Visualise model term contributions to predicted response

The plotting function to visualise the predicted response from a regression model as a stacked bar-chart showing the contributions (predictor coefficient * predictor value) of each model term to the total predicted value (represented by the total height of the bar). Requires the output of the `prediction_contributions_data` as an input in the `data` parameter.

Usage

prediction_contributions_plot(
  data,
  colours = NULL,
  se = FALSE,
  bar_orientation = c("vertical", "horizontal"),
  facet_var = NULL,
  nrow = 0,
  ncol = 0
)

Arguments

data

A data-frame which is the output of the `prediction_contributions_data` function, consisting of the predicted response split into the contributions by the different coefficients.

colours

A character vector specifying the colours for the contributions of the different coefficients. If not specified, a default colour-scheme would be chosen, however it could be uninformative and it is recommended to manually choose contrasting colours for each coefficient group to render a more informative plot.

se

A logical value indicating whether to show prediction intervals for predictions in the plot.

bar_orientation

One of "vertical" or "horizontal" indicating the orientation of the bars. Defaults to a vertical orientation.

facet_var

A character string or numeric index identifying the column in the data to be used for faceting the plot into multiple panels.

nrow

Number of rows in which to arrange the final plot (when `add_var` is specified).

ncol

Number of columns in which to arrange the final plot (when `add_var` is specified).

Value

A ggmultiplot (ggplot if single plot is returned) class object or data-frame (if `plot = FALSE`)

Examples

library(DImodels)
library(dplyr)

## Load data
data(sim2)
sim2$AV <- DI_data_E_AV(data = sim2, prop = 3:6)$AV

## Fit model
mod <- glm(response ~ 0 + (p1 + p2 + p3 + p4 + AV), data = sim2)

## Create data for plotting
plot_data <- prediction_contributions_data(data = sim2[c(1,5,9,11,15,19,23), ],
                                           model = mod)
#> ✔ Finished data preparation.
## Create plot
prediction_contributions_plot(data = plot_data)
#> Warning: No colours were specified for the response contributions.
#> ℹ The default colours might not result in an informative
#>                          plot, consider choosing specific colours to contrast
#>                          the contributions of different groups in the response.
#> This warning is displayed once per session.
#> ✔ Created plot.


## Choose distinct colours for groups of coefficients for better visibility
ID_cols <- get_colours(4, FG = c("G", "G", "H", "H"))
int_cols <- "#808080"
cols <- c(ID_cols, int_cols)
## Specify colours using `cols`
prediction_contributions_plot(data = plot_data, colours = cols)
#> ✔ Created plot.


## Show prediction intervals
prediction_contributions_plot(data = plot_data, colours = cols, se = TRUE)
#> ✔ Created plot.


## Change orientation of bars using `bar_orientation`
prediction_contributions_plot(data = plot_data, colours = cols,
                              se = TRUE, bar_orientation = "horizontal")
#> ✔ Created plot.


## Facet plot based on a variable in the data
prediction_contributions_plot(data = plot_data, colours = cols,
                              se = TRUE, bar_orientation = "horizontal",
                              facet_var = "block")
#> ✔ Created plot.


## If multiple plots are desired `add_var` can be specified during
## data preparation and the plots can be arranged using nrow and ncol
sim2$block <- as.character(sim2$block)
new_mod <- update(mod, ~. + block, data = sim2)
plot_data <- prediction_contributions_data(data = sim2[c(1,5,9,11,15,19,23), c(3:6, 8)],
                                           model = new_mod,
                                           add_var = list("block" = c("1", "2")))
#> ✔ Finished data preparation.
## Arrange in two columns
prediction_contributions_plot(data = plot_data, ncol = 2)
#> ✔ Created all plots.