model_erorrs <- function(cs_input, errorType){
#' Model Errors Function
#'
#' This function allows you to computes the error for each indiviudal model (or conformer)
#' @param cs_input chemical shift dataframe. Should contain field: resname, resid, nucleus, expCS, weight.
#' @param n_total total number of samples. Defaults to 1000
#' @param cycles number of random ranking to generate. Default to 10000.
#' @export
#' @examples
#' random_nslr(10, n_total = 100, cycles = 1000)
# Computes the error for each indiviudal model (or conformer)
# Args:
# x: dataframe with columns expCS and predCS -- data frame
# Returns:
# data frame with errors for each model (or conformer)
require(plyr)
errorType <- tolower(errorType)
if (errorType == "cor_p"){
means <- ddply(.data=cs_input, .var=c("model"),.fun=score_pearson)
}
if (errorType == "cor_s"){
means <- ddply(.data=cs_input, .var=c("model"),.fun=score_spearman)
}
if (errorType == "cor_k"){
means <- ddply(.data=cs_input, .var=c("model"),.fun=score_kendall)
}
if (errorType == "mae"){
means <- ddply(cs_input, c("model"), .fun=score_mae)
}
if (errorType == "rmse"){
means <- ddply(cs_input, c("model"), .fun=score_rmse)
}
if (errorType == "geo_mae"){
means <- ddply(cs_input, c("model"), .fun=score_geo_mae)
}
if (errorType == "prob"){
means <- ddply(cs_input, c("model"), .fun=score_probability)
}
if (errorType == "flatchi2_0.25"){
means <- ddply(cs_input, c("model"), .fun=score_flat_chi2, chi2_c = 0.25)
}
if (errorType == "flatchi2_0.5"){
means <- ddply(cs_input, c("model"), .fun=score_flat_chi2, chi2_c = 0.5)
}
if (errorType == "flatchi2_1"){
means <- ddply(cs_input, c("model"), .fun=score_flat_chi2, chi2_c = 1)
}
if (errorType == "flatchi2_2"){
means <- ddply(cs_input, c("model"), .fun=score_flat_chi2, chi2_c = 2)
}
if (errorType == "flatChi2_3"){
means <- ddply(cs_input, c("model"), .fun=score_flat_chi2, chi2_c = 3)
}
colnames(means) <- c("model", "error")
return(means)
}
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