Hi there,
I want to do several bivariate linear regressions and, than, do a
multivariate linear regression including only variables significantly
associated *(p < 0.15)* with y in bivariate analysis, without having to
look manually to those p values.
So, here what I got for the moment.
First, I use this data set:
tolerance <- read.csv("
http://www.ats.ucla.edu/stat/r/examples/alda/data/tolerance1.txt";).
Second, I used this command, allowing me to extract p-values later:
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class
'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)}
Third, I did my bivariate linear regressions:
fit = lm(exposure~tol11, data = tolerance)
fit_2 = lm(exposure~tol12, data= tolerance)
fit_3 = lm(exposure~tol13, data= tolerance)
fit_4 = lm(exposure~tol14, data= tolerance)
fit_5 = lm(exposure~tol15, data= tolerance)
Fourth, I extracted p-values:
lmp(fit)
lmp(fit_2)
lmp(fit_3)
lmp(fit_4)
lmp(fit_5)
Firth, I confirmed that p-values were OK (just to be sure, it's the first
time I used the above procedure) :
summary (fit)
summary (fit_2)
summary (fit_3)
summary (fit_4)
summary (fit_5)
And now, Im, I dont know what to do.
The multivariate linear regression (if all variables were included) is:
fit_multi = lm (exposure ~ tol11 + tol12 + tol13 + tol14 + tol15, data=
tolerance)
I would like to be able to do something like:
fit_multi = lm (exposure ~ tol11 [include only if lmp( fit) < 0.15] +
tol12 [include only if lmp(fit_2) < 0.15] + tol13 [include only if
lmp(fit_3) < 0.15] + tol14 [include only if lmp(fit_4) < 0.15] +
tol15 [include
only if lmp(fit_4) < 0.15], data= tolerance)
Any idea?
Thank you!
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