#' Summary method for class "mkinfit"
#'
#' Lists model equations, initial parameter values, optimised parameters with
#' some uncertainty statistics, the chi2 error levels calculated according to
#' FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
#' and optionally the data, consisting of observed, predicted and residual
#' values.
#'
#' @param object an object of class \code{\link{mkinfit}}.
#' @param x an object of class \code{summary.mkinfit}.
#' @param data logical, indicating whether the data should be included in the
#' summary.
#' @param distimes logical, indicating whether DT50 and DT90 values should be
#' included.
#' @param alpha error level for confidence interval estimation from t
#' distribution
#' @param digits Number of digits to use for printing
#' @param \dots optional arguments passed to methods like \code{print}.
#' @importFrom stats qt pt cov2cor
#' @return The summary function returns a list with components, among others
#' \item{version, Rversion}{The mkin and R versions used}
#' \item{date.fit, date.summary}{The dates where the fit and the summary were
#' produced}
#' \item{diffs}{The differential equations used in the model}
#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions}
#' \item{bpar}{Optimised and backtransformed
#' parameters}
#' \item{data}{The data (see Description above).}
#' \item{start}{The starting values and bounds, if applicable, for optimised
#' parameters.}
#' \item{fixed}{The values of fixed parameters.}
#' \item{errmin }{The chi2 error levels for
#' each observed variable.}
#' \item{bparms.ode}{All backtransformed ODE
#' parameters, for use as starting parameters for related models.}
#' \item{errparms}{Error model parameters.}
#' \item{ff}{The estimated formation fractions derived from the fitted
#' model.}
#' \item{distimes}{The DT50 and DT90 values for each observed variable.}
#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.}
#' The print method is called for its side effect, i.e. printing the summary.
#' @author Johannes Ranke
#' @references FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence
#' and Degradation Kinetics from Environmental Fate Studies on Pesticides in
#' EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,
#' EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
#' \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
#' @examples
#'
#' summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#'
#' @export
summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...) {
param <- object$par
pnames <- names(param)
bpnames <- names(object$bparms.optim)
epnames <- names(object$errparms)
p <- length(param)
mod_vars <- names(object$mkinmod$diffs)
covar <- try(solve(object$hessian), silent = TRUE)
covar_notrans <- try(solve(object$hessian_notrans), silent = TRUE)
rdf <- object$df.residual
if (!is.numeric(covar) | is.na(covar[1])) {
covar <- NULL
se <- lci <- uci <- rep(NA, p)
} else {
rownames(covar) <- colnames(covar) <- pnames
se <- sqrt(diag(covar))
lci <- param + qt(alpha/2, rdf) * se
uci <- param + qt(1-alpha/2, rdf) * se
}
beparms.optim <- c(object$bparms.optim, object$par[epnames])
if (!is.numeric(covar_notrans) | is.na(covar_notrans[1])) {
covar_notrans <- NULL
se_notrans <- tval <- pval <- rep(NA, p)
} else {
rownames(covar_notrans) <- colnames(covar_notrans) <- c(bpnames, epnames)
se_notrans <- sqrt(diag(covar_notrans))
tval <- beparms.optim / se_notrans
pval <- pt(abs(tval), rdf, lower.tail = FALSE)
}
names(se) <- pnames
param <- cbind(param, se, lci, uci)
dimnames(param) <- list(pnames, c("Estimate", "Std. Error", "Lower", "Upper"))
bparam <- cbind(Estimate = beparms.optim, se_notrans,
"t value" = tval, "Pr(>t)" = pval, Lower = NA, Upper = NA)
# Transform boundaries of CI for one parameter at a time,
# with the exception of sets of formation fractions (single fractions are OK).
f_names_skip <- character(0)
for (box in mod_vars) { # Figure out sets of fractions to skip
f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)
n_paths <- length(f_names)
if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
}
for (pname in pnames) {
if (!pname %in% f_names_skip) {
par.lower <- param[pname, "Lower"]
par.upper <- param[pname, "Upper"]
names(par.lower) <- names(par.upper) <- pname
bpl <- backtransform_odeparms(par.lower, object$mkinmod,
object$transform_rates,
object$transform_fractions)
bpu <- backtransform_odeparms(par.upper, object$mkinmod,
object$transform_rates,
object$transform_fractions)
bparam[names(bpl), "Lower"] <- bpl
bparam[names(bpu), "Upper"] <- bpu
}
}
bparam[epnames, c("Lower", "Upper")] <- param[epnames, c("Lower", "Upper")]
ans <- list(
version = as.character(utils::packageVersion("mkin")),
Rversion = paste(R.version$major, R.version$minor, sep="."),
date.fit = object$date,
date.summary = date(),
solution_type = object$solution_type,
warning = object$warning,
use_of_ff = object$mkinmod$use_of_ff,
error_model_algorithm = object$error_model_algorithm,
df = c(p, rdf),
covar = covar,
covar_notrans = covar_notrans,
err_mod = object$err_mod,
niter = object$iterations,
calls = object$calls,
time = object$time,
par = param,
bpar = bparam)
if (!is.null(object$version)) {
ans$fit_version <- object$version
ans$fit_Rversion <- object$Rversion
if (ans$fit_version >= "0.9.49.6") {
ans$AIC = AIC(object)
ans$BIC = BIC(object)
ans$logLik = logLik(object)
}
}
ans$diffs <- object$mkinmod$diffs
if(data) ans$data <- object$data
ans$start <- object$start
ans$start_transformed <- object$start_transformed
ans$fixed <- object$fixed
ans$errmin <- mkinerrmin(object, alpha = 0.05)
if (object$calls > 0) {
if (!is.null(ans$covar)){
Corr <- cov2cor(ans$covar)
rownames(Corr) <- colnames(Corr) <- rownames(ans$par)
ans$Corr <- Corr
} else {
warning("Could not calculate correlation; no covariance matrix")
}
}
ans$bparms.ode <- object$bparms.ode
ans$shapiro.p <- object$shapiro.p
ep <- endpoints(object)
if (length(ep$ff) != 0)
ans$ff <- ep$ff
if (distimes) ans$distimes <- ep$distimes
if (length(ep$SFORB) != 0) ans$SFORB <- ep$SFORB
if (!is.null(object$d_3_message)) ans$d_3_message <- object$d_3_message
class(ans) <- c("summary.mkinfit", "summary.modFit")
return(ans)
}
#' @rdname summary.mkinfit
#' @export
print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), ...) {
if (is.null(x$fit_version)) {
cat("mkin version: ", x$version, "\n")
cat("R version: ", x$Rversion, "\n")
} else {
cat("mkin version used for fitting: ", x$fit_version, "\n")
cat("R version used for fitting: ", x$fit_Rversion, "\n")
}
cat("Date of fit: ", x$date.fit, "\n")
cat("Date of summary:", x$date.summary, "\n")
if (!is.null(x$warning)) cat("\n\nWarning:", x$warning, "\n\n")
cat("\nEquations:\n")
nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])
writeLines(strwrap(nice_diffs, exdent = 11))
df <- x$df
rdf <- df[2]
cat("\nModel predictions using solution type", x$solution_type, "\n")
cat("\nFitted using", x$calls, "model solutions performed in", x$time[["elapsed"]], "s\n")
if (!is.null(x$err_mod)) {
cat("\nError model: ")
cat(switch(x$err_mod,
const = "Constant variance",
obs = "Variance unique to each observed variable",
tc = "Two-component variance function"), "\n")
cat("\nError model algorithm:", x$error_model_algorithm, "\n")
if (!is.null(x$d_3_message)) cat(x$d_3_message, "\n")
}
cat("\nStarting values for parameters to be optimised:\n")
print(x$start)
cat("\nStarting values for the transformed parameters actually optimised:\n")
print(x$start_transformed)
cat("\nFixed parameter values:\n")
if(length(x$fixed$value) == 0) cat("None\n")
else print(x$fixed)
if (!is.null(x$AIC)) {
cat("\nResults:\n\n")
print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,
row.names = " "))
}
if (!is.null(x$shapiro.p) && x$shapiro.p < 0.05) {
warning("The p-value for the Shapiro-Wilk test of normality on standardized residuals is < 0.05")
}
cat("\nOptimised, transformed parameters with symmetric confidence intervals:\n")
print(signif(x$par, digits = digits))
if (x$calls > 0) {
cat("\nParameter correlation:\n")
if (!is.null(x$covar)){
print(x$Corr, digits = digits, ...)
} else {
cat("No covariance matrix")
}
}
cat("\nBacktransformed parameters:\n")
cat("Confidence intervals for internally transformed parameters are asymmetric.\n")
if ((x$version) < "0.9-36") {
cat("To get the usual (questionable) t-test, upgrade mkin and repeat the fit.\n")
print(signif(x$bpar, digits = digits))
} else {
cat("t-test (unrealistically) based on the assumption of normal distribution\n")
cat("for estimators of untransformed parameters.\n")
print(signif(x$bpar[, c(1, 3, 4, 5, 6)], digits = digits))
}
cat("\nFOCUS Chi2 error levels in percent:\n")
x$errmin$err.min <- 100 * x$errmin$err.min
print(x$errmin, digits=digits,...)
printSFORB <- !is.null(x$SFORB)
if(printSFORB){
cat("\nEstimated Eigenvalues of SFORB model(s):\n")
print(x$SFORB, digits=digits,...)
}
printff <- !is.null(x$ff)
if(printff){
cat("\nResulting formation fractions:\n")
print(data.frame(ff = x$ff), digits=digits,...)
}
printdistimes <- !is.null(x$distimes)
if(printdistimes){
cat("\nEstimated disappearance times:\n")
print(x$distimes, digits=digits,...)
}
printdata <- !is.null(x$data)
if (printdata){
cat("\nData:\n")
print(format(x$data, digits = digits, ...), row.names = FALSE)
}
invisible(x)
}
