On Jun 17, 2009, at 10:06 AM, Girish A.R. wrote:
Hi folks,
I'm trying to consolidate the outputs (of anova() and lrm()) from
multiple runs of single-variable logistic regression. Here's how the
output looks:
------------------------------------------------------------
y ~ x1 y ~ x2 y ~ x3 y ~
x4
Chi-Square 0.1342152 1.573538 1.267291 1.518200
d.f. 2 2
2 1
P 0.9350946 0.4553136 0.5306538 0.2178921
R2 0.01003342 0.1272791 0.0954126 0.1184302
-------------------------------------------------------------------
The problem I have is when there are a lot more variables (15+) --- It
would be nice if this output is transposed.
A reproducible code is included below. I tried the transpose function,
but it didn't seem to work. If there is a neater way of getting the
desired output, I'd appreciate that as well.
===========================================
Lines <- "y x1 x2 x3 x4
0 m 1 0 7
1 t 2 1 13
0 f 1 2 18
1 t 1 2 16
1 f 3 0 16
0 t 3 1 16
0 t 1 1 16
0 t 2 1 16
1 t 3 2 14
0 t 1 0 9
0 t 1 0 10
1 m 1 0 4
0 f 2 2 18
1 f 1 1 12
0 t 2 0 13
0 t 1 1 16
1 t 1 2 7
0 f 2 1 18"
my.data <- read.table(textConnection(Lines), header = TRUE)
my.data$x1 <- as.factor(my.data$x1)
my.data$x2 <- as.factor(my.data$x2)
my.data$x3 <- as.factor(my.data$x3)
my.data$y <- as.logical(my.data$y)
sapply(paste("y ~", names(my.data)[2:dim(my.data)[2]]),
function(f){tab <- cbind(as.data.frame(t(anova(lrm(as.formula(f),data
= my.data,x=T,y=T))[1,])),
as.data.frame(t(lrm(as.formula(f),data = my.data,x=T,y=T)$stats[10])))
})
=================================
Thanks,
- Girish
You can try something like this:
library(Design)
my.func <- function(x)
{
mod <- lrm(my.data$y ~ x)
data.frame(t(anova(mod)[1, ]), R2 = mod$stats[10])
}
> t(sapply(my.data[, -1], my.func))
Chi.Square d.f. P R2
x1 0.1342152 2 0.9350946 0.01003342
x2 1.573538 2 0.4553136 0.1272791
x3 1.267291 2 0.5306538 0.0954126
x4 1.518200 1 0.2178921 0.1184302
I am not sure what your end game might be, but would simply express
the appropriate caution if this is a step in any approach to variable
selection for subsequent model development...
HTH,
Marc Schwartz
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