R version 2.4.0 Under development (unstable) (2006-05-18 r38118)
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ISBN 3-900051-07-0

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> #attach("../.Data")
> #dyn.load('../loadmod.o')
> postscript(file='Rtest.ps')
> library(survival)
Loading required package: splines
> if (R.version$minor>2) options(na.action="na.exclude")
> #options(na.action='na.omit', contrasts='contr.treatment')
> #
> # This data set caused problems for Splus 3.4 due to a mistake in
> #  my initial value code.  Data courtesy Bob Treder at Statsci
> #
> capacitor <- read.table('data.capacitor', row.names=1,
+ 			col.names=c('', 'days', 'event', 'voltage'))
> 
> fitig <- survreg(Surv(days, event)~voltage, 
+ 	dist = "gaussian", data = capacitor)
> summary(fitig)

Call:
survreg(formula = Surv(days, event) ~ voltage, data = capacitor, 
    dist = "gaussian")
             Value Std. Error     z        p
(Intercept) 1764.9    163.387 10.80 3.36e-27
voltage      -53.9      5.545 -9.72 2.56e-22
Log(scale)     4.8      0.105 45.56 0.00e+00

Scale= 121 

Gaussian distribution
Loglik(model)= -361.9   Loglik(intercept only)= -420.1
	Chisq= 116.33 on 1 degrees of freedom, p= 0 
Number of Newton-Raphson Iterations: 6 
n= 125 

> 
> fitix <- survreg(Surv(days, event)~voltage, 
+ 	dist = "extreme", data = capacitor)
> summary(fitix)

Call:
survreg(formula = Surv(days, event) ~ voltage, data = capacitor, 
    dist = "extreme")
              Value Std. Error     z        p
(Intercept) 2055.59    180.349  11.4 4.28e-30
voltage      -62.21      5.967 -10.4 1.88e-25
Log(scale)     4.53      0.108  41.9 0.00e+00

Scale= 92.9 

Extreme value distribution
Loglik(model)= -360   Loglik(intercept only)= -427.1
	Chisq= 134.25 on 1 degrees of freedom, p= 0 
Number of Newton-Raphson Iterations: 7 
n= 125 

> 
> fitil <- survreg(Surv(days, event)~voltage, 
+ 	dist = "logistic", data = capacitor)
> summary(fitil)

Call:
survreg(formula = Surv(days, event) ~ voltage, data = capacitor, 
    dist = "logistic")
              Value Std. Error     z         p
(Intercept) 1811.56    148.853  12.2  4.48e-34
voltage      -55.48      4.986 -11.1  9.39e-29
Log(scale)     4.19      0.117  35.8 2.03e-280

Scale= 66.3 

Logistic distribution
Loglik(model)= -360.4   Loglik(intercept only)= -423.7
	Chisq= 126.5 on 1 degrees of freedom, p= 0 
Number of Newton-Raphson Iterations: 6 
n= 125 

> 
> rm(fitil, fitig, fitix)
> #
> # Good initial values are key to this data set
> #   It killed v4 of survreg; 
> #   data courtesy of Deborah Donnell, Fred Hutchinson Cancer Center
> #
> 
> donnell <- scan("data.donnell", what=list(time1=0, time2=0, status=0))
> donnell <- data.frame(donnell)
> 
> dfit <- survreg(Surv(time1, time2, status, type='interval') ~1, donnell)
> summary(dfit)

Call:
survreg(formula = Surv(time1, time2, status, type = "interval") ~ 
    1, data = donnell)
             Value Std. Error      z       p
(Intercept)  2.390      0.804  2.972 0.00295
Log(scale)  -0.237      0.346 -0.687 0.49232

Scale= 0.789 

Weibull distribution
Loglik(model)= -51   Loglik(intercept only)= -51
Number of Newton-Raphson Iterations: 10 
n= 210 

> 
> #
> # Do a contour plot of the donnell data
> #
> npt <- 25
> beta0  <- seq(.4, 2.4, length=npt)
> logsig <- seq(-1.4, 0.41, length=npt)
> donlog <- matrix(0,npt, npt)
> 
> for (i in 1:npt) {
+     for (j in 1:npt) {
+ 	fit <- survreg(Surv(time1, time2, status, type='interval') ~1,
+ 			donnell, init=c(beta0[i],logsig[j]),
+ 		        control=list(maxiter=0))
+ 	donlog[i,j] <- fit$log[1]
+ 	}
+     }
> 
> clev <- -c(51, 51.5, 52:60, 65, 75, 85, 100, 150)
> contour(beta0, logsig, pmax(donlog, -200), levels=clev, xlab="Intercept",
+ 	ylab="Log(sigma)")
> points(2.39, log(.7885), pch=1, col=2)
> title("Donnell data")
> 
> #
> # Compute the path of the iteration
> #   Step 2 isn't so good, and is followed by 3 iters of step-halving
> #
> niter <- 14
> donpath <- matrix(0,niter+1,2)
> for (i in 0:niter){
+     fit <- survreg(Surv(time1, time2, status, type='interval') ~1,
+ 		    donnell, maxiter=i)
+     donpath[i+1,] <- c(fit$coef, log(fit$scale))
+     }
> points(donpath[,1], donpath[,2])
> lines(donpath[,1], donpath[,2], col=4)
> 
> rm(beta0, logsig, niter, fit, npt, donlog, clev)
> #lfit1 <- censorReg(censor(time, status) ~ age + ph.ecog + strata(sex),lung)
> data(lung)
> lfit2 <- survreg(Surv(time, status) ~ age + ph.ecog + strata(sex), lung)
> lfit3 <- survreg(Surv(time, status) ~ sex + (age+ph.ecog)*strata(sex), lung)
> 
> lfit4 <-  survreg(Surv(time, status) ~ age + ph.ecog , lung,
+ 		  subset=(sex==1))
> lfit5 <- survreg(Surv(time, status) ~ age + ph.ecog , lung,
+ 		  subset=(sex==2))
> 
> aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
> #aeq(lfit4$coef, lfit1[[1]]$coef)
> #aeq(lfit4$scale, lfit1[[1]]$scale)
> aeq(c(lfit4$scale, lfit5$scale), lfit3$scale )
[1] TRUE
> #aeq(c(lfit4$scale, lfit5$scale), sapply(lfit1, function(x) x$scale))
> 
> #
> # Test out ridge regression and splines
> #
> lfit0 <- survreg(Surv(time, status) ~1, lung)
> lfit1 <- survreg(Surv(time, status) ~ age + ridge(ph.ecog, theta=5), lung)
> lfit2 <- survreg(Surv(time, status) ~ sex + ridge(age, ph.ecog, theta=1), lung)
> lfit3 <- survreg(Surv(time, status) ~ sex + age + ph.ecog, lung)
> 
> lfit0
Call:
survreg(formula = Surv(time, status) ~ 1, data = lung)

Coefficients:
(Intercept) 
   6.034904 

Scale= 0.7593936 

Loglik(model)= -1153.9   Loglik(intercept only)= -1153.9
n= 228 
> lfit1
Call:
survreg(formula = Surv(time, status) ~ age + ridge(ph.ecog, theta = 5), 
    data = lung)

               coef     se(coef) se2     Chisq DF p      
(Intercept)     6.83082 0.42860  0.42860 254.0 1  0.00000
age            -0.00783 0.00687  0.00687   1.3 1  0.25000
ridge(ph.ecog) -0.32032 0.08484  0.08405  14.2 1  0.00016

Scale= 0.738 

Iterations: 1 outer, 5 Newton-Raphson
Degrees of freedom for terms= 1 1 1 1 
Likelihood ratio test=18.6  on 2 df, p=8.73e-05
  n=227 (1 observation deleted due to missingness)
> lfit2
Call:
survreg(formula = Surv(time, status) ~ sex + ridge(age, ph.ecog, 
    theta = 1), data = lung)

               coef     se(coef) se2     Chisq  DF p      
(Intercept)     6.27163 0.45280  0.45210 191.84 1  0.0e+00
sex             0.40096 0.12371  0.12371  10.50 1  1.2e-03
ridge(age)     -0.00746 0.00675  0.00674   1.22 1  2.7e-01
ridge(ph.ecog) -0.33848 0.08329  0.08314  16.51 1  4.8e-05

Scale= 0.731 

Iterations: 1 outer, 6 Newton-Raphson
Degrees of freedom for terms= 1 1 2 1 
Likelihood ratio test=30  on 3 df, p=1.37e-06
  n=227 (1 observation deleted due to missingness)
> lfit3
Call:
survreg(formula = Surv(time, status) ~ sex + age + ph.ecog, data = lung)

Coefficients:
(Intercept)         sex         age     ph.ecog 
 6.27343525  0.40109054 -0.00747544 -0.33963810 

Scale= 0.731109 

Loglik(model)= -1132.4   Loglik(intercept only)= -1147.4
	Chisq= 29.98 on 3 degrees of freedom, p= 1.4e-06 
n=227 (1 observation deleted due to missingness)
> 
> 
> xx <- pspline(lung$age, nterm=3, theta=.3)
> xx <- matrix(unclass(xx), ncol=ncol(xx))   # the raw matrix
> lfit4 <- survreg(Surv(time, status) ~xx, lung)
> lfit5 <- survreg(Surv(time, status) ~age, lung)
> 
> lfit6 <- survreg(Surv(time, status)~pspline(age, df=2), lung)
> plot(lung$age, predict(lfit6), xlab='Age', ylab="Spline prediction")
> title("Lung Data")
>      
> lfit7 <- survreg(Surv(time, status) ~ offset(lfit6$lin), lung)
> 
> lfit4
Call:
survreg(formula = Surv(time, status) ~ xx, data = lung)

Coefficients:
(Intercept)         xx1         xx2         xx3         xx4         xx5 
  13.551290   -7.615741   -7.424565   -7.533378   -7.571272  -14.527489 

Scale= 0.755741 

Loglik(model)= -1150.1   Loglik(intercept only)= -1153.9
	Chisq= 7.52 on 5 degrees of freedom, p= 0.19 
n= 228 
> lfit5
Call:
survreg(formula = Surv(time, status) ~ age, data = lung)

Coefficients:
(Intercept)         age 
 6.88712062 -0.01360829 

Scale= 0.7587515 

Loglik(model)= -1151.9   Loglik(intercept only)= -1153.9
	Chisq= 3.91 on 1 degrees of freedom, p= 0.048 
n= 228 
> lfit6
Call:
survreg(formula = Surv(time, status) ~ pspline(age, df = 2), 
    data = lung)

                          coef    se(coef) se2     Chisq  DF   p    
(Intercept)                6.5918 0.63681  0.41853 107.15 1.00 0.000
pspline(age, df = 2), lin -0.0136 0.00687  0.00687   3.94 1.00 0.047
pspline(age, df = 2), non                            0.78 1.06 0.400

Scale= 0.756 

Iterations: 4 outer, 12 Newton-Raphson
     Theta= 0.926 
Degrees of freedom for terms= 0.4 2.1 1.0 
Likelihood ratio test=5.2  on 1.5 df, p=0.0441  n= 228 
> lfit7$coef
 (Intercept) 
1.478892e-09 
> 
> rm(lfit1, lfit2, lfit3, lfit4, lfit5, lfit6, lfit7)
> rm(xx, lfit0)
> #
> # Data courtesy of Bercedis Peterson, Duke University.
> #  v4 of survreg fails due to 2 groups that have only 1 subject; the coef
> #  for them easily gets out of hand.  In fact, this data set is my toughest
> #  test of the minimizer.
> #
> # A shrinkage model for this coefficient is therefore interesting
> 
> 
> peterson <- data.frame(
+ 		  scan('data.peterson', what=list(grp=0, time=0, status=0)))
> 
> fitp <- survreg(Surv(time, status) ~ factor(grp), peterson)
> summary(fitp)

Call:
survreg(formula = Surv(time, status) ~ factor(grp), data = peterson)
              Value Std. Error     z        p
(Intercept)   2.291      0.115 19.92 2.93e-88
factor(grp)2  0.786      0.177  4.44 8.79e-06
factor(grp)3  0.728      0.183  3.97 7.09e-05
factor(grp)4 -1.598      0.218 -7.32 2.48e-13
factor(grp)5 -0.500      0.218 -2.29 2.21e-02
factor(grp)6  0.475      0.170  2.79 5.23e-03
Log(scale)   -1.684      0.257 -6.54 6.09e-11

Scale= 0.186 

Weibull distribution
Loglik(model)= -26.7   Loglik(intercept only)= -40.7
	Chisq= 28.18 on 5 degrees of freedom, p= 3.4e-05 
Number of Newton-Raphson Iterations: 9 
n= 19 

> 
> # Now a shrinkage model.  Give the group coefficients
> #  about 1/2 the scale parameter of the original model, i.e., .18.
> #
> ffit <- survreg(Surv(time, status) ~ frailty(grp, theta=.1), peterson)
> ffit
Call:
survreg(formula = Surv(time, status) ~ frailty(grp, theta = 0.1), 
    data = peterson)

                          coef se(coef) se2    Chisq DF   p     
(Intercept)               2.62 0.172    0.0874 232.0 1.00 0.0000
frailty(grp, theta = 0.1)                       10.4 2.15 0.0067

Scale= 0.301 

Iterations: 1 outer, 7 Newton-Raphson
     Variance of random effect= 0.1   I-likelihood = -11.8 
Degrees of freedom for terms= 0.3 2.2 0.7 
Likelihood ratio test=13.8  on 1.1 df, p=0.00027  n= 19 
> 
> #
> # Try 3 degrees of freedom Gaussian fit, since there are 6 groups.
> #   Compare them to the unconstrained ones.  The frailty coefs are
> #   on a "sum to 0" constraint rather than "first coef=0", so
> #   some conversion is neccessary
> #
> ffit3 <- survreg(Surv(time, status) ~ frailty(grp, df=3, dist='gauss'), 
+ 		 peterson)
> print(ffit3)
Call:
survreg(formula = Surv(time, status) ~ frailty(grp, df = 3, dist = "gauss"), 
    data = peterson)

                          coef se(coef) se2   Chisq DF p      
(Intercept)               2.44 0.223    0.066 119.7 1  0.00000
frailty(grp, df = 3, dist                      16.4 3  0.00096

Scale= 0.251 

Iterations: 8 outer, 33 Newton-Raphson
     Variance of random effect= 0.197 
Degrees of freedom for terms= 0.1 3.0 0.6 
Likelihood ratio test=20.1  on 1.7 df, p=2.79e-05  n= 19 
> 
> temp <- mean(c(0, fitp$coef[-1])) 
> temp2 <- c(fitp$coef[1] + temp, c(0,fitp$coef[-1]) - temp)
> xx <- rbind(c(nrow(peterson), table(peterson$grp)),
+ 	    temp2,
+ 	    c(ffit3$coef, ffit3$frail))
> dimnames(xx) <- list(c("N", "factor fit", "frailty fit"),
+ 		     c("Intercept", paste("grp", 1:6)))
> signif(xx,2)
            Intercept  grp 1 grp 2 grp 3 grp 4 grp 5 grp 6
N                19.0  3.000  6.00  6.00  1.00  1.00  2.00
factor fit        2.3  0.018  0.80  0.75 -1.60 -0.48  0.49
frailty fit       2.4 -0.180  0.58  0.55 -0.77 -0.44  0.26
> #
> # All but the first coef are shrunk towards zero.
> #
> rm(ffit, ffit3, temp, temp2, xx, fitp)
> 
> #
> # Look at predicted values
> #
> data(ovarian)
> ofit1 <- survreg(Surv(futime, fustat) ~ age + ridge(ecog.ps, rx), ovarian)
> 
> predict(ofit1)
 [1]  207.7548  172.7986  358.7735 1426.6498 1353.7357  843.8627 1102.1652
 [8]  859.5084  416.3290 1280.4094  820.7329 1882.7269  876.1267 1041.8963
[15] 3477.0615 2622.9894 3761.5364 2207.8901 1362.2021 3113.9802  879.2010
[22]  180.8417 2501.0794  645.2434  555.8305  936.0103
> predict(ofit1, type='response')
 [1]  207.7548  172.7986  358.7735 1426.6498 1353.7357  843.8627 1102.1652
 [8]  859.5084  416.3290 1280.4094  820.7329 1882.7269  876.1267 1041.8963
[15] 3477.0615 2622.9894 3761.5364 2207.8901 1362.2021 3113.9802  879.2010
[22]  180.8417 2501.0794  645.2434  555.8305  936.0103
> predict(ofit1, type='terms', se=T)
$fit
           age ridge(ecog.ps, rx)
1  -1.37776207         -0.1765482
2  -1.56199426         -0.1765482
3  -0.87785423         -0.1301235
4   0.23872053          0.1336947
5   0.49650242         -0.1765482
6  -0.02255561         -0.1301235
7  -0.06575647          0.1801193
8  -0.31442775          0.1801193
9  -0.68264498         -0.1765482
10  0.08414685          0.1801193
11 -0.05034768         -0.1301235
12  0.51611283          0.1336947
13 -0.29527755          0.1801193
14 -0.07556594          0.1336947
15  1.43982204         -0.1765482
16  1.11152449         -0.1301235
17  1.47203732         -0.1301235
18  0.98567183         -0.1765482
19  0.19249416          0.1336947
20  1.01929333          0.1336947
21 -0.29177478          0.1801193
22 -1.56292322         -0.1301235
23  1.11035690         -0.1765482
24 -0.60116078          0.1801193
25 -0.70390024          0.1336947
26 -0.18273714          0.1336947
attr(,"constant")
(Intercept) 
   11.14419 

$se.fit
            age ridge(ecog.ps, rx)
1  0.0303419941         0.04119540
2  0.0343992782         0.04119540
3  0.0193326906         0.04176016
4  0.0052572625         0.04159720
5  0.0109343070         0.04119540
6  0.0004967347         0.04176016
7  0.0014481328         0.04129287
8  0.0069245373         0.04129287
9  0.0150336625         0.04119540
10 0.0018531379         0.04129287
11 0.0011087902         0.04176016
12 0.0113661806         0.04159720
13 0.0065027990         0.04129287
14 0.0016641635         0.04159720
15 0.0317087202         0.04119540
16 0.0244787327         0.04176016
17 0.0324181865         0.04176016
18 0.0217071217         0.04119540
19 0.0042392346         0.04159720
20 0.0224475568         0.04159720
21 0.0064256586         0.04129287
22 0.0344197363         0.04176016
23 0.0244530192         0.04119540
24 0.0132391631         0.04129287
25 0.0155017601         0.04159720
26 0.0040243590         0.04159720

> 
> temp1 <- predict(ofit1,type="link", se=T)
> temp2 <- predict(ofit1, type= 'response', se=T)
> all.equal(temp2$se.fit, temp1$se.fit* exp(temp1$fit))
[1] TRUE
> #
> # The Stanford data from 1980 is used in Escobar and Meeker
> #	t5 = T5 mismatch score
> #  Their case numbers correspond to a data set sorted by age
> #
> stanford2 <- read.table('data.stanford', 
+ 			col.names=c('id', 'time', 'status', 'age', 't5'))
>  
> stanford2$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 + cage^2, stanford2,
> ###		dist='lognormal')
> 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= 0.0053 
n= 184 
> ldcase <- resid(fit1, type='ldcase')
> ldresp <- resid(fit1, type='ldresp')
> print(ldresp)
         139          159          181          119           74          120 
1.379202e-01 1.452449e-01 2.628088e-02 7.320182e-02 7.624325e-02 3.994793e-02 
          99          108          179           43          134          160 
6.328460e-02 6.128977e-02 9.685668e-03 4.767550e-02 2.980553e-02 1.036051e-01 
         177          153          136          133          176           66 
8.990601e-03 2.114950e-02 2.557694e-02 1.591463e-01 8.618405e-03 3.389342e-02 
         157          114           46           65          184           88 
1.141319e-02 1.990887e-02 2.044977e-02 2.480540e-02 1.085737e-05 5.474389e-02 
         182          180          163           84           90           68 
1.786494e-03 2.574818e-03 7.654075e-03 2.024457e-02 8.561193e-02 3.894985e-02 
          48          174          151          125           73          105 
7.007563e-02 3.767424e-03 8.314677e-03 1.248554e-02 1.954896e-02 1.831982e-02 
         117           96           39           38          106           14 
1.739299e-02 1.789440e-02 2.406180e-02 2.364315e-02 4.717184e-02 2.051895e-02 
         123          135          111           83          143           69 
4.763900e-02 1.663805e-02 1.367017e-02 3.204509e-02 1.857902e-02 2.058865e-02 
          27          113          167          156          141           30 
3.896724e-02 3.775024e-02 5.091513e-03 1.528402e-02 8.682136e-03 1.746134e-02 
         144          158           79          102           77           36 
2.593291e-02 6.620379e-03 1.375918e-02 1.547851e-02 1.786268e-02 2.330671e-02 
         183          122          162          121           87            2 
3.720939e-05 1.696469e-02 5.954816e-03 1.233287e-02 1.655940e-02 1.089489e-01 
          64          150           85           71           19           21 
6.015390e-02 7.469432e-03 1.666501e-02 1.893415e-02 2.645489e-02 1.843298e-01 
         175          169          148          138           98          104 
1.789942e-02 4.379957e-03 7.619698e-03 9.332609e-03 1.428798e-02 1.445962e-02 
         103           12           89            3          100           55 
1.449500e-02 3.404298e-02 3.358405e-02 3.113308e-02 1.412657e-02 1.179741e-02 
         142           63          168           72          137           10 
8.641594e-03 1.426955e-02 4.554043e-03 1.094162e-02 9.645966e-03 1.226564e-02 
         124           17           94           82          170          149 
1.222512e-02 1.088510e-02 1.493684e-02 1.844221e-02 3.988063e-02 3.038321e-02 
          42          128           67          109           75           26 
2.127744e-02 1.439502e-02 1.285836e-02 8.944979e-03 1.997791e-02 2.757124e-02 
          97           58          178          140           32          126 
2.549339e-02 2.356049e-02 2.057505e-03 1.269584e-02 1.103392e-02 1.253031e-02 
          51          101           29           33          164           60 
1.430228e-02 1.637415e-02 2.201027e-02 1.118993e-02 6.417566e-03 8.492275e-03 
         152          145          112           76           47          118 
8.651522e-03 9.608673e-03 1.609215e-02 2.168279e-02 2.622512e-02 2.274275e-02 
           5          129           31           35           40          130 
1.184996e-02 9.391147e-03 8.772106e-03 8.526052e-03 9.451658e-03 1.295998e-02 
          28           56           91           44           23           37 
1.285986e-02 1.536640e-02 2.031499e-02 2.807956e-02 1.965943e-02 1.733256e-02 
          70          132            9           81           59          127 
9.129009e-03 9.121716e-03 9.083024e-03 1.025238e-02 1.032186e-02 1.183693e-02 
         131           80           20           25          165           24 
1.403298e-02 2.363944e-02 2.181249e-02 2.723391e-02 2.043510e-02 2.019541e-02 
         172          146           86          107           95          116 
1.152649e-02 1.265907e-02 1.538526e-02 2.107503e-02 2.298470e-02 2.128394e-02 
          41           61          155          166          154            4 
1.791007e-02 1.763098e-02 1.345059e-02 1.285115e-02 1.218090e-02 1.470506e-02 
          92           93           62           34           15          173 
2.599207e-02 3.098462e-02 3.037745e-02 2.166521e-02 1.478524e-02 7.517967e-03 
         171           52          110           50           45           53 
8.681586e-03 1.679632e-02 2.540017e-02 3.470668e-02 3.229506e-02 3.017736e-02 
          54          147          115           16            1            6 
2.416303e-02 1.870027e-02 2.172488e-02 1.164271e-01 4.257797e-02 2.459122e-02 
           7           57           78          161           11            8 
3.585527e-02 3.587688e-02 2.865161e-02 2.603297e-02 5.640968e-02 4.338250e-02 
          49           13           22           18 
3.425475e-02 6.262791e-02 1.029315e-01 1.442429e-01 
> # The ldcase and ldresp should be compared to table 1 in Escobar and 
> #  Meeker, Biometrics 1992, p519; the colum they label as (1/2) A_{ii}
> 
> plot1 <- function() {
+     # make their figure 1, 2, and 6
+     plot(stanford2$age, stanford2$time, log='y', xlab="Age", ylab="Days",
+ 	 ylim=c(.01, 10^6))
+     temp <- predict(fit1, type='response', se.fit=T) 
+     matlines(stanford2$age, cbind(temp$fit, temp$fit-1.96*temp$se.fit,
+ 				            temp$fit+1.96*temp$se.fit),
+ 	     lty=c(1,2,2))
+     # these are the wrong CI lines, he plotted std dev, I plotted std err
+     # here are the right ones
+     #  Using uncentered age gives different coefs, but makes prediction over an
+     #    extended range somewhat simpler 
+     refit <- survreg(Surv(time,status)~ age + age^2, stanford2,
+ 		     dist='lognormal')
+     plot(stanford2$age, stanford2$time, log='y', xlab="Age", ylab="Days",
+ 	 ylim=c(.01, 10^6), xlim=c(0,75))
+     temp2 <- predict(refit, list(age=1:75), type='quantile', p=c(.05, .5, .95))
+     matlines(1:75, temp2, lty=c(1,2,2), col=2)
+ 
+     plot(ldcase, xlab="Case Number", ylab="(1/2) A")
+     title (main="Case weight pertubations")
+     plot(ldresp, xlab="Case Number", ylab="(1/2) A")
+     title(main="Response pertubations")
+     }
> 
> plot1()
> #
> # 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$fit)
[1] TRUE
> 
> aeq(p4$fit, p5$fit)
[1] TRUE
> #!aeq(p3$se.fit, p4$se.fit)  #this one should be false
> aeq(p4$se.fit, p5$se.fit)  #this one true
[1] TRUE
> 
> #
> # Verify that scale can be fixed at a value
> #    coefs will differ slightly due to different iteration paths
> tol <- survreg.control()$rel.tolerance
> 
> # Intercept only models
> fit1 <- survreg(Surv(time,status) ~ 1, lung)
> fit2 <- survreg(Surv(time,status) ~ 1, lung, scale=fit1$scale)
> #all.equal(fit1$coef, fit2$coef, tolerance= tol)
> #all.equal(fit1$loglik, fit2$loglik, tolerance= tol)
> all.equal(fit1$coef, fit2$coef)
[1] TRUE
> all.equal(fit1$loglik, fit2$loglik)
[1] TRUE
> 
> # multiple covariates
> fit1 <- survreg(Surv(time,status) ~ age + ph.karno, lung)
> fit2 <- survreg(Surv(time,status) ~ age + ph.karno, lung,
+ 		scale=fit1$scale)
> ##all.equal(fit1$coef, fit2$coef, tolerance=tol)
> ##all.equal(fit1$loglik[2], fit2$loglik[2], tolerance=tol)
> all.equal(fit1$coef, fit2$coef)
[1] TRUE
> all.equal(fit1$loglik[2], fit2$loglik[2])
[1] TRUE
> 
> # penalized models
> fit1 <- survreg(Surv(time, status) ~ pspline(age), lung)
> fit2 <- survreg(Surv(time, status) ~ pspline(age), lung, scale=fit1$scale)
> #all.equal(fit1$coef, fit2$coef, tolerance=tol)
> #all.equal(fit1$loglik[2], fit2$loglik[2], tolerance=tol)
> all.equal(fit1$coef, fit2$coef)
[1] "Mean relative  difference: 0.0002457368"
> all.equal(fit1$loglik[2], fit2$loglik[2])
[1] "Mean relative  difference: 4.971155e-07"
> 
> rm(fit1, fit2, tol)
> 
> #
> # Test out the strata capabilities
> #
> tol <- survreg.control()$rel.tolerance
> aeq <- function(x,y,...) all.equal(as.vector(x), as.vector(y))
> 
> # intercept only models
> fit1 <- survreg(Surv(time, status) ~ strata(sex), lung)
> fit2 <- survreg(Surv(time, status) ~ strata(sex) + sex, lung)
> fit3a<- survreg(Surv(time,status) ~1, lung, subset=(sex==1))
> fit3b<- survreg(Surv(time,status) ~1, lung, subset=(sex==2))
> 
> fit1
Call:
survreg(formula = Surv(time, status) ~ strata(sex), data = lung)

Coefficients:
(Intercept) 
   6.062171 

Scale:
    sex=1     sex=2 
0.8167551 0.6533036 

Loglik(model)= -1152.5   Loglik(intercept only)= -1152.5
n= 228 
> fit2
Call:
survreg(formula = Surv(time, status) ~ strata(sex) + sex, data = lung)

Coefficients:
(Intercept)         sex 
   5.494409    0.380171 

Scale:
    sex=1     sex=2 
0.8084294 0.6355816 

Loglik(model)= -1147.1   Loglik(intercept only)= -1152.5
	Chisq= 10.9 on 1 degrees of freedom, p= 0.00096 
n= 228 
> aeq(fit2$scale, c(fit3a$scale, fit3b$scale), tolerance=tol)
[1] TRUE
> aeq(fit2$loglik[2], (fit3a$loglik + fit3b$loglik)[2], tolerance=tol)
[1] TRUE
> aeq(fit2$coef[1] + 1:2*fit2$coef[2], c(fit3a$coef, fit3b$coef), tolerance=tol)
[1] TRUE
> 
> #penalized models
> fit1 <- survreg(Surv(time, status) ~ pspline(age, theta=.92)+strata(sex), lung)
> fit2 <- survreg(Surv(time, status) ~  pspline(age, theta=.92)+ 
+ 		strata(sex) + sex, lung)
> fit1
Call:
survreg(formula = Surv(time, status) ~ pspline(age, theta = 0.92) + 
    strata(sex), data = lung)

                          coef    se(coef) se2    Chisq DF   p      
(Intercept)                6.9036 0.8469   0.5688 66.45 1.00 3.3e-16
pspline(age, theta = 0.92 -0.0124 0.0067   0.0067  3.45 1.00 6.3e-02
pspline(age, theta = 0.92                          2.53 2.65 4.0e-01

Scale:
sex=1 sex=2 
0.807 0.654 

Iterations: 1 outer, 4 Newton-Raphson
     Theta= 0.92 
Degrees of freedom for terms= 0.5 3.6 2.0 
Likelihood ratio test=6.54  on 3.1 df, p=0.0937  n= 228 
> fit2
Call:
survreg(formula = Surv(time, status) ~ pspline(age, theta = 0.92) + 
    strata(sex) + sex, data = lung)

                          coef    se(coef) se2     Chisq DF   p      
(Intercept)                6.3729 0.84471  0.59118 56.92 1.00 4.5e-14
pspline(age, theta = 0.92 -0.0111 0.00666  0.00666  2.77 1.00 9.6e-02
pspline(age, theta = 0.92                           2.46 2.68 4.2e-01
sex                        0.3686 0.11711  0.11685  9.91 1.00 1.6e-03

Scale:
sex=1 sex=2 
0.800 0.636 

Iterations: 1 outer, 5 Newton-Raphson
     Theta= 0.92 
Degrees of freedom for terms= 0.5 3.7 1.0 2.0 
Likelihood ratio test=16.8  on 4.2 df, p=0.00245  n= 228 
> 
> age1 <- ifelse(lung$sex==1, lung$age, mean(lung$age))
> age2 <- ifelse(lung$sex==2, lung$age, mean(lung$age))
> fit3 <- survreg(Surv(time,status) ~ pspline(age1, theta=.92) +
+ 		pspline(age2, theta=.95) + sex + strata(sex), lung,
+ 		rel.tol=1e-6)
> fit3a<- survreg(Surv(time,status) ~pspline(age, theta=.92), lung, 
+ 		    subset=(sex==1))
> fit3b<- survreg(Surv(time,status) ~pspline(age, theta=.95), lung, 
+ 		     subset=(sex==2))
> 
> # relax the tolerance a little, since the above has lots of parameters
> #  I still don't exactly match the second group, but very close
> aeq(fit3$scale, c(fit3a$scale, fit3b$scale), tolerance=tol*10)
[1] "Mean relative  difference: 0.001270879"
> aeq(fit3$loglik[2], (fit3a$loglik + fit3b$loglik)[2], tolerance=tol*10)
[1] "Mean relative  difference: 8.673582e-05"
> pred <- predict(fit3)
> aeq(pred[lung$sex==1] , predict(fit3a), tolerance=tol*10)
[1] TRUE
> aeq(pred[lung$sex==2],  predict(fit3b), tolerance=tol*10)###????FIXME
[1] "Mean relative  difference: 0.01158253"
> 
> 
> 
> 
> #
> # Some tests using the rat data
> #
> rats <- read.table('../testfrail/data.rats', 
+ 		   col.names=c('litter', 'rx', 'time', 'status'))
> 
> rfitnull <- survreg(Surv(time, status) ~1, rats)
> temp <- rfitnull$scale^2 * pi^2/6
> cat("Effective n =", round(temp*(solve(rfitnull$var))[1,1],1), "\n")
Effective n = 65.8 
> 
> rfit0 <- survreg(Surv(time, status) ~ rx , rats)
> print(rfit0)
Call:
survreg(formula = Surv(time, status) ~ rx, data = rats)

Coefficients:
(Intercept)          rx 
  4.9831358  -0.2385112 

Scale= 0.2637875 

Loglik(model)= -242.3   Loglik(intercept only)= -246.3
	Chisq= 8 on 1 degrees of freedom, p= 0.0047 
n= 150 
> 
> rfit1 <- survreg(Surv(time, status) ~ rx + factor(litter), rats)
> temp <- rbind(c(rfit0$coef, rfit0$scale), c(rfit1$coef[1:2], rfit1$scale))
> dimnames(temp) <- list(c("rfit0", "rfit1"), c("Intercept", "rx", "scale"))
> temp
      Intercept         rx     scale
rfit0  4.983136 -0.2385112 0.2637875
rfit1  4.902438 -0.2189410 0.2025434
> 
> 
> rfit2a <- survreg(Surv(time, status) ~ rx +
+ 		  frailty.gaussian(litter, df=13, sparse=F), rats )
> rfit2b <- survreg(Surv(time, status) ~ rx +
+ 		  frailty.gaussian(litter, df=13, sparse=T), rats )
> 
> rfit3a <- coxph(Surv(time,status) ~ rx + 
+ 		  frailty.gaussian(litter, df=13, sparse=F), rats )
> rfit3b <- coxph(Surv(time,status) ~ rx + 
+ 		frailty(litter, df=13, dist='gauss'), rats)
> 
> temp <- cbind(rfit2a$coef[3:52], rfit2b$frail, rfit3a$coef[2:51], rfit3b$frail)
> dimnames(temp) <- list(NULL, c("surv","surv.sparse","cox","cox.sparse"))
> pairs(temp)
> apply(temp,2,var)/c(rfit2a$scale, rfit2b$scale, 1,1)^2
       surv surv.sparse         cox  cox.sparse 
  0.1346218   0.1346218   0.1224049   0.1207863 
> apply(temp,2,mean)
         surv   surv.sparse           cox    cox.sparse 
 6.546887e-19  1.242665e-18 -1.301043e-17 -2.279128e-18 
> 
> # The parametric model gives the coefficients less variance for the
> #  two fits, for the same df, but the scaled results are similar.
> # 13 df is near to the rmle for the rats
> 
> rm(temp, rfit2a, rfit2b, rfit3a, rfit3b, rfitnull, rfit0, rfit1)
> options(na.action="na.exclude")
> temp <- matrix(scan("data.mpip", skip=23), ncol=13, byrow=T)
> dimnames(temp) <- list(NULL, c('ved', 'angina', 'education', 'prior.mi',
+                      'nyha', 'rales', 'ef', 'ecg', 'angina2', 'futime', 
+                      'status', 'admit', 'betab'))
>  
> mpip <- data.frame(temp)
> lved <- log(mpip$ved + .02)
> 
> fit1 <- coxph(Surv(futime, status) ~ pspline(lved) + factor(nyha) + 
+ 	      rales + pspline(ef), mpip)
> 
> temp <- predict(fit1, type='terms', se.fit=T)
> yy <- cbind(temp$fit[,4], temp$fit[,4] + 1.96*temp$se[,4],
+ 	                  temp$fit[,4] - 1.96*temp$se[,4])
> index <- order(mpip$ef)
> index<-index[!is.na(yy[index,1])]
> matplot(mpip$ef[index], yy[index,], type='l', lty=c(1,2,2), col=1,
+         xlab="Ejection Fraction", ylab="Cox model risk", 
+         main="Post-Infarction Survival")
> 
> fit2 <- coxph(Surv(futime, status) ~ lved + factor(nyha) + rales +
+ 	      pspline(ef, df=0), mpip)
> temp <- predict(fit2, type='terms', se.fit=T)
> yy <- cbind(temp$fit[,4], temp$fit[,4] + 1.96*temp$se[,4],
+ 	                  temp$fit[,4] - 1.96*temp$se[,4])
> matplot(mpip$ef[index], yy[index,], type='l', lty=c(1,2,2), col=1,
+         xlab="Ejection Fraction", ylab="Cox model risk", 
+         main="Post-Infarction Survival, AIC")
> 
> 
> fit3 <- survreg(Surv(futime, status) ~ lved + factor(nyha) + rales +
+ 		pspline(ef, df=2), mpip, dist='lognormal')
> temp <- predict(fit3, type='terms', se.fit=T)
> yy <- cbind(temp$fit[,4], temp$fit[,4] + 1.96*temp$se[,4],
+ 	                  temp$fit[,4] - 1.96*temp$se[,4])
> matplot(mpip$ef[index], yy[index,], type='l', lty=c(1,2,2), col=1,
+         xlab="Ejection Fraction", ylab="Log-normal model predictor", 
+         main="Post-Infarction Survival")
> q()