R Under development (unstable) (2023-08-12 r84939 ucrt) -- "Unsuffered Consequences"
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> options(na.action=na.exclude) # preserve missings
> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
> library(survival)
> 
> #
> # The Stanford data from 1980 is used in Escobar and Meeker, Biometrics 1992.
> #	t5 = T5 mismatch score
> #  Their case numbers correspond to a data set sorted by age
> #
> aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
> 
> stanford2$t5 <- ifelse(stanford2$t5 <0, NA, stanford2$t5)
> stanford2 <- stanford2[order(stanford2$age, stanford2$time),]
> stanford2$time <- ifelse(stanford2$time==0, .5, stanford2$time)
> 
> cage <- stanford2$age - mean(stanford2$age)
> fit1 <- survreg(Surv(time, status) ~ cage + I(cage^2), stanford2,
+ 		dist='lognormal')
> fit1
Call:
survreg(formula = Surv(time, status) ~ cage + I(cage^2), data = stanford2, 
    dist = "lognormal")

Coefficients:
 (Intercept)         cage    I(cage^2) 
 6.717591081 -0.061908619 -0.003504315 

Scale= 2.362872 

Loglik(model)= -863.6   Loglik(intercept only)= -868.8
	Chisq= 10.5 on 2 degrees of freedom, p= 0.00526 
n= 184 
> ldcase <- resid(fit1, type='ldcase')
> ldresp <- resid(fit1, type='ldresp')
> # The ldcase and ldresp should be compared to table 1 in Escobar and 
> #  Meeker, Biometrics 1992, p519; the colums they label as (1/2) A_{ii}
> #  They give data for selected cases, entered below as mdata
> mdata <- cbind(c(1,2,4,5,12,16,23,61,66,72,172,182,183,184),
+                c(.035, .244, .141, .159, .194, .402, 0,0, .143, .403,
+                  .178, .033, .005, .015),
+                c(.138, .145, .073, .076, .104, .159, 0,0, .109, .184,
+                  .116, .063, .103, .144))
> dimnames(mdata) <- list(NULL, c("case#", "ldcase", "ldresp"))
> aeq(round(ldcase[mdata[,1]],3), mdata[,2])
[1] TRUE
> aeq(round(ldresp[mdata[,1]],3), mdata[,3])             
[1] TRUE
> 
> plot1 <- function() {
+     # make their figure 1, 2, and 6
+     temp <- predict(fit1, type='quantile', p=c(.1, .5, .9)) 
+     plot(stanford2$age, stanford2$time, log='y', xlab="Age", ylab="Days",
+ 	 ylim=range(stanford2$time, temp))
+     matlines(stanford2$age, temp, lty=c(1,2,2), col=1)
+ 
+     n <- length(ldcase)
+     plot(1:n, ldcase, xlab="Case Number", ylab="(1/2) A", type='l')
+     title (main="Case weight pertubations")
+     plot(1:n, ldresp, xlab="Case Number", ylab="(1/2) A", 
+          ylim=c(0, .2), type='l')
+     title(main="Response pertubations")
+     indx <- which(ldresp > .07)
+     text(indx, ldresp[indx]+ .005, indx%%10, cex=.6)
+     }
> 
> postscript('meekerplot.ps')
> plot1()
> dev.off()
null device 
          1 
> #
> # Stanford predictions in other ways
> #
> fit2 <- survreg(Surv(time, status) ~ poly(age,2), stanford2,
+ 		dist='lognormal')
> 
> p1 <- predict(fit1, type='response')
> p2 <- predict(fit2, type='response')
> aeq(p1, p2)
[1] TRUE
> 
> p3 <- predict(fit2, type='terms', se=T)
> p4 <- predict(fit2, type='lp', se=T)
> p5 <- predict(fit1, type='lp', se=T)
> # aeq(p3$fit + attr(p3$fit, 'constant'), p4$fit)  #R is missing the attribute
> aeq(p4$fit, p5$fit)
[1] TRUE
> aeq(p3$se.fit, p4$se.fit)  #this one should be false
[1] "Mean relative difference: 0.758395"
> aeq(p4$se.fit, p5$se.fit)  #this one true
[1] TRUE
> 
> 
> proc.time()
   user  system elapsed 
   0.84    0.18    1.01