R help - Nov 2013 - Subject: Regress multiple independent variables on multiple dependent variables

I want to estimate the effect of several independent variables on several
dependent
variables. In the example below I wanted to estimate the
effect of three independent variables on ozone and temperature.  My aim is
to create a list of dependent and independent variables and automate the
process rather than writing every dependent and independent variable in
each model as I have done below.

Example data is provided by the following library:
library(faraway)

data(ozone)

mo3 <- glm(O3 ~ humidity + ibh + ibt, data=ozone)

mtemp<- glm(temp ~  humidity + ibh + ibt, data=ozone)


Thanks

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It's not clear exactly what you mean by 'automate' but you can
simplify
a bit by fitting a multivariate linear model to all the responses 
together, and using . on the RHS of the formula to represent all
other variables in the data set as independent variables,

m.all <- glm(cbind(O3, temp) ~ ., data=ozone)

(assuming that only humidity, ibh and ibt remain; otherwise, use
data=subset(ozone, ...))

-Michael

On 11/4/2013 2:55 AM, Kumar Raj wrote:
> I want to estimate the effect of several independent variables on several
> dependent
> variables. In the example below I wanted to estimate the
> effect of three independent variables on ozone and temperature.  My aim is
> to create a list of dependent and independent variables and automate the
> process rather than writing every dependent and independent variable in
> each model as I have done below.
>
> Example data is provided by the following library:
> library(faraway)
>
> data(ozone)
>
> mo3 <- glm(O3 ~ humidity + ibh + ibt, data=ozone)
>
> mtemp<- glm(temp ~  humidity + ibh + ibt, data=ozone)
>
>
> Thanks
>
> 	[[alternative HTML version deleted]]
>

-- 
Michael Friendly     Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
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