diff options
Diffstat (limited to 'R/kinresults.R')
| -rw-r--r-- | R/kinresults.R | 74 |
1 files changed, 74 insertions, 0 deletions
diff --git a/R/kinresults.R b/R/kinresults.R new file mode 100644 index 0000000..6bbff28 --- /dev/null +++ b/R/kinresults.R @@ -0,0 +1,74 @@ +kinresults <- function(kinfits, alpha = 0.05, SFORB=TRUE)
+{
+ kindata <- data.frame(t = kinfits[[1]]$model$t, parent = kinfits[[1]]$model$parent)
+ kindata.means <- aggregate(kindata, list(kindata$t), mean)
+ kindata.means.mean <- mean(kindata.means$parent, na.rm=TRUE)
+ n.times <- length(kindata.means$parent)
+ parms <- list()
+ df <- err.min <- RSS <- vector()
+ DT50 <- DT90 <- vector()
+ f <- list()
+ for (kinmodel in names(kinfits))
+ {
+ m = kinfits[[kinmodel]]
+ if(class(m) == "nls") {
+ kindata.means$est <- predict(m, kindata.means)
+ parms[[kinmodel]] <- switch(kinmodel,
+ SFO = list(parent.0 = coef(m)[["parent.0"]],
+ k = coef(m)[["k"]]),
+ FOMC = list(parent.0 = coef(m)[["parent.0"]],
+ alpha = coef(m)[["alpha"]],
+ beta = coef(m)[["beta"]]),
+ HS = list(parent.0 = coef(m)[["parent.0"]],
+ k1 = coef(m)[["k1"]],
+ k2 = coef(m)[["k2"]],
+ tb = coef(m)[["tb"]]),
+ DFOP = list(parent.0 = coef(m)[["parent.0"]],
+ k1 = coef(m)[["k1"]],
+ k2 = coef(m)[["k2"]],
+ g = coef(m)[["g"]]))
+ if(kinmodel == "DFOP" & SFORB) {
+ k1 = coef(m)[["k1"]]
+ k2 = coef(m)[["k2"]]
+ g = coef(m)[["g"]]
+ parms[["SFORB"]] =
+ list(parent.0 = coef(m)[["parent.0"]],
+ k1out = g * k1 + (1 - g) * k2,
+ k21 = k1 * k2 / (g * k1 + (1 - g) * k2),
+ k12 = (g * (1 - g) * (k1 - k2)^2) / (g * k1 + (1 - g) * k2))
+ }
+ n.parms = length(coef(m))
+ f[[kinmodel]] = switch(kinmodel,
+ HS = function(t, x) {
+ (HS(t, coef(m)[["parent.0"]],
+ coef(m)[["k1"]], coef(m)[["k2"]], coef(m)[["tb"]]) -
+ (1 - x/100) * coef(m)[["parent.0"]])^2
+ },
+ DFOP = function(t, x) {
+ (DFOP(t, coef(m)[["parent.0"]],
+ coef(m)[["k1"]], coef(m)[["k2"]], coef(m)[["g"]]) -
+ (1 - x/100) * coef(m)[["parent.0"]])^2
+ }
+ )
+ coef(m)
+
+ df[[kinmodel]] = n.times - n.parms
+ RSS[[kinmodel]] = sum(summary(m)$residuals^2)
+ DT50[[kinmodel]] = switch(kinmodel,
+ SFO = log(2)/coef(m)[["k"]],
+ FOMC = coef(m)[["beta"]] * (2^(1/coef(m)[["alpha"]]) - 1),
+ HS = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=50)$minimum,
+ DFOP = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=50)$minimum)
+ DT90[[kinmodel]] = switch(kinmodel,
+ SFO = log(10)/coef(m)[["k"]],
+ FOMC = coef(m)[["beta"]] * (10^(1/coef(m)[["alpha"]]) - 1),
+ HS = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=90)$minimum,
+ DFOP = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=90)$minimum)
+ err.min[[kinmodel]] <- kinerrmin(kinfits, kinmodel)
+ }
+ }
+ stats <- data.frame(n.times = n.times, df = df, mean.means = kindata.means.mean,
+ RSS = RSS, err.min = err.min)
+ results <- data.frame(DT50 = DT50, DT90 = DT90)
+ list(parms = parms, stats = stats, results = results)
+}
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