Learn R Programming

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

survey (version 3.20)

analysis of complex survey samples

Description

Summary statistics, 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. Predictive margins by direct standardization. PPS sampling without replacement. Principal components, factor analysis.

Copy Link

Version

Install

install.packages('survey')

Monthly Downloads

62,531

Version

3.20

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

February 9th, 2010

Functions in survey (3.20)

update.survey.design

Add variables to a survey design

Estimated weights for missing data

Survey sample analysis.

Plots for survey data

Calibration (GREG) estimators

Histograms and boxplots

Post-stratify a survey

barplot.svystat

Barplots and Dotplots

Factor analysis in complex surveys (experimental).

Variance estimation for multistage surveys

US 2004 presidential election data at state or county level

Wald test for a term in a regression model

Compute replicate weights

Convert a survey design to use replicate weights

Standardised predictions (predictive margins) for regression models.

Sampling-weighted principal component analysis

Specify survey design with replicate weights

subset.survey.design

Subset of survey

Design-based t-test

Package sample and population size data

compressWeights

Compress replicate weight matrix

Proportional odds and related models

Scatterplot smoothing and density estimation

Ratio estimation

Sandwich variance estimator for glms

Calibration metrics

Update to the new survey design format

Distribution of quadratic forms

Experimental: Construct non-response weights

Conditioning plots of survey data

Survey-weighted generalised linear models.

Extract standard errors

Paley-type Hadamard matrices

Confidence intervals for regression parameters

Compute variance from replicates

Linear and nonlinearconstrasts of survey statistics

Compute variances by replicate weighting

Two-phase designs

dimnames.DBIsvydesign

Dimensions of survey designs

Raking of replicate weight design

Compute survey bootstrap weights

weights.survey.design

Survey design weights

Estimate survival function.

Summary statistics for sample surveys

open.DBIsvydesign

Open and close DBI connections

Household crowding

Two-stage sample from MU284

Quantiles for sample surveys

Loglinear models

Survey statistics on subsets

Wrappers for specifying PPS designs

Cohen's kappa for agreement

Maximum pseudolikelihood estimation in complex surveys

Computations for survey variances

Contingency tables for survey data

Lay out tables of survey statistics

Options for the survey package

Survey-weighted Cox models.

Student performance in California schools

Hadamard matrices

Confidence intervals for proportions

Cumulative Distribution Function

with.svyimputationList

Analyse multiple imputations

Small survey example

Sample of obstetric hospitals

Survival in cardiac arrest