[R-sig-ME] MCMCglmm multivariate multilevel meta-analysis

[Jarrod Hadfield]

Hi Nico,

You should just be able to pass c(dataset$var1, dataset$var2) to mev.  
If you have sampling covariances between the measures then its a bit  
more complicated, but I think you don't?

There's also an in-built function for creating the dummy variables:  
at.level(trait,1):predictor1 fits an effect of predictor1 for trait 1,  
if you want to fit separate predictor1 effects for both traits you can  
just use trait:predictor1.

Cheers,

Jarrod



Quoting Nico N <nic43614 at gmail.com> on Wed, 10 Apr 2013 21:47:09 +1000:

> Hey everyone,
>
> Presently, I am trying to conduct a multivariate multilevel
> meta-analysis using the MCMCglmm package.
>
> However, I encountered the following problem and I was hoping to
> obtain some advice.
>
> The following example would describe my situation. The dependent
> variable is some performance measure, which is captured in two
> different ways (2 different measures for the same construct).
>
> Hence, I have two measures for each student, similar to the
> multi-response model examples in the MCMCglmm tutorial notes.
>
> Moreover, I have a big data set with students nested in schools, and
> schools nested in countries (3 additional levels).
>
> In sum, I require four levels (sorry, social science jargon I think):
> level 1= measures, level2=students, level3=schools, and
> level4=country.
>
> I only would like to have random-intercepts for each level above the
> measure-level.
>
>
> Now I think there may be two ways. Originally, the data is in the
> "wide" format. That is:
>
> Columns headers are the following:  student, school, country, measure
> 1, measure 2, var1 ,  var2, predictor1, predictor 2,…   (for
> simplicity, let's assume two predictors only - both only fixed
> effects).
>
>
> Var2 and var2 are the known variances for the two measures of
> interest. The variances need to be provided to the first level of the
> model, while the estimated covariance matrix is fixed to 1. As far as
> I understood, the "mev" command does this.
>
> Now, given the "wide" format, I noticed that MCMCglmm seems to have a
> convenient way to estimate measure-specific intercepts through the
> "trait"-command (i.e., without changing the data).
>
> If I am not wrong, the code would be as follows.
>
>
> prior = list(R = list( V =diag(2), n=2,fix = 1), G = list(
>
>                        G1 = list ( V = diag(2), n = 2),
>
>                        G2 = list ( V = diag(2), n = 2),
>
>                        G3 = list ( V = diag(2), n = 2) ))
>
> model1 <- MCMCglmm( cbind(measure1, measure2) ~ -1 + trait +
>
>                                predictor1 +
>
>                                predictor2 ,
>
>                        random = ~us(trait):student +
>
>                                us(trait):school +
>
>                                idh(trait):country + ,
>
>                        rcov = ~idh(trait):units,
>
>                        prior=prior,
>
>                        data = dataset,
>
>                        mev= ?????,
>
>                        family = c("gaussian","gaussian"))
>
>
> However, I was wondering how I can provide the variances to this model
> with two measures? In my case, I would need a 2xn matrix and I am not
> sure whether the "mev" command can handle this. Has anyone ever tried
> something like this?
>
> I guess there would be an alternative, in case "mev" must be a 1xn
> vector: I guess I could create the stacked (or long) data myself
> before running the model. Then, as Hox (2010) recommends, one can
> create dummy variables indicating to which measure the dependent
> variable column (DV) refers to.
>
> In my case: MD1 for measure 1 and MD2 for measure2. Then, each
> predictor would have to be multiplied with these two dummies, such
> that it looks like the following table (pred=predictor):
>
> DV  	measure	var	MD1	MD2	student  school	country  pred1
> pred2	pred1XMD1 pred1XMD2    pred2XMD1   pred2XMD2
> 3.4	1		0.2	1	0	1		1	1		2	1.3	2		   0		             1.3	    0
> 5.6	2		0.4	0	1	1		1	1		3	1.4	0		   3		             0	           1.4
> 6.7	1		0.5	1	0	2		1	1		4	1.4	4		   0		             1.4	    0
> 4.5	2		0.3	0	1	2		1	1		4	1	0		   4		              0	            1
> 5.5	1		0.5	1	0	3		1	1		2	1.9	2		   0		             1.9	    0
> 4.5	2		0.7	0	1	3		1	1		4	1.6	0		   4		             0	          1.6
> 6.7	1		0.6	1	0	4		2	1		2	1.7	2		   0		             1.7	    0
> 4.5	2		0.6	0	1	4		2	1		4	1.3	0		   4		              0	1.3
>
>
> Then, the following code should work (with same prior):
>
>
> model2 <- MCMCglmm( DV ~ -1 + MD1 + MD2 +
>
>                                pred1xMD1 +
>
>                                pred1xMD2 +
>
>                                pred2xMD1 +
>
>                                pred2xMD2 +,
>
>                        random = ~us(MD1+MD2):student +
>
>                                us(MD1+MD2):school  +
>
>                                idh(MD1+MD2):country + ,
>
>                        rcov = ~idh(MD1+MD2):units,
>
>                        prior=prior,
>
>                        data = dataset,
>
>                        mev= dataset$var,
>
>                        family = "gaussian"))
>
>
>
> Now I wanted to check whether this seems sensible within the Bayesian
> framework? ? I generally would prefer a solution for the
> "model1"-approach above as I have many more predictors than in this
> example.
>
> I would highly appreciate any comment! I can imagine that other people
> may be interested in conducing a similar hierarchical multiresponse
> meta-analysis.
>
>
> Best regards
>
> Nico
>
> PhD-candidate
>
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>
>


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