Importance / performance matrix

Plots an ipm_model() table as a scatter of feature performance (x) vs. importance (y), coloured by performance band, with decision bands across the top: the importance / performance slide of a survey deck, in one call.

Usage

plot_ipm(model, title = NULL, repel = TRUE, bands = bands_rating_3())

Arguments

model

An ipm_model() output (feature, importance, performance, perf_class).

title

Optional title; defaults to the average performance.

repel

Use ggrepel for non-overlapping labels when available. Defaults to TRUE.

bands

Decision-band spec drawn across the top and used to colour the points. Defaults to bands_rating_3() (BAD 1-3, OK 3-4, GOOD 4-5).

Value

A ggplot object.

Details

Each feature is a point: performance on the x-axis (mean 1–5 rating, with a reference line at the average) and importance on the y-axis (relative weight, as a percentage). Each point takes the colour of the decision band it falls in, so a feature averaging 2.4 is red because it sits in the BAD band – the point and the band behind it can never disagree. Read it by quadrant – high-importance, low-performance features (upper left) are the priorities to fix, while high-importance, high-performance features (upper right) are strengths to protect. Feed it an ipm_model() table; uses ggrepel for non-overlapping labels when available.

See also

ipm_model(), compare_values().

Other plots: plot_bars(), plot_gauges(), plot_nps(), plot_nps_gauge(), plot_quotes_tree(), plot_rating_grid(), plot_stacked_rating()

Examples

ipm_model(podracing_survey, nps_value, "ratings_") %>% plot_ipm()
#> Parsing `nps_value` as a non-binary variable.
#> Applying multiple regression to calculate relative weights...