Learn R Programming

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

survey (version 3.23-2)

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

3.23-2

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

March 14th, 2011

Functions in survey (3.23-2)

barplot.svystat

Barplots and Dotplots

Compute replicate weights

Standardised predictions (predictive margins) for regression models.

Estimated weights for missing data

Student performance in California schools

Take a stratified sample

Wrappers for specifying PPS designs

Household crowding

Extract standard errors

Post-stratify a survey

Package sample and population size data

Options for the survey package

Compute survey bootstrap weights

Cumulative Distribution Function

US 2004 presidential election data at state or county level

Update to the new survey design format

Convert a survey design to use replicate weights

Wald test for a term in a regression model

Conditioning plots of survey data

compressWeights

Compress replicate weight matrix

Lay out tables of survey statistics

Compute variance from replicates

Calibration (GREG) estimators

open.DBIsvydesign

Open and close DBI connections

Sandwich variance estimator for glms

Two-stage sample from MU284

Distribution of quadratic forms

Raking of replicate weight design

Survey statistics on subsets

subset.survey.design

Subset of survey

Specify survey design with replicate weights

Survey-weighted Cox models.

Linear and nonlinearconstrasts of survey statistics

Histograms and boxplots

Factor analysis in complex surveys (experimental).

Computations for survey variances

dimnames.DBIsvydesign

Dimensions of survey designs

Survey sample analysis.

Paley-type Hadamard matrices

Hadamard matrices

Two-phase designs

Summary statistics for sample surveys

Confidence intervals for proportions

Survey-weighted generalised linear models.

Estimate survival function.

Confidence intervals for regression parameters

Contingency tables for survey data

Plots for survey data

Quantiles for sample surveys

Proportional odds and related models

Variance estimation for multistage surveys

with.svyimputationList

Analyse multiple imputations

weights.survey.design

Survey design weights

Calibration metrics

Ratio estimation

Scatterplot smoothing and density estimation

Loglinear models

Sampling-weighted principal component analysis

Experimental: Construct non-response weights

Maximum pseudolikelihood estimation in complex surveys

Trim sampling weights

Compute variances by replicate weighting

Design-based t-test

Cohen's kappa for agreement

update.survey.design

Add variables to a survey design

Small survey example

Sample of obstetric hospitals

Survival in cardiac arrest