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#' GlobalGrowthFit class
#'
#' @description
#' `r lifecycle::badge("stable")`
#'
#' The `GlobalGrowthFit` class contains a growth model fitted to data
#' using a global approach. Its constructor is [fit_growth()].
#'
#' It is a subclass of list with the items:
#'
#' - algorithm: type of algorithm as in [fit_growth()]
#' - data: data used for model fitting
#' - start: initial guess of the model parameters
#' - known: fixed model parameters
#' - primary_model: a character describing the primary model
#' - fit_results: an instance of modFit or modMCMC with the results of the fit
#' - best_prediction: Instance of [GrowthPrediction] with the best growth fit
#' - sec_models: a named vector with the secondary models assigned for each
#' environmental factor. `NULL` for `environment="constant"`
#' - env_conditions: a list with the environmental conditions used for model
#' fitting. `NULL` for `environment="constant"`
#' - niter: number of iterations of the Markov chain. `NULL` if `algorithm != "MCMC"`
#' - logbase_mu: base of the logarithm for the definition of parameter mu
#' (check the relevant vignette)
#' - logbase_logN: base of the logarithm for the definition of the population size
#' (check the relevant vignette)
#' - environment: "dynamic". Always
#'
#' @name GlobalGrowthFit
#'
NULL
#' @describeIn GlobalGrowthFit print of the model
#'
#' @param x An instance of [GlobalGrowthFit].
#' @param ... ignored
#'
#' @export
#'
print.GlobalGrowthFit <- function(x, ...) {
cat("Growth model fitted to data following a global approach conditions using ")
cat(x$algorithm)
cat("\n\n")
cat("Number of experiments: ")
cat(length(x$data))
cat("\n\n")
env <- names(x$best_prediction[[1]]$sec_models) # Index does not matter, they are all the same
cat(paste("Environmental factors included:", paste(env, collapse = ", "), "\n\n"))
for (i in 1:length(x$best_prediction[[1]]$sec_models)) {
cat(paste("Secondary model for ", names(x$best_prediction[[1]]$sec_models)[i], ": ",
x$best_prediction[[1]]$sec_models[[i]]$model, sep = ""))
cat("\n")
}
cat("\n")
cat("Parameter estimates:\n")
print(coef(x))
cat("\nFixed parameters:\n")
print(unlist(x$known))
logbase <- x$logbase_mu
if ( abs(logbase - exp(1)) < .1 ) {
logbase <- "e"
}
cat(paste0("Parameter mu defined in log-", logbase, " scale"))
cat("\n")
logbase <- x$logbase_logN
if ( abs(logbase - exp(1)) < .1 ) {
logbase <- "e"
}
cat(paste0("Population size defined in log-", logbase, " scale"))
cat("\n")
}
#' @describeIn GlobalGrowthFit vector of fitted model parameters.
#'
#' @param object an instance of [GlobalGrowthFit].
#' @param ... ignored
#'
#' @importFrom stats coef
#'
#' @export
#'
coef.GlobalGrowthFit <- function(object, ...) {
if (object$algorithm == "regression") {
coef(object$fit_results)
} else {
object$fit_results$bestpar
}
}
#' @describeIn GlobalGrowthFit statistical summary of the fit.
#'
#' @param object Instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @export
#'
summary.GlobalGrowthFit <- function(object, ...) {
out <- summary(object$fit_results)
if (object$algorithm != "MCMC") { # The summary of MCMC is a data.frame, so this would add a column
out$logbase_mu <- object$logbase_mu
out$logbase_logN <- object$logbase_logN
}
out
}
#' @describeIn GlobalGrowthFit vector of model predictions
#'
#' @param object Instance of `GlobalGrowthFit`.
#' @param ... ignored
#' @param times A numeric vector with the time points for the simulations. `NULL`
#' by default (using the same time points as the ones defined in `env_conditions`).
#' @param env_conditions a tibble describing the environmental conditions (as
#' in [predict_growth()].
#'
#' @importFrom dplyr bind_rows
#'
#' @export
#'
predict.GlobalGrowthFit <- function(object, env_conditions, times=NULL, ...) {
if (is.null(times)) {
times <- env_conditions$time
}
my_model <- object$best_prediction[[1]] # Index does not matter, parameters are the same
pred <- predict_growth(environment = "dynamic",
times,
my_model$primary_model,
my_model$sec_models,
env_conditions,
logbase_mu = object$logbase_mu ,
logbase_logN = object$logbase_logN
)
pred$simulation$logN
}
#' @describeIn GlobalGrowthFit model residuals. They are returned as a tibble
#' with 4 columns: time (storage time), logN (observed count),
#' exp (name of the experiment) and res (residual).
#'
#' @param object Instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @importFrom stats residuals
#'
#' @export
#'
residuals.GlobalGrowthFit <- function(object, ...) {
if (object$algorithm == "MCMC") {
out <- lapply(names(object$data), function(each_sim) {
simulations <- object$best_prediction[[each_sim]]$simulation %>%
select("time", "logN") %>%
as.data.frame()
modCost(model = simulations,
obs = as.data.frame(object$data[[each_sim]]$data))$residuals %>%
select(time = "x", logN = "obs", res = "res") %>%
mutate(exp = each_sim)
})
bind_rows(out)
} else {
object$data %>%
map(~ .$data) %>%
imap_dfr(~ mutate(.x, exp = .y)) %>%
mutate(res = residuals(object$fit_results))
}
}
#' @describeIn GlobalGrowthFit variance-covariance matrix of the model, estimated
#' as 1/(0.5*Hessian) for regression and as the variance-covariance of the draws
#' for MCMC
#'
#' @param object an instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @export
#'
vcov.GlobalGrowthFit <- function(object, ...) {
if (object$algorithm == "MCMC") {
cov(object$fit_results$pars)
} else {
# The code has been adapted from the one of summary.modFit
covar <- try(solve(0.5*object$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 GlobalGrowthFit deviance of the model.
#'
#' @param object an instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @importFrom stats deviance
#'
#' @export
#'
deviance.GlobalGrowthFit <- function(object, ...) {
if (object$algorithm == "MCMC") {
sum(residuals(object)$res^2)
} else {
deviance(object$fit_results)
}
}
#' @describeIn GlobalGrowthFit fitted values. They are returned as a
#' tibble with 3 columns: time (storage time), exp (experiment
#' identifier) and fitted (fitted value).
#'
#' @param object an instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @importFrom rlang .data
#' @importFrom dplyr select mutate
#' @importFrom purrr %>%
#'
#' @export
#'
fitted.GlobalGrowthFit <- function(object, ...) {
residuals(object) %>%
mutate(fitted = .data$logN + .data$res) %>%
select("exp", "time", "fitted")
}
#' @describeIn GlobalGrowthFit loglikelihood of the model
#'
#' @param object an instance of [GlobalGrowthFit]
#' @param ... ignored
#'
#' @export
#'
logLik.GlobalGrowthFit <- function(object, ...) {
n <- object$data %>% map_dfr(~.$data) %>% nrow()
df <- n - length(coef(object))
SS <- sum(residuals(object)$res^2)
sigma <- sqrt(SS/df)
lL <- - n/2*log(2*pi) -n/2 * log(sigma^2) - 1/2/sigma^2*SS
lL
}
#' @describeIn GlobalGrowthFit Akaike Information Criterion
#'
#' @param object an instance of [GlobalGrowthFit]
#' @param ... ignored
#' @param k penalty for the parameters (k=2 by default)
#'
#' @export
#'
AIC.GlobalGrowthFit <- function(object, ..., k=2) {
## Normal AIC
p <- length(coef(object))
lL <- logLik(object)
AIC <- 2*p - 2*lL
## Calculate the penalty
n <- object$data %>% map_dfr(~.$data) %>% nrow()
penalty <- (k*p^2 + k*p)/(n - p - 1)
## Return
AIC + penalty
}
#' @describeIn GlobalGrowthFit comparison between the fitted model and
#' the experimental data.
#'
#' @inheritParams plot.DynamicGrowth
#' @param x an instance of GlobalGrowthFit
#' @param point_size Size of the data points
#' @param point_shape shape of the data points
#' @param subplot_labels labels of the subplots according to `plot_grid`.
#' @param label_x label of the x-axis
#'
#' @importFrom ggplot2 geom_point
#' @importFrom cowplot plot_grid
#' @importFrom rlang .data
#'
#' @export
#'
plot.GlobalGrowthFit <- function(x, y=NULL, ...,
add_factor = NULL,
ylims = NULL,
label_x = "time",
label_y1 = NULL,
label_y2 = add_factor,
line_col = "black",
line_size = 1,
line_type = "solid",
line_col2 = "black",
line_size2 = 1,
line_type2 = "dashed",
point_size = 3,
point_shape = 16,
subplot_labels = "AUTO"
) {
my_plots <- lapply(1:length(x$data), function(i) {
this_d <- x$data[[i]]$data
this_sim <- x$best_prediction[[i]]
plot(this_sim,
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) +
geom_point(aes(x = .data$time, y = .data$logN), data = this_d,
size = point_size, shape = point_shape) +
xlab(label_x)
})
plot_grid(plotlist = my_plots, labels = subplot_labels)
}
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