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mirror of https://github.com/msberends/AMR.git synced 2025-07-10 00:23:03 +02:00

(v1.2.0.9034) code cleaning

This commit is contained in:
2020-07-13 09:17:24 +02:00
parent c0cf7ab02b
commit 6ab468362d
36 changed files with 266 additions and 265 deletions

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@ -122,12 +122,12 @@ resistance_predict <- function(x,
preserve_measurements = TRUE,
info = interactive(),
...) {
stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
stop_if(any(dim(x) == 0), "`x` must contain rows and columns")
stop_if(is.null(model), 'choose a regression model with the `model` parameter, e.g. resistance_predict(..., model = "binomial")')
stop_ifnot(col_ab %in% colnames(x),
"column `", col_ab, "` not found")
"column `", col_ab, "` not found")
dots <- unlist(list(...))
if (length(dots) != 0) {
@ -140,7 +140,7 @@ resistance_predict <- function(x,
warning("`I_as_R is deprecated - use I_as_S instead.", call. = FALSE)
}
}
# -- date
if (is.null(col_date)) {
col_date <- search_type_in_df(x = x, type = "date")
@ -148,7 +148,7 @@ resistance_predict <- function(x,
}
stop_ifnot(col_date %in% colnames(x),
"column `", col_date, "` not found")
# no grouped tibbles
x <- as.data.frame(x, stringsAsFactors = FALSE)
@ -178,7 +178,7 @@ resistance_predict <- function(x,
df <- as.data.frame(rbind(table(df[, c("year", col_ab)])), stringsAsFactors = FALSE)
df$year <- as.integer(rownames(df))
rownames(df) <- NULL
df <- subset(df, sum(df$R + df$S, na.rm = TRUE) >= minimum)
df_matrix <- as.matrix(df[, c("R", "S"), drop = FALSE])
@ -193,9 +193,9 @@ resistance_predict <- function(x,
if (is.null(year_max)) {
year_max <- year(Sys.Date()) + 10
}
years <- list(year = seq(from = year_min, to = year_max, by = year_every))
if (model %in% c("binomial", "binom", "logit")) {
model <- "binomial"
model_lm <- with(df, glm(df_matrix ~ year, family = binomial))
@ -204,11 +204,11 @@ resistance_predict <- function(x,
cat("\n------------------------------------------------------------\n")
print(summary(model_lm))
}
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} else if (model %in% c("loglin", "poisson")) {
model <- "poisson"
model_lm <- with(df, glm(R ~ year, family = poisson))
@ -217,11 +217,11 @@ resistance_predict <- function(x,
cat("\n--------------------------------------------------------------\n")
print(summary(model_lm))
}
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} else if (model %in% c("lin", "linear")) {
model <- "linear"
model_lm <- with(df, lm((R / (R + S)) ~ year))
@ -230,22 +230,22 @@ resistance_predict <- function(x,
cat("\n-----------------------\n")
print(summary(model_lm))
}
predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} else {
stop("no valid model selected. See ?resistance_predict.")
}
# prepare the output dataframe
df_prediction <- data.frame(year = unlist(years),
value = prediction,
se_min = prediction - se,
se_max = prediction + se,
stringsAsFactors = FALSE)
if (model == "poisson") {
df_prediction$value <- as.integer(format(df_prediction$value, scientific = FALSE))
df_prediction$se_min <- as.integer(df_prediction$se_min)
@ -257,7 +257,7 @@ resistance_predict <- function(x,
}
# se_min not below 0
df_prediction$se_min <- ifelse(df_prediction$se_min < 0, 0, df_prediction$se_min)
df_observations <- data.frame(year = df$year,
observations = df$R + df$S,
observed = df$R / (df$R + df$S),
@ -265,17 +265,17 @@ resistance_predict <- function(x,
df_prediction <- df_prediction %>%
left_join(df_observations, by = "year")
df_prediction$estimated <- df_prediction$value
if (preserve_measurements == TRUE) {
# replace estimated data by observed data
df_prediction$value <- ifelse(!is.na(df_prediction$observed), df_prediction$observed, df_prediction$value)
df_prediction$se_min <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_min)
df_prediction$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 <- df_prediction[order(df_prediction$year), ]
structure(
.Data = df_prediction,
class = c("resistance_predict", "data.frame"),
@ -296,7 +296,7 @@ rsi_predict <- resistance_predict
#' @rdname resistance_predict
plot.resistance_predict <- function(x, main = paste("Resistance Prediction of", x_name), ...) {
x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
if (attributes(x)$I_as_S == TRUE) {
ylab <- "%R"
} else {
@ -319,17 +319,17 @@ plot.resistance_predict <- function(x, main = paste("Resistance Prediction of",
sub = paste0("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"),
cex.sub = 0.75)
axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
# hack for error bars: https://stackoverflow.com/a/22037078/4575331
arrows(x0 = x$year,
y0 = x$se_min,
x1 = x$year,
y1 = x$se_max,
length = 0.05, angle = 90, code = 3, lwd = 1.5)
# overlay grey points for prediction
points(x = subset(x, is.na(observations))$year,
y = subset(x, is.na(observations))$value,
@ -346,15 +346,15 @@ ggplot_rsi_predict <- function(x,
stop_ifnot_installed("ggplot2")
stop_ifnot(inherits(x, "resistance_predict"), "`x` must be a resistance prediction model created with resistance_predict()")
x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
if (attributes(x)$I_as_S == TRUE) {
ylab <- "%R"
} else {
ylab <- "%IR"
}
p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
ggplot2::geom_point(data = subset(x, !is.na(observations)),
size = 2) +
@ -364,7 +364,7 @@ ggplot_rsi_predict <- function(x,
x = "Year",
caption = paste0("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"))
if (ribbon == TRUE) {
p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25)
} else {