GTDL (version 1.0.0)

random.quantile.GTDL: 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.

Value

Randomized quantile residuals

Details

The randomized quantile residual (Dunn and Smyth, 1996), which follow a standard normal distribution is used to assess departures from the GTDL model.

References

• Dunn, P. K. e Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236<U+2013>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<U+00B4><U+0131>a, L. F. A., Colombo, D., Oliveira, P. G. O., Santos, H. F. L., e Magalh<U+02DC>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 <U+2013> 219774.

• de Oliveira, L. E. F., dos Santos L. S., da Silva, P. H. F., Fabio, L. C., Carrasco, J. M. F.(2022). An<U+00E1>lise de res<U+00ED>duos para o modelo log<U+00ED>stico generalizado dependente do tempo (GTDL). Submitted.

Examples

# NOT RUN {
### 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
# }