View source: R/estimatr_lh_robust.R
lh_robust | R Documentation |
This function fits a linear model with robust standard errors and performs linear hypothesis test.
lh_robust(..., data, linear_hypothesis)
... |
Other arguments to be passed to |
data |
A |
linear_hypothesis |
A character string or a matrix specifying combination, to be passed to the hypothesis.matrix argument of car::linearHypothesis
See |
This function is a wrapper for lm_robust
and for
linearHypothesis
. It first runs lm_robust
and
next passes "lm_robust"
object as an argument to linearHypothesis
.
An object of class "lh_robust"
containing the two following components:
lm_robust |
an object as returned by |
lh |
A data frame with most of its columns pulled from |
The only analyis directly performed by lh_robust
is a t-test
for the null hypothesis of no effects of the linear combination of coefficients as specified by the user.
All other output components are either extracted from linearHypothesis
or lm_robust
.
The original output returned by linearHypothesis
is added as an attribute under the "linear_hypothesis"
attribute.
library(fabricatr) dat <- fabricate( N = 40, y = rpois(N, lambda = 4), x = rnorm(N), z = rbinom(N, 1, prob = 0.4), clusterID = sample(1:4, 40, replace = TRUE) ) # Default variance estimator is HC2 robust standard errors lhro <- lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z + 2x = 0") # The linear hypothesis argument can be specified equivalently as: lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z = 2x") lh_robust(y ~ x + z, data = dat, linear_hypothesis = "2*x +1*z") lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z + 2x = 0") # Also recovers other sorts of standard erorrs just as specified in \code{\link{lm_robust}} lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z + 2x = 0", se_type = "classical") lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z + 2x = 0", se_type = "HC1") # Can tidy() main output and subcomponents in to a data.frame lhro <- lh_robust(y ~ x + z, data = dat, linear_hypothesis = "z + 2x = 0") tidy(lhro ) tidy(lhro$lm_robust) tidy(lhro$lh) # Can use summary() to get more statistics on the main output and subcomponents. summary(lhro) summary(lhro$lm_robust) summary(lhro$lh)
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