schools: PISA Literacy Data

In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description

The data in Program for International Assessment (PISA) on academic proficiency in schools around the world.

Format

A data frame with 334 observations on the following 6 variables.

y

a numeric vector indicating student literacy

socio

a numeric vector

sex

a numeric vector

age

a numeric vector

region

a numeric vector indicating four regions

school

a numeric vector indicating the schools within region

References

OECD (2010). PISA 2009 Results. http://www.oecd.org/

Examples

#
# The example takes a few seconds to run, so in order to 
# save CRAN's testing time it has been commented out. 
# To run, simply uncomment and execute.
#

# data(schools)
# rlme.fit = rlme(y ~ 1 + sex + age + (1 | region) + (1 | region:school), 
#	schools, method="gr")
# summary(rlme.fit)

rlme documentation built on May 2, 2019, 3:47 p.m.