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survey (version 3.29-4)

analysis of complex survey samples

Description

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

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Version

Install

install.packages('survey')

Monthly Downloads

77,056

Version

3.29-4

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

March 11th, 2013

Functions in survey (3.29-4)

Two-stage sample from MU284

Model comparison for glms.

Wald test for a term in a regression model

Extract standard errors

barplot.svystat

Barplots and Dotplots

Compute replicate weights

Convert a survey design to use replicate weights

Conditioning plots of survey data

Student performance in California schools

Estimated weights for missing data

Paley-type Hadamard matrices

Compute variance from replicates

Package sample and population size data

Sampling-weighted principal component analysis

Hadamard matrices

Confidence intervals for regression parameters

Loglinear models

compressWeights

Compress replicate weight matrix

Lay out tables of survey statistics

Survey-weighted Cox models.

with.svyimputationList

Analyse multiple imputations

Maximum pseudolikelihood estimation in complex surveys

US 2004 presidential election data at state or county level

Plots for survey data

Two-phase designs

Update to the new survey design format

Scatterplot smoothing and density estimation

subset.survey.design

Subset of survey

Summary statistics for sample surveys

Proportional odds and related models

Household crowding

Compute variances by replicate weighting

Compute survey bootstrap weights

Design-based t-test

Direct standardization within domains

Raking of replicate weight design

Variance estimation for multistage surveys

dimnames.DBIsvydesign

Dimensions of survey designs

Confidence intervals for proportions

Ratio estimation

Options for the survey package

Histograms and boxplots

Cholesterol data from a US survey

Experimental: Construct non-response weights

Standardised predictions (predictive margins) for regression models.

Computations for survey variances

update.survey.design

Add variables to a survey design

Cumulative Distribution Function

Wrappers for specifying PPS designs

Calibration metrics

Contingency tables for survey data

Survey-weighted generalised linear models.

Take a stratified sample

Post-stratify a survey

Calibration (GREG) estimators

Estimate survival function.

Linear and nonlinearconstrasts of survey statistics

Survey sample analysis.

weights.survey.design

Survey design weights

Quantiles for sample surveys

Specify survey design with replicate weights

Factor analysis in complex surveys (experimental).

Trim sampling weights

Compare survival distributions

Design-based rank tests

Distribution of quadratic forms

Sandwich variance estimator for glms

open.DBIsvydesign

Open and close DBI connections

Cohen's kappa for agreement

Survey statistics on subsets

Sample of obstetric hospitals

Survival in cardiac arrest

Small survey example