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#' FitDynamicGrowth class
#'
#' @description
#' `r lifecycle::badge("superseded")`
#'
#' The class [FitDynamicGrowth] has been superseded by the top-level
#' class [GrowthFit], which provides a unified approach for growth modelling.
#'
#' Still, it is still returned if the superseded [fit_dynamic_growth()] is called.
#'
#' It is a subclass of list with the items:
#' \itemize{
#' \item fit_results: the object returned by `modFit`.
#' \item best_prediction: the model prediction for the fitted parameters.
#' \item env_conditions: environmental conditions for the fit.
#' \item data: data used for the fit.
#' \item starting: starting values for model fitting
#' \item known: parameter values set as known.
#' \item sec_models: a named vector with the secondary model
#' for each environmental factor
#' }
#'
#' @name FitDynamicGrowth
#'
NULL
#' @describeIn FitDynamicGrowth comparison between the fitted model and the data.
#'
#'
#' @param x An instance of `FitDynamicGrowth`.
#' @param ... ignored
#'
#' @export
#'
print.FitDynamicGrowth <- function(x, ...) {
cat("Growth model fitted to data under dynamic conditions\n\n")
env <- names(x$env_conditions)
cat(paste("Environmental factors included:", paste(env, collapse = ", "), "\n\n"))
cat("Parameters of the primary model:\n")
print(unlist(x$best_prediction$primary_pars))
cat("\n")
logbase <- x$logbase_mu
if ( abs(logbase - exp(1)) < .1 ) {
logbase <- "e"
}
cat(paste0("Parameter mu defined in log-", logbase, " scale"))
cat("\n\n")
for (i in 1:length(x$best_prediction$sec_models)) {
cat(paste("Secondary model for ", names(x$best_prediction$sec_models)[i], ":\n", sep = ""))
print(unlist(x$best_prediction$sec_models[[i]]))
cat("\n")
}
}
#' @describeIn FitDynamicGrowth comparison between the fitted model and the data.
#'
#' @param x The object of class `FitDynamicGrowth` to plot.
#' @param y ignored
#' @param ... ignored.
#' @param add_factor whether to plot also one environmental factor.
#' If `NULL` (default), no environmental factor is plotted. If set
#' to one character string that matches one entry of x$env_conditions,
#' that condition is plotted in the secondary axis
#' @param ylims A two dimensional vector with the limits of the primary y-axis.
#' @param label_y1 Label of the primary y-axis.
#' @param label_y2 Label of the secondary y-axis.
#' @param line_col Aesthetic parameter to change the colour of the line geom in the plot, see: [geom_line()]
#' @param line_size Aesthetic parameter to change the thickness of the line geom in the plot, see: [geom_line()]
#' @param line_type Aesthetic parameter to change the type of the line geom in the plot, takes numbers (1-6) or strings ("solid") see: [geom_line()]
#' @param point_col Aesthetic parameter to change the colour of the point geom, see: [geom_point()]
#' @param point_size Aesthetic parameter to change the size of the point geom, see: [geom_point()]
#' @param point_shape Aesthetic parameter to change the shape of the point geom, see: [geom_point()]
#' @param line_col2 Same as lin_col, but for the environmental factor.
#' @param line_size2 Same as line_size, but for the environmental factor.
#' @param line_type2 Same as lin_type, but for the environmental factor.
#'
#' @export
#'
#' @importFrom ggplot2 ggplot geom_point
#' @importFrom graphics plot
#' @importFrom rlang .data
#' @importFrom cowplot theme_cowplot
#'
plot.FitDynamicGrowth <- function(x, y=NULL, ...,
add_factor = NULL,
ylims = NULL,
label_y1 = "logN",
label_y2 = add_factor,
line_col = "black",
line_size = 1,
line_type = 1,
point_col = "black",
point_size = 3,
point_shape = 16,
line_col2 = "black",
line_size2 = 1,
line_type2 = "dashed"
) {
p <- plot(x$best_prediction,
add_factor = add_factor,
ylims = ylims,
label_y1 = label_y1,
label_y2 = label_y2,
line_col = line_col,
line_size = line_size,
line_type = line_type,
line_col2 = line_col2,
line_size2 = line_size2,
line_type2 = line_type2
)
p + geom_point(aes(x = .data$time, y = .data$logN), data = x$data,
inherit.aes = FALSE, col = point_col,
size = point_size, shape = point_shape) +
theme_cowplot()
}
#' @describeIn FitDynamicGrowth statistical summary of the fit.
#'
#' @param object Instance of FitDynamicGrowth.
#' @param ... ignored.
#'
#' @export
#'
summary.FitDynamicGrowth <- function(object, ...) {
out <- summary(object$fit)
out$logbase_mu <- object$logbase_mu
out
}
#' @describeIn FitDynamicGrowth residuals of the model.
#'
#' @param object Instance of FitDynamicGrowth
#' @param ... ignored
#'
#' @importFrom stats residuals
#'
#' @export
#'
residuals.FitDynamicGrowth <- function(object, ...) {
residuals(object$fit_results)
}
#' @describeIn FitDynamicGrowth vector of fitted parameters.
#'
#' @param object an instance of `FitDynamicGrowth`.
#' @param ... ignored
#'
#' @importFrom stats coef
#'
#' @export
#'
coef.FitDynamicGrowth <- function(object, ...) {
coef(object$fit_results)
}
#' @describeIn FitDynamicGrowth (unscaled) variance-covariance matrix of the model,
#' calculated as 1/(0.5*Hessian)
#'
#' @param object an instance of `FitDynamicGrowth`.
#' @param ... ignored
#'
#' @export
#'
vcov.FitDynamicGrowth <- function(object, ...) {
# The code has been adapted from the one of summary.modFit
covar <- try(solve(0.5*object$fit_results$hessian), silent = TRUE)
if (!is.numeric(covar)) {
warning("Cannot estimate covariance; system is singular")
param <- object$par
p <- length(param)
covar <- matrix(data = NA, nrow = p, ncol = p)
}
covar
}
#' @describeIn FitDynamicGrowth deviance of the model.
#'
#' @param object an instance of `FitDynamicGrowth`.
#' @param ... ignored
#'
#' @importFrom stats deviance
#'
#' @export
#'
deviance.FitDynamicGrowth <- function(object, ...) {
deviance(object$fit_results)
}
#' @describeIn FitDynamicGrowth fitted values.
#'
#' @param object an instance of `FitDynamicGrowth`.
#' @param ... ignored
#'
#' @export
#'
fitted.FitDynamicGrowth <- function(object, ...) {
object$data$logN + residuals(object)
}
#' @describeIn FitDynamicGrowth model predictions.
#'
#' @param object an instance of `FitDynamicGrowth`.
#' @param ... ignored
#' @param times A numeric vector with the time points for the simulations. `NULL`
#' by default (using the same time points as those for the simulation).
#' @param newdata a tibble describing the environmental conditions (as `env_conditions`)
#' in [predict_dynamic_growth()].
#' If `NULL` (default), uses the same conditions as those for fitting.
#'
#' @export
#'
predict.FitDynamicGrowth <- function(object, times = NULL, newdata = NULL, ...) {
if (is.null(newdata)) {
newdata <- object$env_conditions
}
if (is.null(times)) {
times <- object$data$time
}
# pred <- predict_dynamic_growth(
# times,
# newdata,
# object$best_prediction$primary_pars,
# object$best_prediction$sec_models
# )
pred <- predict_growth(environment = "dynamic",
times,
object$best_prediction$primary_pars,
object$best_prediction$sec_models,
newdata,
logbase_mu = object$logbase_mu
)
pred$simulation$logN
}
#' @describeIn FitDynamicGrowth loglikelihood of the model
#'
#' @param object an instance of FitDynamicGrowth
#' @param ... ignored
#'
#' @export
#'
logLik.FitDynamicGrowth <- function(object, ...) {
## AIC without penalty
n <- nrow(object$data)
sigma <- sqrt(object$fit_results$ssr/object$fit_results$df.residual)
lL <- - n/2*log(2*pi) -n/2 * log(sigma^2) - 1/2/sigma^2*object$fit_results$ssr
lL
}
#' @describeIn FitDynamicGrowth Akaike Information Criterion
#'
#' @param object an instance of FitDynamicGrowth
#' @param ... ignored
#' @param k penalty for the parameters (k=2 by default)
#'
#' @importFrom stats logLik
#'
#' @export
#'
AIC.FitDynamicGrowth <- function(object, ..., k=2) {
## Normal AIC
p <- length(coef(object))
lL <- logLik(object)
AIC <- 2*p - 2*lL
## Calculate the penalty
n <- nrow(object$data)
penalty <- (k*p^2 + k*p)/(n - p - 1)
## Return
AIC + penalty
}
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