Confidence intervals and testing for linear combinations of fixed effects estimated via betta() or betta_random()

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)

Arguments

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.

Value

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.

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See also

betta;

Author

David Clausen

Examples

# 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,
#>   Estimates Standard Errors Lower CIs Upper CIs p-values
#> 1  2995.366        511.8334  1992.191  3998.541 2.43e-09
# where beta_0 is the intercept, and beta_1 is the (true value of the) coefficient on Cont_var