`R/test_submodel.R`

`test_submodel.Rd`

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.

test_submodel( fitted_betta, submodel_formula, method = "bootstrap", nboot = 1000 )

fitted_betta | A fitted betta object -- i.e., the output of either betta() or betta_random() -- containing fixed effect estimates of interest. |
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submodel_formula | A formula defining which submodel to treat as the null. It is not necessary to include random effects in this formula (they will be ignored if included -- the submodel will be fit with the same random effect structure as the full model regardless of input.) |

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". |

A list containing

The p-value

The calculated F statistic

A vector of bootstrapped F statistics if bootstrap has been used. Otherwise NULL.

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

David Clausen

# 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)