[R-meta] Post-hoc weighted analysis based on number of observations

Cesar Terrer Moreno cesar.terrer at me.com
Thu Jan 25 07:56:47 CET 2018

Dear Wolfgang,

Thanks so much for your reply. You have captured the essence of the question perfectly. 

I have successfully scaled the meta-analysis-derived SE, so I have basically produced a global map of the SE of the effect:

SE <- predict(meta, 
                    newmods = cbind(s.df$precipitation, s.df$temperature, CO2inc, s.df$temperature*CO2inc))$se

However, as you said, some locations, in this case ecosystems (e.g. tropical forests) are poorly represented in the dataset. Therefore, a proper assessment of the uncertainties of the approach should account for the uncertainty associated with the sampling effort (or the lack of) in some regions. Reviewers will check this for sure.

It turns out that ecosystem type, per se, is not a good predictor, thus including it in the meta-regression probably does not make much sense (or maybe yes). I was thus thinking more on a post-hoc solution, not necessarily in a meta-analytic context, so maybe this distribution list is not the right place to ask this question. The idea is to increase SE in pixels dominated by ecosystems that are poorly sampled. The final quantification of uncertainties would thus be an aggregation of the SEs and some sort of multiplier that adds uncertainty in a particular pixel as a function of the representativeness of the type of ecosystem in that pixel.

For example:

group_by(ecosystem_type) %>% summarise(n = n()) %>% mutate (weight = n/sum(n))
SEw= max(SE,na.rm=T) - max(SE,na.rm=T)*weight, 

SEsum = SE + SEw

SEsum would thus be the sum of SE and another level of error driven by the sample size of the type of ecosystem, and constrained to fall within the range of observed SE from the dataset.

But I think this approach is not very elegant. Any other ideas?

> On 24 Jan 2018, at 23:56, Viechtbauer Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
> Dear Cesar,
> Let me try to understand the essence of your question/issue and abstract it a bit from the specifics of your data. So, if I understand things correctly, you have data from various places on Earth. Let's pretend those places are on a 2d surface, so something like this (where * indicates a place where you have data):
> +------------------------+
> |     *                  |
> |  *                     |
> |     *                  |
> |                     *  |
> |                 *  *   |
> |                        |
> +------------------------+
> You have fitted a model that relates an outcome to some predictor variables based on the data for these places. Now you actually have the values of the predictor variables for *all* places on that surface and you have computed the corresponding predicted values. But there are locations for which there were no data to begin with (e.g., upper right and lower left) and hence you want the SEs of the predicted values to reflect this lack of information in those areas and you are wondering how to do that. Does that capture the essence of your question?
> 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: Monday, 22 January, 2018 18:52
>> To: r-sig-meta-analysis at r-project.org
>> Subject: [R-meta] Post-hoc weighted analysis based on number of
>> observations
>> I have a gridded dataset representing the standard error (SE) of an
>> effect. This SE was calculated through a meta-analysis and subsequent
>> predictive model applied on a grid:
>> ECMmeta <- rma(es, var, data=ecm.df ,control=list(stepadj=.5), mods= ~ 1
>> + MAP + MAT*CO2dif, knha=TRUE)
>> options(na.action = "na.pass")
>> ECMpred <- predict(ECMmeta,
>>                   newmods = cbind(s.df$precipitation, s.df$temperature,
>> CO2inc, s.df$temperature*CO2inc))
>> ECMrelSE <- rasterFromXYZ(ECMpred[,c("x", "y", "se")],crs="+proj=longlat
>> +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
>> I would like to add a further level of uncertainty to SE based on the
>> number of measurements (observations) per type of ecosystem in the
>> dataset. The idea is that ecosystems that are poorly represented by
>> experiments in the dataset should have a higher SE than ecosystems with
>> plenty of measurements in the dataset.
>> I thought I could, for example, calculate an ecosystem-based weight as:
>> weight = n/sum(n)
>> That is, number of observations in a particular ecosystem divided by the
>> total of observations.
>> The next step would be to apply a weighting approach to each pixel. First
>> approach I've come up with is to simply multiply SE and the inverse of
>> the weight:
>> SEw=SE*(1/weight)
>> But the values are extremely high.
>> An approach like this would be more like an post-hoc patch. I am sure
>> something like this can be done within the meta-analysis at the
>> beginning. Alternatively, a better post-hoc approach or ideas to
>> investigate further would be welcome. Any recommendation or basic
>> approach commonly used to add further uncertainty to areas with low
>> representativeness?
>> Thanks

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