# $Id$
# Copyright (C) 2010-2012 Johannes Ranke
# Contact: jranke@uni-bremen.de
# The summary function is an adapted and extended version of summary.modFit
# from the FME package, v 1.1 by Soetart and Petzoldt, which was in turn
# inspired by summary.nls.lm
# This file is part of the R package mkin
# mkin is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>
mkinfit <- function(mkinmod, observed,
parms.ini = "auto",
state.ini = c(100, rep(0, length(mkinmod$diffs) - 1)),
fixed_parms = NULL,
fixed_initials = names(mkinmod$diffs)[-1],
solution_type = "auto",
plot = FALSE, quiet = FALSE,
err = NULL, weight = "none", scaleVar = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
trace_parms = FALSE,
...)
{
# Get the names of the state variables in the model
mod_vars <- names(mkinmod$diffs)
# Subset observed data with names of observed data in the model
observed <- subset(observed, name %in% names(mkinmod$map))
# Get names of observed variables
obs_vars = unique(as.character(observed$name))
# Define starting values for parameters where not specified by the user
if (parms.ini[[1]] == "auto") parms.ini = vector()
defaultpar.names <- setdiff(mkinmod$parms, names(parms.ini))
for (parmname in defaultpar.names) {
# Default values for rate constants, depending on the parameterisation
if (substr(parmname, 1, 2) == "k_") parms.ini[parmname] = 0.1
# Default values for rate constants for reversible binding
if (grepl("free_bound$", parmname)) parms.ini[parmname] = 0.1
if (grepl("bound_free$", parmname)) parms.ini[parmname] = 0.02
# Default values for formation fractions
if (substr(parmname, 1, 2) == "f_") parms.ini[parmname] = 0.2
# Default values for the FOMC, DFOP and HS models
if (parmname == "alpha") parms.ini[parmname] = 1
if (parmname == "beta") parms.ini[parmname] = 10
if (parmname == "k1") parms.ini[parmname] = 0.1
if (parmname == "k2") parms.ini[parmname] = 0.01
if (parmname == "tb") parms.ini[parmname] = 5
if (parmname == "g") parms.ini[parmname] = 0.5
}
# Name the inital state variable values if they are not named yet
if(is.null(names(state.ini))) names(state.ini) <- mod_vars
# Transform initial parameter values for fitting
transparms.ini <- transform_odeparms(parms.ini, mod_vars)
# Parameters to be optimised:
# Kinetic parameters in parms.ini whose names are not in fixed_parms
parms.fixed <- transparms.ini[fixed_parms]
parms.optim <- transparms.ini[setdiff(names(transparms.ini), fixed_parms)]
# Inital state variables in state.ini whose names are not in fixed_initials
state.ini.fixed <- state.ini[fixed_initials]
state.ini.optim <- state.ini[setdiff(names(state.ini), fixed_initials)]
# Preserve names of state variables before renaming initial state variable parameters
state.ini.optim.boxnames <- names(state.ini.optim)
if(length(state.ini.optim) > 0) {
names(state.ini.optim) <- paste(names(state.ini.optim), "0", sep="_")
}
# Decide if the solution of the model can be based on a simple analytical
# formula, the spectral decomposition of the matrix (fundamental system)
# or a numeric ode solver from the deSolve package
if (!solution_type %in% c("auto", "analytical", "eigen", "deSolve"))
stop("solution_type must be auto, analytical, eigen or de Solve")
if (solution_type == "analytical" && length(mkinmod$map) > 1)
stop("Analytical solution not implemented for models with metabolites.")
if (solution_type == "eigen" && !is.matrix(mkinmod$coefmat))
stop("Eigenvalue based solution not possible, coefficient matrix not present.")
if (solution_type == "auto") {
if (length(mkinmod$map) == 1) {
solution_type = "analytical"
} else {
if (is.matrix(mkinmod$coefmat)) {
solution_type = "eigen"
} else {
solution_type = "deSolve"
}
}
}
cost.old <- 1e100 # The first model cost should be smaller than this value
calls <- 0 # Counter for number of model solutions
out_predicted <- NA
# Define the model cost function
cost <- function(P)
{
assign("calls", calls+1, inherits=TRUE) # Increase the model solution counter
# Trace parameter values if quiet is off
if(trace_parms) cat(P, "\n")
# Time points at which observed data are available
# Make sure we include time 0, so initial values for state variables are for time 0
outtimes = sort(unique(c(observed$time,
seq(min(observed$time), max(observed$time), length.out=n.outtimes))))
if(length(state.ini.optim) > 0) {
odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed)
names(odeini) <- c(state.ini.optim.boxnames, names(state.ini.fixed))
} else odeini <- state.ini.fixed
odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], parms.fixed)
parms <- backtransform_odeparms(odeparms, mod_vars)
# Solve the system with current transformed parameter values
out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, atol = atol, rtol = rtol, ...)
assign("out_predicted", out, inherits=TRUE)
mC <- modCost(out, observed, y = "value",
err = err, weight = weight, scaleVar = scaleVar)
# Report and/or plot if the model is improved
if (mC$model < cost.old) {
if(!quiet) cat("Model cost at call ", calls, ": ", mC$model, "\n")
# Plot the data and current model output if requested
if(plot) {
outtimes_plot = seq(min(observed$time), max(observed$time), length.out=100)
out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot,
solution_type = solution_type, atol = atol, rtol = rtol, ...)
plot(0, type="n",
xlim = range(observed$time), ylim = range(observed$value, na.rm=TRUE),
xlab = "Time", ylab = "Observed")
col_obs <- pch_obs <- 1:length(obs_vars)
names(col_obs) <- names(pch_obs) <- obs_vars
for (obs_var in obs_vars) {
points(subset(observed, name == obs_var, c(time, value)),
pch = pch_obs[obs_var], col = col_obs[obs_var])
}
matlines(out_plot$time, out_plot[-1])
legend("topright", inset=c(0.05, 0.05), legend=obs_vars,
col=col_obs, pch=pch_obs, lty=1:length(pch_obs))
}
assign("cost.old", mC$model, inherits=TRUE)
}
return(mC)
}
fit <- modFit(cost, c(state.ini.optim, parms.optim), ...)
# We need to return some more data for summary and plotting
fit$solution_type <- solution_type
# We also need the model for summary and plotting
fit$mkinmod <- mkinmod
# We need data and predictions for summary and plotting
fit$observed <- observed
fit$obs_vars <- obs_vars
fit$predicted <- mkin_wide_to_long(out_predicted, time = "time")
# Collect initial parameter values in two dataframes
fit$start <- data.frame(initial = c(state.ini.optim,
backtransform_odeparms(parms.optim, mod_vars)))
fit$start$type = c(rep("state", length(state.ini.optim)), rep("deparm", length(parms.optim)))
fit$start$transformed = c(state.ini.optim, parms.optim)
fit$fixed <- data.frame(
value = c(state.ini.fixed, parms.fixed))
fit$fixed$type = c(rep("state", length(state.ini.fixed)), rep("deparm", length(parms.fixed)))
parms.all = backtransform_odeparms(c(fit$par, parms.fixed), mod_vars)
# Collect observed, predicted and residuals
data <- merge(fit$observed, fit$predicted, by = c("time", "name"))
names(data) <- c("time", "variable", "observed", "predicted")
data$residual <- data$observed - data$predicted
data$variable <- ordered(data$variable, levels = obs_vars)
fit$data <- data[order(data$variable, data$time), ]
fit$atol <- atol
fit$rtol <- rtol
fit$parms.all <- parms.all # Return all backtransformed parameters for summary
fit$odeparms.final <- parms.all[mkinmod$parms] # Return ode parameters for further fitting
fit$date <- date()
class(fit) <- c("mkinfit", "modFit")
return(fit)
}
summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {
param <- object$par
pnames <- names(param)
p <- length(param)
covar <- try(solve(0.5*object$hessian), silent = TRUE) # unscaled covariance
if (!is.numeric(covar)) {
message <- "Cannot estimate covariance; system is singular"
warning(message)
covar <- matrix(data = NA, nrow = p, ncol = p)
} else message <- "ok"
rownames(covar) <- colnames(covar) <- pnames
rdf <- object$df.residual
resvar <- object$ssr / rdf
se <- sqrt(diag(covar) * resvar)
names(se) <- pnames
tval <- param / se
modVariance <- object$ssr / length(object$residuals)
param <- cbind(param, se)
dimnames(param) <- list(pnames, c("Estimate", "Std. Error"))
bparam <- as.matrix(object$parms.all)
dimnames(bparam) <- list(pnames, c("Estimate"))
ans <- list(
version = as.character(packageVersion("mkin")),
Rversion = paste(R.version$major, R.version$minor, sep="."),
date.fit = object$date,
date.summary = date(),
use_of_ff = object$mkinmod$use_of_ff,
residuals = object$residuals,
residualVariance = resvar,
sigma = sqrt(resvar),
modVariance = modVariance,
df = c(p, rdf), cov.unscaled = covar,
cov.scaled = covar * resvar,
info = object$info, niter = object$iterations,
stopmess = message,
par = param,
bpar = bparam)
ans$diffs <- object$mkinmod$diffs
if(data) ans$data <- object$data
ans$start <- object$start
ans$fixed <- object$fixed
ans$errmin <- mkinerrmin(object, alpha = 0.05)
ans$parms.all <- object$parms.all
ep <- endpoints(object)
if (!is.null(ep$ff))
ans$ff <- ep$ff
if(distimes) ans$distimes <- ep$distimes
if(length(ep$SFORB) != 0) ans$SFORB <- ep$SFORB
class(ans) <- c("summary.mkinfit", "summary.modFit")
return(ans)
}
# Expanded from print.summary.modFit
print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), ...) {
cat("mkin version: ", x$version, "\n")
cat("R version: ", x$Rversion, "\n")
cat("Date of fit: ", x$date.fit, "\n")
cat("Date of summary:", x$date.summary, "\n")
cat("\nEquations:\n")
print(noquote(as.character(x[["diffs"]])))
df <- x$df
rdf <- df[2]
cat("\nStarting values for optimised parameters:\n")
print(x$start)
cat("\nFixed parameter values:\n")
if(length(x$fixed$value) == 0) cat("None\n")
else print(x$fixed)
cat("\nOptimised, transformed parameters:\n")
printCoefmat(x$par, digits = digits, ...)
cat("\nBacktransformed parameters:\n")
printCoefmat(x$bpar, digits = digits, ...)
cat("\nResidual standard error:",
format(signif(x$sigma, digits)), "on", rdf, "degrees of freedom\n")
cat("\nChi2 error levels in percent:\n")
x$errmin$err.min <- 100 * x$errmin$err.min
print(x$errmin, digits=digits,...)
printdistimes <- !is.null(x$distimes)
if(printdistimes){
cat("\nEstimated disappearance times:\n")
print(x$distimes, digits=digits,...)
}
printff <- !is.null(x$ff)
if(printff & x$use_of_ff == "min"){
cat("\nEstimated formation fractions:\n")
print(data.frame(ff = x$ff), digits=digits,...)
}
printSFORB <- !is.null(x$SFORB)
if(printSFORB){
cat("\nEstimated Eigenvalues of SFORB model(s):\n")
print(x$SFORB, digits=digits,...)
}
printcor <- is.numeric(x$cov.unscaled)
if (printcor){
Corr <- cov2cor(x$cov.unscaled)
rownames(Corr) <- colnames(Corr) <- rownames(x$par)
cat("\nParameter correlation:\n")
print(Corr, digits = digits, ...)
}
printdata <- !is.null(x$data)
if (printdata){
cat("\nData:\n")
print(format(x$data, digits = digits, scientific = FALSE,...), row.names = FALSE)
}
invisible(x)
}
