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survey (version 3.35-1)

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

75,924

Version

3.35-1

License

GPL-2 | GPL-3

Maintainer

Thomas Lumley

Last Published

January 29th, 2019

Functions in survey (3.35-1)

Model comparison for glms.

Wrappers for specifying PPS designs

Extract standard errors

Standardised predictions (predictive margins) for regression models.

Package sample and population size data

Convert a survey design to use replicate weights

Confidence intervals for regression parameters

Two-stage sample from MU284

dimnames.DBIsvydesign

Dimensions of survey designs

Wald test for a term in a regression model

Survival in cardiac arrest

Survey-weighted Cox models.

Cronbach's alpha

Household crowding

US 2004 presidential election data at state or county level

Compare survival distributions

Loglinear models

open.DBIsvydesign

Open and close DBI connections

Paley-type Hadamard matrices

Options for the survey package

Direct standardization within domains

Summary statistics for sample surveys

Fit accelerated failure models to survey data

Survey statistics on subsets

Take a stratified sample

Cumulative Distribution Function

weights.survey.design

Survey design weights

subset.survey.design

Subset of survey

Contingency tables for survey data

with.svyimputationList

Analyse multiple imputations

Confidence intervals for proportions

Cohen's kappa for agreement

Cholesterol data from a US survey

Experimental: Construct non-response weights

Estimate survival function.

Linear and nonlinearconstrasts of survey statistics

Conditioning plots of survey data

Update to the new survey design format

barplot.svystat

Barplots and Dotplots

Maximum pseudolikelihood estimation in complex surveys

Lay out tables of survey statistics

Proportional odds and related models

Plots for survey data

Design-based rank tests

Sampling-weighted principal component analysis

Hadamard matrices

Ratio estimation

Two-phase designs

Distribution of quadratic forms

update.survey.design

Add variables to a survey design

Post-stratify a survey

Pseudo-Rsquareds

Raking of replicate weight design

Survey sample analysis.

Compute survey bootstrap weights

Factor analysis in complex surveys (experimental).

Variance estimation for multistage surveys

Scatterplot smoothing and density estimation

Design-based t-test

Estimated weights for missing data

Compute replicate weights

Small survey example

Sample of obstetric hospitals

Trim sampling weights

Calibration metrics

Compute variance from replicates

Specify survey design with replicate weights

Sandwich variance estimator for glms

Computations for survey variances

Survey-weighted generalised linear models.

Histograms and boxplots

Predictive marginal means

Quantiles for sample surveys

Compute variances by replicate weighting

One variable from the Youth Risk Behaviors Survey, 2015.

Student performance in California schools

Calibration (GREG) estimators

compressWeights

Compress replicate weight matrix