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Hello, everybody, There is the first time for me to post a question, because I really cannot find answer from books, websites or my colleagues. Thank you in advance for your help! I am running likelihood ratio test to find if the simpler model is not significant from more complicated model. However, when I run LRT to compare them, the test did not return F value and p-value for me. What's the

Dear R users, I encounter a problem regarding space control in xyplot. Basically, I want to control spaces between label, tick and axis. I remember there is a function called mgp in general plot. Is there a similar function for xyplot or xYplot? Below is my basic code: myplotkid<-xyplot(expected_offspringnumber~afr|decade,groups=SES,data1,

Dear R users, I have a problem in plotting 3 dimensional graph using mixed models. My model is sur_prop ~ afr_c+I(afr_c^2)+I(afr_c^3)+byear_c+I(byear_c^2)+I(byear_c^3)+I(byear_c^4)+(1|Studyparish)+afr_c:byear_c +afr_c:I(byear_c^2)+afr_c:I(byear_c^3)+afr_c:I(byear_c^4)+I(afr_c^2):byear_c+I(afr_c^2):I(byear_c^2)+I(afr_c^2):I(byear_c^3)+I(afr_c^2):I(byear_c^4) This is a study on the effect of

Dear R users, I encounter a problem regarding number of significant digits on y-axis. Below is my basic code: myplotkid<-xyplot(expected_offspringnumber~afr|decade,groups=SES,data1, auto.key=list(space="right"),layout=c(9,1),xlab="",ylab="Offspring number", aspect="fill",scales=list(x=list(draw=F)),strip=T) >From this code, you can see there are 9

I am a little bit confusing about the following help message on how to fit a GAM model with interaction between factor and smooth terms from http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html: ?Sometimes models of the form: E(y)=b0+f(x)z need to be estimated (where f is a smooth function, as usual.) The appropriate formula is: y~z+s(x,by=z) - the by argument ensures that the smooth

Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg38537.html" which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something,

Dear R users, I want to draw standard error lines for the predicted regression line estimated by logistic regression using lmer. I have two predictors: cafr and its quadratic form I(cafr^2), where cafr is a variable centered around the mean of original variable. Now, the estimated value from the fitted model will be, (model@X)%*%fixef(model) In the logit scale, the mean sum of square from fitted