Running 'doRUnit.R' [10s]
  Running 'forecastTests.R' [9s]
  Running 'offsetTests.R' [1s]
  Running 'randomisedResidsTest.R' [175s]
Running the tests in 'tests/randomisedResidsTest.R' failed.
Complete output:
  > require(glarma)
  Loading required package: glarma
  > ### Boat Race:
  > data(OxBoatRace)
  > 
  > y1 <- OxBoatRace$Camwin
  > n1 <- rep(1, length(OxBoatRace$Year))
  > Y <- cbind(y1, n1 - y1)
  > X <- cbind(OxBoatRace$Intercept, OxBoatRace$Diff)
  > colnames(X) <- c("Intercept", "Weight Diff")
  > 
  > oxcamglm <- glm(Y ~ Diff + I(Diff^2),
  +                 data = OxBoatRace,
  +                 family = binomial(link = "logit"), x = TRUE)
  > 
  > X <- oxcamglm$x
  > 
  > glarmamod <- glarma(Y, X, thetaLags = c(1, 2), type = "Bin", method = "NR",
  +                     residuals = "Pearson", maxit = 100, grad = 1e-6)
  > 
  > summary(glarmamod)
  
  Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson", 
      thetaLags = c(1, 2), maxit = 100, grad = 1e-06)
  
  Pearson Residuals:
      Min       1Q   Median       3Q      Max  
  -1.8865  -0.7968   0.4395   0.8436   2.9658  
  
  GLARMA Coefficients:
          Estimate Std.Error z-ratio Pr(>|z|)    
  theta_1   0.3396    0.1709   1.987 0.046975 *  
  theta_2   0.5552    0.1459   3.804 0.000142 ***
  
  Linear Model Coefficients:
               Estimate Std.Error z-ratio Pr(>|z|)   
  (Intercept)  0.349954  0.267249   1.309  0.19038   
  Diff         0.114755  0.038238   3.001  0.00269 **
  I(Diff^2)   -0.011333  0.004916  -2.305  0.02114 * 
  
      Null deviance: 216.16  on 155  degrees of freedom
  Residual deviance: 148.53  on 151  degrees of freedom
  AIC: 193.0553 
  
  Number of Newton Raphson iterations: 5
  
  LRT and Wald Test:
  Alternative hypothesis: model is a GLARMA process
  Null hypothesis: model is a GLM with the same regression structure
            Statistic  p-value    
  LR Test       15.56 0.000417 ***
  Wald Test     17.51 0.000158 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  
  > 
  > par(mfrow=c(3,2))
  > plot(glarmamod)
  > ### model adequate - test using randomized PIT?
  > rt <- normRandPIT(glarmamod)$rt
  > par(mfrow = c(2,2))
  > hist(rt, main = "Histogram of Randomized Residuals",
  +      xlab = expression(r[t]))
  > qqnorm(rt, main = "Q-Q Plot of  Randomized Residuals" )
  > abline(0, 1, lty = 2)
  > acf(rt, main = "ACF of Randomized Residuals")
  > pacf(rt, main = "PACF of Randomized Residuals")
  > 
  > proc.time()
     user  system elapsed 
     0.45    0.12    0.53