Survival analysis for quantitative ordinal scale data.
CompMuCens.RdSurvival analysis for quantitative ordinal scale data.
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.
Value
Returns a list containing the score statistic, hypothesis tests, adjusted significance level, and conclusion based on pairwise comparisons.
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
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)
#> Loading required package: survival
#> Loading required package: perm
#> Loading required package: Icens
#> Loading required package: MLEcens
#> Depends on Icens package available on bioconductor.
#> To install use for example:
#> install.packages('BiocManager')
#> BiocManager::install('Icens')
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:igraph’:
#>
#> as_data_frame, groups, union
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
#> $inputData
#> treatment ClassValue intervals
#> 1 A 5 [12, 25]
#> 2 A 4 [ 6, 12]
#> 3 A 2 [ 0, 3]
#> 4 A 5 [12, 25]
#> 5 A 5 [12, 25]
#> 6 A 4 [ 6, 12]
#> 7 A 4 [ 6, 12]
#> 8 A 2 [ 0, 3]
#> 9 A 5 [12, 25]
#> 10 A 2 [ 0, 3]
#> 11 A 2 [ 0, 3]
#> 12 A 3 [ 3, 6]
#> 13 A 4 [ 6, 12]
#> 14 A 3 [ 3, 6]
#> 15 A 2 [ 0, 3]
#> 16 A 2 [ 0, 3]
#> 17 A 6 [25, 50]
#> 18 A 2 [ 0, 3]
#> 19 A 2 [ 0, 3]
#> 20 A 4 [ 6, 12]
#> 21 A 2 [ 0, 3]
#> 22 A 4 [ 6, 12]
#> 23 A 2 [ 0, 3]
#> 24 A 4 [ 6, 12]
#> 25 A 5 [12, 25]
#> 26 A 3 [ 3, 6]
#> 27 A 4 [ 6, 12]
#> 28 A 2 [ 0, 3]
#> 29 A 2 [ 0, 3]
#> 30 A 3 [ 3, 6]
#> 31 B 5 [12, 25]
#> 32 B 3 [ 3, 6]
#> 33 B 2 [ 0, 3]
#> 34 B 4 [ 6, 12]
#> 35 B 4 [ 6, 12]
#> 36 B 5 [12, 25]
#> 37 B 4 [ 6, 12]
#> 38 B 5 [12, 25]
#> 39 B 4 [ 6, 12]
#> 40 B 4 [ 6, 12]
#> 41 B 6 [25, 50]
#> 42 B 4 [ 6, 12]
#> 43 B 5 [12, 25]
#> 44 B 5 [12, 25]
#> 45 B 5 [12, 25]
#> 46 B 2 [ 0, 3]
#> 47 B 6 [25, 50]
#> 48 B 2 [ 0, 3]
#> 49 B 3 [ 3, 6]
#> 50 B 5 [12, 25]
#> 51 B 2 [ 0, 3]
#> 52 B 6 [25, 50]
#> 53 B 4 [ 6, 12]
#> 54 B 3 [ 3, 6]
#> 55 B 2 [ 0, 3]
#> 56 B 5 [12, 25]
#> 57 B 3 [ 3, 6]
#> 58 B 5 [12, 25]
#> 59 B 4 [ 6, 12]
#> 60 B 5 [12, 25]
#> 61 C 2 [ 0, 3]
#> 62 C 3 [ 3, 6]
#> 63 C 1 0
#> 64 C 4 [ 6, 12]
#> 65 C 1 0
#> 66 C 1 0
#> 67 C 4 [ 6, 12]
#> 68 C 1 0
#> 69 C 1 0
#> 70 C 3 [ 3, 6]
#> 71 C 2 [ 0, 3]
#> 72 C 1 0
#> 73 C 4 [ 6, 12]
#> 74 C 1 0
#> 75 C 1 0
#> 76 C 2 [ 0, 3]
#> 77 C 5 [12, 25]
#> 78 C 2 [ 0, 3]
#> 79 C 1 0
#> 80 C 3 [ 3, 6]
#> 81 C 1 0
#> 82 C 4 [ 6, 12]
#> 83 C 2 [ 0, 3]
#> 84 C 2 [ 0, 3]
#> 85 C 2 [ 0, 3]
#> 86 C 4 [ 6, 12]
#> 87 C 2 [ 0, 3]
#> 88 C 3 [ 3, 6]
#> 89 C 2 [ 0, 3]
#> 90 C 2 [ 0, 3]
#> 91 D 5 [12, 25]
#> 92 D 5 [12, 25]
#> 93 D 4 [ 6, 12]
#> 94 D 5 [12, 25]
#> 95 D 5 [12, 25]
#> 96 D 6 [25, 50]
#> 97 D 6 [25, 50]
#> 98 D 4 [ 6, 12]
#> 99 D 6 [25, 50]
#> 100 D 4 [ 6, 12]
#> 101 D 3 [ 3, 6]
#> 102 D 5 [12, 25]
#> 103 D 5 [12, 25]
#> 104 D 6 [25, 50]
#> 105 D 4 [ 6, 12]
#> 106 D 6 [25, 50]
#> 107 D 5 [12, 25]
#> 108 D 6 [25, 50]
#> 109 D 5 [12, 25]
#> 110 D 4 [ 6, 12]
#> 111 D 5 [12, 25]
#> 112 D 5 [12, 25]
#> 113 D 5 [12, 25]
#> 114 D 3 [ 3, 6]
#> 115 D 5 [12, 25]
#> 116 D 6 [25, 50]
#> 117 D 5 [12, 25]
#> 118 D 5 [12, 25]
#> 119 D 5 [12, 25]
#> 120 D 6 [25, 50]
#>
#> $U.Score
#> treatment score
#> 1 D -15.125000
#> 2 B -4.541667
#> 3 A 4.225000
#> 4 C 15.441667
#>
#> $Hypothesis.test
#> treat1 treat2 p-value for H0: treat1 ≤ treat2 p-value for H0: treat1 = treat2
#> 1 D B 0.0018767018 NA
#> 2 B A 0.0113215174 NA
#> 3 A C 0.0007456484 NA
#>
#> $adj.Signif
#> [1] 0.01666667
#>
#> $Conclusion
#> [1] "D>B>A>C"
#>