# [R] custom selfStart model works with getInitial but not nls

Michael A. Gilchrist mikeg at utk.edu
Sat Oct 17 02:09:18 CEST 2009

```Hello,

I'm having problems creating and using a selfStart model with nlme.  Briefly,
I've defined the model, a selfStart object, and then combined them to make a
selfStart.default model.

If I apply getInitial to the selfStart model, I get results.  However, if I try
usint it with nls or nlsList, these routines complain about a lack of initial
conditions.

If someone could point out what I'm doing wrong, I'd greatly appreciate it.

Details:
## Nonlinear model I want to fit to the data
const.PBMC.tcell.model <- function(B0, t, aL, aN, T0){

Tb0 = B0;

x = exp(-log(aL) + log(T0*aL+(-1+exp(t * aL))*Tb0 * aN) - t * aL);

return(x);
}

##Define selfStart routine
const.PBMC.tcell.selfStart<- function(mCall, LHS, data){

t0 = 0;
t1 = 24;
t2 = 48;

##Get B0 Value
B0 =  data[1, "B0"];

T0 = mean(data[data\$Time==t0, "Count"]);
T1 = mean(data[data\$Time==t1, "Count"]);
T2 = mean(data[data\$Time==t2, "Count"]);

if(T0 < T2){ ##increase -- doesn't work
stop(paste("Error in const.PBMC.tcell.start: T0 < T2 for data: ", data[1,
]));

}
##Estimate aL based on exponential decline from t=0 to t=24
aLVal = -(log(T1) - log(T0))/(t1-t0);

##Estimate aNVal based on final value
aNVal = aLVal*T2/B0;

values = list(aLVal, aNVal, T0);
names(values) <- mCall[c("aL", "aN", "T0")]; #mimic syntax used by P&B
return(values)
}

##Now create new model with selfStart attributes
const.PBMC.tcell.modelSS<-  selfStart(model = const.PBMC.tcell.model,
initial=const.PBMC.tcell.selfStart)

##Test routines using getInitial -- This works
> getInitial(Count ~ const.PBMC.tcell.modelSS(B0, Time,aL, aN, T0), data =
> tissueData)
[1] 0.05720924
\$aL
[1] 0.05720924

\$aN
[1] 0.1981895

\$T0
[1] 1360.292

##Now try to use the SS model -- this doesn't work
> nls(Count ~ const.PBMC.tcell.modelSS(B0, Time,aL, aN, T0), data = tissueData)
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model