| random.quantile.GTDL {GTDL} | R Documentation |
Randomized quantile residuals for the GTDL model
Description
Randomized quantile residuals for the GTDL model
Usage
random.quantile.GTDL(t, formula, pHat, censur)
Arguments
t |
non-negative random variable representing the failure time and leave the snapshot failure rate, or danger. |
formula |
The structure matrix of covariates of dimension n x p. |
pHat |
Estimate of the parameters from the GTDL model. |
censur |
censoring status 0=censored, a=fail. |
Details
The randomized quantile residual (Dunn and Smyth, 1996), which follow a standard normal distribution is used to assess departures from the GTDL model.
Value
Randomized quantile residuals
References
Dunn, P. K. e Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236–244.
Louzada, F., Cuminato, J. A., Rodriguez, O. M. H., Tomazella, V. L. D., Milani, E. A., Ferreira, P. H., Ramos, P. L., Bochio, G., Perissini, I. C., Junior, O. A. G., Mota, A. L., Alegr´ıa, L. F. A., Colombo, D., Oliveira, P. G. O., Santos, H. F. L., e Magalh˜aes, M. V. C. (2020). Incorporation of frailties into a non-proportional hazard regression model and its diagnostics for reliability modeling of downhole safety valves. IEEE Access, 8, 219757 – 219774.
de Oliveira, L. E. F., dos Santos L. S., da Silva, P. H. F., Fabio, L. C., Carrasco, J. M. F.(2022). Análise de resíduos para o modelo logístico generalizado dependente do tempo (GTDL). Submitted.
Examples
### Example 1
require(survival)
data(lung)
lung <- lung[-14,]
lung$sex <- ifelse(lung$sex==2, 1, 0)
lung$ph.ecog[lung$ph.ecog==3]<-2
t1 <- lung$time
formula1 <- ~lung$sex+factor(lung$ph.ecog)+lung$age
censur1 <- ifelse(lung$status==1,0,1)
start1 <- c(0.03,0.05,-1,0.7,2,-0.1)
fit.model1 <- mle2.GTDL(t = t1,start = start1,
formula = formula1,
censur = censur1)
r1 <- random.quantile.GTDL(t = t1,formula = formula1 ,pHat = fit.model1$Coefficients[,1],
censur = censur1)
r1
### Example 2
data(tumor)
t2 <- tumor$time
formula2 <- ~tumor$group
censur2 <- tumor$censured
start2 <- c(1,-0.05,1.7)
fit.model2 <- mle2.GTDL(t = t2,start = start2,
formula = formula2,
censur = censur2)
r2 <- random.quantile.GTDL(t = t2,formula = formula2, pHat = fit.model2$Coefficients[,1],
censur = censur2)
r2