Extract standard errors from tsls output - r

Question

I want to extract the standard errors from the output of the tsls command in the sem R package.
Using some generic code as an example:
fit = tsls(Y ~ X, ~Z)
summary(fit)
The summary function outputs several things besides the regression estimates (e.g., model formulas, summary of the residuals).
I want an equivalent to fit$coef that outputs standard errors. But that doesn't seem to be an option. All the code used to do the equivalent for glm and lm output doesn't seem to work here. Is there any way to hack the output?

Answer 1

Sometimes it takes a little bit of digging to find where these values are coming from. The best place to look, if you don't get any clues from str(fit), would be to look at what summary.tsls is doing.
With some help from getAnywhere("summary.tsls"), we see:
getAnywhere("summary.tsls")
# A single object matching ‘summary.tsls’ was found
# It was found in the following places
# registered S3 method for summary from namespace sem
# namespace:sem
# with value
#
# function (object, digits = getOption("digits"), ...)
# {
# ###
# ### \\\SNIP///
# ###
# std.errors <- sqrt(diag(object$V))
# ###
# ### \\\SNIP///
# ###
# }
# <bytecode: 0x503c530>
# <environment: namespace:sem>
So, to get the value you are looking for, you need to calculate it yourself with:
sqrt(diag(fit$V))
A reproducible example:
library(sem)
fit <- tsls(Q ~ P + D, ~ D + F + A, data=Kmenta)
summary(fit)
#
# 2SLS Estimates
#
# Model Formula: Q ~ P + D
#
# Instruments: ~D + F + A
#
# Residuals:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -3.4300 -1.2430 -0.1895 0.0000 1.5760 2.4920
#
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 94.63330387 7.92083831 11.94738 1.0762e-09 ***
# P -0.24355654 0.09648429 -2.52431 0.021832 *
# D 0.31399179 0.04694366 6.68869 3.8109e-06 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 1.9663207 on 17 degrees of freedom
sqrt(diag(fit$V))
# (Intercept) P D
# 7.92083831 0.09648429 0.04694366