[R] Subject: Regress multiple independent variables on multiple dependent variables

[arun]

Hi,

This gives an error.  

glm(cbind(O3, temp) ~ ., data=ozone)
Error in x[good, , drop = FALSE] : (subscript) logical subscript too long


 lm(cbind(O3, temp) ~ ., data=ozone) #works



R version 3.0.2 (2013-09-25)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] faraway_1.0.5   ggplot2_0.9.3.1 plotrix_3.5-1   stringr_0.6.2  
[5] reshape2_1.2.2 

loaded via a namespace (and not attached):
 [1] colorspace_1.2-3   dichromat_2.0-0    digest_0.6.3       grid_3.0.2        
 [5] gtable_0.1.2       labeling_0.2       MASS_7.3-29        munsell_0.4.2     
 [9] plyr_1.8           proto_0.3-10       RColorBrewer_1.0-5 scales_0.2.3      
[13] tcltk_3.0.2        tools_3.0.2       



On Monday, November 4, 2013 8:55 AM, Michael Friendly <friendly at yorku.ca> wrote:
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
York University      Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street    Web:  http://www.datavis.ca
Toronto, ONT  M3J 1P3 CANADA


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