betta_lincom | R Documentation |
This function provides point estimates, standard errors, and equal-tailed confidence intervals for linear combinations of fixed effects estimated via betta() or betta_random(). A p-value for a Wald test of the null that the linear combination of effects is equal to zero (against a general alternative) is also returned.
betta_lincom(fitted_betta, linear_com, signif_cutoff = 0.05)
fitted_betta |
A fitted betta object – i.e., the output of either betta() or betta_random() – containing fixed effect estimates of interest. |
linear_com |
The linear combination of fixed effects for which a point estimate, confidence interval, and hypothesis test are to be produced. |
signif_cutoff |
The type-I significance threshold for confidence intervals. Defaults to 0.05. |
table |
A table containing a point estimate, standard error, lower and upper confidence bounds, and a p-value for the linear combination of fixed effects specified in input. The p-value is generated via a two-sided Wald test of the null that the linear combination of fixed effects is equal to zero. |
David Clausen
Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.
betta
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# generate example data df <- data.frame(chats = c(2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180), Cont_var = c(100, 150, 100, 50)) # fit betta() example_fit <- betta(formula = chats ~ Cont_var, ses = ses, data = df) # generate point estimate and 95% CI for mean richness at Cont_var = 125 betta_lincom(fitted_betta = example_fit, linear_com = c(1, 125)) # this tells betta_lincom to estimate value of beta_0 + 125*beta_1, # where beta_0 is the intercept, and beta_1 is the (true value of the) coefficient on Cont_var
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