compute_score_metrics {cmpsR}

Compute Different Metrics Based on Scores

Description

Compute Different Metrics Based on Scores

Usage

compute_score_metrics(
  land1,
  land2,
  score,
  addNA = TRUE,
  na.rm = TRUE,
  include = NULL,
  out_names = NULL
)

Arguments

land1

(numeric) vector with land ids of bullet 1

land2

(numeric) vector with land ids of bullet 2

score

numeric vector of scores to be summarized into a single number

addNA

logical value. In case of missing lands, are scores set to 0 (addNA = FALSE) or set to NA (addNA = TRUE)

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds

include

a character vector specifying which metrics to be included in the result; if include = NULL, including all metrics

out_names

a character vector specifying the variable names of each metric; if out_names = NULL, using the default names

Details

By default, this helper function computes four metrics.

diff: the difference between the mean score of the foreground phase and the mean score of the background phases diff.med: the difference between the median score of the foreground phase and the median score of the background phases max: the max score maxbar: the mean score of the foreground phase

Value

a data frame containing values of the metrics

Examples

library(tidyverse)

data("bullets")

lands <- unique(bullets$bulletland)

comparisons <- data.frame(expand.grid(land1 = lands[1:6], land2 = lands[7:12]),
                          stringsAsFactors = FALSE)

comparisons <- comparisons %>%
  left_join(bullets %>% select(bulletland, sig1=sigs),
            by = c("land1" = "bulletland")) %>%
  left_join(bullets %>% select(bulletland, sig2=sigs),
            by = c("land2" = "bulletland"))

comparisons <- comparisons %>% mutate(
  cmps = purrr::map2(sig1, sig2, .f = function(x, y) {
    extract_feature_cmps(x$sig, y$sig, include = "full_result")
  })
)

comparisons <- comparisons %>%
  mutate(
    cmps_score = sapply(comparisons$cmps, function(x) x$CMPS_score),
    cmps_nseg = sapply(comparisons$cmps, function(x) x$nseg)
  )
  
cp1 <- comparisons %>% select(land1, land2, cmps_score, cmps_nseg)
cp1 <- cp1 %>% mutate(
  land1idx = land1 %>% str_sub(-1, -1) %>% as.numeric(),
  land2idx = land2 %>% str_sub(-1, -1) %>% as.numeric()
)

with(cp1, {
  compute_score_metrics(land1idx, land2idx, cmps_score)
})