Learn R Programming

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

survey (version 3.33-2)

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

73,193

Version

3.33-2

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

March 13th, 2018

Functions in survey (3.33-2)

Design-based t-test

with.svyimputationList

Analyse multiple imputations

Direct standardization within domains

Model comparison for glms.

Wrappers for specifying PPS designs

Student performance in California schools

Extract standard errors

Calibration (GREG) estimators

compressWeights

Compress replicate weight matrix

Cholesterol data from a US survey

Lay out tables of survey statistics

Hadamard matrices

Experimental: Construct non-response weights

open.DBIsvydesign

Open and close DBI connections

Compute variance from replicates

Specify survey design with replicate weights

Paley-type Hadamard matrices

Survey-weighted Cox models.

Survey sample analysis.

Estimate survival function.

Variance estimation for multistage surveys

Take a stratified sample

update.survey.design

Add variables to a survey design

Package sample and population size data

Loglinear models

Convert a survey design to use replicate weights

weights.survey.design

Survey design weights

Sampling-weighted principal component analysis

subset.survey.design

Subset of survey

Survey statistics on subsets

Cumulative Distribution Function

Compare survival distributions

Maximum pseudolikelihood estimation in complex surveys

Confidence intervals for regression parameters

Fit accelerated failure models to survey data

One variable from the Youth Risk Behaviors Survey, 2015.

Predictive marginal means

Household crowding

Trim sampling weights

Standardised predictions (predictive margins) for regression models.

Two-stage sample from MU284

Pseudo-Rsquareds

Raking of replicate weight design

Contingency tables for survey data

Confidence intervals for proportions

Proportional odds and related models

Plots for survey data

Scatterplot smoothing and density estimation

Compute variances by replicate weighting

Compute survey bootstrap weights

Compute replicate weights

Update to the new survey design format

barplot.svystat

Barplots and Dotplots

Estimated weights for missing data

dimnames.DBIsvydesign

Dimensions of survey designs

US 2004 presidential election data at state or county level

Small survey example

Distribution of quadratic forms

Sample of obstetric hospitals

Post-stratify a survey

Calibration metrics

Wald test for a term in a regression model

Survival in cardiac arrest

Sandwich variance estimator for glms

Options for the survey package

Computations for survey variances

Summary statistics for sample surveys

Factor analysis in complex surveys (experimental).

Design-based rank tests

Survey-weighted generalised linear models.

Quantiles for sample surveys

Linear and nonlinearconstrasts of survey statistics

Conditioning plots of survey data

Histograms and boxplots

Cohen's kappa for agreement

Ratio estimation

Two-phase designs