### R2MLwiN with mice imputed data (with sampling weights)

Posted:

**Thu Nov 30, 2017 4:41 pm**Hello,

I'm having difficulty 'pooling' multilevel regression results using data that has been multiply imputed using MICE software through the R2MLwiN package (I'm a little stuck!).

Run 10 imputed datasets through the runMLwiN() function, and apply survey weights at level 1.

This is the code I am using

The Level1Weight is in the imputed data (mids object not data.frame), and it seems like Formula = F1 works but the error comes up when I try and apply the weights. If I remove the sampling weights, then I can obtain the pooled estimates. For example, see the following code:

I just can't seem to apply survey weights AND get pooled estimates!

Alternative approach that I'm thinking about

I've deconstructed the imputed data into 10 datasets and am running the following code:

When I apply the weights like this, I am able to run the models without errors BUT i'm unsure of how to pool the results!!

Is it possible to pool the results of several objects stored as "Formal class mlwinfitIGLS"?

Thanks so much. Please let me know if I can clarify anything!

I'm having difficulty 'pooling' multilevel regression results using data that has been multiply imputed using MICE software through the R2MLwiN package (I'm a little stuck!).

**What I am trying to do**Run 10 imputed datasets through the runMLwiN() function, and apply survey weights at level 1.

This is the code I am using

Code: Select all

```
Formula: F1 <- Y ~ 1 + x1 + x2 + (1|level2) + (1|level1)
model <- with(imputed, runMLwin(Formula = F1, estoptions=list(weighting=list(weightvar=c(NA, "Level1weight")), standardised=TRUE))
Error in eval(predvars, data, env) : object 'Level1weight' not found <---- NOT WORKING
If I do this:
model <model <- with(imputed, runMLwin(Formula = F1, estoptions=list(weighting=list(weightvar=c(NA, "Level1weight")), standardised=TRUE), data=imputed_as_dataframe) <-- WORKS but, treats the imputed datasets as one big dataset!
```

Code: Select all

```
model <- with(imputed, runMLwin(Formula = F1))
summary(pool(model)) <--- THIS WORKS
```

Alternative approach that I'm thinking about

I've deconstructed the imputed data into 10 datasets and am running the following code:

Code: Select all

```
model1 <- runMLwin(Formula = F1, estoptions=list(weighting=list(weightvar=c(NA, "Level1weight")), standardised=TRUE), data=imputed1)
model2 <- runMLwin(Formula = F1, estoptions=list(weighting=list(weightvar=c(NA, "Level1weight")), standardised=TRUE), data=imputed2)
**
**
model10 <- runMLwin(Formula = F1, estoptions=list(weighting=list(weightvar=c(NA, "Level1weight")), standardised=TRUE), data=imputed10)
```

When I apply the weights like this, I am able to run the models without errors BUT i'm unsure of how to pool the results!!

Is it possible to pool the results of several objects stored as "Formal class mlwinfitIGLS"?

Thanks so much. Please let me know if I can clarify anything!