[R-meta] standard error in predictive nonlinear meta-regression - R-SIG-meta-analysis

Fri, Jan 19, 2018 4:45 AM #

Dear all,

Yesterday I could solve my question re SE in a nonlinear model following Phillip and Wolfgang?s great suggestions using the delta method. 

Now I need to compute SE for a linear meta-regression:

Model Results:

            estimate      se     tval    pval    ci.lb    ci.ub     
intrcpt       0.5754  0.1828   3.1481  0.0031   0.2057   0.9451   **
MAP           0.0002  0.0001   2.6648  0.0111   0.0000   0.0003    *
MAT          -0.0589  0.0179  -3.2842  0.0022  -0.0952  -0.0226   **
CO2dif       -0.0019  0.0007  -2.7384  0.0093  -0.0032  -0.0005   **
MAT:CO2dif    0.0002  0.0001   3.6366  0.0008   0.0001   0.0003  ***


How can I compute SE for a particular pixel with known MAP, MAT and CO2dif?
Thanks

Viechtbauer Wolfgang (STAT)

Sat, Jan 20, 2018 1:33 AM #

Use predict(). In this case:

predict(ECMmeta, newmods = c(2, 3, 4, 3*4))

where MAP=2, MAT=3, CO2dif=4, and hence MAT*CO2dif=3*4.

Best,
Wolfgang

Cesar Terrer Moreno

Sat, Jan 20, 2018 10:08 AM #

Hi Wolfgang,

Thanks for your response. 

Do you know how I could apply this model to predict effect size on a grid (i.e. on a per pixel basis) for the entire world, with known MAP (precipitation) and MAT (temperature) per pixel coming from maps, and a fix COdif=300?

Something like:

ECMrelSE <- overlay(s[["temperature"]], s[["precipitation"]],  # raster maps for MAT and MAP, respectively
                    fun=predict(ECMmeta, newmods = c(MAP, MAT, 300, MAT*300)))

The above doesn?t work.

Thanks
C?sar

Viechtbauer Wolfgang (STAT)

Sat, Jan 20, 2018 11:27 AM #

I do not know the overlay() function, but the 'newmods' argument of predict() can also take multiple rows, so something like this:

MAPvals <- seq(0, 2, by=0.1)
MATvals <- seq(1, 10, by=1)
X <- expand.grid(MAP=MAPvals, MAT=MATvals)
X <- cbind(X, 300, X$MAT*300)
predict(ECMmeta, newmods = X)

You still might need to do some further restructuring.

Best,
Wolfgang

-----Original Message-----
From: Cesar Terrer Moreno [mailto:cesar.terrer at me.com]
Sent: Saturday, 20 January, 2018 19:08
To: Viechtbauer Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] standard error in predictive nonlinear meta-
regression

Hi Wolfgang,

Thanks for your response.

Do you know how I could apply this model to predict effect size on a grid
(i.e. on a per pixel basis) for the entire world, with known MAP
(precipitation) and MAT (temperature) per pixel coming from maps, and a
fix COdif=300?

Something like:

ECMrelSE <- overlay(s[["temperature"]], s[["precipitation"]],  # raster
maps for MAT and MAP, respectively
                   fun=predict(ECMmeta, newmods = c(MAP, MAT, 300,
MAT*300)))

The above doesn?t work.

Thanks
C?sar

On 20 Jan 2018, at 10:33, Viechtbauer Wolfgang (SP)

<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

Use predict(). In this case:

predict(ECMmeta, newmods = c(2, 3, 4, 3*4))

where MAP=2, MAT=3, CO2dif=4, and hence MAT*CO2dif=3*4.

Best,
Wolfgang

-----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, 19 January, 2018 13:45
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] standard error in predictive nonlinear meta-

regression

Dear all,

Yesterday I could solve my question re SE in a nonlinear model

following

Phillip and Wolfgang?s great suggestions using the delta method.

Now I need to compute SE for a linear meta-regression:

summary(ECMmeta <- rma(es, var, data=ecm ,control=list(stepadj=.5),

mods= ~ 1 + MAP + MAT*CO2dif, knha=TRUE))

Model Results:

          estimate      se     tval    pval    ci.lb    ci.ub
intrcpt       0.5754  0.1828   3.1481  0.0031   0.2057   0.9451   **
MAP           0.0002  0.0001   2.6648  0.0111   0.0000   0.0003    *
MAT          -0.0589  0.0179  -3.2842  0.0022  -0.0952  -0.0226   **
CO2dif       -0.0019  0.0007  -2.7384  0.0093  -0.0032  -0.0005   **
MAT:CO2dif    0.0002  0.0001   3.6366  0.0008   0.0001   0.0003  ***

How can I compute SE for a particular pixel with known MAP, MAT and
CO2dif?
Thanks