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...) fit3 <- geese(pseudo ~ as.factor(AGE) + as.factor(WEIGHT) + as.factor(HEIGHT) , data =b, id=id, jack = TRUE, scale.fix=TRUE, mean.link = "logit", corstr="unstructured") #The results using the AJ variance estimate cbind(mean = round(fit3$beta,4), SD = round(sqrt(diag(fit3$vbeta.ajs)),4), Z = round(fit3$beta/sqrt(diag(fit3$vbeta.ajs)),4), PVal = round(2-2*pnorm(abs(fit3$beta/sqrt(diag(fit3$vbeta.ajs)))),4)) [[alternative HTML version deleted]]

 Dear R-helpers, My question is related to how to impose constraints when when sampling from a distribution. For example, suppose I'm sampling a vector from a multivariate normal distribution vbeta <- 100*diag(2) mbeta <- c(1,1) ans <- beta <- c(rmvnorm(1,mbeta,vbeta)) ans will thus be a vector with two elements. My question is how do I place a restriction on one of the elements of the ans vector. For example, suppose my goal is to reject any draw for ans[2] below -1 and above...

...", std.err = "san.se") mm3 <- yags(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", alphainit=0.) # S.E. # gee: sqrt(diag(mm$robust.variance)) #(Intercept) tensionM tensionH # 5.77 7.46 3.73 # geeglm: sqrt(diag(mm2$geese$vbeta)) # [1] 8.17 10.56 5.28 # yags: sqrt(diag(slot(mm3, "robust.parmvar"))) # [1] 5.77 7.46 3.73 Any explanation of this behavior is welcome. Best wishes Torleif Markussen Lunde PhD candidate Centre for International Health/ Bjerkens Centre for Climate Research University of Bergen Norway

...-0.04217 0.00000 Inf <2e-16 *** Mes4 -0.22612 0.00000 Inf <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’1 Estimated Scale Parameters: Estimate Std.err (Intercept) 0.309 0.0818 (B) anova(modelo) Error en solve.default(vbeta[zeroidx, zeroidx, drop = FALSE]) : rutina Lapack dgesv: sistema es exactamente singular It’s so necessary for me get this comparisons too. Please, I appreciate if you can help me. Marylin Bejarano pH student Instituto de Ecología-UNAM [[alternative HTML version deleted]]