This package provides functions for inference the difference in quantiles and probability indices comparing two treatment arms.
devtools::install_github(repo = 'zrmacc/Ordinal')
Simulated example data.
library(Ordinal)
arm0 <- as.numeric(rmultinom(n = 1, size = 200, prob = c(0.25, 0.25, 0.25, 0.25)))
arm1 <- as.numeric(rmultinom(n = 1, size = 200, prob = c(0.10, 0.30, 0.30, 0.30)))
names(arm0) = names(arm1) <- paste0('Category ', seq(1:4))
show(arm0)
## Category 1 Category 2 Category 3 Category 4
## 43 45 62 50
show(arm1)
## Category 1 Category 2 Category 3 Category 4
## 28 46 70 56
To find a confidence interval and p-vaue for the difference in medians $q = 0.5$:
# Confidence interval calculation.
ci <- QuantDiffCI(
counts0 = arm0,
counts1 = arm1,
q = 0.5
)
round(ci, digits = 2)
## Quantile Arm1 Arm0 Delta L U
## 0.50 2.82 2.33 0.49 0.25 0.71
# P-value calculation.
pval <- QuantDiffP(
counts0 = arm0,
counts1 = arm1,
q = 0.5
)
round(pval, digits = 2)
## Quantile Arm1 Arm0 Delta P
## 0.50 2.82 2.33 0.49 0.00
Note that the medians are linearly interpolated within integer bins.
The difference in probability indices is defined as: $$ \Delta = P(Y_{1} > Y_{0}) - P(Y_{0} > Y_{1}). $$
To find a confidence interval and p-vaue for the difference in probability indices:
# Confidence interval calculation.
ci <- ProbIndexDiffCI(
counts0 = arm0,
counts1 = arm1
)
round(ci, digits = 2)
## Arm1 Arm0 Delta L U
## 0.49 0.26 0.23 0.13 0.32
# P-value calculation.
pval <- ProbIndexDiffP(
counts0 = arm0,
counts1 = arm1
)
round(pval, digits = 2)
## Arm1 Arm0 Delta P
## 0.49 0.26 0.23 0.00
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.