[R-meta] Partial residual plots and meta-analysis
Michael Dewey
lists at dewey.myzen.co.uk
Sat Jul 15 13:53:59 CEST 2017
Dear Cesar
You could try data.frame(unlist(MAPpred)) although I am not absolutely
sure it gives you what ggplot2 expects (I am not a ggplot2 user)
Michael
On 15/07/2017 08:46, Cesar Terrer Moreno wrote:
> Hi Wolfgang,
>
> Many thanks, I like this approach too.
>
> One minor question: when I try to plot the result in ggplot, it complains about the class of the output from pred:
>
> MAPpred <- predict(ECM, newmods=cbind(645:1750, mean(ecmdat$MAT), 300, mean(ecmdat$MAT) * 300), addx = T)
>
> ggplot(MAPpred, aes(X[,"MAP"], make_pct(pred))) + …
>
> Error: ggplot2 doesn't know how to deal with data of class list.rma
>
> I tried to do “as.data.frame”, but it doesn’t work. Any suggestion?
> Thanks
> César
>
>> On 14 Jul 2017, at 17:42, Viechtbauer Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
>>
>> Hi César,
>>
>> To show how the expected value of 'es' changes as a function of a moderator, just use the predict() function, computing predicted values for varying values of the moderator. An example for the simplest case (with a single moderator) is shown here:
>>
>> http://www.metafor-project.org/doku.php/plots:meta_analytic_scatterplot
>>
>> In models with multiple moderators, you can do the same thing. "Taking into account the effect of the rest of the moderators" simply means that you hold the other moderators constant. A common practice is to hold continuous moderators constant at their mean (although one can choose any other sensible value). Using the same example but with two moderators:
>>
>> library(metafor)
>> dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
>> res <- rma(yi, vi, mods = ~ ablat + year, data=dat)
>> predict(res, newmods=cbind(13:55, mean(dat$year))) # holding year constant at the mean
>> predict(res, newmods=cbind(mean(dat$ablat), 1948:1980)) # holding ablat constant at the mean
>>
>> For models with interactions, you do the same thing. For example:
>>
>> res <- rma(yi, vi, mods = ~ ablat + year + ablat:year, data=dat)
>> predict(res, newmods=cbind(13:55, mean(dat$year), 13:55 * mean(dat$year)))
>> predict(res, newmods=cbind(mean(dat$ablat), 1948:1980, mean(dat$ablat) * 1948:1980))
>>
>> Since the slope of one moderator changes as a function of the value of the other moderator when the two moderators interact, one may also want to obtain predicted values when holding the other moderator constant at a couple different values. For example:
>>
>> predict(res, newmods=cbind(13:55, 1948, 13:55 * 1948)) # holding year constant at lower bound
>> predict(res, newmods=cbind(13:55, 1966, 13:55 * 1966)) # holding year constant at the mean (rounded)
>> predict(res, newmods=cbind(13:55, 1980, 13:55 * 1980)) # holding year constant at upper bound
>>
>> One can then plot the three lines to show how the expected value of 'es' changes as a function of ablat when year is held constant at its lower bound, mean, and upper bound.
>>
>> Best,
>> Wolfgang
>>
>> --
>> Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
>> Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
>> Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
>>
>>> -----Original Message-----
>>> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>>> project.org] On Behalf Of Cesar Terrer Moreno
>>> Sent: Friday, July 14, 2017 13:52
>>> To: r-sig-meta-analysis at r-project.org
>>> Subject: [R-meta] Partial residual plots and meta-analysis
>>>
>>> Dear All,
>>>
>>> Using our beloved metafor and model selection, I got to the conclusion
>>> that my best model was of the form:
>>>
>>> mymodel <- rma(es, var, data=dat , mods= ~ 1 + A + B*C)
>>>
>>> I want to show in a figure (to be included in a paper) the relationship
>>> between es and the individual moderators, including the interaction, while
>>> taking into account the effect of the rest of the moderators. Although I
>>> don’t have experience in this matter, it seems that partial residual plots
>>> is what I am looking for. I have tried to use the package visreg for this
>>> purpose, but apparently it cannot handle a rma output:
>>>
>>> visreg(mymodel)
>>> Error in formula.default(fit) : invalid formula
>>>
>>> Do you know how to visualise the individual effects of the moderators in a
>>> meta-regression?
>>> Thanks
>>> César
>
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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