R version 4.5.0 RC (2025-04-04 r88113 ucrt) -- "How About a Twenty-Six"
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Platform: x86_64-w64-mingw32/x64

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> library(plasma)
> fls <- try(loadESCAdata())
> if (inherits(fls, "try-error")) {
+   stop("Unable to load data from remote server.")
+ }
> ls()
[1] "fls"
> ## prepare MultiOmics
> MO <- with(plasmaEnv, prepareMultiOmics(assemble, Outcome))
> MO <- MO[c("ClinicalBin", "ClinicalCont", "RPPA"),]
> summary(MO)
Datasets:
     ClinicalBin ClinicalCont RPPA
[1,]          53            6  192
[2,]         185          185  185
Outcomes:
   patient_id  vital_status days_to_death    days_to_last_followup
 a3i8   :  1   alive:108    Min.   :   9.0   Min.   :   4.0       
 a3ql   :  1   dead : 77    1st Qu.: 180.0   1st Qu.: 336.5       
 a3y9   :  1                Median : 351.0   Median : 402.5       
 a3ya   :  1                Mean   : 495.2   Mean   : 570.1       
 a3yb   :  1                3rd Qu.: 650.0   3rd Qu.: 696.8       
 a3yc   :  1                Max.   :2532.0   Max.   :3714.0       
 (Other):179                NA's   :108      NA's   :77           
      Days       
 Min.   :   4.0  
 1st Qu.: 232.0  
 Median : 400.0  
 Mean   : 538.9  
 3rd Qu.: 681.0  
 Max.   :3714.0  
                 
> ## test complete cox models
> set.seed(12345)
> train <- rep(FALSE, 185)
> train[sample(185, 113)] <- TRUE
> MO2 <- MO[, train]
> summary(MO2)
Datasets:
     ClinicalBin ClinicalCont RPPA
[1,]          53            6  192
[2,]         113          113  113
Outcomes:
   patient_id  vital_status days_to_death    days_to_last_followup
 a3ql   :  1   alive:64     Min.   :   9.0   Min.   :  11.0       
 a3y9   :  1   dead :49     1st Qu.: 157.0   1st Qu.: 318.8       
 a3ya   :  1                Median : 351.0   Median : 404.5       
 a3yc   :  1                Mean   : 494.6   Mean   : 572.7       
 a43c   :  1                3rd Qu.: 600.0   3rd Qu.: 652.8       
 a43h   :  1                Max.   :2532.0   Max.   :3714.0       
 (Other):107                NA's   :64       NA's   :49           
      Days       
 Min.   :   9.0  
 1st Qu.: 238.0  
 Median : 400.0  
 Mean   : 538.8  
 3rd Qu.: 632.0  
 Max.   :3714.0  
                 
> bigfit <- fitCoxModels(MO2, timevar = "Days",
+                        eventvar = "vital_status",
+                        eventvalue = "dead")
Fitting model with  ClinicalBin 
____************************************************____
____There are some NAs in X but not in Y____
____Component____ 1 ____
____Component____ 2 ____
____Component____ 3 ____
____Predicting X with NA in X and not in Y____
****________________________________________________****

Fitting model with  ClinicalCont 
____************************************************____
____There are some NAs in X but not in Y____
____Component____ 1 ____
____Component____ 2 ____
____Predicting X with NA in X and not in Y____
****________________________________________________****

Fitting model with  RPPA 
____************************************************____
____Component____ 1 ____
____Component____ 2 ____
____Component____ 3 ____
____Predicting X without NA neither in X nor in Y____
****________________________________________________****

Warning messages:
1: In coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1 ; coefficient may be infinite. 
2: In coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2 ; coefficient may be infinite. 
3: In coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1,2,3 ; coefficient may be infinite. 
> ## test core methods
> class(bigfit)
[1] "MultiplePLSCoxModels"
attr(,"package")
[1] "plasma"
> summary(bigfit)
An object containing MultiplePLSCoxModels based on:
[1] "ClinicalBin"  "ClinicalCont" "RPPA"        
> getSizes(bigfit)
             NT cNT      p.value
ClinicalBin   3   3 5.363395e-06
ClinicalCont  2   2 5.056035e-01
RPPA          3   3 6.993030e-11
> plot(bigfit)
> preds <- predict(bigfit)
> ## make sure we can predict on new data
> testobj <- MO[, !train]
> 
> predsB <- predict(bigfit, newdata = testobj)
> predsR <- predict(bigfit, newdata = testobj, type = "risk")
> predsS <- predict(bigfit, newdata = testobj, type = "split")
> 
> proc.time()
   user  system elapsed 
   9.76    1.21   12.34