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survey (version 4.0)

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.

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Version

Install

install.packages('survey')

Monthly Downloads

77,056

Version

4.0

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

April 3rd, 2020

Functions in survey (4.0)

barplot.svystat

Barplots and Dotplots

Update to the new survey design format

Compute survey bootstrap weights

Compute replicate weights

Wrappers for specifying PPS designs

Package sample and population size data

Convert a survey design to use replicate weights

Household crowding

Confidence intervals for regression parameters

Model comparison for glms.

Estimated weights for missing data

Standardised predictions (predictive margins) for regression models.

Lay out tables of survey statistics

Cholesterol data from a US survey

Extract standard errors

Hadamard matrices

Post-stratify a survey

Two-stage sample from MU284

Pseudo-Rsquareds

Small survey example

Distribution of quadratic forms

Student performance in California schools

Calibration (GREG) estimators

Survival in cardiac arrest

Sample of obstetric hospitals

Wald test for a term in a regression model

Sandwich variance estimator for glms

compressWeights

Compress replicate weight matrix

US 2004 presidential election data at state or county level

Raking of replicate weight design

Computations for survey variances

Survey statistics on subsets

Maximum pseudolikelihood estimation in complex surveys

Survey sample analysis.

Experimental: Construct non-response weights

Compare survival distributions

Calibration metrics

Scatterplot smoothing and density estimation

open.DBIsvydesign

Open and close DBI connections

Predictive marginal means

Factor analysis in complex surveys (experimental).

dimnames.DBIsvydesign

Dimensions of survey designs

Variance estimation for multistage surveys

Paley-type Hadamard matrices

Confidence intervals for proportions

Cumulative Distribution Function

Options for the survey package

Take a stratified sample

subset.survey.design

Subset of survey

Contingency tables for survey data

Quantiles for sample surveys

with.svyimputationList

Analyse multiple imputations

Survey-weighted Cox models.

Estimate survival function.

Compute variance from replicates

Trim sampling weights

Probability-weighted nonlinear least squares

One variable from the Youth Risk Behaviors Survey, 2015.

Summary statistics for sample surveys

Design-based t-test

Cronbach's alpha

Proportional odds and related models

weights.survey.design

Survey design weights

Cohen's kappa for agreement

Loglinear models

withPV.survey.design

Analyse plausible values in surveys

Sampling-weighted principal component analysis

Two-stage least-squares for instrumental variable regression

Linear and nonlinearconstrasts of survey statistics

Survey-weighted generalised linear models.

Plots for survey data

Direct standardization within domains

Specify survey design with replicate weights

update.survey.design

Add variables to a survey design

Two-phase designs

Design-based rank tests

Compute variances by replicate weighting

Ratio estimation

Histograms and boxplots

Conditioning plots of survey data

Fit accelerated failure models to survey data