Running examples in ‘tram-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: tram-methods
> ### Title: Methods for Stratified Linear Transformation Models
> ### Aliases: as.mlt.tram model.frame.tram model.matrix.tram
> ###   model.matrix.stram coef.tram coef.Lm coef.Survreg vcov.tram
> ###   logLik.tram estfun.tram predict.tram predict.stram residuals.tram
> ###   plot.tram plot.ROCtram PI PI.tram PI.default OVL OVL.tram OVL.default
> ###   TV TV.tram TV.default L1 L1.tram L1.default ROC ROC.tram ROC.default
> 
> ### ** Examples
> 
> 
>     data("BostonHousing2", package = "mlbench")
> 
>     ### fit non-normal Box-Cox type linear model with two
>     ### baseline functions (for houses near and off Charles River)
>     BC_BH_2 <- BoxCox(cmedv | 0 + chas ~ crim + zn + indus + nox + 
+                       rm + age + dis + rad + tax + ptratio + b + lstat,
+                       data = BostonHousing2)
>     logLik(BC_BH_2)
'log Lik.' -1334.509 (df=26)
> 
>     ### classical likelihood inference
>     summary(BC_BH_2)

 (Stratified) Non-normal (Box-Cox-Type) Linear Regression Model 

Call:
BoxCox(formula = cmedv | 0 + chas ~ crim + zn + indus + nox + 
    rm + age + dis + rad + tax + ptratio + b + lstat, data = BostonHousing2)

Coefficients:
          Estimate Std. Error z value Pr(>|z|)    
crim    -0.0467906  0.0074130  -6.312 2.76e-10 ***
zn       0.0061513  0.0029332   2.097    0.036 *  
indus    0.0140681  0.0131135   1.073    0.283    
nox     -4.9487919  0.8464110  -5.847 5.01e-09 ***
rm       0.4368418  0.0948225   4.607 4.09e-06 ***
age     -0.0016568  0.0028368  -0.584    0.559    
dis     -0.2991249  0.0437646  -6.835 8.21e-12 ***
rad      0.0811888  0.0142694   5.690 1.27e-08 ***
tax     -0.0037180  0.0008041  -4.624 3.76e-06 ***
ptratio -0.2184113  0.0285718  -7.644 2.11e-14 ***
b        0.0026673  0.0005776   4.618 3.87e-06 ***
lstat   -0.1669453  0.0123199 -13.551  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Log-Likelihood:
 -1334.509 (df = 26)
Likelihood-ratio Test: Chisq = 817.2314 on 12 degrees of freedom; p = < 2.2e-16

> 
>     ### coefficients of the linear predictor
>     coef(BC_BH_2)
        crim           zn        indus          nox           rm          age 
-0.046790563  0.006151332  0.014068066 -4.948791933  0.436841809 -0.001656839 
         dis          rad          tax      ptratio            b        lstat 
-0.299124862  0.081188783 -0.003717984 -0.218411314  0.002667338 -0.166945349 
> 
>     ### plot linear predictor (mean of _transformed_ response) 
>     ### vs. observed values
>     plot(predict(BC_BH_2, type = "lp"), BostonHousing2$cmedv)
> 
>     ### all coefficients
>     coef(BC_BH_2, with_baseline = TRUE)
Bs1(cmedv):chas0 Bs2(cmedv):chas0 Bs3(cmedv):chas0 Bs4(cmedv):chas0 
   -13.299892745    -11.572055177    -11.572055179     -4.413500468 
Bs5(cmedv):chas0 Bs6(cmedv):chas0 Bs7(cmedv):chas0 Bs1(cmedv):chas1 
    -2.922815888     -2.922815896     -2.176114273    -17.466541536 
Bs2(cmedv):chas1 Bs3(cmedv):chas1 Bs4(cmedv):chas1 Bs5(cmedv):chas1 
   -17.466541536     -8.197577847     -4.375130082     -4.375130049 
Bs6(cmedv):chas1 Bs7(cmedv):chas1             crim               zn 
    -4.375130134     -3.375504890     -0.046790563      0.006151332 
           indus              nox               rm              age 
     0.014068066     -4.948791933      0.436841809     -0.001656839 
             dis              rad              tax          ptratio 
    -0.299124862      0.081188783     -0.003717984     -0.218411314 
               b            lstat 
     0.002667338     -0.166945349 
> 
>     ### compute predicted median along with 10% and 90% quantile for the first
>     ### observations
>     predict(BC_BH_2, newdata = BostonHousing2[1:3,], type = "quantile",
+             prob = c(.1, .5, .9))
       
prob        [,1]     [,2]     [,3]
    0.1 22.77239 19.83903 23.39704
    0.5 27.98349 23.65863 29.08549
    0.9 38.50440 29.55907 41.48202
> 
>     ### plot the predicted density for these observations
>     plot(BC_BH_2, newdata = BostonHousing2[1:3, -1],
+          which = "distribution", type = "density", K = 1000)
> 
>     ### evaluate the two baseline transformations, with confidence intervals
>     nd <- model.frame(BC_BH_2)[1:2, -1]
>     nd$chas <- factor(c("0", "1"))
>     library("colorspace")
>     col <- diverge_hcl(2, h = c(246, 40), c = 96, l = c(65, 90))
>     fill <- diverge_hcl(2, h = c(246, 40), c = 96, l = c(65, 90), alpha = .3)
>     plot(BC_BH_2, which = "baseline only", newdata = nd, col = col,
+          confidence = "interval", fill = fill, lwd = 2,
+          xlab = "Median Value", ylab = expression(h[Y]))
>     legend("bottomright", lty = 1, col = col, 
+             title = "Near Charles River", legend = c("no", "yes"), bty = "n")
> 
>     ### cars data; with quantile functions
>     plot(dist ~ speed, data = cars)
>     m <- Colr(dist ~ speed, data = cars)
>     q <- predict(as.mlt(m), newdata = data.frame(speed = s <- 7:20),
+                  type = "quantile", prob = c(1, 5, 9) / 10)
>     lines(s, q[1,])
>     lines(s, q[2,])
>     lines(s, q[3,])
> 
>     nd <- data.frame(speed = s <- as.double(1:5 * 5))
>     
>     # Prob(dist at speed s > dist at speed 0)
>     # speed 0 is reference, not a good choice here
>     PI(m, newdata = nd)
          [,1]     [,2]      [,3]      [,4]      [,5]
[1,] 0.8593495 0.978335 0.9975546 0.9997618 0.9999786
> 
>     # Prob(dist at speed s > dist at speed 15)
>     lp15 <- c(predict(m, newdata = data.frame(speed = 15)))
>     PI(m, newdata = nd, reference = lp15)
           [,1]      [,2] [,3]      [,4]     [,5]
[1,] 0.02166504 0.1406505  0.5 0.8593495 0.978335
>     PI(m, newdata = nd, reference = nd[3,,drop = FALSE])
           [,1]      [,2] [,3]      [,4]     [,5]
[1,] 0.02166504 0.1406505  0.5 0.8593495 0.978335
> 
>     # Prob(dist at speed s' > dist at speed s)
>     PI(m, newdata = nd, reference = nd)
          1         2         3         4
2 0.8593495                              
3 0.9783350 0.8593495                    
4 0.9975546 0.9783350 0.8593495          
5 0.9997618 0.9975546 0.9783350 0.8593495
>     # essentially:
>     lp <- predict(m, newdata = nd)
>     PI(object = dist(lp))
          1         2         3         4
2 0.8593495                              
3 0.9783350 0.8593495                    
4 0.9975546 0.9783350 0.8593495          
5 0.9997618 0.9975546 0.9783350 0.8593495
>     # same, with simultaneous confidence intervals
>     PI(m, newdata = nd, reference = nd, conf.level = .95)
     Estimate       lwr       upr
1-2 0.8593495 0.7811056 0.9141291
1-3 0.9783350 0.9324841 0.9937147
2-3 0.8593495 0.7811056 0.9141291
1-4 0.9975546 0.9835574 0.9996740
2-4 0.9783350 0.9324841 0.9937147
3-4 0.8593495 0.7811056 0.9141291
1-5 0.9997618 0.9965137 0.9999854
2-5 0.9975546 0.9835574 0.9996740
3-5 0.9783350 0.9324841 0.9937147
4-5 0.8593495 0.7811056 0.9141291
attr(,"conf.level")
[1] 0.95
attr(,"calpha")
[1] 1.960362
> 
>     # plot ROC curves + confidence bands
>     # compare speed 20 and 25 to speed 15
>     plot(ROC(m, newdata = nd[4:5,,drop = FALSE],
+              reference = nd[3,,drop = FALSE],
+              conf.level = 0.95))
> 
>     # Overlap of conditional densities at speed s' and s
>     OVL(m, newdata = nd, reference = nd)
            1           2           3           4
2 0.419779466                                    
3 0.131832586 0.419779466                        
4 0.036802262 0.131832586 0.419779466            
5 0.009910296 0.036802262 0.131832586 0.419779466
> 
>     ### ROC analysis (takes too long for CRAN Windows)
>     if (require("mlbench") && .Platform$OS.type != "windows") {
+ 
+         layout(matrix(1:4, nrow = 2))
+         data("PimaIndiansDiabetes2", package = "mlbench")
+         dia <- sort(unique(PimaIndiansDiabetes2$diabetes))
+         nd <- data.frame(diabetes = dia, 
+                          age = 29, mass = 32) ### median values
+ 
+         ### unconditional ROC analysis: glucose tolerance test
+         m0 <- Colr(glucose ~ diabetes, data = PimaIndiansDiabetes2)
+         # ROC curve + confidence band
+         plot(ROC(m0, newdata = nd[2,,drop = FALSE], conf.level = .95)) 
+         # Wald interval for AUC
+         PI(m0, newdata = nd[2,,drop = FALSE], conf.level = .95)
+         # score interval for AUC
+         PI(-c(coef(m0), score_test(m0)$conf.int[2:1]))
+ 
+         ### adjusted ROC analysis for age and mass
+         m1 <- Colr(glucose ~ diabetes + age + mass, data = PimaIndiansDiabetes2)
+         # ROC curve + confidence band (this is the same for all ages /
+         # masses)
+         plot(ROC(m1, newdata = nd[2,,drop = FALSE], 
+                      reference = nd[1,,drop = FALSE], 
+                  conf.level = .95))
+         # Wald interval for adjusted AUC
+         PI(m1, newdata = nd[2,,drop = FALSE], reference = nd[1,,drop = FALSE], 
+            conf.level = .95)
+         # Score interval for adjusted AUC
+         PI(-c(coef(m1)[1], score_test(m1, names(coef(m1))[1])$conf.int[2:1]))
+ 
+         ### conditional ROC analysis: AUC regression ~ age + mass
+         m2 <- Colr(glucose ~ diabetes * (age + mass), data = PimaIndiansDiabetes2)
+         # ROC curve for a person with age = 29 and mass = 32
+         plot(ROC(m2, newdata = nd[2,,drop = FALSE], 
+                      reference = nd[1,,drop = FALSE], 
+                  conf.level = .95))
+         # AUC for persons ages 21:81, all with mass = 32
+         nd1 <- data.frame(diabetes = nd[1,"diabetes"], age = 21:81, mass = 32)
+         nd2 <- data.frame(diabetes = nd[2,"diabetes"], age = 21:81, mass = 32)
+         auc <- PI(m2, newdata = nd2, reference = nd1, one2one = TRUE,
+                   conf.level = 0.95)
+         plot(nd1$age, auc[, "Estimate"], xlab = "Age (in years)", ylab =
+              "AUC", ylim = c(0, 1), type = "l")
+         lines(nd1$age, auc[, "lwr"], lty = 3)
+         lines(nd1$age, auc[, "upr"], lty = 3)
+     }
Loading required package: mlbench
Warning in data("PimaIndiansDiabetes2", package = "mlbench") :
  data set ‘PimaIndiansDiabetes2’ not found
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
            user system elapsed
mmlt       4.928  0.149   7.401
perm_test  4.146  0.135   6.078
score_test 3.967  0.147   5.125