search for: pdidnot

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <-...

....but that the amount of smoothing is also changing per level of the factor?? Or is it only the intercept that is changing? And where can I find some explanation on the magic output below? Thanks Piet summary(b2$lme) Random effects: Formula: ~Xr.1 - 1 | g.1 Structure: pdIdnot Xr.11 Xr.12 Xr.13 Xr.14 Xr.15 Xr.16 Xr.17 Xr.18 StdDev: 1.680679 1.680679 1.680679 1.680679 1.680679 1.680679 1.680679 1.680679 Formula: ~Xr.2 - 1 | g.2 %in% g.1 Structure: pdIdnot Xr.21 Xr.22 Xr.23 Xr.24 Xr.25 Xr.26 Xr.27 Xr.28 StdDev: 1....

...********************** g1 $lme Linear mixed-effects model fit by maximum likelihood Data: data Log-likelihood: -437.696 Fixed: fixed X(Intercept) Xz Xint Xs(xc)Fx1 0.6738466 -2.5688317 0.0137415 -0.1801294 Random effects: Formula: ~Xr - 1 | g Structure: pdIdnot Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8 Residual StdDev: 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 1.693177 Correlation Structure: AR(1) Formula: ~1 | g Parameter est...

...of the assumed covariance structure (up to a constant) of random effects c1 and c2, respectively, such that b1 ~ N(0,sig.1^2*I) and c1 ~ N(0,sig.1^2*K1) , where K1 = Z1*t(Z1), and c1 = Z1*b1. The model form I've been using is just the following: m<-glmmPQL(y~1,random=list(f=pdBlocked(list(pdIdnot(~Z.1-1),pdIdnot(~Z.2-1)))) ,family=binomial(link="logit")) I've been extracting the variance components using VarCorr(), but I've noticed that the reported variances associated with my random effects are not even close to the values I get if I evaluate their variances empiric...

...o not match the length of object [0] All the other output seems to be ok: > summary(test$lme) Linear mixed-effects model fit by maximum likelihood Data: strip.offset(mf) AIC BIC logLik 299.9468 312.9727 -144.9734 Random effects: Formula: ~Xr.1 - 1 | g.1 Structure: pdIdnot Xr.11 Xr.12 Xr.13 Xr.14 Xr.15 StdDev: 0.0001073404 0.0001073404 0.0001073404 0.0001073404 0.0001073404 Xr.16 Xr.17 Xr.18 Residual StdDev: 0.0001073404 0.0001073404 0.0001073404 1.045916 Variance function: Structure: Differ...

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems