Running ‘KM.R’ [4s/4s]
  Running ‘confint.R’ [8s/9s]
  Comparing ‘confint.Rout’ to ‘confint.Rout.save’ ... OK
  Running ‘csep.R’ [8s/9s]
  Running ‘edges.R’ [2s/2s]
  Running ‘empirical.R’ [1s/2s]
  Comparing ‘empirical.Rout’ to ‘empirical.Rout.save’ ...
7,12c7,286
< > proc.time()
<    user  system elapsed 
<   1.149   0.113   1.545 
< Warning message:
< In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
<   there is no package called 'rms'
---
> 
> Attaching package: 'Hmisc'
> 
> The following objects are masked from 'package:base':
> 
>     format.pval, units
> 
> > 
> > options(digits = 5)
> > set.seed(29)
> > 
> > N <- 500
> > x <- gl(2, N)
> > y <- rlogis(length(x), location = c(0, 2)[x])
> > 
> > ci <- confint(m <- orm(y ~ x))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>    x=2  2.5 % 97.5 % 
> 1.9213 1.6797 2.1629 
> > logLik(m)
> 'log Lik.' -6774.9 (df=1000)
> > 
> > d <- data.frame(y = y, x = x)
> > 
> > (ft <- free1way(y = y, groups = x))
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 15.6, p-value <2e-16
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > coef(ft)
>     x2 
> 1.9213 
> > confint(ft)
>     2.5 % 97.5 %
> x2 1.6726   2.17
> > logLik(ft)
> [1] -6774.9
> > (ft <- free1way(y ~ x, data = d))
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 15.6, p-value <2e-16
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > (ft <- free1way(y = y, groups = x, nbins = 99))
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 15.6, p-value <2e-16
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > (ft <- free1way(y = y, groups = x, nbins = 100))
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 15.6, p-value <2e-16
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > 
> > -coef(m <- coxph(Surv(y, rep(TRUE, length(y))) ~ x))
>      x2 
> 0.98652 
> > -rev(confint(m))
> [1] 0.85647 1.11657
> > ### rms: cloglog for P(Y < y) is loglog for P(Y >= y), the latter
> > ### being used in orm()
> > ci <- confint(m <- orm(y ~ x, family = "loglog"))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>     x=2   2.5 %  97.5 % 
> 0.98795 0.85789 1.11802 
> > logLik(m)
> 'log Lik.' -6800 (df=1000)
> > 
> > ft <- free1way(y = y, groups = x, link = "cloglog")
> > coef(ft)
>      x2 
> 0.98795 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.83898 1.1369
> > logLik(ft)
> [1] -6800
> > ft <- free1way(y = y, groups = x, link = "cloglog", nbins = 99)
> > coef(ft)
>      x2 
> 0.99438 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.84516 1.1436
> > ft <- free1way(y = y, groups = x, link = "cloglog", nbins = 100)
> > coef(ft)
>      x2 
> 0.99326 
> > confint(ft)
>     2.5 % 97.5 %
> x2 0.8441 1.1424
> > 
> > ci <- confint(m <- orm(y ~ x, family = "cloglog"))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>     x=2   2.5 %  97.5 % 
> 0.94540 0.81641 1.07439 
> > logLik(m)
> 'log Lik.' -6807.3 (df=1000)
> > 
> > ft <- free1way(y = y, groups = x, link = "loglog")
> > coef(ft)
>     x2 
> 0.9454 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.79934 1.0915
> > logLik(ft)
> [1] -6807.3
> > ft <- free1way(y = y, groups = x, link = "loglog", nbins = 99)
> > coef(ft)
>     x2 
> 0.9506 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.80439 1.0968
> > ft <- free1way(y = y, groups = x, link = "loglog", nbins = 100)
> > coef(ft)
>      x2 
> 0.95052 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.80429 1.0967
> > 
> > ### probit
> > ci <- confint(m <- orm(y ~ x, family = "probit"))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>     x=2   2.5 %  97.5 % 
> 1.08725 0.95392 1.22059 
> > logLik(m)
> 'log Lik.' -6779.2 (df=1000)
> > 
> > ft <- free1way(y = y, groups = x, link = "probit")
> > coef(ft)
>     x2 
> 1.0873 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.94523 1.2293
> > logLik(ft)
> [1] -6779.2
> > ft <- free1way(y = y, groups = x, link = "probit", nbins = 99)
> > coef(ft)
>     x2 
> 1.0888 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.94676 1.2308
> > ft <- free1way(y = y, groups = x, link = "probit", nbins = 100)
> > coef(ft)
>     x2 
> 1.0879 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.94592 1.2299
> > 
> > 
> > ### with offset
> > mu <- 1
> > off <- (x == levels(x)[2]) * mu
> > 
> > ci <- confint(m <- orm(y ~ x + offset(off)))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>     x=2   2.5 %  97.5 % 
> 0.92128 0.67969 1.16287 
> > logLik(m)
> 'log Lik.' -6774.9 (df=1000)
> > 
> > ft <- free1way(y = y, groups = x, mu = 1)
> > coef(ft)
>      x2 
> 0.92128 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.67258   1.17
> > logLik(m)
> 'log Lik.' -6774.9 (df=1000)
> > ft <- free1way(y = y, groups = x, mu = 1, nbins = 99)
> > coef(ft)
>      x2 
> 0.92232 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.67357 1.1711
> > ft <- free1way(y = y, groups = x, mu = 1, nbins = 100)
> > coef(ft)
>      x2 
> 0.92024 
> > confint(ft)
>      2.5 % 97.5 %
> x2 0.67158 1.1689
> > 
> > ### permutations
> > N <- 15
> > x <- gl(2, N)
> > y <- rlogis(length(x), location = c(0, 2)[x])
> > 
> > ci <- confint(m <- orm(y ~ x))
> > c(rev(coef(m))[1], ci[nrow(ci),])
>      x=2    2.5 %   97.5 % 
>  1.06712 -0.22078  2.35502 
> > logLik(m)
> 'log Lik.' -100.68 (df=30)
> > 
> > ft <- free1way(y = y, groups = x)
> > print(ft, test = "Wald")
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Wald chi-squared = 2.64, df = 1, p-value = 0.1
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > coef(ft)
>     x2 
> 1.0671 
> > confint(ft, test = "Wald")
>       2.5 % 97.5 %
> x2 -0.22078  2.355
> > logLik(m)
> 'log Lik.' -100.68 (df=30)
> > print(ft, test = "Rao")
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Rao chi-squared = 2.69, df = 1, p-value = 0.1
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > confint(ft, test = "Rao")
>       2.5 % 97.5 %
> x2 -0.20344 2.3392
> > print(ft, test = "LRT")
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> logLR chi-squared = 2.71, df = 1, p-value = 0.1
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > confint(ft, test = "LRT")
>      2.5 % 97.5 %
> x2 -0.2006 2.3929
> > print(ft, test = "Permutation")
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 1.6, p-value = 0.11
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > confint(ft, test = "Permutation")
>       2.5 % 97.5 %
> x2 -0.23643 2.3724
> > 
> > ft <- free1way(y = y, groups = x, B = 10000)
> > print(ft, test = "Permutation")
> 
> 	2-sample Wilcoxon test against proportional odds alternatives
> 
> data:  y by x (1, 2)
> Perm Z = 1.6, p-value = 0.12
> alternative hypothesis: true log-odds ratio is not equal to 0
> 
> > confint(ft, test = "Permutation")
>       2.5 % 97.5 %
> x2 -0.26984  2.379
> attr(,"Attained level")
> [1] 0.9597
> > 
  Running ‘exact.R’ [16s/18s]
  Comparing ‘exact.Rout’ to ‘exact.Rout.save’ ... OK
  Running ‘free1way-Ex.R’ [6s/8s]
  Comparing ‘free1way-Ex.Rout’ to ‘free1way-Ex.Rout.save’ ... OK
  Running ‘glm.R’ [2s/3s]
  Comparing ‘glm.Rout’ to ‘glm.Rout.save’ ... OK
  Running ‘jobsatisfaction.R’ [2s/3s]
  Comparing ‘jobsatisfaction.Rout’ to ‘jobsatisfaction.Rout.save’ ... OK
  Running ‘logrank.R’ [3s/4s]
  Comparing ‘logrank.Rout’ to ‘logrank.Rout.save’ ... OK
  Running ‘polr.R’ [10s/11s]
  Comparing ‘polr.Rout’ to ‘polr.Rout.save’ ... OK
  Running ‘power.R’ [53s/62s]
  Comparing ‘power.Rout’ to ‘power.Rout.save’ ... OK
  Running ‘speed.R’ [0s/0s]