Hi all,
I've been attempting to fit a logistic glmm using glmmPQL in order to
estimate variance components for a score test, where the model is of the
form logit(mu) = X*a+ Z1*b1 + Z2*b2. Z1 and Z2 are actually reduced rank
square root matrices of the assumed covariance structure (up to a constan
Flip date and q in your formula, you've got them backwards from what you've
said you're trying to model.
armstrwa wrote:
>
>
> Yeah, I did look at the help(loess) page, but I wasn't really sure what to
> do with that. I was inputting it as:
>
>> test<-loess(date ~ q,data.frame(date,q),span=0.
Just use vect_2_id as a subsetting index for vect_1, ie:
vect_2<-vect_1[vect_2_id]
Vincy Pyne wrote:
>
> Dear R helpers
>
> Suppose I have a vector as
>
> vect_1 = c("AAA", "AA", "A", "BBB", "BB", "B", "CCC")
>
> vect_1_id = c(1:length(vect_1))
>
> Through some process I obtain
>
> vect_2_
Try using which()
Something like data.frame(dataset[which(dataset[,3]=="text3"),])
e-letter wrote:
>
> Readers,
>
> For a data set:
>
> text1,23,text2,45
> text1,23,text3,78
> text1,23,text3,56
> text1,23,text2,45
>
> The following command was entered:
>
> datasubset<-data.frame(dataset[,3]
64 Bit R w/JAGS seems to be stalling out as well, I ran a test run of 100
iterations and it's been hanging for 8 hours so that doesn't seem to be the
solution. I'll take a look at PYMC. That CppBUGS package looks pretty
interesting, I'll keep my eye on it.
My C programming book arrives today fro
That actually won't work. max(y) will give a value, not a coordinate, so
x[max(y)] is definitely not what you want.
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This is where which.max() comes in handy
n<-length(x)
x.c<-rep(0,n)
for(i in 1:n){
x.c[i]<-which.max[y1]
}
x.c is then a vector of x coordinates for the maximum for columns
y1,y2,...,yn
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You mean like cumsum()?
> a<-1:10
> b<-cumsum(a)
> b
[1] 1 3 6 10 15 21 28 36 45 55
-Nick Larson
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The reason this doesn't work is because R thinks that in the command
as.character(c(first,second)) that first and second are variables that
exist within the working environment. Since they don't (I assume), R
doesn't know what to do with the command. Using the quotes indicates to R
that you're s
Has anybody had issues running MCMC (either BUGS or JAGS) on data sets of
this magnitude (ie 30k x 20-30). I've been trying to run a hierarchical
random effects model on expression data but R completely stalls out on jobs
run on 32bit R on our server (doesn't respond...then eventually crashes out
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