bootstrap_pvals | R Documentation |
Calculate Bootstrap p-values for fixed effects
Perform bootstrap tests based on the t-statistic for each fixed effect in order to calculate approximate p-values.
bootstrap_pvals( model, type, B, resample = NULL, reb_type = NULL, hccme = NULL, aux.dist = NULL ) ## S3 method for class 'merMod' bootstrap_pvals( model, type, B, resample = NULL, reb_type = NULL, hccme = NULL, aux.dist = NULL ) ## S3 method for class 'lme' bootstrap_pvals( model, type, B, resample = NULL, reb_type = NULL, hccme = NULL, aux.dist = NULL )
model |
The model object you wish to bootstrap. |
type |
A character string indicating the type of bootstrap that is being
requested. Possible values are |
B |
The number of bootstrap resamples. |
resample |
A logical vector specifying whether each level of the model should be resampled in the cases bootstrap. The levels should be specified from the highest level (largest cluster) of the hierarchy to the lowest (observation-level); for example for students within a school, specify the school level first, then the student level. |
reb_type |
Specification of what random effect block bootstrap version to
implement. Possible values are |
hccme |
either |
aux.dist |
one of |
The bootstrap test compares the fitted model specified by the user to reduced models that eliminate a single fixed effect, the same comparison summarized by the table of coefficients in the summary. The bootstrap p-value is then calculated as $(n_extreme + 1) / (B + 1)$.
A tibble giving the table of coefficients from the model summary with a column appended containing bootstrap p-values.
Davison, A., & Hinkley, D. (1997). Tests. In Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics, pp. 136-190). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511802843.005
## Not run: # This takes a while to run bootstrap_pvals.merMod(jsp_mod, type = "wild", B = 1000, hccme = "hc2", aux.dist = "mammen") ## End(Not run)
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