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-rw-r--r--CakeSummary.R330
1 files changed, 200 insertions, 130 deletions
diff --git a/CakeSummary.R b/CakeSummary.R
index 6c7c661..70f01e6 100644
--- a/CakeSummary.R
+++ b/CakeSummary.R
@@ -3,10 +3,10 @@
# and display the summary itself.
# Parts modified from package mkin
-# Parts Tessella (Rob Nelson/Richard Smith/Tamar Christina) for Syngenta: Copyright (C) 2011 Syngenta
-# Tessella Project Reference: 6245, 7247
+# Parts Tessella for Syngenta: Copyright (C) 2011-2016 Syngenta
+# Tessella Project Reference: 6245, 7247, 8361, 7414
-# This program is free software: you can redistribute it and/or modify
+# The CAKE R modules are 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.
@@ -17,27 +17,84 @@
# 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/>.”
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
# Calculate the extra decay times for the other k values in DFOP
# and HS
# Arguments
# type - type of compartment
# parms.all - list of parameters
-CakeExtraDT<-function(type, parms.all) {
+CakeExtraDT<-function(type, obs_var, parms.all) {
DT50k1 = NA
- DT50k2 = NA
- if (!is.na(parms.all["k1"]) && !is.na(parms.all["k2"])) {
- k1 = parms.all["k1"]
- k2 = parms.all["k2"]
- DT50k1 = log(2)/k1
- DT50k2 = log(2)/k2
+ DT50k2 = NA
+ if (type == "DFOP"){
+ k1_name = paste("k1", obs_var, sep="_")
+ k2_name = paste("k2", obs_var, sep="_")
+
+ if (!is.na(parms.all[k1_name]) && !is.na(parms.all[k2_name])) {
+ k1 = parms.all[k1_name]
+ k2 = parms.all[k2_name]
+ DT50k1 = log(2)/k1
+ DT50k2 = log(2)/k2
+ }
}
+
+ if (type == "HS"){
+ if (!is.na(parms.all["k1"]) && !is.na(parms.all["k2"])) {
+ k1 = parms.all["k1"]
+ k2 = parms.all["k2"]
+ DT50k1 = log(2)/k1
+ DT50k2 = log(2)/k2
+ }
+ }
+
ret<-c(DT50k1, DT50k2)
names(ret)<-c("k1", "k2")
ret
}
+# Calculate the representative decay time for an IORE model.
+# Arguments
+# obs_var - compartment name
+# parms.all - list of parameters
+CakeIORERepresentativeDT<-function(obs_var, parms.all) {
+ repDT = NA
+
+ if ("N" %in% names(parms.all)){
+ k_name = paste("k", obs_var, sep="_")
+ k_tot = parms.all[[k_name]]
+ Nval = parms.all[["N"]]
+ M0_name = paste(obs_var, "0", sep="_")
+
+ if (!(M0_name %in% names(parms.all))){
+ M0_name = obs_var
+ }
+
+ M0val = parms.all[[M0_name]]
+ if (Nval == 1){
+ repDT = log(2)/k_tot
+ }
+ else{
+ repDT = (log(2)/log(10)) * ((10^(Nval - 1) - 1) * M0val^(1 - Nval))/((Nval - 1) * k_tot)
+ }
+ }
+
+ repDT
+}
+
+# Calculate the "back-calculated" DT50 for an FOMC model.
+# Arguments
+# parms.all - list of parameters
+CakeFOMCBackCalculatedDT<-function(parms.all) {
+ repDT = NA
+
+ if ("alpha" %in% names(parms.all) && "beta" %in% names(parms.all)){
+ repDT = parms.all[["beta"]] * (10^(1/parms.all[["alpha"]]) - 1) * (log(2)/log(10))
+ }
+
+ repDT
+}
+
# Calculate the decay times for a compartment
# Arguments
# type - type of compartment
@@ -46,8 +103,6 @@ CakeExtraDT<-function(type, parms.all) {
# sannMaxIter - the maximum amount of iterations to do for SANN
CakeDT<-function(type, obs_var, parms.all, sannMaxIter) {
if (type == "SFO") {
- # Changed to new parameterisation
- #k_names = grep(paste("k", obs_var, sep="_"), names(parms.all), value=TRUE)
k_name = paste("k", obs_var, sep="_")
k_tot = parms.all[k_name]
DT50 = log(2)/k_tot
@@ -60,24 +115,56 @@ CakeDT<-function(type, obs_var, parms.all, sannMaxIter) {
DT90 = beta * (10^(1/alpha) - 1)
}
if (type == "DFOP") {
- k1 = parms.all["k1"]
- k2 = parms.all["k2"]
- g = parms.all["g"]
+ k1 = parms.all[paste("k1", obs_var, sep="_")]
+ k2 = parms.all[paste("k2", obs_var, sep="_")]
+ g = parms.all[paste("g", obs_var, sep="_")]
f <- function(t, x) {
fraction <- g * exp( - k1 * t) + (1 - g) * exp( - k2 * t)
(fraction - (1 - x/100))^2
}
DTminBounds <- 0.001
- DTminInitial <- 1
- DT50.temp <- optim(DTminInitial, f, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 50, lower = DTminBounds)
- DT50.o = DT50.temp$par
- DT50.converged = DT50.temp$convergence == 0
- DT50 = ifelse(!DT50.converged, NA, DT50.o)
- DT90.temp <- optim(DTminInitial, f, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 90, lower = DTminBounds)
- DT90.o = DT90.temp$par
- DT90.converged = DT90.temp$convergence == 0
- DT90 = ifelse(!DT90.converged, NA, DT90.o)
+
+ # Determine a decent starting point for numerical iteration. The latter two terms are also lower bounds for DT50.
+ DT50min <- max(0.001, (1 / k1) * log(g * 2), (1 / k2) * log((1 - g) * 2))
+
+ # Set the starting point for the numerical solver to a be a bit smaller than the computed minimum as the R optim function seems to get confused if the min is
+ # greater than the answer it's trying to converge to (up to its level of accuracy).
+ DT50Initial <- DT50min * 0.9
+
+ # Results greater than 10,000 are not interesting. The R optim routine also handles very large values unreliably and can claim to converge when it is nowhere near.
+ if (DT50min > 10000){
+ DT50 = ">10,000"
+ }else{
+ DT50.temp <- optim(DT50Initial, f, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 50, lower = DTminBounds)
+ DT50.o = DT50.temp$par
+ DT50.converged = DT50.temp$convergence == 0
+ DT50 = ifelse(!DT50.converged || DT50.o <= 0, NA, DT50.o)
+
+ if (DT50.converged && DT50 > 10000){
+ DT50 = ">10,000"
+ }
+ }
+
+ # Determine a decent starting point for numerical iteration. The latter two terms are also lower bounds for DT90.
+ DT90min <- max(0.001, (1 / k1) * log(g * 10), (1 / k2) * log((1 - g) * 10))
+
+ # Set the starting point for the numerical solver to a be a bit smaller than the computed minimum as the R optim function seems to get confused if the min is
+ # greater than the answer it's trying to converge to (up to its level of accuracy).
+ DT90Initial <- DT90min * 0.9
+
+ if (DT90min > 10000){
+ DT90 = ">10,000"
+ }else{
+ DT90.temp <- optim(DT90Initial, f, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 90, lower = DTminBounds)
+ DT90.o = DT90.temp$par
+ DT90.converged = DT90.temp$convergence == 0
+ DT90 = ifelse(!DT90.converged || DT90.o <= 0, NA, DT90.o)
+
+ if (DT90.converged && DT90 > 10000){
+ DT90 = ">10,000"
+ }
+ }
}
if (type == "HS") {
k1 = parms.all["k1"]
@@ -93,34 +180,28 @@ CakeDT<-function(type, obs_var, parms.all, sannMaxIter) {
DT50 <- DTx(50)
DT90 <- DTx(90)
}
- if (type == "SFORB") {
- # FOCUS kinetics (2006), p. 60 f
- k_out_names = grep(paste("k", obs_var, "free", sep="_"), names(parms.all), value=TRUE)
- k_out_names = setdiff(k_out_names, paste("k", obs_var, "free", "bound", sep="_"))
- k_1output = sum(parms.all[k_out_names])
- k_12 = parms.all[paste("k", obs_var, "free", "bound", sep="_")]
- k_21 = parms.all[paste("k", obs_var, "bound", "free", sep="_")]
-
- sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 + k_12 * k_21 - (k_12 + k_1output) * k_21)
- b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp
- b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp
-
- SFORB_fraction = function(t) {
- ((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 * t) +
- ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t)
- }
- f_50 <- function(t) (SFORB_fraction(t) - 0.5)^2
- DTmin <- 0.001
- DT50.temp <- optim(DTmin, f_50, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 50, lower = DTmin)
- DT50.o = DT50.temp$par
- DT50.converged = DT50.temp$convergence == 0
- if (!DT50.converged) DT50 = NA else DT50 = DT50.o
- f_90 <- function(t) (SFORB_fraction(t) - 0.1)^2
- DT90.temp <- optim(DTmin, f_90, method="SANN", control=list(fnscale=0.05, maxit=sannMaxIter), x = 90, lower = DTmin)
- DT90.o = DT90.temp$par
- DT90.converged = DT90.temp$convergence == 0
- if (!DT90.converged) DT90 = NA else DT90 = DT90.o
+ if (type == "IORE") {
+ k_name = paste("k", obs_var, sep="_")
+ k_tot = parms.all[k_name]
+ Nval = parms.all["N"]
+ M0_name = paste(obs_var, "0", sep="_")
+
+ if (!(M0_name %in% names(parms.all))){
+ M0_name = obs_var
+ }
+
+ M0val = parms.all[M0_name]
+
+ if (Nval == 1){
+ DT50 = log(2)/k_tot
+ DT90 = log(10)/k_tot
+ }
+ else{
+ DT50 = ((2^(Nval - 1) - 1) * M0val^(1 - Nval))/((Nval - 1) * k_tot)
+ DT90 = ((10^(Nval - 1) - 1) * M0val^(1 - Nval))/((Nval - 1) * k_tot)
+ }
}
+
ret<-c(DT50, DT90)
names(ret)<-c("DT50","DT90")
ret
@@ -133,68 +214,43 @@ CakeDT<-function(type, obs_var, parms.all, sannMaxIter) {
# obs_vars - compartment names
# parms.optim - list of fitted parameters
# state,ini.optim - list of fitted initial values
+# fixed - parameters that were fixed (taken from fit$fixed usually)
#
-CakeChi2<-function(mkinmod, observed, predicted_long, obs_vars, parms.optim, state.ini.optim, state.ini, parms.ini) {
+CakeChi2<-function(mkinmod, observed, predicted_long, obs_vars, parms.optim, state.ini.optim, state.ini, parms.ini, fixed) {
# Calculate chi2 error levels according to FOCUS (2006)
- if(getRversion() >= '2.14.0'){
# You would usually call mkinfit to fit the data, however it seems that we've already fit the data
# bundle <- mkinfit(mkinmod, observed, parms.ini = parms.optim, state.ini = state.ini) #, err='err')
# Somewhere else. Calling mkinfit may result in an error during fitting. So Instead we just create
- # the values that the new version of mkinerrmin expects. There is no class check so it's fine.
+ # the values that the new version of CakeErrMin expects. There is no class check so it's fine.
bundle<-vector()
bundle$predicted <- predicted_long
bundle$observed <- observed
bundle$obs_vars <- obs_vars
bundle$par <- c(parms.optim, state.ini.optim)
- errmin.overall <- mkinerrmin(bundle)
+ bundle$fixed <- fixed
+ errmin.overall <- CakeErrMin(bundle)
errmin.overall
- } else {
- means <- aggregate(value ~ time + name, data = observed, mean, na.rm=TRUE)
- errdata <- merge(means, predicted_long, by = c("time", "name"), suffixes = c("_mean", "_pred"))
- errdata <- errdata[order(errdata$time, errdata$name), ]
- errmin.overall <- mkinerrmin(errdata, length(parms.optim) + length(state.ini.optim))
-
- errmin <- data.frame(err.min = errmin.overall$err.min,
- n.optim = errmin.overall$n.optim, df = errmin.overall$df)
- rownames(errmin) <- "All data"
- for (obs_var in obs_vars)
- {
- errdata.var <- subset(errdata, name == obs_var)
- n.k.optim <- length(grep(paste("k", obs_var, sep="_"), names(parms.optim)))
- n.initials.optim <- length(grep(paste(obs_var, ".*", "_0", sep=""), names(state.ini.optim)))
- n.optim <- n.k.optim + n.initials.optim
- if ("alpha" %in% names(parms.optim)) n.optim <- n.optim + 1
- if ("beta" %in% names(parms.optim)) n.optim <- n.optim + 1
- if ("k1" %in% names(parms.optim)) n.optim <- n.optim + 1
- if ("k2" %in% names(parms.optim)) n.optim <- n.optim + 1
- if ("g" %in% names(parms.optim)) n.optim <- n.optim + 1
- if ("tb" %in% names(parms.optim)) n.optim <- n.optim + 1
- errmin.tmp <- mkinerrmin(errdata.var, n.optim)
- errmin[obs_var, c("err.min", "n.optim", "df")] <- errmin.tmp
- }
- errmin
- }
}
-# Compute additional statistics (r²/efficiency) of obs v pred for each compartment and the whole problem
+# Compute additional statistics (r?/efficiency) of obs v pred for each compartment and the whole problem
CakeAdditionalStats<-function(obsvspred){
- agg<-aggregate(obsvspred[c('observed', 'err-var', 'predicted', 'residual')], list(name=obsvspred$variable), function(x){x}, simplify=FALSE)
- frames<-apply(agg, 1, as.data.frame);
- names(frames)<-agg$name;
- frames$all<-obsvspred
-
- t(sapply(frames, function(frame){
- # This function takes a data frame for a single variable and makes the stats for it
- # r²: FOCUS eq 6.5 p. 97
- # Efficiency: eq 6.6 p. 99
- do <- frame$observed - mean(frame$observed)
- dp <- frame$predicted - mean(frame$predicted)
- r2 <- ( sum(do * dp) / sqrt(sum(do^2) * sum(dp^2)) ) ^ 2
- eff <- 1 - ( sum((frame$predicted - frame$observed)^2) / sum(do^2) )
- list(r2=r2, eff=eff)
- }))
+ agg<-aggregate(obsvspred[c('observed', 'err-var', 'predicted', 'residual')], list(name=obsvspred$variable), function(x){x}, simplify=FALSE)
+ frames<-apply(agg, 1, as.data.frame);
+ names(frames)<-agg$name;
+ frames$all<-obsvspred
+
+ t(sapply(frames, function(frame){
+ # This function takes a data frame for a single variable and makes the stats for it
+ # r?: FOCUS eq 6.5 p. 97
+ # Efficiency: eq 6.6 p. 99
+ do <- frame$observed - mean(frame$observed)
+ dp <- frame$predicted - mean(frame$predicted)
+ r2 <- ( sum(do * dp) / sqrt(sum(do^2) * sum(dp^2)) ) ^ 2
+ eff <- 1 - ( sum((frame$predicted - frame$observed)^2) / sum(do^2) )
+ list(r2=r2, eff=eff)
+ }))
}
# Summarise a fit - used for CakeIrlsFit and CakeOlsFit
@@ -202,22 +258,33 @@ summary.CakeFit <- function(object, data = TRUE, distimes = TRUE, halflives = TR
param <- object$par
pnames <- names(param)
p <- length(param)
- covar <- try(solve(0.5*object$hessian), silent = TRUE) # unscaled covariance
- # covar <- try(ginv(0.5*object$hessian)) # unscaled covariance, calculate using generic inversion
+
+ # If the Hessian comes back as the identity matrix, there are no residuals and so we can't provide statistics
+ if(object$hessian==diag(dim(object$hessian)[1])) {
+ covar=NULL
+ } else {
+ covar <- try(solve(0.5*object$hessian), silent = TRUE) # unscaled covariance
+ }
rdf <- object$df.residual
+
+ compartmentsFixedConcentration <- gsub("_0$", "", rownames(subset(object$fixed, type=="state")))
+
+ # As for chi squared values, observations at time 0 should not count towards number of degrees of freedom if the corresponding
+ # initial concentration is 0.
+ for (compartment in compartmentsFixedConcentration){
+ timeZeroObservations <- subset(object$data, time==0 & variable==compartment)
+
+ if (length(row.names(timeZeroObservations)) > 0){
+ rdf <- rdf - length(row.names(timeZeroObservations))
+ }
+ }
+
resvar <- object$ssr / rdf
modVariance <- object$ssr / length(object$residuals)
-
- # if(!is.numeric(covar)){
- # covar <- try(ginv(0.5*object$hessian))
- # }
if (!is.numeric(covar)) {
-# message <- "Cannot estimate covariance; system is singular"
-# warning(message)
-# covar <- matrix(data = NA, nrow = p, ncol = p)
- covar=NULL
+ covar=NULL
} else {
message <- "ok"
rownames(covar) <- colnames(covar) <-pnames
@@ -233,7 +300,6 @@ summary.CakeFit <- function(object, data = TRUE, distimes = TRUE, halflives = TR
} else message <- "ok"
if(cov && !is.null(covar)) {
-
# Filter the values for t-test, only apply t-test to k-values
t.names <- grep("k(\\d+)|k_(.*)", names(tval), value = TRUE)
t.rest <- setdiff(names(tval), t.names)
@@ -241,19 +307,15 @@ summary.CakeFit <- function(object, data = TRUE, distimes = TRUE, halflives = TR
t.values[t.rest] <- NA
t.result <- pt(t.values, rdf, lower.tail = FALSE)
- # Now set the values we're not interested in for the lower
- # and upper bound we're not interested in to NA
- t.param = c(param)
- t.param[t.names] <- NA
# calculate the 90% confidence interval
alpha <- 0.10
- lci90 <- t.param + qt(alpha/2,rdf)*se
- uci90 <- t.param + qt(1-alpha/2,rdf)*se
+ lci90 <- c(param) + qt(alpha/2,rdf)*se
+ uci90 <- c(param) + qt(1-alpha/2,rdf)*se
# calculate the 95% confidence interval
alpha <- 0.05
- lci95 <- t.param + qt(alpha/2,rdf)*se
- uci95 <- t.param + qt(1-alpha/2,rdf)*se
+ lci95 <- c(param) + qt(alpha/2,rdf)*se
+ uci95 <- c(param) + qt(1-alpha/2,rdf)*se
param <- cbind(param, se, tval, t.result, lci90, uci90, lci95, uci95)
dimnames(param) <- list(pnames, c("Estimate", "Std. Error",
@@ -282,15 +344,19 @@ summary.CakeFit <- function(object, data = TRUE, distimes = TRUE, halflives = TR
ans$diffs <- object$diffs
if(data) {
- ans$data <- object$data
- ans$additionalstats = CakeAdditionalStats(object$data)
+ ans$data <- object$data
+ ans$additionalstats = CakeAdditionalStats(object$data)
}
ans$start <- object$start
ans$fixed <- object$fixed
ans$errmin <- object$errmin
ans$penalties <- object$penalties
if(distimes) ans$distimes <- object$distimes
- if(halflives) ans$extraDT50 <- object$extraDT50
+ if(halflives){
+ ans$extraDT50 <- object$extraDT50
+ ans$ioreRepDT <- object$ioreRepDT
+ ans$fomcRepDT <- object$fomcRepDT
+ }
if(ff) ans$ff <- object$ff
if(length(object$SFORB) != 0) ans$SFORB <- object$SFORB
class(ans) <- c("summary.CakeFit","summary.mkinfit", "summary.cakeModFit")
@@ -334,20 +400,24 @@ print.summary.CakeFit <- function(x, digits = max(3, getOption("digits") - 3), .
if(printextraDT50){
cat("\nHalf Lives for Extra k values:\n")
print(x$extraDT50, digits=digits,...)
+ }
+
+ printIoreRepDT <- !is.null(x$ioreRepDT)
+ if (printIoreRepDT){
+ cat("\nRepresentative Half Life for IORE:", format(signif(x$ioreRepDT, digits)))
}
+ printFomcRepDT <- !is.null(x$fomcRepDT)
+ if (printFomcRepDT){
+ cat("\nBack-calculated Half Life for FOMC:", format(signif(x$fomcRepDT, digits)))
+ }
+
printff <- !is.null(x$ff)
if(printff){
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$SFORB, digits=digits,...)
- }
-
printcor <- !is.null(x$cov.unscaled)
if (printcor){
Corr <- cov2cor(x$cov.unscaled)
@@ -363,13 +433,13 @@ print.summary.CakeFit <- function(x, digits = max(3, getOption("digits") - 3), .
}
if(!is.null(x$additionalstats)){
- cat("\nAdditional Stats:\n");
- print(x$additionalstats, digits=digits, ...)
+ cat("\nAdditional Stats:\n");
+ print(x$additionalstats, digits=digits, ...)
}
if(!(is.null(x$penalties) || 0 == dim(x$penalties)[[1]])){
- cat("\nPenalties:\n");
- print(x$penalties, digits=digits, row.names = FALSE, ...)
+ cat("\nPenalties:\n");
+ print(x$penalties, digits=digits, row.names = FALSE, ...)
}
if ( !is.null(x$seed) ) {

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