F_test {breakaway} | R Documentation |
Conduct F test of null hypothesis LB = 0 using output from betta() or betta_random()
Description
This function performs an F-test of a null hypothesis LB = 0 where B is a vector of p fixed effects returned by betta() or betta_random() and L is an m x p matrix with linearly independent rows.
Usage
F_test(fitted_betta, L, method = "bootstrap", nboot = 1000)
Arguments
fitted_betta |
A fitted betta object – i.e., the output of either betta() or betta_random() – containing fixed effect estimates of interest. |
L |
An m x p matrix defining the null LB = 0. L must have full row rank. |
method |
A character variable indicating which method should be used to estimate the distribution of the test statistic under the null. |
nboot |
Number of bootstrap samples to use if method = "bootstrap". Ignored if method = "asymptotic". |
Value
A list containing
pval |
The p-value |
F_stat |
The calculated F statistic |
boot_F |
A vector of bootstrapped F statistics if bootstrap has been used. Otherwise NULL. |
Author(s)
David Clausen
References
Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.
See Also
Examples
# generate example data
df <- data.frame(chats = c(2000, 3000, 4000, 3000,
2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180,
100, 200, 150, 180),
Cont_var = c(100, 150, 100, 50,
100, 150, 100, 50),
Cont_var_2 = c(50,200,25,125,
50,200,25,125))
# fit betta()
example_fit <- betta(formula = chats ~ Cont_var + Cont_var_2, ses = ses, data = df)
# construct L for hypothesis that B_cont_var = B_cont_var_2 = 0
L <- rbind(c(0,1,0),
c(0,0,1))
F_test_results <- F_test(example_fit, L, nboot = 100)