# General Instructions
#
# email support - techsupport@pharmastatsci.com
# feedback survey - Feedback link, please send your comments and suggestions
#
# This script documents general instructions for executing the examples posted
# using S-Plus 2000 for Windows. The programs are written with S Version 3.4
# and NLME Version 3.3. 
# The datasets and functions required to run the examples are all included
# with S-Plus 2000.
#
# The commands can be executed directly from the command line or from the Script
# Window. Execution from the Script Window is recommended, with execution of one or
# a few commands at a time, as opposed to running the entire script at once.
# To execute a script from the Script Window, follow the steps below.
#
# 1) Open the Script in the Script Window using the File, Open commands from the drop-down menu
# 2) With the Script Window active, highlight the command or section of code to execute
#    by holding down the 'shift' key and using the up and down arrow keys to select
#    the text
# 3) Use the F10 key or select 'Script, Run' from the drop-down menu to execute the code
# 4) Review the output in the Script Output Window for text or the graphsheet Window for plots
# 5) Repeat for the next section of code
# 6) You can use the 'Windows, Tile Vertical' to view multiple windows simultaneously
#
# Running the entire script at once is not recommended, in many cases there are several
# models within one script and it will take several minutes to run.
#
# Please email techsupport@pharmastatsci.com if you have questions, problems, comments, or
# suggestions.
# 
# Simple examples
# This script generates plots of Theophylline (example data frame Theoph), fits
# the data to two nonlinear models using the nlme function (one model specifies
# Ke, Ka, and Cl with random effects and another specifies just Ka and Cl with
# random effects). The two models are compared using the anova() fucntion.
# The individual coefficients are listed using the coef() function.
trellis.device()
plot(Theoph)
plot(Theoph,outer=~1,inner=~Subject,aspect="fill")

#fit the complete model - use the self-starting function
#SSfol for initial estimates - save the model as an object
#named fit

fit <- nlme(conc ~ SSfol(Dose, Time, lKe, lKa, lCl),
	fixed = lKe + lKa + lCl ~ 1,
	random = lKe + lKa + lCl ~ 1,
	data=Theoph)
	
plot(fit)
plot(augPred(fit))
plot(ranef(fit))
fit
fixef(fit)
ranef(fit)
coef(fit)
summary(fit)

#update the model fit and specify random effects for only Ka and Cl
#name the model fit.fix.ke
fit.fix.ke <- update(fit, random = lKa + lCl ~ 1)
	
plot(fit.fix.ke)
plot(augPred(fit.fix.ke))
plot(ranef(fit.fix.ke))
fit.fix.ke

#compare the models
anova(fit,fit.fix.ke)