Running 'MLcens-savedvalues.R' [0s/1s]
  Running 'MLcensreg-savedvalues.R' [2s/3s]
  Running 'MMnpcensreg-savedvalues.R' [1s/1s]
  Running 'QTau-savedvalues.R' [0s/1s]
  Running 'Snpcensreg-savedvalues.R' [1s/1s]
  Running 'TQTau-savedvalues.R' [0s/0s]
  Running 'TWQ-savedvalues.R' [0s/0s]
  Running 'TWQTau-savedvalues.R' [0s/1s]
  Running 'WL-savedvalues.R' [0s/1s]
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  Running 'loggammacenslmrob.ML.R' [9s/10s]
  Running 'loggammacenslmrob.MM.R' [6s/7s]
  Running 'loggammacenslmrob.S.R' [8s/10s]
  Running 'loggammacenslmrob.oneTML.R' [10s/12s]
  Running 'loggammacenslmrob.oneWL.R' [9s/10s]
  Running 'loggammacensrob.ML.R' [4s/4s]
  Running 'loggammacensrob.TQTatu.R' [4s/4s]
  Running 'loggammacensrob.TWQTatu.R' [5s/6s]
  Running 'loggammacensrob.oneTML.R' [5s/6s]
  Running 'loggammacensrob.oneWL.R' [4s/4s]
  Running 'loggammarob.QTau.R' [3s/3s]
  Running 'loggammarob.WL.R' [4s/4s]
  Running 'loggammarob.WQTau.R' [3s/4s]
  Running 'loggammarob.oneWL.R' [3s/4s]
  Running 'oneTMLemp-savedvalues.R' [0s/1s]
  Running 'oneTMLregemp-savedvalues.R' [2s/2s]
  Running 'oneWL-savedvalues.R' [0s/1s]
  Running 'oneWLemp-savedvalues.R' [0s/1s]
  Running 'oneWLexp-savedvalues.R' [0s/1s]
  Running 'oneWLregemp-savedvalues.R' [2s/3s]
  Running 'sumWL-savedvalues.R' [0s/1s]
  Running 'sumoneWL-savedvalues.R' [0s/0s]
Running the tests in 'tests/loggammacenslmrob.ML.R' failed.
Complete output:
  > library(robustloggamma)
  > 
  > do.test <- TRUE
  > if (!do.test) {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   ML <- loggammacenslmrob(y~X, delta=delta, method="ML")
  +   ML$call <- NULL
  +   dput(ML, file='MLcensreg-savedvalues.R')
  + } else {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   MLTest <- loggammacenslmrob(y~X, delta=delta, method="ML")
  +   MLTest$call <- NULL
  +   ML <- dget(file='MLcensreg-savedvalues.R')
  +   stopifnot(
  +     all.equal(MLTest, ML, tol = 2e-6)
  +   )
  + }
  Error: MLTest and ML are not equal:
    Component "mu": Mean relative difference: 1.324353e-05
    Component "coefficients": Mean relative difference: 4.916313e-06
    Component "sigma": Mean relative difference: 4.089281e-06
    ....
  In addition: Warning message:
  In survreg.fit(X, Y, weights, offset, init = init, controlvals = control,  :
    Ran out of iterations and did not converge
  Execution halted
Running the tests in 'tests/loggammacenslmrob.MM.R' failed.
Complete output:
  > library(robustloggamma)
  > 
  > do.test <- TRUE
  > if (!do.test) {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   IX <- cbind(1,X)  
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   set.seed(2345)
  +   MMNP <- robustloggamma:::loggammacenslmrob.MM(IX,y,delta,control=robustloggamma:::loggammacenslmrob.MM.control())
  +   dput(MMNP, file='MMnpcensreg-savedvalues.R')
  + } else {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   IX <- cbind(1,X)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   MMNPTest <- robustloggamma:::loggammacenslmrob.MM(IX,y,delta,control=robustloggamma:::loggammacenslmrob.MM.control())  
  +   MMNP <- dget(file='MMnpcensreg-savedvalues.R')
  +   stopifnot(
  +     all.equal(MMNPTest, MMNP, tol = 2e-6)
  +   )
  + }
  Error: MMNPTest and MMNP are not equal:
    Component "coefficients": Mean relative difference: 0.007036949
    Component "scale": Mean relative difference: 0.002941794
    Component "fitted.values": Mean relative difference: 0.005523447
    ....
  In addition: Warning message:
  In survreg.fit(X, Y, weights, offset, init = init, controlvals = control,  :
    Ran out of iterations and did not converge
  Execution halted
Running the tests in 'tests/loggammacenslmrob.S.R' failed.
Complete output:
  > library(robustloggamma)
  > 
  > do.test <- TRUE
  > if (!do.test) {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   IX <- cbind(1,X)  
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   set.seed(2345)
  +   SNP <- robustloggamma:::loggammacenslmrob.S(IX,y,delta,control=robustloggamma:::loggammacenslmrob.S.control())
  +   dput(SNP, file='Snpcensreg-savedvalues.R')
  + } else {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   IX <- cbind(1,X)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   SNPTest <- robustloggamma:::loggammacenslmrob.S(IX,y,delta,control=robustloggamma:::loggammacenslmrob.S.control())  
  +   SNP <- dget(file='Snpcensreg-savedvalues.R')
  +   stopifnot(
  +     all.equal(SNPTest, SNP, tol = 2e-5)
  +   )
  + }
  Error: SNPTest and SNP are not equal:
    Component "coefficients": Mean relative difference: 0.05346586
    Component "scale": Mean relative difference: 0.0008444163
    Component "fitted.values": Mean relative difference: 0.07052369
    ....
  Execution halted
Running the tests in 'tests/loggammacenslmrob.oneTML.R' failed.
Complete output:
  > library(robustloggamma)
  > 
  > do.test <- TRUE
  > if (!do.test) {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   TMLoneemp <- loggammacenslmrob(y~X, delta=delta, method="oneTML")
  +   TMLoneemp$call <- NULL
  +   dput(TMLoneemp, file='oneTMLregemp-savedvalues.R')
  + } else {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   TMLoneempTest <- loggammacenslmrob(y~X, delta=delta, method="oneTML")
  +   TMLoneempTest$call <- NULL
  +   TMLoneemp <- dget(file='oneTMLregemp-savedvalues.R')
  +   stopifnot(
  +     all.equal(TMLoneempTest, TMLoneemp, tol = 2e-6)
  +   )
  + }
  Error: TMLoneempTest and TMLoneemp are not equal:
    Component "coefficients": Mean relative difference: 0.007907289
    Component "mu": Mean relative difference: 0.01296166
    Component "sigma": Mean relative difference: 0.05503404
    ....
  In addition: Warning message:
  In survreg.fit(X, Y, weights, offset, init = init, controlvals = control,  :
    Ran out of iterations and did not converge
  Execution halted
Running the tests in 'tests/loggammacenslmrob.oneWL.R' failed.
Complete output:
  > library(robustloggamma)
  > 
  > do.test <- TRUE
  > if (!do.test) {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   WLoneemp <- loggammacenslmrob(y~X, delta=delta, method="oneWL")
  +   WLoneemp$call <- NULL
  +   dput(WLoneemp, file='oneWLregemp-savedvalues.R')
  + } else {
  +   n <- 200
  +   p <- 4
  +   set.seed(1234)
  +   X <- matrix(rnorm(p*n, sd=2), ncol=p)
  +   mu <- 2
  +   beta <- rep(2,p)
  +   sigma <- 2
  +   lambda <- 1
  +   linear <- mu + X%*%beta
  +   y <- rloggamma(n=n, mu=linear, sigma=sigma, lambda=lambda)
  +   cens <- rloggamma(n=n, mu=linear+2.5, sigma=sigma, lambda=lambda)
  +   delta <- as.numeric(y <= cens)
  +   y[delta==0] <- cens[delta==0]
  +   w <- rep(1,n)
  +   set.seed(2345)
  +   WLoneempTest <- loggammacenslmrob(y~X, delta=delta, method="oneWL")
  +   WLoneempTest$call <- NULL
  +   WLoneemp <- dget(file='oneWLregemp-savedvalues.R')
  +   stopifnot(
  +     all.equal(WLoneempTest, WLoneemp, tol = 2e-6)
  +   )
  + }
  Error: WLoneempTest and WLoneemp are not equal:
    Component "coefficients": Mean relative difference: 0.008262107
    Component "mu": Mean relative difference: 0.02474369
    Component "sigma": Mean relative difference: 0.00240464
    ....
  In addition: Warning message:
  In survreg.fit(X, Y, weights, offset, init = init, controlvals = control,  :
    Ran out of iterations and did not converge
  Execution halted