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

arun smartpink111 at yahoo.com
Mon Nov 4 15:18:25 CET 2013


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)

 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=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, ...))


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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