nlme

I'm doing Linear mixed-effects model fit by REML in nlme package. And these are codes that work for me: # Linear mixed-effects model fit by REML (intercept and not slope) x <- lme (DV ~ IV1 + IV2 + IV1*IV2, data=a.frame, random=~1|speaker) summary(x) # Linear mixed-effects model fit by REML (slope and no intercept) x1 <- lme (DV ~ IV1 + IV2 + IV1*IV2, data=a.frame, random=~IV3-1|speaker) summary(x1) # Linear mixed-effects model fit by REML (slope and intercept) x2 <- lme (DV ~ IV1 + IV2 + IV1*IV2, data=a.frame, random=~IV3|speaker) summary(x2) #nested random effect x5 <- lme (DV ~ IV1 + IV2 +

I can use gls() from the nlme package to build mod1 with no random effects. I can then compare mod1 using AIC to mod2 built using lme() which does include a random effect. mod1 = gls(response ~ fixed1 + fixed2, method="REML", data) mod2 = lme(response ~ fixed1 + fixed2, random = ~1 | random1, method="REML",data) AIC(mod1,mod2) Is there something similar to gls() for the lme4 package which would allow me to build mod3 with no random effects and compare it to mod4 built using lmer() which does include a random effect? mod3 = ???(response ~ fixed1 + fixed2, REML=T, data) mod4 = lmer(response ~

I am using LME model defined like: mod4.lme <- lme(pRNFL ~ Init.Age + Status + I(Time^2), random= ~1|Patient/EyeID,data = long1, na.action = na.omit) The output is: > summary(mod4.lme) Linear mixed-effects model fit by REML Data: long1 AIC BIC logLik 2055.295 2089.432 -1018.647 Random effects: Formula: ~1 | Patient (Intercept) StdDev: 7.949465 Formula: ~1 | EyeID %in% Patient (Intercept) Residual StdDev: 12.10405 2.279917 Fixed effects: pRNFL ~ Init.Age + Status + I(Time^2) Value Std.Error DF t-value p-value (Intercept) 97.27827 6.156093 212 15.801950 0.0000 Init.Age 0.02114 0.131122 57 0

How could I extract coefficients (b0 and b1) with their respectively standard errors for each experimental unit (plot )in a linear mixed model such as this one: Better fits for a linear model with this same dataset(df), and for the fitted model (fitL1): how could I get a data frame as this one... plot b0 b0_se b1 b1_se 1 2898.69 53.85 -7.5 4.3 ... ... ... ... ... The first comment is that this is actually a non-trivial theoretical question: there is a rather long thread on r-sig-mixed-models that goes into some of the technical details; you should definitely have a look, even though it gets a

I am trying to run a lme model with these data: tot_nochc=runif(10,1,15) cor_partner=factor(c(1,1,0,1,0,0,0,0,1,0)) age=runif(10,18,75) agecu=age^3 day=factor(c(1,2,2,3,3,NA,NA,4,4,4)) dt=as.data.frame(cbind(tot_nochc,cor_partner,agecu,day)) attach(dt) corpart.lme.1=lme(tot_nochc~cor_partner+agecu+cor_partner *agecu, random = ~cor_partner+agecu+cor_partner *agecu |day, na.exclude(day)) I get this error code: Error in na.fail.default(list(cor_partner = c(1L, 1L, 2L, 1L, 1L, 1L, : missing values in object I am aware there are similar questions in the forum. However, in my case: cor_partner has

I used lmList to fit 480 relationships and I would like the R2 of each of these. Here is an example dataset and model which are pretty close to what it really looks like, except I have 480 eu (experimental units): eu mass day 11 .02 1 11 .03 2 11 .04 3 11 .06 4 12 .01 1 12 .03 2 12 .04 3 12 .05 4 fit<-lmList(mass ~ day | eu, data=df) Printing fit or summary does not give me the information I want. I am ultimately trying to make a new dataframe that will look like: eu intercept slope R2 11 .01 .95 .98 12 .01 .96 .98 I've got the coefficients through coef , now I need the R-squared. Here you go: