Compute Profile Repeatability Score
Arguments
- id_list
The list of the names of the individuals
- df_list
A list of data frames, each of which correspond to one of the names in the individual list
- n_replicates
The number of replicate columns (number of columns in a df in df_list)
- n_trials
The number of trials per individual (number of rows in a df in df_list)
- verbose
A boolean parameter the defaults to FALSE. Determines whether messages are printed.
Value
A dataframe of the calculated metrics. The column structure is as follows:
- Column 1: "individual" - the unique identifier of an individual or sample
- Column 2: "n_crossings" - the calculated number of crossings.
- Column 3: "max_variance" - the maximum of the variances of the replicate measurements at a single time for the individual or sample.
- Column 4: "ave_variance" - the average of the variances of the replicate measurements at a single time for the individual or sample.
- Column 5: "base_score" - the original, unnormalized profile repeatability score. Smaller numbers rank higher.
- Column 6: "final_score" - the base score, normalized by the sigmoid function. Constrained to be between 0 and 1. Scores closer to 1 rank higher.Details
Works on multiple elements of data.
Splits the data into the data frame for a particular individual from the id_list, then calculates metrics to compute the profile repeatability score. Returns a data frame with the individuals name and the score.
Examples
df <- data.frame(
col_a = c('A', 'A', 'B', 'B'),
col_b = c(5, 15, 5, 15),
col_c = c(5, 10, 1, 2),
col_d = c(10, 15, 3, 4)
)
id_list <- unique(df[, 1])
individuals <- list()
for (i in 1:length(id_list)) {
individuals[[i]] <- df[df[, 1] == id_list[i], ]
}
ret_df <- score_dfs(id_list=id_list, df_list=individuals, n_replicates=2, n_trials=2)
print(ret_df)
#> individual n_crossings max_variance ave_variance base_score final_score
#> 1 A 0 12.5 12.5 25.02 0.9914
#> 2 B 0 2.0 2.0 4.00 0.9930