[R-meta] Partial dependence plots

[Cesar Terrer Moreno]

Hi Wolfgang,

Many thanks for your quick response. 

How should I modify your code if e.g. C is a factor?

Best wishes,
César

> On 19 Jun 2018, at 17:15, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> 
> Hi César,
> 
> I do not know of an automated way of doing this. I looked at the 'pdp' package, but it might take some effort to make it work together with metafor. However, doing this manually shouldn't be too complicated. Here is an example using a relatively simple model with two predictors:
> 
> library(metafor)
> 
> dat <- get(data(dat.bcg))
> dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
> res <- rma(yi, vi, mods = ~ ablat + year, data=dat)
> res
> 
> xs <- seq(min(dat$ablat), max(dat$ablat), by=1)
> pred <- sapply(xs, function(x) mean(predict(res, newmods = cbind(x, dat$year))$pred))
> plot(xs, pred, type="o")
> 
> ### same as:
> pred <- predict(res, newmods = cbind(xs, mean(dat$year)))$pred
> lines(xs, pred, col="red")
> 
> xs <- seq(min(dat$year), max(dat$year), by=1)
> pred <- sapply(xs, function(x) mean(predict(res, newmods = cbind(dat$ablat, x))$pred))
> plot(xs, pred, type="o")
> 
> ### same as:
> pred <- predict(res, newmods = cbind(mean(dat$ablat), xs))$pred
> lines(xs, pred, col="red")
> 
> In your case:
> 
> M <- rma(yi, vi, dat, mods= ~ A*B + C + D)
> 
> xs <- seq(min(dat$C), max(dat$C), by=1) # or use an appropriate 'by' value
> pred <- sapply(xs, function(x) mean(predict(res, newmods = cbind(dat$A, dat$B, dat$A*dat$B, x, dat$D))$pred))
> plot(xs, pred, type="o")
> 
> xs <- seq(min(dat$D), max(dat$D), by=1) # or use an appropriate 'by' value
> pred <- sapply(xs, function(x) mean(predict(res, newmods = cbind(dat$A, dat$B, dat$A*dat$B, dat$C, x))$pred))
> plot(xs, pred, type="o")
> 
> Best,
> Wolfgang
> 
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Cesar Terrer Moreno
> Sent: Tuesday, 19 June, 2018 14:52
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] Partial dependence plots
> 
> Dear all,
> 
> I have a mixed-effects model of the form:
> 
> M <- rma(yi, vi, dat, mods= ~ A*B + C + D)
> 
> How can I produce partial dependence plots of e.g. C and D (predicted effect sizes of C and D as a function of the value of each predictor variable). The idea is to show that once the interaction A*B is taken into account, C and D explain very little of the overall effect size.
> 
> Can this be coded into a function to make partial dependence plots for all variables?
> 
> Thanks
> César
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