L Nathan - 1 year ago 98

R Question

This is a follow up question to a previous post (How to modify slots lme4 >1.0). I have a similar pairwise data structure and want the random effect to consider both "pops" in the pair. I have a functional random intercept model using the code previously suggested:

`dat <- data.frame(pop1 = c(2,1,1,1,1,3,2,2,2,3,5,3,5,4,6),`

pop2 = c(1,3,4,5,6,2,4,5,6,4,3,6,4,6,5),

X = c(20,25,18,40,36,70,68,72,78,76,97,100,115,110,108),

Y = c(18,16,15,40,22,18,18,18,18,45,10,47,67,5,6))

#build random effects matrix

Zl<-lapply(c("pop1","pop2"),function(nm)Matrix:::fac2sparse(dat[[nm]],"d",drop=FALSE))

ZZ<-Reduce("+",Zl[-1],Zl[[1]])

#specify model structure

mod<-lFormula(Y~X+(1|pop1),data=dat,REML=TRUE)

#replace slot

mod$reTrms$Zt <- ZZ

#fit model

dfun<-do.call(mkLmerDevfun,mod)

opt<-optimizeLmer(dfun)

mkMerMod(environment(dfun),opt,mod$reTrms,fr=mod$fr)

However, when attempting to add a random slope variable:

`mod2<-lFormula(Y~X+(1+X|pop1),data=dat,REML=TRUE)`

mod2$reTrms$Zt <- ZZ

dfun<-do.call(mkLmerDevfun,mod2)

Results in the same error identified in the previous post (where the issue was calling the wrong data frame): "Error in Lambdat %*% Ut :

Cholmod error 'A and B inner dimensions must match' at file ../MatrixOps/cholmod_ssmult.c, line 82"

`plot(1,type="n",xlim=c(0,150),ylim=c(0,75),ylab = "Y",xlab="X")`

for(i in 1:length(unique(c(dat$pop1,dat$pop2)))){

subdat<-dat[which(dat$pop1==i | dat$pop2==i),]

out<-summary(lm(subdat$Y~subdat$X))

x=subdat$X

y=x*out$coefficients[2,1]+out$coefficients[1,1]

lines(x,y,col=i))

}

legend(125,60,1:6,col=1:6,lty=1,title="Pop")

Answer Source

```
dat <- data.frame(pop1 = c(2,1,1,1,1,3,2,2,2,3,5,3,5,4,6),
pop2 = c(1,3,4,5,6,2,4,5,6,4,3,6,4,6,5),
X = c(20,25,18,40,36,70,68,72,78,76,97,100,115,110,108),
Y = c(18,16,15,32,22,29,32,38,44,45,51,47,67,59,61))
```

It helps to try to understand what the original code is actually doing:

```
## build random effects matrix
## 1. sparse dummy-variable matrices for each population ID
Zl <- lapply(dat[c("pop1","pop2")],
Matrix::fac2sparse,to="d",drop.unused.levels=FALSE)
## 2. take the sum of all components of the list of dummy-variable matrices ...
ZZ <- Reduce("+",Zl[-1],Zl[[1]])
```

The `Reduce`

form is convenient in general if we have a long list, but it helps to see that in this case it's just `Zl[[1]]+Zl[[2]]`

...

```
all.equal(Zl[[1]]+Zl[[2]],ZZ) ## TRUE
```

What does this RE structure look like?

```
library(gridExtra)
grid.arrange(
image(t(Zl[[1]]),main="pop 1",sub="",xlab="pop",ylab="obs"),
image(t(Zl[[2]]),main="pop 2",sub="",xlab="pop",ylab="obs"),
image(t(ZZ),main="combined",sub="",xlab="RE",ylab="obs"),
nrow=1)
```

For the random slope, I *think* we want to take each filled element of `ZZ`

and replace it with the `X`

value observed for the corresponding observation/row of `dat`

: the indexing here is a bit obscure - in this case it boils down to there being 2 filled values in each row of `Z`

/column of `Zt`

(the `@p`

slot of the sparse matrix gives a zero-indexed pointer to the first non-zero element in each column ...)

```
vals <- dat$X[rep(1:(length(ZZ@p)-1),diff(ZZ@p))]
ZZX <- ZZ
ZZX@x <- vals
image(t(ZZX))
```

```
library(lme4)
mod <- lFormula(Y~X+(X|pop1),data=dat,REML=TRUE)
## replace slot
mod$reTrms$Zt <- rbind(ZZ,ZZX)
## fit model
dfun <- do.call(mkLmerDevfun,mod)
opt <- optimizeLmer(dfun)
m1 <- mkMerMod(environment(dfun),opt,mod$reTrms,fr=mod$fr)
```

This *seems* to work, but you should certainly check it with your own knowledge of what's supposed to be going on here ...