README.md

In a-lenz/presize: Precision Based Sample Size Calculation

presize

Warning: R-package in development.

The goal of presize is to provide functions for precision based sample size calculation. For a given sample size, the functions will return the precision (half the width of the confidence interval), and vice versa.

Installation

You can install presize from github with:

# install.packages("devtools")
devtools::install_github("CTU-Bern/presize")

Overview

presize will provide functions for

• descriptive statistics
  • mean (prec_mean)
  • proportion (prec_prop)
  • rate (prec_rate)
• absolute and relative differences
  • mean difference (prec_meandiff)
  • risk difference (prec_riskdiff)
  • odds ration (prec_or)
  • risk ratio (prec_riskratio)
  • rate ratio
  • hazard ratio
• correlation measures
  • correlation coefficient (prec_cor)
  • Cohens kappa
  • ICC (prec_icc)
  • limit of agreement from Bland Altman plot
• diagnostic measures
  • sens
  • spec
  • positive LR
  • negative LR
  • AUC

Example

This is a basic example which shows you how to solve a common problem:

library(presize)

# calculate sample size for a proportion of 0.2, or 0.4 with a precision of 0.2
prec_prop(p = c(.2, .4), n = 10, method = "wilson")
#> 
#>      Sample size or precision for a proportion with wilson confidence interval. 
#> 
#>     p  n      prec      padj conf.level        lwr       upr
#> 1 0.2 10 0.2265777 0.2832598       0.95 0.05668215 0.5098375
#> 2 0.4 10 0.2595730 0.4277533       0.95 0.16818033 0.6873262
#> 
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.

a-lenz/presize documentation built on May 17, 2019, 7:44 a.m.