#' plot.stAirPol.prediction
#'
#' Plot the results of a cross validation.
#'
#' @param prediction an object as returned by \link{predict.stAirPol.model}
#'
#' @return a \link{ggplot2} object which contains the plot
#' @export
#'
#' @examples
#' data("mini_dataset")
#' mini_dataset <- clean_model_data(mini_dataset)
#' formula = value ~ humi + temp + rainhist + windhist +
#' trafficvol + log(sensor_age)
#' training_set <- get_test_and_training_set(mini_dataset, sampel_size = 0.75,
#' random.seed = 220292)
#' model.gp <- fit_sp_model(data = mini_dataset, formula = formula,
#' model = 'GP', training_set = training_set)
#' pred.gp <- predict(model.gp, mini_dataset, training_set)
#' plot(pred.gp)
#' plot(pred.gp, time_dimension = TRUE)
plot.stAirPol.prediction <- function(prediction, time_dimension = FALSE) {
if (!time_dimension) {
g <- ggplot(data = prediction, aes(x = value, y = prediction)) +
geom_point() +
theme_classic() +
scale_x_continuous(limits = c(0, max(prediction$value))) +
scale_y_continuous(limits = c(0, max(prediction$prediction))) +
geom_abline(slope = 1)
} else {
g <- ggplot(data = melt(prediction[, .(obs_value = value, prediction, timestamp)],
id.vars = c('timestamp')),
aes(x = timestamp, y = value, col = variable)) +
geom_point() +
theme(legend.position = 'bottom') +
theme_classic() +
geom_hline(yintercept = 0, lty = 2) +
xlab(NULL) +
xlab(NULL)
}
g
}
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