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survey (version 3.9-1)

analysis of complex survey samples

Description

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

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Version

Install

install.packages('survey')

Monthly Downloads

60,281

Version

3.9-1

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

March 20th, 2024

Functions in survey (3.9-1)

Package sample and population size data

Student performance in California schools

Household crowding

G-calibration (GREG) estimators

Sandwich variance estimator for glms

Variance estimation for multistage surveys

Maximum pseudolikelihood estimation in complex surveys

Raking of replicate weight design

Survey-weighted Cox models.

Histograms and boxplots

Cohen's kappa for agreement

Quantiles for sample surveys

Paley-type Hadamard matrices

Two-phase designs

Wald test for a term in a regression model

Scatterplot smoothing and density estimation

Options for the survey package

subset.survey.design

Subset of survey

open.DBIsvydesign

Open and close DBI connections

Extract standard errors

Specify survey design with replicate weights

Distribution of quadratic forms

Compute replicate weights

Survey sample analysis.

update.survey.design

Add variables to a survey design

Contingency tables for survey data

Compute variances by replicate weighting

Hadamard matrices

with.svyimputationList

Analyse multiple imputations

Proportional odds and related models

Estimate survival function.

weights.survey.design

Survey design weights

Update to the new survey design format

Two-stage sample from MU284

Survey statistics on subsets

Post-stratify a survey

Survey-weighted generalised linear models.

Compute survey bootstrap weights

Calibration metrics

Conditioning plots of survey data

Compute variance from replicates

Plots for survey data

Ratio estimation

Estimated weights for missing data

Linear constrasts of survey statistics

Summary statistics for sample surveys

Convert a survey design to use replicate weights

compressWeights

Compress replicate weight matrix

Computations for survey variances

Lay out tables of survey statistics

Cumulative Distribution Function

Experimental: Construct non-response weights

Small survey example

Sample of obstetric hospitals

Survival in cardiac arrest