Package index • svyplan

Overview

Package overview and the argument-to-symbol notation map.

svyplan-package: svyplan: Survey Sample Size Determination and Precision Analysis

Survey plan profiles

Reusable design defaults for survey planning.

svyplan() update(<svyplan>): Survey plan profile

Sample size determination

Compute required sample sizes for different designs.

n_prop(): Sample size for a proportion
n_mean(): Sample size for a mean
n_change(): Sample size for a change between two occasions
n_panel(): Recruitment size for a panel that loses units between waves
n_pooled(): Sample size for an estimate pooled across occasions
n_cluster(): Optimal multistage cluster allocation
n_multi(): Multi-indicator sample size
n_multi_cluster(): Multi-indicator sample size for cluster designs
n_alloc(): Constrained stratified allocation
n_twophase(): Two-phase sample allocation

Precision analysis

Evaluate sampling precision for a given design.

prec_prop(): Sampling precision for a proportion
prec_mean(): Sampling precision for a mean
prec_change(): Sampling precision for a change between two occasions
prec_panel(): Precision a panel recruitment delivers, wave by wave
prec_pooled(): Sampling precision for an estimate pooled across occasions
prec_cluster(): Sampling precision for a multistage cluster allocation
prec_multi(): Multi-indicator sampling precision
prec_multi_cluster(): Multi-indicator precision for cluster designs
prec_alloc(): Precision for a constrained allocation
prec_twophase(): Precision of a two-phase allocation

Power analysis

Statistical power for two-sample tests.

power_prop(): Power analysis for proportions
power_mean(): Power analysis for means
power_did(): Power analysis for difference-in-differences designs

Variance components

Estimate variance components from frame data.

varcomp(): Estimate variance components

Stratification

Optimal strata boundary determination.

strata_bound(): Strata boundaries for survey design
predict(<svyplan_strata>): Assign observations to strata

Design effects and degrees of freedom

Plan design effects, effective sample sizes, design df, and schedules.

design_effect(): Planning design effect
design_df(): Design degrees of freedom
design_overlap(): Sample overlap a rotation schedule produces
design_schedule(): Construct an operational schedule from a rotating panel plan
effective_n(): Effective sample size

Confidence intervals

Confidence intervals for a planned design.

confint(<svyplan_n>) confint(<svyplan_prec>): Confidence intervals for svyplan results

Print and summary methods

Format and display svyplan objects.

print(<svyplan_n>) print(<svyplan_cluster>) print(<svyplan_prec>) print(<svyplan_varcomp>) print(<svyplan_power>) format(<svyplan_n>) format(<svyplan_cluster>) format(<svyplan_prec>) format(<svyplan_varcomp>) format(<svyplan_power>) as.integer(<svyplan_n>) as.double(<svyplan_n>) as.integer(<svyplan_cluster>) as.double(<svyplan_cluster>) as.data.frame(<svyplan_n>) as.data.frame(<svyplan_prec>) as.data.frame(<svyplan_varcomp>) as.data.frame(<svyplan_cluster>) as.integer(<svyplan_power>) as.double(<svyplan_power>) as.data.frame(<svyplan_power>) print(<svyplan_strata>) format(<svyplan_strata>) as.data.frame(<svyplan_strata>) as.integer(<svyplan_strata>) as.double(<svyplan_strata>) print(<svyplan_twophase>): Print svyplan objects
print(<svyplan_deff>) summary(<svyplan_deff>) print(<summary.svyplan_deff>) format(<svyplan_deff>) as.double(<svyplan_deff>) as.list(<svyplan_deff>) `$`(<svyplan_deff>) `[[`(<svyplan_deff>) as.data.frame(<svyplan_deff>) Ops(<svyplan_deff>) Math(<svyplan_deff>) `[<-`(<svyplan_deff>) `[[<-`(<svyplan_deff>): Print and coerce planning design effects
print(<svyplan_df>) summary(<svyplan_df>) print(<summary.svyplan_df>) format(<svyplan_df>) as.double(<svyplan_df>) as.list(<svyplan_df>) `$`(<svyplan_df>) `[[`(<svyplan_df>) as.data.frame(<svyplan_df>) Ops(<svyplan_df>) Math(<svyplan_df>) `[<-`(<svyplan_df>) `[[<-`(<svyplan_df>): Print and coerce design degrees of freedom
print(<svyplan_overlap>) format(<svyplan_overlap>) as.double(<svyplan_overlap>) as.list(<svyplan_overlap>) `[`(<svyplan_overlap>) Ops(<svyplan_overlap>) Math(<svyplan_overlap>) `[[`(<svyplan_overlap>) `$`(<svyplan_overlap>) as.data.frame(<svyplan_overlap>) `[<-`(<svyplan_overlap>) `[[<-`(<svyplan_overlap>): Print, format and coerce a rotation overlap
print(<svyplan_panel>) summary(<svyplan_panel>) print(<summary.svyplan_panel>) format(<svyplan_panel>) as.double(<svyplan_panel>) as.integer(<svyplan_panel>) as.data.frame(<svyplan_panel>): Print, summarise, format and coerce a panel recruitment
print(<svyplan_schedule>) summary(<svyplan_schedule>) print(<summary.svyplan_schedule>) format(<svyplan_schedule>) as.data.frame(<svyplan_schedule>) `$<-`(<svyplan_schedule>) `[<-`(<svyplan_schedule>) `[[<-`(<svyplan_schedule>): Print, summarise and coerce a longitudinal design schedule
summary(<svyplan_n>) summary(<svyplan_prec>) print(<summary.svyplan_bethel>) print(<summary.svyplan_alloc>): Summarize stratified allocation results
summary(<svyplan_twophase>) print(<summary.svyplan_twophase>): Analyse a two-phase allocation

Predict methods

Grid exploration for svyplan objects.

predict(<svyplan_n>) predict(<svyplan_cluster>) predict(<svyplan_power>) predict(<svyplan_prec>): Grid exploration for svyplan objects

Plot methods

Plot svyplan objects.

plot(<svyplan_strata>) plot(<svyplan_power>) plot(<svyplan_n>): Plot svyplan objects
plot(<svyplan_overlap>): Chart a rotation schedule