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survey (version 3.25)

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.

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Version

Install

install.packages('survey')

Monthly Downloads

62,531

Version

3.25

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

August 5th, 2011

Functions in survey (3.25)

Student performance in California schools

Standardised predictions (predictive margins) for regression models.

Post-stratify a survey

Raking of replicate weight design

Calibration (GREG) estimators

Distribution of quadratic forms

Wald test for a term in a regression model

Take a stratified sample

Compute replicate weights

Computations for survey variances

Two-stage sample from MU284

Calibration metrics

Compute variance from replicates

Summary statistics for sample surveys

Hadamard matrices

Wrappers for specifying PPS designs

Lay out tables of survey statistics

Survey statistics on subsets

Contingency tables for survey data

Quantiles for sample surveys

Factor analysis in complex surveys (experimental).

dimnames.DBIsvydesign

Dimensions of survey designs

Confidence intervals for proportions

subset.survey.design

Subset of survey

Ratio estimation

Design-based rank tests

Extract standard errors

Cohen's kappa for agreement

Package sample and population size data

Specify survey design with replicate weights

Survey-weighted generalised linear models.

compressWeights

Compress replicate weight matrix

Proportional odds and related models

Conditioning plots of survey data

Maximum pseudolikelihood estimation in complex surveys

Compute survey bootstrap weights

Linear and nonlinearconstrasts of survey statistics

Experimental: Construct non-response weights

Survey-weighted Cox models.

Compare survival distributions

Convert a survey design to use replicate weights

Estimated weights for missing data

Histograms and boxplots

Design-based t-test

US 2004 presidential election data at state or county level

Two-phase designs

Survey sample analysis.

Loglinear models

Variance estimation for multistage surveys

Confidence intervals for regression parameters

Cumulative Distribution Function

Trim sampling weights

barplot.svystat

Barplots and Dotplots

Sampling-weighted principal component analysis

Options for the survey package

Sandwich variance estimator for glms

Plots for survey data

open.DBIsvydesign

Open and close DBI connections

update.survey.design

Add variables to a survey design

Household crowding

Scatterplot smoothing and density estimation

Compute variances by replicate weighting

with.svyimputationList

Analyse multiple imputations

Paley-type Hadamard matrices

Update to the new survey design format

Estimate survival function.

weights.survey.design

Survey design weights

Small survey example

Survival in cardiac arrest

Sample of obstetric hospitals