Hello, Indrajit,
a "quick and dirty" solution could be to loop over the names of the
relevant variables of the data frame (in your case over the first three
variables) and use "[[ ]]" for indexing:
for (i in names( df)[ 1:3]) {
z <- glm( df[[ i]] ~ df$Independent1)
print( z)
}
An output that is a little bit more informative w. r. t. the actually used
model formula is obtained by
for( i in names( df)[ 1:3]) {
modelform <- formula( paste( i, "~ Independent1"))
z <- eval( substitute( lm( f, data = airquality),
list( f = modelform) ))
print( z)
}
Hth,
Gerrit
On Tue, 15 Mar 2011, Indrajit Chatterjee wrote:
Hi,
I have created a dataframe (lets call is df) that contains the following
variables
"Dependent1" "Dependent2" Dependent3" "Independent1"
I want to do the following regressions:
z<- glm( df$Dependent1 ~ df$Independent1)
z<- glm( df$Dependent2 ~ df$Independent1)
z<- glm( df$Dependent3 ~ df$Independent1)
and so on
I wanted to put this in a for loop e.g.
for (i in 1:3) {
z<- glm( df$ColumnName_i ~ df$Independent1)
print(z)
}
How do I specify the column name in the regression above for the dependent
variable
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and provide commented, minimal, self-contained, reproducible code.
