Author: Richard Meitern
Date: 2019-06-11

# Plot rms model summaries and predictions

#### Richard Meitern

#### 06/11/2019

### Load the required libraries

    #the regression modelling by F. Harrell
    library(rms)

    #the extension for plotting the rms summary objects
    library(ormPlot)
    #> Registered S3 method overwritten by 'ormPlot':
    #>   method           from
    #>   plot.summary.rms rms

    #to modify plots
    library(ggplot2)

### 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)

educ_3 | Rural | sex | max_SEP_3 | n_siblings | cran_rzs | height_rzs | FW_rzs | YOBc | YOB
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
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")

### Creating a model

    #see help(orm) for further info
    orm_model<-orm(educ_3 ~ Rural + sex + n_siblings + cran_rzs + height_rzs +
        FW_rzs + YOBc + (YOBc * sex) + (YOBc * Rural), data = educ_data)

### 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]]

    plots[[2]]

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)