Plot Super Learner weights for LMTP nuisance fits
Source:R/margot_learners.R
margot_plot_lmtp_learners.RdProduces a heatmap of average Super Learner weights by wave, learner, shift, and nuisance component (outcome regression `m` and density-ratio regression `r`). Colours encode the mean Super Learner weight averaged across cross-fitting folds.
Usage
margot_plot_lmtp_learners(
x,
outcome,
shifts = NULL,
label_mapping = NULL,
waves = NULL,
remove_waves = NULL,
component = c("both", "outcome", "treatment")
)Arguments
- x
LMTP run output (e.g., the result of [margot_lmtp()]) or any object that exposes `$density_ratios` in the same structure as the plot helpers.
- outcome
Character scalar giving the outcome name to summarise.
- shifts
Optional character vector of shifts to include (either full names such as `t5_pwi_z_shift_up` or cleaned suffixes such as `shift_up`). If `NULL`, all available shifts for the outcome are used.
- label_mapping
Optional named list passed to [transform_label()] for readable outcome/shift labels.
- waves
Optional integer vector of waves to keep (matching the wave index used by the LMTP fits).
- remove_waves
Optional integer vector of waves to drop after subsetting.
- component
Which nuisance models to include: `"both"` (default), `"outcome"` (only `m`), or `"treatment"` (only `r`).