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survey (version 2.8-3)

analysis of complex survey samples

Description

Summary statistics, generalised linear models, and general maximum pseudolikelihood estimation for stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification and raking.

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Version

Install

install.packages('survey')

Monthly Downloads

73,193

Version

2.8-3

License

LGPL

Maintainer

Thomas Lumley

Last Published

March 20th, 2024

Functions in survey (2.8-3)

Survey-weighted generalised linear models.

Sandwich variance estimator for glms

Raking of replicate weight design

Post-stratify a survey

Compute replicate weights

Household crowding

Compute variance from replicates

Hadamard matrices

compressWeights

Compress replicate weight matrix

Convert a survey design to use replicate weights

Survey-weighted generalised linear models.

update.survey.design

Add variables to a survey design

Lay out tables of survey statistics

Student performance in California schools

Maximum pseudolikelihood estimation in complex surveys

Summary statistics for sample surveys

Experimental: Construct non-response weights

Survey sample analysis.

Quantiles for sample surveys

subset.survey.design

Subset of survey

Specify survey design with replicate weights

Compute variances by replicate weighting

Ratio estimation

Survey statistics on subsets

Contingency tables for survey data

weights.survey.design

Survey design weights

Plots for survey data

Computations for survey variances

Wald test for a term in a regression model

Extract standard errors

Survival in cardiac arrest

Sample of obstetric hospitals

Small survey example