I've examining a number of linear regression models on a large dataset
following the basic ideas presented here
http://www.r-bloggers.com/r-calculating-all-possible-linear-regression-models-for-a-given-set-of-predictors/
Calculating all possible linear regressions . I run into a problem with
ldply when I have a formula that includes no intercept. Here's a simple test
to show what happens.
# data and two linear model regressions
xy <- data.frame(cbind(x=(0:10),y=2*x + 0.2*rnorm(11)))
models <- as.list(c('y ~ x', 'y ~ -1 + x'))
models <- lapply(models, function(x) (as.formula(x)) )
fits <- lapply(models, function(x) lm(x, data=xy))
# regression summaries specified individually (OK)
coef(summary(fits[[1]]))
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.0594176 0.10507394 -0.5654837 5.855640e-01
# x 2.0163534 0.01776074 113.5286997 1.620614e-15
coef(summary(fits[[2]]))
# Estimate Std. Error t value Pr(>|t|)
# x 2.007865 0.00916494 219.0811 9.652427e-20
# Coefficients as a dataframe using ldply (OK)
ldply(fits, function(x) as.data.frame(t(coef(x))))
# (Intercept) x
# 1 -0.0594176 2.016353
# 2 NA 2.007865
# Std Errors as a dataframe using ldply (FAIL)
# variable name 'x' is missed in the second model which has no intercept.
Default variable
# name V1 is added to the output instead.
# The same behaviour is observed for 't value' and 'Pr(>|t|)'
ldply(fits, function(x) as.data.frame(t(coef(summary(x))[,'Std. Error'])))
# (Intercept) x V1
# 1 0.1050739 0.01776074 NA
# 2 NA NA 0.00916494
Is this a bug or (hopefully) user error? Any ideas for a workaround?
Thanks.
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