betta_lincom {breakaway}

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)

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.

Author(s)

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