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New mo algorithm, prepare for 2.0

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Dr. Matthijs Berends
2022-10-05 09:12:22 +02:00
committed by GitHub
parent 63fe160322
commit cd2acc4a29
182 changed files with 4054 additions and 90905 deletions
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32
R/resistance_predict.R
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@ -1,12 +1,16 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
@ -132,18 +136,6 @@ resistance_predict <- function(x,
x.bak <- x
x <- as.data.frame(x, stringsAsFactors = FALSE)
dots <- unlist(list(...))
if (length(dots) != 0) {
# backwards compatibility with old arguments
dots.names <- names(dots)
if ("tbl" %in% dots.names) {
x <- dots[which(dots.names == "tbl")]
}
if ("I_as_R" %in% dots.names) {
warning_("in `resistance_predict()`: I_as_R is deprecated - use I_as_S instead.")
}
}
# -- date
if (is.null(col_date)) {
col_date <- search_type_in_df(x = x, type = "date")
@ -167,10 +159,10 @@ resistance_predict <- function(x,
df[, col_ab] <- droplevels(as.rsi(df[, col_ab, drop = TRUE]))
if (I_as_S == TRUE) {
# then I as S
df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE])
df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE], fixed = TRUE)
} else {
# then I as R
df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE])
df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE], fixed = TRUE)
}
df[, col_ab] <- ifelse(is.na(df[, col_ab, drop = TRUE]), 0, df[, col_ab, drop = TRUE])
@ -257,10 +249,10 @@ resistance_predict <- function(x,
df_prediction$se_max <- as.integer(df_prediction$se_max)
} else {
# se_max not above 1
df_prediction$se_max <- ifelse(df_prediction$se_max > 1, 1, df_prediction$se_max)
df_prediction$se_max <- pmin(df_prediction$se_max, 1)
}
# se_min not below 0
df_prediction$se_min <- ifelse(df_prediction$se_min < 0, 0, df_prediction$se_min)
df_prediction$se_min <- pmax(df_prediction$se_min, 0)
df_observations <- data.frame(
year = df$year,
@ -279,7 +271,7 @@ resistance_predict <- function(x,
df_prediction$se_max <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_max)
}
df_prediction$value <- ifelse(df_prediction$value > 1, 1, ifelse(df_prediction$value < 0, 0, df_prediction$value))
df_prediction$value <- ifelse(df_prediction$value > 1, 1, pmax(df_prediction$value, 0))
df_prediction <- df_prediction[order(df_prediction$year), , drop = FALSE]
out <- as_original_data_class(df_prediction, class(x.bak))