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Jarkko HautalaParticipant
>fit=mixed(log(A)~B+C+(1+Bid)+(1+Bitem), method=”S”, data=dat2, progress=TRUE, cl=cl)
>summary(fit)
here i would like to get regression estimates and random effect variance on the original values, not on logtransformedanother problem I’m encountering is that when I try to use emmeans emmip or summary functions it fails when the model includes the expand_re=TRUE specification:
>fit=mixed(log(A)~B+C+(1+Bid)+(1+Bitem), method=”S”, data=dat2, progress=TRUE, expand_re=TRUE, cl=cl)
>Grid=ref_grid(fit, at=list(C=c(2, 1, 0, 1, 2)))
>EMM=emmeans(Grid, specs=c(“C”, “B”))
>emmip(EMM, type=”response”, B~C, CIs=TRUE)Error in eval(predvars, data, env) : object ‘re1.B1’ not found
Jarkko HautalaParticipantOne thing more:
Is there a way to backtransform the values returned by summaryfunction for a mixed object?
Jarkko HautalaParticipantMany thanks I was now being able to plot figures:
>R=ref_grid(fit, type=”response”, at = list(a = c(2, 1, 0, 1, 2)))
>A2=emmeans(R, specs=c(“a”,”b”, “c”))
>emmip(A2, b~ac, CIs=T)And also confidence intervals for a result table, e.g:
>R1=ref_grid(fit, type=”response”)
>A3=emmeans(R1, specs=c(“a”))Here a=continuous predictor, b, and c categorical predictors.
Its a pity if no variance explained or equivalent value cannot be computed, that would be highly appreciated by researchers.
Of course we get sense of how strong the predictors are by looking at the figures.Jarkko HautalaParticipantOk, so the emmeans seems to be the way to go as I have a rather complex models to report, and in some models I have factors with three levels (=multiple parameters?).
Jarkko HautalaParticipantThanks, I opened an issue also on sjPlot GIT page.
I believe I need to few other values, at least confidence intervals and variance explained for the fixed and random parts of the model.
What would be best way to get these?I’ve used:
>r.squaredGLMM(fit$full_model)
>confint(fit$full_model)but at least the confint seem to take a lot of computation time at least if continuous predictors are involved.

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