Dr. Bolker,
Thanks for the response. One last question with regard to negative t
values when using log10 transformed data. I am assuming the correct
interpretation of the following output is: if the t value is negative
and you're using log10 data, to get the fixed effect CI, you must add
your own negative sign to 10^(est.+1/96*SE), such that the
backtransformed CI from the output below would be:
([1] "95 % REML Confidence interval"
[1] -0.58261813 0.02578124
becomes
-.295 -1.05
Is this correct,
Thanks again for the help
Gus
[1] "###############NH4 Results Year Two##################"
Data: data.sub
Models:
Mod.NH4.2.2: log10(NH4Nyeartwo) ~ 1 + (1 | pr)
Mod.NH4.2.1: log10(NH4Nyeartwo) ~ 1 + sitett + (1 | pr)
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
Mod.NH4.2.2 3 26.427 29.700 -10.2136
Mod.NH4.2.1 4 25.243 29.607 -8.6216 3.1841 1 0.07436 .
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Linear mixed model fit by REML
Formula: log10(NH4Nyeartwo) ~ 1 + sitett + (1 | pr)
Data: data.sub
AIC BIC logLik deviance REMLdev
30.37 34.73 -11.18 17.24 22.37
Random effects:
Groups Name Variance Std.Dev.
pr (Intercept) 0.010942 0.10460
Residual 0.130473 0.36121
Number of obs: 22, groups: pr, 12
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.7305 0.1086 6.729
sitettToeAdditionsTreatment -0.2784 0.1552 -1.794
[1] "95 % REML Confidence interval"
[1] -0.58261813 0.02578124
Gus Jespersen <jesper <at> u.washington.edu> writes:
Thank you Thierry, I have looked through the glht function in multcomp, and have two further questions:
[snip]
Yet I get the following error message:
Error in parse(text = ex[i]) : <text>:1:20: unexpected symbol
1: sitettMossAddition Treatment
^
Any ideas on what I'm doing wrong here?
It is very likely that the glht function is having trouble
with the spaces in your level names. I would strongly suggest
that you reformulate them as legal R variable names: something like
levels(mydata$myfactor) <- make.names(levels(mydata$myfactor))
should work.
(2) As you can see, I am working with a log10 transformed response variable. I'd like to stay with this for homog. of variance reasons, and for reporting the "Treatment -Control" CI previously mentioned, I'd like to report the backtransformed limits of the CI. At what point in this process should the back-transformation happen? When attempting this calculation without glht, I am uncertain of where in the process to back-transform as well. I had been hoping to simply use 10^ for each fixed effect and its SE, as well as each element of the vcov matrix, but I fear I am overlooking some basic math here.
Yes, you are. You need to back-transform the confidence intervals, not the elements and their standard errors. The basic math you are thinking about is 10^(est+1.96*stderr) !== 10^est+10^(1.96*stderr) If you back-transform first, the RHS is what you will be doing; you want the LHS (this is for the upper CI, the obvious parallel applies for the lower CI)
R. Gus Jespersen PhD Candidate College of Forest Resources University of Washington Box 352100 Seattle, WA 98195-2100 (206) 543-5777 jesper at u.washington.edu