Hello,
I'm trying to do some non-linear regression with 2 cell types and 4 tissue
type treatments using selfStart models
Following Ritz and Streibig (2009), I wrote the following routines:
##Selfstart
expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){
exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL
}
expDecayAndConstantInflowModelInit <- function(mCall, LHS, data){
print(paste("SelfStart mCall:", mCall));
print(attributes(mCall));
print(mCall[["aN"]]);
xy <- sortedXyData(mCall[["time"]], LHS, data);
lmFit <- lm(log(xy[, "y"]) ~ xy[, "x"]);
coefs <- coef(lmFit);
T0 <- exp(coefs[1]);
aL <- -coefs[2]*0.99;
aN <- 0.0001;
value <- c(aL, aN, T0);
names(value) <- mCall[c("aL", "aN", "T0")];
value
}
SSexpDecayAndConstantInflow <- selfStart(expDecayAndConstantInflowModel,
expDecayAndConstantInflowModelInit, c("aL", "aN", "T0"))
Ignoring the treatment effects, the routines seem to work find:
-----------------------------------
getInitial(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL, aN, T0), data =
tissueData)
aL aN T0
4.600144e-02 1.000000e-04 1.082172e+03
nls(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL, aN, T0), data =
tissueData)
Nonlinear regression model
model: Count ~ SSexpDecayAndConstantInflow(B0, Time, aL, aN, T0)
data: tissueData
aL aN T0
0.1131 0.1206 1348.0646
residual sum-of-squares: 38821317
Number of iterations to convergence: 11
Achieved convergence tolerance: 6.267e-06
----------------------------------
However, I need to be able to test for these treatment effects (they are quite
heterogenous), but when I try using the standard syntax for nls(), the
selfStart model only estimates a set of global values and these don't work
with nls()
getInitial(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL[Type], aN[Type],
T0[TypeTissue]), data = tissueData)
aL[Type] aN[Type] T0[TypeTissue]
4.600144e-02 1.000000e-04 1.082172e+03
nls(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL[Type], aN[Type],
T0[TypeTissue]), data = tissueData)
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
In addition: Warning message:
In nls(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL[Type], aN[Type], :
No starting values specified for some parameters.
Intializing 'aL', 'aN', 'T0' to '1.'.
Consider specifying 'start' or using a selfStart model
----------------------------------
I've tried the following syntax, but it seems that the global estimates give
singularities when applied to each group
----------------------------------
nls(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL[Type], aN[Type],
T0[TypeTissue]),
data = tissueData,
start = getInitial(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL, aN,
T0), data = tissueData))
nls(Count ~ SSexpDecayAndConstantInflow(B0, Time, aL[Type], aN[Type],
T0[TypeTissue]),
+ data = tissueData,
+ start = getInitial(Count ~ SSexpDecayAndConstantInflow(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
----------------------------------
Does anyone have any suggestions for how to work around this problem or have
an example of a selfStart model that can deal with treatment effects? The
dataset is quite heterogenous and so I need to explore a number of alternative
transformations and models so I really need something that works without me
having to continually reset the start values by hand.
Many thanks.
Mike
-----------------------------------------------------
Department of Ecology & Evolutionary Biology
569 Dabney Hall
University of Tennessee
Knoxville, TN 37996-1610
phone:(865) 974-6453
fax: (865) 974-6042
web: http://eeb.bio.utk.edu/gilchrist.asp
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