# [R] NLME model question

From: Darren Shaw (Darren.Shaw@ed.ac.uk)
Date: Fri 14 May 2004 - 20:11:07 EST

```Message-id: <5.1.0.14.2.20040514110535.00b80c70@staffmail.ed.ac.uk>

```

Dear R-helpers

I have a problem related to the use of NLME

I think is simply a matter of getting the nlme coding correct, but i cannot
get my brain around it

I am analysing some 24 growth curves of some cells , and i wanted to say
that there are significant differences between the curves in two parameters
that describe the pattern of growth. these parameters are from a logistic
(r & k) .

i have attempted to construct a self starting routine for nlme ie:

SSGrowth_function(x, r, k)
{
.expr2 <- (k - 100000)/100000
.expr5 <- exp(((r * -1) * x))
.expr7 <- 1 + (.expr2 * .expr5)
.expr13 <- .expr7^2
.value <- k/.expr7
.actualArgs <- match.call()[c("r", "k")]
if(all(unlist(lapply(as.list(.actualArgs), is.name)))) {
.grad <- array(0, c(length(.value), 2), list(NULL, c("r",
"k")))
.grad[, "r"] <- - ((k * (.expr2 * (.expr5 * (-1 *
x))))/.expr13)
.grad[, "k"] <- (1/.expr7) - ((k * (1e-005 * .expr5))/.expr13)
}
.value
}

where x = time, 100000 = known starting conditions, r = growth and k =
carrying capacity

i guessed i should then write

nlme(NoofCells~SSGrowth(Time,r,k),fixed=r+k~1,data=CellData,random=r+k~1)

This runs and tells me that r & k's do differ

BUT. The "CellData" actually consists of replicates - ie there are 4 cell
types, but they are done 6 times each. Therefore, I do not want to ask if
there are significant differences in r & k between 24 sets of data
("Runs")- rather I want to be able to say that there are differences
between the four cell types occurring 6 times each. So how do
i incorporate "CellType" explicitly into my model structure??

i.e. If i was lust looking at say linear growth and was using lme I would
have written something like

lme(NoofCells~Time*CellType,random=~1|Runs,data=CellData)

Darren Shaw

-----------------------------------------------------------------
Dr Darren J Shaw
Centre for Tropical Veterinary Medicine (CTVM)
Royal School of Veterinary Studies
The University of Edinburgh
Scotland

______________________________________________
R-help@stat.math.ethz.ch mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help