Dear friends - I'm having troubles with nlme fitting a simplified model
as shown below eliciting the error
Error in chol.default((value + t(value))/2) :
? the leading minor of order 1 is not positive definite -
I have seen the threads on this error but it didn't help me solve the
problem.
The model runs well in brms and identifies the used parameters even with
fixed effects for TRT? - but here in nlme TRT is ignored and I guess
this is not the reason for the said error
Below is the quite clumsy simulated data set and specification of call
to nlme - the start values are taken from fitted values in brms
library(ggplot2)
windows(record=TRUE)
#generate 3*10? rats - add fixed effects to the four parameters
according to the three groups - add random effects pr each rat - add
residual random effect
#Parameter values taken from Sapirstein AJP 181:330-6, 1955
set.seed(1234)
Time <- seq(1,60,by=1)
A <- 275; B <-? 140;? g1 <- 0.1105; g2 <- .0161
N <- 30
AA <- rep(A,30)+rnorm(30,0,30);BB <- rep(B,30)+rnorm(30,0,15) ;
gg1 <- rep(g1,30)+rnorm(30,0,0.01); gg2 <- rep(g2,30)+rnorm(30,0,0.001)
TRT <- gl(3,10*60)
levels(TRT) <- c("CTRL","DIAB","HYPER")
AA1 <- AA + c(rep(0,10),rep(10,10),rep(-10,10))
BB1 <- BB + c(rep(0,10),rep(5,10),rep(-5,10))
Gg1 <- gg1 + c(rep(0,10),rep(0.01,10),rep(-0.01,10))
Gg2 <- gg2 + c(rep(0,10),rep(0.005,10),rep(-0.005,10))
getY <- function(A,B,g1,g2) {
Y? <- A*exp(-g1*Time) + B*exp(-g2*Time)
Y <- Y + rnorm(60,0,20)
}
YY <-? c()
for (i in 1:N) YY <- c(YY,getY(AA1[i],BB1[i],Gg1[i],Gg2[i]))
TT <- rep(Time,N)
RAT <- gl(N,length(Time))
dats? <- data.frame(RAT,TRT,TT,YY)
Dats <- dats
names(Dats)[c(3,4)] <- c("Time","Y")
dput(Dats,"dats0505.dat")
with(Dats,plot(Time,Y,pch=19,cex=.1,col=TRT))
ggplot(data=Dats,aes(x=Time,y=Y,group=RAT,col=TRT)) + geom_line()
library(nlme)
gfr.nlme <- nlme(Y ~ A*exp(-Time*g1)+B*exp(-Time*g2),
data = Dats,
fixed = A+g1+B+g2 ~1,
random = A+g1+B+g2 ~1,groups = ~ RAT,
start = c(255,115,130*1e-3,17*1e-3),
na.action = na.omit,verbose=TRUE,control = list(msVerbose = TRUE))
summary(gfr.nlme)
error in chol.default((value + t(value))/2) : , the leading minor of order 1 is not positive definite
2 messages · Troels Ring, David Winsemius
2 days later
On May 5, 2018, at 1:19 AM, Troels Ring <tring at gvdnet.dk> wrote:
Dear friends - I'm having troubles with nlme fitting a simplified model as shown below eliciting the error
Error in chol.default((value + t(value))/2) :
the leading minor of order 1 is not positive definite -
I have seen the threads on this error but it didn't help me solve the problem.
The model runs well in brms and identifies the used parameters even with fixed effects for TRT - but here in nlme TRT is ignored and I guess this is not the reason for the said error
Below is the quite clumsy simulated data set and specification of call to nlme - the start values are taken from fitted values in brms
library(ggplot2)
windows(record=TRUE)
#generate 3*10 rats - add fixed effects to the four parameters according to the three groups - add random effects pr each rat - add residual random effect
#Parameter values taken from Sapirstein AJP 181:330-6, 1955
set.seed(1234)
Time <- seq(1,60,by=1)
A <- 275; B <- 140; g1 <- 0.1105; g2 <- .0161
N <- 30
AA <- rep(A,30)+rnorm(30,0,30);BB <- rep(B,30)+rnorm(30,0,15) ;
gg1 <- rep(g1,30)+rnorm(30,0,0.01); gg2 <- rep(g2,30)+rnorm(30,0,0.001)
TRT <- gl(3,10*60)
levels(TRT) <- c("CTRL","DIAB","HYPER")
AA1 <- AA + c(rep(0,10),rep(10,10),rep(-10,10))
BB1 <- BB + c(rep(0,10),rep(5,10),rep(-5,10))
Gg1 <- gg1 + c(rep(0,10),rep(0.01,10),rep(-0.01,10))
Gg2 <- gg2 + c(rep(0,10),rep(0.005,10),rep(-0.005,10))
getY <- function(A,B,g1,g2) {
Y <- A*exp(-g1*Time) + B*exp(-g2*Time)
Y <- Y + rnorm(60,0,20)
}
YY <- c()
for (i in 1:N) YY <- c(YY,getY(AA1[i],BB1[i],Gg1[i],Gg2[i]))
TT <- rep(Time,N)
RAT <- gl(N,length(Time))
dats <- data.frame(RAT,TRT,TT,YY)
Dats <- dats
names(Dats)[c(3,4)] <- c("Time","Y")
dput(Dats,"dats0505.dat")
with(Dats,plot(Time,Y,pch=19,cex=.1,col=TRT))
ggplot(data=Dats,aes(x=Time,y=Y,group=RAT,col=TRT)) + geom_line()
library(nlme)
gfr.nlme <- nlme(Y ~ A*exp(-Time*g1)+B*exp(-Time*g2),
data = Dats,
fixed = A+g1+B+g2 ~1,
random = A+g1+B+g2 ~1,groups = ~ RAT,
Your fixed and random formulae look the same. That would seem to create problems, at leas the way I understand mixed models analysis. At any rate this is much more likely to get expert eyes (which mine definitely are not) on the problem if it were posted to the mixed models list.
start = c(255,115,130*1e-3,17*1e-3), na.action = na.omit,verbose=TRUE,control = list(msVerbose = TRUE)) summary(gfr.nlme)
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