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survey (version 3.18)

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.

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Version

Install

install.packages('survey')

Monthly Downloads

62,531

Version

3.18

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

September 22nd, 2009

Functions in survey (3.18)

barplot.svystat

Barplots and Dotplots

Options for the survey package

Cumulative Distribution Function

Variance estimation for multistage surveys

update.survey.design

Add variables to a survey design

Specify survey design with replicate weights

Package sample and population size data

Convert a survey design to use replicate weights

Sandwich variance estimator for glms

Two-stage sample from MU284

Design-based t-test

Plots for survey data

open.DBIsvydesign

Open and close DBI connections

Estimate survival function.

Calibration (GREG) estimators

Two-phase designs

dimnames.DBIsvydesign

Dimensions of survey designs

Survey-weighted Cox models.

Extract standard errors

Compute variance from replicates

Proportional odds and related models

Quantiles for sample surveys

Calibration metrics

Survey statistics on subsets

Distribution of quadratic forms

compressWeights

Compress replicate weight matrix

Paley-type Hadamard matrices

Household crowding

Compute variances by replicate weighting

Conditioning plots of survey data

Lay out tables of survey statistics

Linear and nonlinearconstrasts of survey statistics

Experimental: Construct non-response weights

Summary statistics for sample surveys

Compute replicate weights

Standardised predictions (predictive margins) for regression models.

Survey-weighted generalised linear models.

Scatterplot smoothing and density estimation

Maximum pseudolikelihood estimation in complex surveys

Loglinear models

weights.survey.design

Survey design weights

Compute survey bootstrap weights

Student performance in California schools

US 2004 presidential election data at state or county level

Histograms and boxplots

Wald test for a term in a regression model

Post-stratify a survey

Confidence intervals for proportions

Wrappers for specifying PPS designs

Raking of replicate weight design

Survey sample analysis.

Estimated weights for missing data

Confidence intervals for regression parameters

Contingency tables for survey data

Update to the new survey design format

subset.survey.design

Subset of survey

Hadamard matrices

Computations for survey variances

Cohen's kappa for agreement

Ratio estimation

with.svyimputationList

Analyse multiple imputations

Sample of obstetric hospitals

Small survey example

Survival in cardiac arrest