Package 'ABSurvTDC'

Title: Survival Analysis using Time Dependent Covariate for Animal Breeding
Description: Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and Moeschberger (2003) <doi:10.1007/b97377>.
Authors: Dr. Himadri Ghosh [aut, cre], Mr. Saikath Das [aut], Dr. Md Yeasin [aut], Dr. Amrit Kumar Paul [aut]
Maintainer: Dr. Himadri Ghosh <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-02-13 04:27:46 UTC
Source: https://github.com/cran/ABSurvTDC

Help Index

Cox-PH Model for Animal Breeding

Description

Data preparation for ABCoxPH

Usage

ABCoxPH(wide_data, lact)

Arguments

wide_data

Dataset from DataPrep function
lact

Number of lactation to be used for model building

Value

  • Cox_Model - ABCoxPH model

  • LongData- Long data

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. <doi:10.1002/9781118032985>

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York. <doi:10.1007/b97377>

Examples

library("ABSurvTDC")
library("readxl")
data_test<-read_excel(path = system.file("extdata/data_test.xlsx", package = "ABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))
ABCoxPH(PropData)

ABCoxPH Prediction

Description

Prediction for ABCoxPH model

Usage

CoxPred(Model, NewData, AFC, HYS)

Arguments

Model

ABCoxPH model
NewData

New data
AFC

Age (in days) at first calving
HYS

Combine effect of herd, year and season

Value

  • SurvProb - Survival probabilities

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. <doi:10.1002/9781118032985>

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York. <doi:10.1007/b97377>

Examples

library("ABSurvTDC")
library("readxl")
data_test<-read_excel(path = system.file("extdata/data_test.xlsx", package = "ABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))
model<-ABCoxPH(PropData)
Lact_1<-c("Yes","Yes","Yes","No","No","No","No","No","No","No","No")
Lact_2<-c("No","No","No","No","Yes","Yes","No","No","No","No","No")
Lact_3<-c("No","No","No","No","No","No","No","No","Yes","Yes","Yes")
Lact_4<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_5<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_6<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_7<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_8<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_9<-c("No","No","No","No","No","No","No","No","No","No","No")
ndata<- data.frame(Lact_1,Lact_2,Lact_3,Lact_4,Lact_5,Lact_6,Lact_7,
                   Lact_8,Lact_9)
HYS<-2033
AFC <- 1400
CoxPred(Model=model, NewData=ndata, AFC, HYS)

Data Preparation

Description

Data preparation for ABCoxPH

Usage

DataPrep(data, t_int, max_lac)

Arguments

data

Raw data sets
t_int

No of days to be considered as single time interval (Default value: 90)
max_lac

Maximum no of lactation to be considered for data preparation (Default value: Max Lactation)

Value

  • wide_data - Processed data for ABCoxPH

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. <doi:10.1002/9781118032985>

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York. <doi:10.1007/b97377>

Examples

library("ABSurvTDC")
library("readxl")
data_test<-read_excel(path = system.file("extdata/data_test.xlsx", package = "ABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))