Learn R Programming

⚠️There's a newer version (4.4-2) of this package.Take me there.

survey (version 3.35)

Analysis of Complex Survey Samples

Description

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear 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 subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.

Copy Link

Version

Install

install.packages('survey')

Monthly Downloads

77,056

Version

3.35

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

December 17th, 2018

Functions in survey (3.35)

Wrappers for specifying PPS designs

Compute replicate weights

Confidence intervals for regression parameters

Household crowding

Hadamard matrices

Distribution of quadratic forms

Post-stratify a survey

Take a stratified sample

subset.survey.design

Subset of survey

Sample of obstetric hospitals

Sandwich variance estimator for glms

Calibration metrics

Computations for survey variances

Compute variance from replicates

Model comparison for glms.

Specify survey design with replicate weights

Student performance in California schools

Linear and nonlinearconstrasts of survey statistics

Estimated weights for missing data

Package sample and population size data

Convert a survey design to use replicate weights

Calibration (GREG) estimators

Conditioning plots of survey data

compressWeights

Compress replicate weight matrix

Maximum pseudolikelihood estimation in complex surveys

Cholesterol data from a US survey

Experimental: Construct non-response weights

Proportional odds and related models

Plots for survey data

Loglinear models

Options for the survey package

Small survey example

Summary statistics for sample surveys

Sampling-weighted principal component analysis

Update to the new survey design format

Compare survival distributions

Trim sampling weights

Design-based t-test

Survey-weighted Cox models.

barplot.svystat

Barplots and Dotplots

Variance estimation for multistage surveys

Cronbach's alpha

Cohen's kappa for agreement

open.DBIsvydesign

Open and close DBI connections

Paley-type Hadamard matrices

Wald test for a term in a regression model

Survey statistics on subsets

dimnames.DBIsvydesign

Dimensions of survey designs

Scatterplot smoothing and density estimation

weights.survey.design

Survey design weights

Cumulative Distribution Function

Survival in cardiac arrest

US 2004 presidential election data at state or county level

Raking of replicate weight design

Two-stage sample from MU284

Pseudo-Rsquareds

with.svyimputationList

Analyse multiple imputations

Standardised predictions (predictive margins) for regression models.

Contingency tables for survey data

Estimate survival function.

Direct standardization within domains

Fit accelerated failure models to survey data

Confidence intervals for proportions

Survey-weighted generalised linear models.

Factor analysis in complex surveys (experimental).

Predictive marginal means

Survey sample analysis.

Two-phase designs

Quantiles for sample surveys

update.survey.design

Add variables to a survey design

Histograms and boxplots

Design-based rank tests

Compute variances by replicate weighting

One variable from the Youth Risk Behaviors Survey, 2015.

Ratio estimation

Compute survey bootstrap weights

Lay out tables of survey statistics

Extract standard errors