mikropml (version 1.6.1)

calc_model_sensspec: Calculate and summarize performance for ROC and PRC plots

Description

Use these functions to calculate cumulative sensitivity, specificity, recall, etc. on single models, concatenate the results together from multiple models, and compute mean ROC and PRC. You can then plot mean ROC and PRC curves to visualize the results. Note: These functions assume a binary outcome.

Usage

calc_model_sensspec(trained_model, test_data, outcome_colname = NULL)
calc_mean_roc(sensspec_dat)
calc_mean_prc(sensspec_dat)

Value

data frame with summarized performance

Arguments

trained_model

Trained model from caret::train().

test_data

Held out test data: dataframe of outcome and features.

outcome_colname

Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).

sensspec_dat

data frame created by concatenating results of calc_model_sensspec() for multiple models.

Functions

• calc_model_sensspec(): Get sensitivity, specificity, and precision for a model.

• calc_mean_roc(): Calculate mean sensitivity over specificity for multiple models

• calc_mean_prc(): Calculate mean precision over recall for multiple models

Author

Courtney Armour

Kelly Sovacool,

Examples

if (FALSE) {
library(dplyr)
# get cumulative performance for a single model
sensspec_1 <- calc_model_sensspec(
  otu_mini_bin_results_glmnet$trained_model,
  otu_mini_bin_results_glmnet$test_data,
  "dx"
)
head(sensspec_1)

# get performance for multiple models
get_sensspec_seed <- function(seed) {
  ml_result <- run_ml(otu_mini_bin, "glmnet", seed = seed)
  sensspec <- calc_model_sensspec(
    ml_result$trained_model,
    ml_result$test_data,
    "dx"
  ) %>%
    dplyr::mutate(seed = seed)
  return(sensspec)
}
sensspec_dat <- purrr::map_dfr(seq(100, 102), get_sensspec_seed)

# calculate mean sensitivity over specificity
roc_dat <- calc_mean_roc(sensspec_dat)
head(roc_dat)

# calculate mean precision over recall
prc_dat <- calc_mean_prc(sensspec_dat)
head(prc_dat)

# plot ROC & PRC
roc_dat %>% plot_mean_roc()
baseline_prec <- calc_baseline_precision(otu_mini_bin, "dx", "cancer")
prc_dat %>%
  plot_mean_prc(baseline_precision = baseline_prec)

# balanced precision
prior <- calc_baseline_precision(otu_mini_bin,
  outcome_colname = "dx",
  pos_outcome = "cancer"
)
bprc_dat <- sensspec_dat %>%
  dplyr::mutate(balanced_precision = calc_balanced_precision(precision, prior)) %>%
  dplyr::rename(recall = sensitivity) %>%
  calc_mean_perf(group_var = recall, sum_var = balanced_precision)
bprc_dat %>% plot_mean_prc(ycol = mean_balanced_precision) + ylab("Mean Bal. Precision")
}