=c("Mean", "Lower CI", "Upper CI"),
position = "top",
name = NULL)
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
# the x axis is actually y axis because the cordinates are flipped with coord_flip()
p2 <- plots[[2]] + scale_y_continuous(breaks = c(0.5, 0.7, 0.9, 1.1),
position = "right")
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.
forestplot<-join_ggplots(p1,p2)
To save as svg fur further editing just use ggsave from
ggplot2
ggsave("forestplot.svg",forestplot)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.