srswrRe {qape}

Bootstrap sample of predicted random effects

Description

The function draws at random a simple random sample with replacement from predicted random effects, where the sample size is equal the number of random effects in the whole population.

Usage

srswrRe(listRanef, reg)

Arguments

listRanef

ranef(model) object where model is an lmer object.

reg

the population matrix of auxiliary variables named in fixed.part and random.part.

Value

tablsrswrRe

a vector of a simple random sample with replacement from predicted random effects, where the sample size is equal the number of random effects in the whole population.

lsrswrRe

a list of length equal the number of grouping variables taken into account in the random part of the model. Each list consists of 4 sublists: $raneftotal - a vector of a simple random sample with replacement from all predicted random effects under the cosidered grouping variable, $ranefname - a name of the grouping variable, $k - the number of random effects under the considered grouping variable, $df - a data frame of predicted random effects under the considered grouping variable, $dfsamp - a data frame of a simple random sample with replacement from predicted random effects under the considered grouping variable.

Author(s)

Alicja Wolny-Dominiak, Tomasz Zadlo

References

1. Carpenter, J.R., Goldstein, H. and Rasbash, J. (2003), A novel bootstrap procedure for assessing the relationship between class size and achievement. Journal of the Royal Statistical Society: Series C (Applied Statistics), 52, 431-443.

2. Chambers, R. and Chandra, H. (2013) A Random Effect Block Bootstrap for Clustered Data, Journal of Computational and Graphical Statistics, 22(2), 452-470.

3. Thai, H.-T., Mentre, F., Holford, N.H., Veyrat-Follet, C. and Comets, E. (2013), A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models. Pharmaceutical Statistics, 12, 129-140.

Examples

library(lme4)
data(invData)
# data from one period are considered: 
invData2018 <- invData[invData$year == 2018,] 
attach(invData2018)
N <- nrow(invData2018) # population size
n <- 100 # sample size

set.seed(12345)
sampled_elements <- sample(N,n)
reg <- invData2018[, -which(names(invData2018) == 'investments')]

detach(invData2018)

invData2018sample <- invData2018[sampled_elements,]
attach(invData2018sample)
model <- lmer(investments ~ newly_registered + (1|NUTS2) + (1|NUTS4type))
srswrRe(ranef(model),reg)$tablsrswrRe
srswrRe(ranef(model),reg)$lsrswrRe

detach(invData2018sample)