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survey (version 3.28-1)

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

62,531

Version

3.28-1

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

May 24th, 2012

Functions in survey (3.28-1)

Student performance in California schools

Distribution of quadratic forms

Experimental: Construct non-response weights

Take a stratified sample

Summary statistics for sample surveys

Estimated weights for missing data

Extract standard errors

Update to the new survey design format

Two-stage sample from MU284

Convert a survey design to use replicate weights

Design-based rank tests

Survey sample analysis.

Compute variance from replicates

Variance estimation for multistage surveys

Package sample and population size data

Confidence intervals for regression parameters

Plots for survey data

Scatterplot smoothing and density estimation

dimnames.DBIsvydesign

Dimensions of survey designs

Paley-type Hadamard matrices

update.survey.design

Add variables to a survey design

US 2004 presidential election data at state or county level

barplot.svystat

Barplots and Dotplots

Compute replicate weights

Factor analysis in complex surveys (experimental).

Compare survival distributions

Compute survey bootstrap weights

Design-based t-test

open.DBIsvydesign

Open and close DBI connections

weights.survey.design

Survey design weights

Sampling-weighted principal component analysis

compressWeights

Compress replicate weight matrix

Loglinear models

Quantiles for sample surveys

Standardised predictions (predictive margins) for regression models.

Maximum pseudolikelihood estimation in complex surveys

Lay out tables of survey statistics

Model comparison for glms.

Calibration metrics

Confidence intervals for proportions

Options for the survey package

Specify survey design with replicate weights

Computations for survey variances

Household crowding

Wrappers for specifying PPS designs

Raking of replicate weight design

Cohen's kappa for agreement

Survey statistics on subsets

Contingency tables for survey data

Survey-weighted Cox models.

Conditioning plots of survey data

Cumulative Distribution Function

Sandwich variance estimator for glms

Calibration (GREG) estimators

Survey-weighted generalised linear models.

Wald test for a term in a regression model

Hadamard matrices

Proportional odds and related models

Histograms and boxplots

Estimate survival function.

Two-phase designs

Ratio estimation

Linear and nonlinearconstrasts of survey statistics

Post-stratify a survey

Compute variances by replicate weighting

Trim sampling weights

subset.survey.design

Subset of survey

with.svyimputationList

Analyse multiple imputations

Small survey example

Survival in cardiac arrest

Sample of obstetric hospitals