r4pde (version 0.1.0)

CompMuCens: Survival analysis for quantitative ordinal scale data.

Description

Survival analysis for quantitative ordinal scale data.

Usage

CompMuCens(dat, scale, grade = TRUE, ckData = FALSE)

Value

Returns a list containing the score statistic, hypothesis tests, adjusted significance level, and conclusion based on pairwise comparisons.

Arguments

dat

Data frame containing the data to be processed.

scale

A numeric vector indicating the scale or order of classes.

grade

Logical. If TRUE, uses the class value. If FALSE, uses the NPE (Non-Parametric Estimate).

ckData

Logical. If TRUE, returns the input data along with the results. If FALSE, returns only the results.

Details

To assist plant pathologists in analyzing quantitative ordinal scale data and encourage the uptake of the interval-censored analysis method, Chiang and collaborators have developed this function and provided comprehensive explanation of the program code used to implement class ratings analyzed through this method in this repository: https://github.com/StatisticalMethodsinPlantProtection/CompMuCens According to results in the paper, the method can be applied to reduce the risk of type II errors when considering quantitative ordinal data, which are widely used in plant pathology and related disciplines.The function starts by converting the data into a censored data format and performs multiple pairwise comparisons to determine significance using the score statistic method.

References

Chiang, K.S., Chang, Y.M., Liu, H.I., Lee, J.Y., El Jarroudi, M. and Bock, C., 2023. Survival Analysis as a Basis to Test Hypotheses When Using Quantitative Ordinal Scale Disease Severity Data. Phytopathology, in press. Available at: https://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-02-23-0055-R

See Also

Other Disease quantification: DSI(), DSI2()

Examples

# Entering your data as ordinal rating scores
trAs=c(5,4,2,5,5,4,4,2,5,2,2,3,4,3,2,2,6,2,2,4,2,4,2,4,5,3,4,2,2,3)
trBs=c(5,3,2,4,4,5,4,5,4,4,6,4,5,5,5,2,6,2,3,5,2,6,4,3,2,5,3,5,4,5)
trCs=c(2,3,1,4,1,1,4,1,1,3,2,1,4,1,1,2,5,2,1,3,1,4,2,2,2,4,2,3,2,2)
trDs=c(5,5,4,5,5,6,6,4,6,4,3,5,5,6,4,6,5,6,5,4,5,5,5,3,5,6,5,5,5,6)
# Data shaping into input format
inputData = data.frame(treatment=c(rep("A",30),rep("B",30),rep("C",30),
rep("D",30)), x=c(trAs, trBs, trCs, trDs))
# Perform analysis using CompMuCens() function
CompMuCens(dat=inputData, scale=c(0,3,6,12,25,50,75,88,94,97,100,100),ckData=TRUE)