Description Usage Arguments Value
View source: R/stats_helpers.R
Parametric bootstrapping
1 2 3 4 5 6 7 8 9 10 11 12  bootstrap_all(
nboot,
mod,
R2_type,
all_comb,
partition,
data_mod,
allow_neg_r2,
parallel,
expct,
overdisp_name
)

nboot 
Number of parametric bootstrap iterations for confidence interval estimation
(defaults to NULL, i.e. no bootstrapping). Larger numbers of bootstraps give a better
asymptotic CI, but may be timeconsuming. Bootstrapping can be switched on by setting

mod 
merMod object, lme4 fit 
R2_type 
"marginal" or "conditional" R2. With "marginal", the variance explained by fixed effects is calculated. With "conditional", the variance explained by both fixed and random effects is calculated. 
all_comb 
list of predictor combinations 
partition 
TRUE or FALSE 
data_mod 
Data for model 
allow_neg_r2 
Calculating part R2 involves fitting two models, one with and one without the predictor of interest. In cases where the predictor has little association with the response, the resulting part R2 value can become negative. By default we set negative values to 0, but by setting this parameter to TRUE, R2 values can become negative. 
parallel 
If TRUE, computation uses 
expct 
A string specifying the method for estimating the expectation in Poisson models with log link and in Binomial models with logit link (in all other cases the argument is ignored). The only valid terms are 'meanobs', 'latent', 'none' (and 'liability for binary and proportion data). With the default 'meanobs', the expectation is estimated as the mean of the observations in the sample. With 'latent', the expectation is estimated from estimates of the intercept and variances on the link scale. While this is a preferred solution, it is susceptible to the distribution of fixed effect covariates and gives appropriate results typically only when all covariances are centered to zero. With 'liability' estimates follow formulae as presented in Nakagawa & Schielzeth (2010). With 'none', R2 is calculated without distribution specific variance in the denominator. 
overdisp_name 
Name of overdispersion term 
Bootstrap samples for all statistics, plus associated warnings
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