From 12934520d7ec3218ce1505787b6066334a24a562 Mon Sep 17 00:00:00 2001
From: jranke <jranke@edb9625f-4e0d-4859-8d74-9fd3b1da38cb>
Date: Tue, 30 Mar 2010 19:49:44 +0000
Subject: Initial import of the kinfit package developed from 2008-07 to
 2010-03 at Harlan Laboratories Ltd (former RCC Ltd).

Supports fitting of parent data with the usual FOCUS kinetic models.



git-svn-id: svn+ssh://svn.r-forge.r-project.org/svnroot/kinfit/pkg/kinfit@2 edb9625f-4e0d-4859-8d74-9fd3b1da38cb
---
 R/kinresults.R | 74 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 R/kinresults.R

(limited to 'R/kinresults.R')

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)
+}
-- 
cgit v1.2.1