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

[Viechtbauer Wolfgang (SP)]

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
>César
>
>> 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