breakaway (version 4.8.4)

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

Description

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.

Usage

betta_lincom(fitted_betta, linear_com, signif_cutoff = 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.

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.

Author

David Clausen

References

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

See Also

betta;

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,
# where beta_0 is the intercept, and beta_1 is the (true value of the) coefficient on Cont_var