Let me clarify the output I want to create:
Source X1 (var) X2 (var) X1&X2
(cov)
gen var(X1) var(X2)
cov(x1X2)
block var(X1) var(X2)
cov(x1x2)
error/ res var(x1) var(x2)
cov(x1x2)
I need to do posterior analysis out of this table
Thanks in advance
Maya
On Sun, Apr 17, 2011 at 9:59 PM, Maya Joshi <maya.d.jo...@gmail.com> wrote:
> Dear list
>
> I need your help: Execuse me for my limited R knowledge.
>
> #example data set
> set.seed (134)
> lm=c(1:4)
>
> block = c(rep(lm,6))
>
> gen <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4),rep(5, 4),rep(6, 4))
>
> X1 = c( rnorm (4, 10, 4), rnorm (4, 12, 6), rnorm (4, 10, 7),rnorm (4, 5,
> 2), rnorm (4, 8, 4), rnorm (4,7, 2))
>
> X2 = X1 + rnorm(length(X1), 0,3)
>
> yvar <- c(X1, X2)
>
> X <- c(rep( 1, length(X1)), rep( 2, length(X2))) # dummy x variable
>
> dataf <- data.frame(as.factor(block), as.factor(gen), as.factor(X), yvar )
>
>
>
> My objective to estimate variance-covariance between two variables X1 and
> X2. Means that I need to fit something like unstructure (UN) covariance
> structure.
>
>
>
> Question 1: I got the following error
>
> require("lme4");
>
> fm1Gen <- lmer(yvar ~ X + gen +(1|block), data= dataf) # Question 1:
> should I consider X fixed or random
>
>
>
> Error in model.frame.default(data = dataf, formula = yvar ~ X + gen + :
> variable lengths differ (found for 'gen')
>
>
>
> A tried nlme too.
>
> require(nlme)
>
> fm2Gen <- lme(yvar ~ X + gen, random= ~ 1|block, data= dataf)
>
> Error in model.frame.default(formula = ~yvar + X + gen + block, data =
> list( :
> variable lengths differ (found for 'gen') # similar error
>
>
>
> Question 2: How can get I covariance matrix between X1 and X2 either using
> lme4 or lmer.
>
> X1 X2
>
> X1 Var (X1) Cov(X1,X2)
>
> X2 Cov(X1, X2) Var(X2)
>
>
>
> Should I put gen in the model to do this? Should I specify something in "*
> correlation* = "
>
> Thank you for your time
>
> Maya
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