CRAN Package Check Results for Package smoothSurv

Last updated on 2017-08-21 09:48:23.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.6 26.45 43.55 70.00 ERROR
r-devel-linux-x86_64-debian-gcc 1.6 19.99 44.41 64.39 ERROR
r-devel-linux-x86_64-fedora-clang 1.6 151.53 NOTE
r-devel-linux-x86_64-fedora-gcc 1.6 132.17 NOTE
r-devel-windows-ix86+x86_64 1.6 66.00 88.00 154.00 ERROR
r-patched-linux-x86_64 1.6 19.53 46.31 65.84 NOTE
r-patched-solaris-x86 1.6 170.20 OK
r-release-linux-x86_64 1.6 19.34 43.79 63.13 NOTE
r-release-windows-ix86+x86_64 1.6 101.00 104.00 205.00 OK
r-release-osx-x86_64 1.6 OK
r-oldrel-windows-ix86+x86_64 1.6 68.00 83.00 151.00 OK
r-oldrel-osx-x86_64 1.6 OK

Check Details

Version: 1.6
Check: compiled code
Result: NOTE
    File ‘smoothSurv/libs/smoothSurv.so’:
     Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’
    
    It is good practice to register native routines and to disable symbol
    search.
    
    See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.6
Check: examples
Result: ERROR
    Running examples in ‘smoothSurv-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: smoothSurvReg
    > ### Title: Regression for a Survival Model with Smoothed Error Distribution
    > ### Aliases: smoothSurvReg
    > ### Keywords: survival smooth
    >
    > ### ** Examples
    >
    > ##### EXAMPLE 1: Common scale
    > ##### ========================
    > ### We generate interval censored data and fit a model with few artificial covariates
    > set.seed(221913282)
    > x1 <- rbinom(50, 1, 0.4) ## binary covariate
    > x2 <- rnorm(50, 180, 10) ## continuous covariate
    > y1 <- 0.5*x1 - 0.01*x2 + 0.005 *x1*x2 + 1.5*rnorm(50, 0, 1) ## generate log(T), left limit
    > t1 <- exp(y1) ## left limit of the survival time
    > t2 <- t1 + rgamma(50, 1, 1) ## right limit of the survival time
    > surv <- Surv(t1, t2, type = "interval2") ## survival object
    >
    > ## Fit the model with an interaction
    > fit1 <- smoothSurvReg(surv ~ x1 * x2, logscale = ~1, info = FALSE, lambda = exp(2:(-1)))
    
    Fit with Log(Lambda) = 2, AIC(7.389056) = -74.86754, df(7.389056) = 5.767029, 5 iterations, fail = 0
    Fit with Log(Lambda) = 1, AIC(2.718282) = -74.84135, df(2.718282) = 6.111356, 21 iterations, fail = 0
    Fit with Log(Lambda) = 0, AIC(1) = -76.18611, df(1) = 8.265108, 11 iterations, fail = 0
    Fit with Log(Lambda) = -1, AIC(0.3678794) = -75.3578, df(0.3678794) = 8.377794, 9 iterations, fail = 0
    >
    > ## Print the summary information
    > summary(fit1, spline = TRUE)
    Call:
    smoothSurvReg(formula = surv ~ x1 * x2, logscale = ~1, lambda = exp(2:(-1)),
     info = FALSE)
    
    Estimated Regression Coefficients:
     Value Std.Error Std.Error2 Z Z2 p p2
    (Intercept) -1.802764 3.27586 3.06748 -0.5503 -0.5877 0.58210 0.55673
    x1 -3.778410 4.39325 4.28869 -0.8600 -0.8810 0.38976 0.37831
    x2 0.006314 0.01804 0.01693 0.3501 0.3729 0.72630 0.70920
    x1:x2 0.025362 0.02438 0.02378 1.0403 1.0664 0.29821 0.28625
    Log(scale) -0.251375 0.13899 0.12857 -1.8086 -1.9551 0.07052 0.05057
    
    Scale = 0.7777
    
    Details on (Fitted) Error Distribution:
     Knot SD basis c coef. Std.Error.c Std.Error2.c Z Z2
    knot[1] -6.0 0.2 3.819e-07 8.967e-06 6.490e-06 0.04259 0.05884
    knot[2] -5.7 0.2 9.662e-07 1.930e-05 1.426e-05 0.05005 0.06773
    knot[3] -5.4 0.2 2.387e-06 4.010e-05 3.029e-05 0.05954 0.07881
    knot[4] -5.1 0.2 5.761e-06 8.019e-05 6.204e-05 0.07184 0.09286
    knot[5] -4.8 0.2 1.358e-05 1.540e-04 1.223e-04 0.08815 0.11105
    knot[6] -4.5 0.2 3.125e-05 2.832e-04 2.312e-04 0.11035 0.13518
    knot[7] -4.2 0.2 7.026e-05 4.964e-04 4.177e-04 0.14155 0.16819
    knot[8] -3.9 0.2 1.542e-04 8.242e-04 7.173e-04 0.18715 0.21505
    knot[9] -3.6 0.2 3.307e-04 1.286e-03 1.161e-03 0.25718 0.28488
    knot[10] -3.3 0.2 6.924e-04 1.863e-03 1.749e-03 0.37163 0.39589
    knot[11] -3.0 0.2 1.416e-03 2.464e-03 2.401e-03 0.57443 0.58954
    knot[12] -2.7 0.2 2.824e-03 2.908e-03 2.878e-03 0.97123 0.98120
    knot[13] -2.4 0.2 5.494e-03 3.049e-03 2.690e-03 1.80172 2.04220
    knot[14] -2.1 0.2 1.040e-02 3.436e-03 7.191e-04 3.02574 14.45721
    knot[15] -1.8 0.2 1.903e-02 5.720e-03 1.340e-03 3.32671 14.19884
    knot[16] -1.5 0.2 3.331e-02 9.984e-03 7.371e-03 3.33631 4.51886
    knot[17] -1.2 0.2 5.469e-02 1.443e-02 1.342e-02 3.79149 4.07491
    knot[18] -0.9 0.2 8.211e-02 1.667e-02 1.592e-02 4.92634 5.15898
    knot[19] -0.6 0.2 1.098e-01 1.615e-02 1.055e-02 6.79988 10.40344
    knot[20] -0.3 0.2 1.283e-01 1.625e-02 NaN 7.90012 NaN
    knot[21] 0.0 0.2 1.307e-01 1.802e-02 8.661e-03 7.25246 15.09289
    knot[22] 0.3 0.2 1.174e-01 1.800e-02 1.539e-02 6.52304 7.62716
    knot[23] 0.6 0.2 9.515e-02 1.589e-02 1.365e-02 5.98918 6.96938
    knot[24] 0.9 0.2 7.137e-02 1.332e-02 6.426e-03 5.35747 11.10517
    knot[25] 1.2 0.2 5.062e-02 1.071e-02 NaN 4.72456 NaN
    knot[26] 1.5 0.2 3.433e-02 7.942e-03 NaN 4.32307 NaN
    knot[27] 1.8 0.2 2.225e-02 5.506e-03 1.322e-03 4.04150 16.82506
    knot[28] 2.1 0.2 1.366e-02 4.080e-03 3.284e-03 3.34741 4.15852
    knot[29] 2.4 0.2 7.862e-03 3.491e-03 2.632e-03 2.25212 2.98768
    knot[30] 2.7 0.2 4.211e-03 2.988e-03 1.348e-03 1.40941 3.12511
    knot[31] 3.0 0.2 2.089e-03 2.299e-03 NaN 0.90894 NaN
    knot[32] 3.3 0.2 9.583e-04 1.557e-03 NaN 0.61539 NaN
    knot[33] 3.6 0.2 4.061e-04 9.332e-04 NaN 0.43520 NaN
    knot[34] 3.9 0.2 1.590e-04 4.982e-04 NaN 0.31908 NaN
    knot[35] 4.2 0.2 5.747e-05 2.384e-04 NaN 0.24101 NaN
    knot[36] 4.5 0.2 1.919e-05 1.028e-04 NaN 0.18665 NaN
    knot[37] 4.8 0.2 5.915e-06 4.006e-05 NaN 0.14764 NaN
    knot[38] 5.1 0.2 1.684e-06 1.416e-05 NaN 0.11893 NaN
    knot[39] 5.4 0.2 4.429e-07 4.551e-06 NaN 0.09732 NaN
    knot[40] 5.7 0.2 1.076e-07 1.332e-06 NaN 0.08074 NaN
    knot[41] 6.0 0.2 2.413e-08 3.558e-07 NaN 0.06780 NaN
     p p2
    knot[1] 9.660e-01 9.531e-01
    knot[2] 9.601e-01 9.460e-01
    knot[3] 9.525e-01 9.372e-01
    knot[4] 9.427e-01 9.260e-01
    knot[5] 9.298e-01 9.116e-01
    knot[6] 9.121e-01 8.925e-01
    knot[7] 8.874e-01 8.664e-01
    knot[8] 8.515e-01 8.297e-01
    knot[9] 7.970e-01 7.757e-01
    knot[10] 7.102e-01 6.922e-01
    knot[11] 5.657e-01 5.555e-01
    knot[12] 3.314e-01 3.265e-01
    knot[13] 7.159e-02 4.113e-02
    knot[14] 2.480e-03 2.258e-47
    knot[15] 8.788e-04 9.314e-46
    knot[16] 8.490e-04 6.217e-06
    knot[17] 1.497e-04 4.603e-05
    knot[18] 8.378e-07 2.483e-07
    knot[19] 1.047e-11 2.392e-25
    knot[20] 2.786e-15 NaN
    knot[21] 4.093e-13 1.804e-51
    knot[22] 6.889e-11 2.400e-14
    knot[23] 2.109e-09 3.183e-12
    knot[24] 8.440e-08 1.184e-28
    knot[25] 2.306e-06 NaN
    knot[26] 1.539e-05 NaN
    knot[27] 5.311e-05 1.599e-63
    knot[28] 8.157e-04 3.203e-05
    knot[29] 2.431e-02 2.811e-03
    knot[30] 1.587e-01 1.777e-03
    knot[31] 3.634e-01 NaN
    knot[32] 5.383e-01 NaN
    knot[33] 6.634e-01 NaN
    knot[34] 7.497e-01 NaN
    knot[35] 8.095e-01 NaN
    knot[36] 8.519e-01 NaN
    knot[37] 8.826e-01 NaN
    knot[38] 9.053e-01 NaN
    knot[39] 9.225e-01 NaN
    knot[40] 9.356e-01 NaN
    knot[41] 9.459e-01 NaN
    
    Penalized Loglikelihood and Its Components:
     Log-likelihood: -68.72999
     Penalty: -0.2994899
     Penalized Log-likelihood: -69.02948
    
    Degree of smoothing:
     Number of parameters: 43
     Mean parameters: 4
     Scale parameters: 1
     Spline parameters: 38
    
     Lambda: 2.718282
     Log(Lambda): 1
     df: 6.111356
    
    AIC (higher is better): -74.84135
    
    Number of Newton-Raphson Iterations: 21
    n = 50
    >
    > ## Plot the fitted error distribution
    > plot(fit1)
    >
    > ## Plot the fitted error distribution with its components
    > plot(fit1, components = TRUE)
    >
    > ## Plot the cumulative distribution function corresponding to the error density
    > survfit(fit1, cdf = TRUE)
    >
    > ## Plot survivor curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > survfit(fit1, cov = cov)
    >
    > ## Plot hazard curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > hazard(fit1, cov = cov)
    >
    > ## Plot densities for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > fdensity(fit1, cov = cov)
    >
    > ## Compute estimates expectations of survival times for persons with
    > ## (x1, x2) = (0, 180), (1, 180), (0, 190), (1, 190), (0, 200), (1, 200)
    > ## and estimates of a difference of these expectations:
    > ## T(0, 180) - T(1, 180), T(0, 190) - T(1, 190), T(0, 200) - T(1, 200),
    > cov1 <- matrix(c(0, 180, 0, 0, 190, 0, 0, 200, 0), ncol = 3, byrow = TRUE)
    > cov2 <- matrix(c(1, 180, 180, 1, 190, 190, 1, 200, 200), ncol = 3, byrow = TRUE)
    > print(estimTdiff(fit1, cov1 = cov1, cov2 = cov2))
    Warning in matrix(rep(sigmasq0, row.cov), ncol = nknots, byrow = TRUE) :
     data length [3] is not a sub-multiple or multiple of the number of columns [41]
    Error in `dimnames<-.data.frame`(`*tmp*`, value = list(n)) :
     invalid 'dimnames' given for data frame
    Calls: print ... as.array.default -> dimnames<- -> dimnames<-.data.frame
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.6
Check: running examples for arch ‘i386’
Result: ERROR
    Running examples in 'smoothSurv-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: smoothSurvReg
    > ### Title: Regression for a Survival Model with Smoothed Error Distribution
    > ### Aliases: smoothSurvReg
    > ### Keywords: survival smooth
    >
    > ### ** Examples
    >
    > ##### EXAMPLE 1: Common scale
    > ##### ========================
    > ### We generate interval censored data and fit a model with few artificial covariates
    > set.seed(221913282)
    > x1 <- rbinom(50, 1, 0.4) ## binary covariate
    > x2 <- rnorm(50, 180, 10) ## continuous covariate
    > y1 <- 0.5*x1 - 0.01*x2 + 0.005 *x1*x2 + 1.5*rnorm(50, 0, 1) ## generate log(T), left limit
    > t1 <- exp(y1) ## left limit of the survival time
    > t2 <- t1 + rgamma(50, 1, 1) ## right limit of the survival time
    > surv <- Surv(t1, t2, type = "interval2") ## survival object
    >
    > ## Fit the model with an interaction
    > fit1 <- smoothSurvReg(surv ~ x1 * x2, logscale = ~1, info = FALSE, lambda = exp(2:(-1)))
    
    Fit with Log(Lambda) = 2, AIC(7.389056) = -74.89445, df(7.389056) = 5.793826, 5 iterations, fail = 0
    Fit with Log(Lambda) = 1, AIC(2.718282) = -74.92716, df(2.718282) = 6.197057, 23 iterations, fail = 0
    Fit with Log(Lambda) = 0, AIC(1) = -75.83258, df(1) = 7.909182, 9 iterations, fail = 10
    Fit with Log(Lambda) = -1, AIC(0.3678794) = -75.52308, df(0.3678794) = 8.543258, 9 iterations, fail = 0
    >
    > ## Print the summary information
    > summary(fit1, spline = TRUE)
    Call:
    smoothSurvReg(formula = surv ~ x1 * x2, logscale = ~1, lambda = exp(2:(-1)),
     info = FALSE)
    
    Estimated Regression Coefficients:
     Value Std.Error Std.Error2 Z Z2 p p2
    (Intercept) -2.210403 3.3145 3.21278 -0.6669 -0.6880 0.50484 0.49145
    x1 -3.501239 4.4452 4.40693 -0.7877 -0.7945 0.43090 0.42691
    x2 0.008493 0.0183 0.01775 0.4642 0.4785 0.64253 0.63229
    x1:x2 0.023998 0.0247 0.02449 0.9716 0.9801 0.33127 0.32704
    Log(scale) -0.234998 0.1286 0.12416 -1.8271 -1.8928 0.06768 0.05839
    
    Scale = 0.7906
    
    Details on (Fitted) Error Distribution:
     Knot SD basis c coef. Std.Error.c Std.Error2.c Z Z2
    knot[1] -6.0 0.2 1.512e-09 2.073e-08 8.837e-09 0.07292 0.1711
    knot[2] -5.7 0.2 8.355e-09 9.825e-08 4.295e-08 0.08504 0.1945
    knot[3] -5.4 0.2 4.253e-08 4.245e-07 1.905e-07 0.10020 0.2232
    knot[4] -5.1 0.2 1.994e-07 1.669e-06 7.704e-07 0.11945 0.2588
    knot[5] -4.8 0.2 8.613e-07 5.967e-06 2.835e-06 0.14435 0.3039
    knot[6] -4.5 0.2 3.426e-06 1.933e-05 9.467e-06 0.17723 0.3619
    knot[7] -4.2 0.2 1.255e-05 5.662e-05 2.862e-05 0.22174 0.4387
    knot[8] -3.9 0.2 4.237e-05 1.493e-04 7.797e-05 0.28378 0.5434
    knot[9] -3.6 0.2 1.317e-04 3.527e-04 1.904e-04 0.37345 0.6918
    knot[10] -3.3 0.2 3.771e-04 7.408e-04 4.129e-04 0.50901 0.9132
    knot[11] -3.0 0.2 9.944e-04 1.370e-03 7.849e-04 0.72609 1.2670
    knot[12] -2.7 0.2 2.415e-03 2.193e-03 1.276e-03 1.10125 1.8933
    knot[13] -2.4 0.2 5.403e-03 2.969e-03 1.684e-03 1.81990 3.2086
    knot[14] -2.1 0.2 1.113e-02 3.290e-03 1.538e-03 3.38170 7.2350
    knot[15] -1.8 0.2 2.106e-02 3.184e-03 NaN 6.61456 NaN
    knot[16] -1.5 0.2 3.656e-02 4.561e-03 1.221e-03 8.01614 29.9361
    knot[17] -1.2 0.2 5.786e-02 8.187e-03 4.701e-03 7.06692 12.3071
    knot[18] -0.9 0.2 8.282e-02 1.174e-02 6.848e-03 7.05455 12.0929
    knot[19] -0.6 0.2 1.064e-01 1.293e-02 5.687e-03 8.22703 18.7049
    knot[20] -0.3 0.2 1.220e-01 1.124e-02 NaN 10.86071 NaN
    knot[21] 0.0 0.2 1.252e-01 9.296e-03 NaN 13.46416 NaN
    knot[22] 0.3 0.2 1.155e-01 1.019e-02 5.440e-03 11.33885 21.2385
    knot[23] 0.6 0.2 9.705e-02 1.165e-02 6.242e-03 8.33120 15.5465
    knot[24] 0.9 0.2 7.503e-02 1.116e-02 3.320e-03 6.72462 22.6010
    knot[25] 1.2 0.2 5.391e-02 8.700e-03 NaN 6.19694 NaN
    knot[26] 1.5 0.2 3.619e-02 5.498e-03 NaN 6.58257 NaN
    knot[27] 1.8 0.2 2.272e-02 3.158e-03 NaN 7.19464 NaN
    knot[28] 2.1 0.2 1.331e-02 2.680e-03 1.437e-03 4.96501 9.2595
    knot[29] 2.4 0.2 7.252e-03 2.752e-03 1.030e-03 2.63499 7.0372
    knot[30] 2.7 0.2 3.668e-03 2.408e-03 NaN 1.52343 NaN
    knot[31] 3.0 0.2 1.720e-03 1.771e-03 NaN 0.97130 NaN
    knot[32] 3.3 0.2 7.478e-04 1.125e-03 NaN 0.66465 NaN
    knot[33] 3.6 0.2 3.012e-04 6.293e-04 NaN 0.47868 NaN
    knot[34] 3.9 0.2 1.124e-04 3.138e-04 NaN 0.35828 NaN
    knot[35] 4.2 0.2 3.888e-05 1.407e-04 NaN 0.27635 NaN
    knot[36] 4.5 0.2 1.246e-05 5.705e-05 NaN 0.21840 NaN
    knot[37] 4.8 0.2 3.700e-06 2.101e-05 NaN 0.17609 NaN
    knot[38] 5.1 0.2 1.018e-06 7.050e-06 NaN 0.14439 NaN
    knot[39] 5.4 0.2 2.595e-07 2.160e-06 NaN 0.12011 NaN
    knot[40] 5.7 0.2 6.130e-08 6.059e-07 NaN 0.10116 NaN
    knot[41] 6.0 0.2 1.342e-08 1.558e-07 NaN 0.08614 NaN
     p p2
    knot[1] 9.419e-01 8.642e-01
    knot[2] 9.322e-01 8.458e-01
    knot[3] 9.202e-01 8.234e-01
    knot[4] 9.049e-01 7.958e-01
    knot[5] 8.852e-01 7.612e-01
    knot[6] 8.593e-01 7.174e-01
    knot[7] 8.245e-01 6.609e-01
    knot[8] 7.766e-01 5.869e-01
    knot[9] 7.088e-01 4.890e-01
    knot[10] 6.107e-01 3.611e-01
    knot[11] 4.678e-01 2.052e-01
    knot[12] 2.708e-01 5.831e-02
    knot[13] 6.877e-02 1.334e-03
    knot[14] 7.204e-04 4.657e-13
    knot[15] 3.727e-11 NaN
    knot[16] 1.091e-15 6.667e-197
    knot[17] 1.584e-12 8.297e-35
    knot[18] 1.732e-12 1.152e-33
    knot[19] 1.919e-16 4.514e-78
    knot[20] 1.774e-27 NaN
    knot[21] 2.542e-41 NaN
    knot[22] 8.425e-30 4.215e-100
    knot[23] 8.002e-17 1.681e-54
    knot[24] 1.760e-11 4.238e-113
    knot[25] 5.757e-10 NaN
    knot[26] 4.624e-11 NaN
    knot[27] 6.263e-13 NaN
    knot[28] 6.870e-07 2.054e-20
    knot[29] 8.414e-03 1.962e-12
    knot[30] 1.277e-01 NaN
    knot[31] 3.314e-01 NaN
    knot[32] 5.063e-01 NaN
    knot[33] 6.322e-01 NaN
    knot[34] 7.201e-01 NaN
    knot[35] 7.823e-01 NaN
    knot[36] 8.271e-01 NaN
    knot[37] 8.602e-01 NaN
    knot[38] 8.852e-01 NaN
    knot[39] 9.044e-01 NaN
    knot[40] 9.194e-01 NaN
    knot[41] 9.314e-01 NaN
    
    Penalized Loglikelihood and Its Components:
     Log-likelihood: -69.10062
     Penalty: -0.1161117
     Penalized Log-likelihood: -69.21674
    
    Degree of smoothing:
     Number of parameters: 43
     Mean parameters: 4
     Scale parameters: 1
     Spline parameters: 38
    
     Lambda: 7.389056
     Log(Lambda): 2
     df: 5.793826
    
    AIC (higher is better): -74.89445
    
    Number of Newton-Raphson Iterations: 5
    n = 50
    >
    > ## Plot the fitted error distribution
    > plot(fit1)
    >
    > ## Plot the fitted error distribution with its components
    > plot(fit1, components = TRUE)
    >
    > ## Plot the cumulative distribution function corresponding to the error density
    > survfit(fit1, cdf = TRUE)
    >
    > ## Plot survivor curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > survfit(fit1, cov = cov)
    >
    > ## Plot hazard curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > hazard(fit1, cov = cov)
    >
    > ## Plot densities for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > fdensity(fit1, cov = cov)
    >
    > ## Compute estimates expectations of survival times for persons with
    > ## (x1, x2) = (0, 180), (1, 180), (0, 190), (1, 190), (0, 200), (1, 200)
    > ## and estimates of a difference of these expectations:
    > ## T(0, 180) - T(1, 180), T(0, 190) - T(1, 190), T(0, 200) - T(1, 200),
    > cov1 <- matrix(c(0, 180, 0, 0, 190, 0, 0, 200, 0), ncol = 3, byrow = TRUE)
    > cov2 <- matrix(c(1, 180, 180, 1, 190, 190, 1, 200, 200), ncol = 3, byrow = TRUE)
    > print(estimTdiff(fit1, cov1 = cov1, cov2 = cov2))
    Warning in matrix(rep(sigmasq0, row.cov), ncol = nknots, byrow = TRUE) :
     data length [3] is not a sub-multiple or multiple of the number of columns [41]
    Error in `dimnames<-.data.frame`(`*tmp*`, value = list(n)) :
     invalid 'dimnames' given for data frame
    Calls: print ... as.array.default -> dimnames<- -> dimnames<-.data.frame
    Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 1.6
Check: running examples for arch ‘x64’
Result: ERROR
    Running examples in 'smoothSurv-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: smoothSurvReg
    > ### Title: Regression for a Survival Model with Smoothed Error Distribution
    > ### Aliases: smoothSurvReg
    > ### Keywords: survival smooth
    >
    > ### ** Examples
    >
    > ##### EXAMPLE 1: Common scale
    > ##### ========================
    > ### We generate interval censored data and fit a model with few artificial covariates
    > set.seed(221913282)
    > x1 <- rbinom(50, 1, 0.4) ## binary covariate
    > x2 <- rnorm(50, 180, 10) ## continuous covariate
    > y1 <- 0.5*x1 - 0.01*x2 + 0.005 *x1*x2 + 1.5*rnorm(50, 0, 1) ## generate log(T), left limit
    > t1 <- exp(y1) ## left limit of the survival time
    > t2 <- t1 + rgamma(50, 1, 1) ## right limit of the survival time
    > surv <- Surv(t1, t2, type = "interval2") ## survival object
    >
    > ## Fit the model with an interaction
    > fit1 <- smoothSurvReg(surv ~ x1 * x2, logscale = ~1, info = FALSE, lambda = exp(2:(-1)))
    
    Fit with Log(Lambda) = 2, AIC(7.389056) = -74.86754, df(7.389056) = 5.767029, 5 iterations, fail = 0
    Fit with Log(Lambda) = 1, AIC(2.718282) = -74.67222, df(2.718282) = 5.943123, 20 iterations, fail = 0
    Fit with Log(Lambda) = 0, AIC(1) = -74.56259, df(1) = 6.642928, 11 iterations, fail = 10
    Fit with Log(Lambda) = -1, AIC(0.3678794) = -75.3578, df(0.3678794) = 8.377794, 9 iterations, fail = 0
    >
    > ## Print the summary information
    > summary(fit1, spline = TRUE)
    Call:
    smoothSurvReg(formula = surv ~ x1 * x2, logscale = ~1, lambda = exp(2:(-1)),
     info = FALSE)
    
    Estimated Regression Coefficients:
     Value Std.Error Std.Error2 Z Z2 p p2
    (Intercept) -1.798521 3.28657 3.09781 -0.5472 -0.5806 0.58422 0.56152
    x1 -3.776682 4.41477 4.33888 -0.8555 -0.8704 0.39229 0.38407
    x2 0.006291 0.01808 0.01706 0.3480 0.3687 0.72785 0.71235
    x1:x2 0.025351 0.02445 0.02395 1.0368 1.0583 0.29983 0.28993
    Log(scale) -0.251593 0.13885 0.12851 -1.8120 -1.9578 0.06999 0.05025
    
    Scale = 0.7776
    
    Details on (Fitted) Error Distribution:
     Knot SD basis c coef. Std.Error.c Std.Error2.c Z Z2
    knot[1] -6.0 0.2 3.718e-07 1.212e-05 1.389e-05 0.03067 0.02677
    knot[2] -5.7 0.2 9.438e-07 2.631e-05 3.045e-05 0.03587 0.03100
    knot[3] -5.4 0.2 2.339e-06 5.507e-05 6.442e-05 0.04247 0.03631
    knot[4] -5.1 0.2 5.660e-06 1.110e-04 1.313e-04 0.05102 0.04311
    knot[5] -4.8 0.2 1.338e-05 2.146e-04 2.572e-04 0.06233 0.05201
    knot[6] -4.5 0.2 3.086e-05 3.971e-04 4.824e-04 0.07771 0.06398
    knot[7] -4.2 0.2 6.953e-05 6.997e-04 8.627e-04 0.09937 0.08059
    knot[8] -3.9 0.2 1.529e-04 1.166e-03 1.462e-03 0.13112 0.10460
    knot[9] -3.6 0.2 3.285e-04 1.823e-03 2.327e-03 0.18023 0.14114
    knot[10] -3.3 0.2 6.888e-04 2.632e-03 3.431e-03 0.26168 0.20080
    knot[11] -3.0 0.2 1.410e-03 3.435e-03 4.570e-03 0.41051 0.30852
    knot[12] -2.7 0.2 2.817e-03 3.894e-03 5.240e-03 0.72336 0.53753
    knot[13] -2.4 0.2 5.484e-03 3.618e-03 4.508e-03 1.51558 1.21653
    knot[14] -2.1 0.2 1.038e-02 3.436e-03 6.047e-04 3.02261 17.17277
    knot[15] -1.8 0.2 1.902e-02 6.999e-03 5.524e-03 2.71761 3.44292
    knot[16] -1.5 0.2 3.331e-02 1.360e-02 1.587e-02 2.44874 2.09861
    knot[17] -1.2 0.2 5.471e-02 1.977e-02 2.572e-02 2.76693 2.12705
    knot[18] -0.9 0.2 8.215e-02 2.127e-02 2.790e-02 3.86137 2.94458
    knot[19] -0.6 0.2 1.098e-01 1.718e-02 1.630e-02 6.39430 6.73673
    knot[20] -0.3 0.2 1.284e-01 1.677e-02 NaN 7.65803 NaN
    knot[21] 0.0 0.2 1.308e-01 2.224e-02 2.215e-02 5.87899 5.90231
    knot[22] 0.3 0.2 1.174e-01 2.310e-02 2.868e-02 5.08393 4.09378
    knot[23] 0.6 0.2 9.511e-02 1.883e-02 2.283e-02 5.04948 4.16564
    knot[24] 0.9 0.2 7.131e-02 1.382e-02 1.117e-02 5.15999 6.38614
    knot[25] 1.2 0.2 5.056e-02 1.039e-02 NaN 4.86461 NaN
    knot[26] 1.5 0.2 3.430e-02 8.059e-03 NaN 4.25585 NaN
    knot[27] 1.8 0.2 2.224e-02 6.232e-03 5.191e-03 3.56813 4.28350
    knot[28] 2.1 0.2 1.366e-02 4.936e-03 5.830e-03 2.76803 2.34369
    knot[29] 2.4 0.2 7.876e-03 4.033e-03 4.610e-03 1.95321 1.70871
    knot[30] 2.7 0.2 4.227e-03 3.213e-03 2.911e-03 1.31578 1.45202
    knot[31] 3.0 0.2 2.102e-03 2.360e-03 1.475e-03 0.89096 1.42485
    knot[32] 3.3 0.2 9.671e-04 1.561e-03 5.203e-04 0.61956 1.85867
    knot[33] 3.6 0.2 4.112e-04 9.256e-04 NaN 0.44424 NaN
    knot[34] 3.9 0.2 1.616e-04 4.927e-04 NaN 0.32790 NaN
    knot[35] 4.2 0.2 5.865e-05 2.361e-04 NaN 0.24840 NaN
    knot[36] 4.5 0.2 1.967e-05 1.022e-04 NaN 0.19253 NaN
    knot[37] 4.8 0.2 6.095e-06 4.003e-05 NaN 0.15226 NaN
    knot[38] 5.1 0.2 1.745e-06 1.424e-05 NaN 0.12254 NaN
    knot[39] 5.4 0.2 4.616e-07 4.609e-06 NaN 0.10016 NaN
    knot[40] 5.7 0.2 1.128e-07 1.359e-06 NaN 0.08300 NaN
    knot[41] 6.0 0.2 2.547e-08 3.660e-07 NaN 0.06961 NaN
     p p2
    knot[1] 9.755e-01 9.786e-01
    knot[2] 9.714e-01 9.753e-01
    knot[3] 9.661e-01 9.710e-01
    knot[4] 9.593e-01 9.656e-01
    knot[5] 9.503e-01 9.585e-01
    knot[6] 9.381e-01 9.490e-01
    knot[7] 9.208e-01 9.358e-01
    knot[8] 8.957e-01 9.167e-01
    knot[9] 8.570e-01 8.878e-01
    knot[10] 7.936e-01 8.409e-01
    knot[11] 6.814e-01 7.577e-01
    knot[12] 4.695e-01 5.909e-01
    knot[13] 1.296e-01 2.238e-01
    knot[14] 2.506e-03 4.246e-66
    knot[15] 6.576e-03 5.755e-04
    knot[16] 1.434e-02 3.585e-02
    knot[17] 5.659e-03 3.342e-02
    knot[18] 1.128e-04 3.234e-03
    knot[19] 1.613e-10 1.620e-11
    knot[20] 1.888e-14 NaN
    knot[21] 4.128e-09 3.585e-09
    knot[22] 3.697e-07 4.244e-05
    knot[23] 4.430e-07 3.105e-05
    knot[24] 2.470e-07 1.701e-10
    knot[25] 1.147e-06 NaN
    knot[26] 2.083e-05 NaN
    knot[27] 3.595e-04 1.840e-05
    knot[28] 5.640e-03 1.909e-02
    knot[29] 5.079e-02 8.750e-02
    knot[30] 1.882e-01 1.465e-01
    knot[31] 3.730e-01 1.542e-01
    knot[32] 5.355e-01 6.307e-02
    knot[33] 6.569e-01 NaN
    knot[34] 7.430e-01 NaN
    knot[35] 8.038e-01 NaN
    knot[36] 8.473e-01 NaN
    knot[37] 8.790e-01 NaN
    knot[38] 9.025e-01 NaN
    knot[39] 9.202e-01 NaN
    knot[40] 9.339e-01 NaN
    knot[41] 9.445e-01 NaN
    
    Penalized Loglikelihood and Its Components:
     Log-likelihood: -68.7291
     Penalty: -0.3003893
     Penalized Log-likelihood: -69.02949
    
    Degree of smoothing:
     Number of parameters: 43
     Mean parameters: 4
     Scale parameters: 1
     Spline parameters: 38
    
     Lambda: 2.718282
     Log(Lambda): 1
     df: 5.943123
    
    AIC (higher is better): -74.67222
    
    Number of Newton-Raphson Iterations: 20
    n = 50
    >
    > ## Plot the fitted error distribution
    > plot(fit1)
    >
    > ## Plot the fitted error distribution with its components
    > plot(fit1, components = TRUE)
    >
    > ## Plot the cumulative distribution function corresponding to the error density
    > survfit(fit1, cdf = TRUE)
    >
    > ## Plot survivor curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > survfit(fit1, cov = cov)
    >
    > ## Plot hazard curves for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > hazard(fit1, cov = cov)
    >
    > ## Plot densities for persons with (x1, x2) = (0, 180) and (1, 180)
    > cov <- matrix(c(0, 180, 0, 1, 180, 180), ncol = 3, byrow = TRUE)
    > fdensity(fit1, cov = cov)
    >
    > ## Compute estimates expectations of survival times for persons with
    > ## (x1, x2) = (0, 180), (1, 180), (0, 190), (1, 190), (0, 200), (1, 200)
    > ## and estimates of a difference of these expectations:
    > ## T(0, 180) - T(1, 180), T(0, 190) - T(1, 190), T(0, 200) - T(1, 200),
    > cov1 <- matrix(c(0, 180, 0, 0, 190, 0, 0, 200, 0), ncol = 3, byrow = TRUE)
    > cov2 <- matrix(c(1, 180, 180, 1, 190, 190, 1, 200, 200), ncol = 3, byrow = TRUE)
    > print(estimTdiff(fit1, cov1 = cov1, cov2 = cov2))
    Warning in matrix(rep(sigmasq0, row.cov), ncol = nknots, byrow = TRUE) :
     data length [3] is not a sub-multiple or multiple of the number of columns [41]
    Error in `dimnames<-.data.frame`(`*tmp*`, value = list(n)) :
     invalid 'dimnames' given for data frame
    Calls: print ... as.array.default -> dimnames<- -> dimnames<-.data.frame
    Execution halted
Flavor: r-devel-windows-ix86+x86_64