Learn R Programming

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

survey (version 3.36)

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

3.36

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

April 27th, 2019

Functions in survey (3.36)

Wrappers for specifying PPS designs

Distribution of quadratic forms

Model comparison for glms.

Compute variance from replicates

Post-stratify a survey

barplot.svystat

Barplots and Dotplots

Specify survey design with replicate weights

Lay out tables of survey statistics

Survey statistics on subsets

Cumulative Distribution Function

withPV.survey.design

Analyse plausible values in surveys

Hadamard matrices

Student performance in California schools

Compute variances by replicate weighting

Estimated weights for missing data

Small survey example

Pseudo-Rsquareds

Raking of replicate weight design

Linear and nonlinearconstrasts of survey statistics

Sample of obstetric hospitals

Calibration metrics

Compute survey bootstrap weights

Compute replicate weights

Conditioning plots of survey data

Calibration (GREG) estimators

Loglinear models

compressWeights

Compress replicate weight matrix

Cholesterol data from a US survey

Take a stratified sample

Experimental: Construct non-response weights

Wald test for a term in a regression model

Compare survival distributions

Plots for survey data

Survival in cardiac arrest

Survey-weighted Cox models.

Sampling-weighted principal component analysis

Cronbach's alpha

weights.survey.design

Survey design weights

Survey-weighted generalised linear models.

Histograms and boxplots

Predictive marginal means

with.svyimputationList

Analyse multiple imputations

Quantiles for sample surveys

Package sample and population size data

Convert a survey design to use replicate weights

dimnames.DBIsvydesign

Dimensions of survey designs

US 2004 presidential election data at state or county level

Standardised predictions (predictive margins) for regression models.

Two-stage sample from MU284

subset.survey.design

Subset of survey

Confidence intervals for regression parameters

Household crowding

Options for the survey package

Sandwich variance estimator for glms

Summary statistics for sample surveys

Computations for survey variances

One variable from the Youth Risk Behaviors Survey, 2015.

open.DBIsvydesign

Open and close DBI connections

Survey sample analysis.

Paley-type Hadamard matrices

Factor analysis in complex surveys (experimental).

Contingency tables for survey data

Cohen's kappa for agreement

Estimate survival function.

Confidence intervals for proportions

Maximum pseudolikelihood estimation in complex surveys

Direct standardization within domains

Fit accelerated failure models to survey data

Design-based rank tests

Ratio estimation

Proportional odds and related models

Variance estimation for multistage surveys

Design-based t-test

Trim sampling weights

Scatterplot smoothing and density estimation

Two-phase designs

update.survey.design

Add variables to a survey design

Update to the new survey design format

Extract standard errors