I needed to run variations of the same regression model: the same explanatory variables with multiple dependent variables. In R, we can do this with a simple `for()`

loop and `assign()`

.

First I specify the dependent variables:

`dv <- c("dv1", "dv2", "dv3")`

Then I create a for() loop to cycle through the different dependent variables:

`for(i in 1:length(dv)){`

Within this loop, I need to create an object to hold the models. I need a separate object for each model, so I create one with `paste()`

. For the first dependent variable, this will be `model1`

; for the second dependent variable `model2`

, and so on.

`model <- paste("model",i, sep="")`

With this object to hold the model in place, I can run the model: the i^{th} dependent variable is used. It is stored in an object called `m`

.

`m <- lm(as.formula(paste(dv[i],"~ ev1 + ev2")), data=mydata)`

Now, I assign the model `m`

to the `model`

object created above: model1 for the first dependent variable, etc. That’s also the end of the `for()`

loop.

`assign(model,m)}`

We can now look at the results:

`summary(model1); summary(model2); summary(model3)`

or, more practical to compare models:

`library(memisc)`

mtable(model1, model2, model3)

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