[R-meta] A question about meta-regression Analysis

[Michael Dewey]

Dear Hamed

If the line is supposed to show a polynomial of degree two then to my 
eyes it does not. It looks linear. If you think there may be a quadratic 
term why do you not fit two models, a linear on and a quadratic one, and 
just compare them with anova()?

Most of the points on your plot lie on one side of the line (above) 
which does not look quite right.

Michael

On 13/07/2018 17:38, Hamed Kianmehr wrote:
> Thank you for your response.
> Please see the attached file regarding the plot.
> 
> On Fri, Jul 13, 2018 at 12:35 PM, Viechtbauer, Wolfgang (SP) 
> <wolfgang.viechtbauer using maastrichtuniversity.nl 
> <mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>> wrote:
> 
>     Dear Hamed,
> 
>     Please post in plain text format. I cannot see any plot. Either
>     attach it as a separate image file or post it somewhere else and
>     provide a link.
> 
>     As for 2 - If you use poly(), try:
> 
>     preds <- predict(res, transf = transf.ilogit, newmods =
>     unname(poly(years, degree=2)))
> 
>     Best,
>     Wolfgang
> 
>     -----Original Message-----
>     From: R-sig-meta-analysis
>     [mailto:r-sig-meta-analysis-bounces using r-project.org
>     <mailto:r-sig-meta-analysis-bounces using r-project.org>] On Behalf Of
>     Hamed Kianmehr
>     Sent: Friday, 13 July, 2018 16:03
>     To: r-sig-meta-analysis using r-project.org
>     <mailto:r-sig-meta-analysis using r-project.org>
>     Subject: [R-meta] A question about meta-regression Analysis
> 
>     I have some questions regarding the meta-regression analysis. I have
>     the database on the proportion (Below bubble plot). My advisor asked
>     me to do the polynomial regression. I have two questions:
> 
>     1- Is it any polynomial regression pattern in this data?
>     2- I wrote below code but I couldn't get the line, Could you help me?
>     ----------------
>     res<-rma(yi=y, vi=s2, data=data, method="DL",mods = ~ poly(year,
>     degree=2))
>     years <- 1989:2016
>     preds <- predict(res, transf = transf.ilogit, newmods
>     =cbind(years,years^2))
>     wi <- 0.5/sqrt(data$s2)
>     wi <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi))
>     plot(data$year, transf.ilogit(y), cex=wi,xlab="Year",ylab="Patient
>     Expectation (%)")
>     lines(years, preds$pred)
> 
> 
> 
> 
> -- 
> 
> Best Regards,
> 
> Hamed Kianmehr, Ph.D. Candidate
> 
> Department of Systems Science and Industrial Engineering
> 
> The State University of New York at Binghamton
> 
> 
> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html