Skip to contents

Test all or selected regression coefficients in a fitted model

Source: R/coef_test.R
coef_test.Rd

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.

Usage

coef_test(
  obj,
  vcov,
  test = "Satterthwaite",
  alternative = c("two-sided", "greater", "less"),
  coefs = "All",
  null_constants = 0,
  p_values = TRUE,
  ...
)

Arguments

obj

Fitted model for which to calculate t-tests.

vcov

Variance covariance matrix estimated using vcovCR or a character string specifying which small-sample adjustment should be used to calculate the variance-covariance.

test

Character vector specifying which small-sample corrections to calculate. "z" returns a z test (i.e., using a standard normal reference distribution). "naive-t" returns a t test with m - 1 degrees of freedom, where m is the number of unique clusters. "naive-tp" returns a t test with m - p degrees of freedom, where p is the number of regression coefficients in obj. "Satterthwaite" returns a Satterthwaite correction. "saddlepoint" returns a saddlepoint correction. Default is "Satterthwaite".

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 "All" will test all estimated coefficients.

null_constants

vector of null values for each coefficient to test. Must have length equal to the number of coefficients specified in coefs. Default is 0, in which case the null values are taken to be zero.

p_values

Logical indicating whether to report p-values. The default value is TRUE.

...

Further arguments passed to vcovCR, which are only needed if vcov is a character string.

Value

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.

See also

vcovCR

Examples


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)
#> Alternative hypothesis: two-sided 
#>       Coef. Estimate   SE Null value t-stat d.f. (Satt) p-val (Satt) Sig.
#>  Diet2:Time     1.77 1.49          0   1.19        18.8       0.2497     
#>  Diet3:Time     4.58 1.35          0   3.39        18.8       0.0031   **
#>  Diet4:Time     2.87 1.01          0   2.85        18.3       0.0105    *

V_CR2 <- vcovCR(lm_fit, cluster = ChickWeight$Chick, type = "CR2")
coef_test(lm_fit, vcov = V_CR2, coefs = diet_index)
#> Alternative hypothesis: two-sided 
#>       Coef. Estimate   SE Null value t-stat d.f. (Satt) p-val (Satt) Sig.
#>  Diet2:Time     1.77 1.49          0   1.19        18.8       0.2497     
#>  Diet3:Time     4.58 1.35          0   3.39        18.8       0.0031   **
#>  Diet4:Time     2.87 1.01          0   2.85        18.3       0.0105    *

# 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")
#> Alternative hypothesis: greater 
#>       Coef. Estimate   SE Null value t-stat d.f. (Satt) p-val (Satt) Sig.
#>  Diet2:Time     1.77 1.49          2 -0.156        18.8       0.5613     
#>  Diet3:Time     4.58 1.35          2  1.911        18.8       0.0357    *
#>  Diet4:Time     2.87 1.01          2  0.866        18.3       0.1990