coef_test | R Documentation |
coef_test
reports one- or two-sided t-tests for each coefficient
estimate in a fitted linear regression model, using a sandwich estimator for
the standard errors and (optionally) a small sample correction for the
p-value. Available small-sample corrections include Satterthwaite
approximation or a saddlepoint approximation. Coefficients can be tested
against non-zero null values by specifying null_constants
.
coef_test(
obj,
vcov,
test = "Satterthwaite",
alternative = c("two-sided", "greater", "less"),
coefs = "All",
null_constants = 0,
p_values = TRUE,
...
)
obj |
Fitted model for which to calculate t-tests. |
vcov |
Variance covariance matrix estimated using |
test |
Character vector specifying which small-sample corrections to
calculate. |
alternative |
Character string specifying the alternative hypothesis, with options "two-sided" (the default), "greater" or "less". |
coefs |
Character, integer, or logical vector specifying which
coefficients should be tested. The default value |
null_constants |
vector of null values for each coefficient to test.
Must have length equal to the number of coefficients specified in
|
p_values |
Logical indicating whether to report p-values. The default
value is |
... |
Further arguments passed to |
A data frame containing estimated regression coefficients, standard errors, specified values of null hypotheses, and test results. For the Satterthwaite approximation, degrees of freedom and a p-value are reported. For the saddlepoint approximation, the saddlepoint and a p-value are reported.
vcovCR
data("ChickWeight", package = "datasets")
lm_fit <- lm(weight ~ Diet * Time, data = ChickWeight)
diet_index <- grepl("Diet.:Time", names(coef(lm_fit)))
coef_test(lm_fit, vcov = "CR2", cluster = ChickWeight$Chick, coefs = diet_index)
V_CR2 <- vcovCR(lm_fit, cluster = ChickWeight$Chick, type = "CR2")
coef_test(lm_fit, vcov = V_CR2, coefs = diet_index)
# non-inferiority test whether time-by-diet interaction effects are 2 or greater
coef_test(lm_fit, vcov = V_CR2, coefs = diet_index, null_constants = 2, alternative = "greater")
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