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* using log directory 'R:/run/building/build_2013-02-15-08-50/RF_PKG_CHECK/PKGS/ordinal2.Rcheck'
* using R version 2.15.2 Patched (2013-01-31 r61797)
* using platform: x86_64-w64-mingw32 (64-bit)
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* this is package 'ordinal2' version '2011.06-9'
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Maintainer: 'Rune Haubo B Christensen '
New submission
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Found the following significant warnings:
  Warning: value of 'strip.white' option should be lowercase
See 'R:/run/building/build_2013-02-15-08-50/RF_PKG_CHECK/PKGS/ordinal2.Rcheck/00install.out' for details.
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Foreign function call with 'PACKAGE' argument in a base package:
  .Call("La_dgesv", ..., PACKAGE = "base")
Packages should not make .C/.Call/.Fortran calls to base packages. They
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* checking R code for possible problems ... NOTE
clmm.finalize: no visible binding for global variable 'ths'
clmm.finalize: no visible binding for global variable 'link'
clmm.finalize: no visible binding for global variable 'threshold'
clmm.finalize: no visible binding for global variable 'optRes'
clmm.finalize: no visible binding for global variable 'Niter'
clmm.finalize: no visible binding for global variable 'nlev'
clmm.finalize: no visible binding for global variable 'random'
clmm.finalize: no visible binding for global variable 'L'
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'data(package=)' call not declared from: 'ordinal'
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* checking installed files from 'inst/doc' ... OK
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'ordinal2-Ex.R' failed
The error most likely occurred in:

> ### Name: clm
> ### Title: Cumulative Link Models
> ### Aliases: clm
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> data(wine)
> fm1 <- clm(rating ~ temp * contact, data = wine)
> fm1 ## print method
formula: rating ~ temp * contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad
 logit flexible  72   -86.42 186.83 6(0)  5.21e-12
           tempwarm          contactyes tempwarm:contactyes 
             2.3212              1.3475              0.3595 

Threshold coefficients:
   1|2    2|3    3|4    4|5 
-1.411  1.144  3.377  4.942 
> summary(fm1)
formula: rating ~ temp * contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.42 186.83 6(0)  5.21e-12 5.1e+01

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)    
tempwarm              2.3212     0.7009   3.311 0.000928 ***
contactyes            1.3475     0.6604   2.041 0.041300 *  
tempwarm:contactyes   0.3595     0.9238   0.389 0.697129    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.4113     0.5454  -2.588
2|3   1.1436     0.5097   2.244
3|4   3.3771     0.6382   5.292
4|5   4.9420     0.7509   6.581
> anova(fm1, update(fm1, ~.-temp:contact))
Likelihood ratio tests of cumulative link models:
 
                               formula:                link: threshold:
update(fm1, ~. - temp:contact) rating ~ temp + contact logit flexible  
fm1                            rating ~ temp * contact logit flexible  

                               no.par    AIC  logLik LR.stat df Pr(>Chisq)
update(fm1, ~. - temp:contact)      6 184.98 -86.492                      
fm1                                 7 186.83 -86.416  0.1514  1     0.6972
> 
> dropterm(fm1, test = "Chi")
Single term deletions

Model:
rating ~ temp * contact
             Df    AIC     LRT Pr(Chi)
          186.83                
temp:contact  1 184.98 0.15145  0.6972
> drop1(fm1, test = "Chi")
Single term deletions

Model:
rating ~ temp * contact
             Df    AIC     LRT Pr(>Chi)
          186.83                 
temp:contact  1 184.98 0.15145   0.6972
> add1(fm1, ~.+judge, test = "Chi")
Single term additions

Model:
rating ~ temp * contact
       Df    AIC    LRT  Pr(>Chi)    
    186.83                     
judge   8 171.80 31.036 0.0001384 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> addterm(fm1, ~.+judge, test = "Chi")
Single term additions

Model:
rating ~ temp * contact
       Df    AIC    LRT   Pr(Chi)    
    186.83                     
judge   8 171.80 31.036 0.0001384 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> fm2 <- step(fm1)
Start:  AIC=186.83
rating ~ temp * contact

               Df    AIC
- temp:contact  1 184.98
            186.83

Step:  AIC=184.98
rating ~ temp + contact

          Df    AIC
       184.98
- contact  1 194.03
- temp     1 209.91
> summary(fm2)
formula: rating ~ temp + contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.49 184.98 6(0)  4.02e-12 2.7e+01

Coefficients:
           Estimate Std. Error z value Pr(>|z|)    
tempwarm     2.5031     0.5287   4.735 2.19e-06 ***
contactyes   1.5278     0.4766   3.205  0.00135 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.3444     0.5171  -2.600
2|3   1.2508     0.4379   2.857
3|4   3.4669     0.5978   5.800
4|5   5.0064     0.7309   6.850
> fm3 <- stepAIC(fm1)
Start:  AIC=186.83
rating ~ temp * contact

               Df    AIC
- temp:contact  1 184.98
            186.83

Step:  AIC=184.98
rating ~ temp + contact

          Df    AIC
       184.98
- contact  1 194.03
- temp     1 209.91
> summary(fm3)
formula: rating ~ temp + contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.49 184.98 6(0)  4.02e-12 2.7e+01

Coefficients:
           Estimate Std. Error z value Pr(>|z|)    
tempwarm     2.5031     0.5287   4.735 2.19e-06 ***
contactyes   1.5278     0.4766   3.205  0.00135 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.3444     0.5171  -2.600
2|3   1.2508     0.4379   2.857
3|4   3.4669     0.5978   5.800
4|5   5.0064     0.7309   6.850
> 
> coef(fm1)
                1|2                 2|3                 3|4                 4|5 
         -1.4112620           1.1435537           3.3770825           4.9419823 
           tempwarm          contactyes tempwarm:contactyes 
          2.3211843           1.3474604           0.3595489 
> vcov(fm1)
                           1|2        2|3        3|4        4|5   tempwarm
1|2                  0.2974102  0.1433319  0.1434281  0.1437944  0.1470096
2|3                  0.1433319  0.2597488  0.2498799  0.2501820  0.2521417
3|4                  0.1434281  0.2498799  0.4072504  0.3976946  0.3249357
4|5                  0.1437944  0.2501820  0.3976946  0.5638677  0.3317317
tempwarm             0.1470096  0.2521417  0.3249357  0.3317317  0.4913280
contactyes           0.1565436  0.2477552  0.2730741  0.2730982  0.2581980
tempwarm:contactyes -0.1598445 -0.2494219 -0.2039255 -0.1408440 -0.4256882
                    contactyes tempwarm:contactyes
1|2                  0.1565436          -0.1598445
2|3                  0.2477552          -0.2494219
3|4                  0.2730741          -0.2039255
4|5                  0.2730982          -0.1408440
tempwarm             0.2581980          -0.4256882
contactyes           0.4360696          -0.4226690
tempwarm:contactyes -0.4226690           0.8534413
> AIC(fm1)
[1] 186.8324
> extractAIC(fm1)
[1]   7.0000 186.8324
> logLik(fm1)
'log Lik.' -86.4162 (df=7)
> fitted(fm1)
 [1] 0.56229641 0.20864908 0.43467309 0.08938852 0.19028226 0.19028226
 [7] 0.28622518 0.28622518 0.19603509 0.56229641 0.05959593 0.43467309
[13] 0.21210373 0.50642742 0.28622518 0.37103562 0.56229641 0.20864908
[19] 0.43467309 0.38960327 0.06781183 0.06781183 0.37103562 0.37103562
[25] 0.20864908 0.56229641 0.43467309 0.38960327 0.50642742 0.21210373
[31] 0.28622518 0.28982109 0.56229641 0.20864908 0.08938852 0.43467309
[37] 0.50642742 0.50642742 0.28982109 0.28982109 0.20864908 0.56229641
[43] 0.43467309 0.38960327 0.21210373 0.19028226 0.28622518 0.37103562
[49] 0.19603509 0.19603509 0.38960327 0.38960327 0.21210373 0.50642742
[55] 0.04859504 0.28982109 0.56229641 0.56229641 0.38960327 0.43467309
[61] 0.50642742 0.50642742 0.28982109 0.37103562 0.19603509 0.56229641
[67] 0.43467309 0.38960327 0.50642742 0.21210373 0.37103562 0.37103562
> 
> confint(fm1) ## type = "profile"
                          2.5 %   97.5 %
tempwarm             0.99435182 3.761793
contactyes           0.08378091 2.694828
tempwarm:contactyes -1.45985126 2.180286
> confint(fm1, type = "Wald")
                          2.5 %     97.5 %
1|2                 -2.48013466 -0.3423893
2|3                  0.14464718  2.1424601
3|4                  2.12630850  4.6278565
4|5                  3.47022323  6.4137413
tempwarm             0.94735154  3.6950170
contactyes           0.05318714  2.6417337
tempwarm:contactyes -1.45110279  2.1702006
> pr1 <- profile(fm1)
> confint(pr1)
                          2.5 %   97.5 %
tempwarm             0.99438454 3.761828
contactyes           0.08379044 2.694864
tempwarm:contactyes -1.45984555 2.180280
> 
> ## plotting the profiles:
> par(mfrow = c(2, 2))
> plot(pr1, root = TRUE) ## check for linearity 
> par(mfrow = c(2, 2))
> plot(pr1)
> par(mfrow = c(2, 2))
> plot(pr1, approx = TRUE)
> par(mfrow = c(2, 2))
> plot(pr1, Log = TRUE)
> par(mfrow = c(2, 2))
> plot(pr1, Log = TRUE, relative = FALSE)
> 
> ## other link functions:
> fm4.lgt <- update(fm1, link = "logit") ## default
> fm4.prt <- update(fm1, link = "probit")
> fm4.ll <- update(fm1, link = "loglog")
> fm4.cll <- update(fm1, link = "cloglog")
> fm4.cct <- update(fm1, link = "cauchit")
> anova(fm4.lgt, fm4.prt, fm4.ll, fm4.cll, fm4.cct)
Likelihood ratio tests of cumulative link models:
 
        formula:                link:   threshold:
fm4.lgt rating ~ temp * contact logit   flexible  
fm4.prt rating ~ temp * contact probit  flexible  
fm4.ll  rating ~ temp * contact loglog  flexible  
fm4.cll rating ~ temp * contact cloglog flexible  
fm4.cct rating ~ temp * contact cauchit flexible  

        no.par    AIC  logLik  LR.stat df Pr(>Chisq)
fm4.lgt      7 186.83 -86.416                       
fm4.prt      7 185.45 -85.723   1.3864  0           
fm4.ll       7 189.14 -87.569  -3.6923  0           
fm4.cll      7 187.22 -86.610   1.9175  0           
fm4.cct      7 198.05 -92.027 -10.8323  0           
> 
> ## structured thresholds:
> fm5 <- update(fm1, threshold = "symmetric")
> fm6 <- update(fm1, threshold = "equidistant")
> anova(fm1, fm5, fm6)
Likelihood ratio tests of cumulative link models:
 
    formula:                link: threshold: 
fm6 rating ~ temp * contact logit equidistant
fm5 rating ~ temp * contact logit symmetric  
fm1 rating ~ temp * contact logit flexible   

    no.par    AIC  logLik LR.stat df Pr(>Chisq)
fm6      5 185.14 -87.570                      
fm5      6 187.05 -87.527  0.0864  1     0.7688
fm1      7 186.83 -86.416  2.2220  1     0.1361
> 
> ## the slice methods:
> slice.fm1 <- slice(fm1)
> par(mfrow = c(3, 3))
> plot(slice.fm1)
> ## see more at '?slice.clm'
> 
> ## Example from MASS::polr:
> data(housing, package = "MASS")
> fm1 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
> summary(fm1)
formula: Sat ~ Infl + Type + Cont
data:    housing

 link  threshold nobs logLik   AIC     niter max.grad cond.H 
 logit flexible  1681 -1739.57 3495.15 4(0)  6.60e-09 4.7e+01

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
InflMedium     0.56639    0.10465   5.412 6.23e-08 ***
InflHigh       1.28882    0.12716  10.136  < 2e-16 ***
TypeApartment -0.57235    0.11924  -4.800 1.59e-06 ***
TypeAtrium    -0.36619    0.15517  -2.360 0.018282 *  
TypeTerrace   -1.09101    0.15149  -7.202 5.93e-13 ***
ContHigh       0.36028    0.09554   3.771 0.000162 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
            Estimate Std. Error z value
Low|Medium   -0.4961     0.1248  -3.974
Medium|High   0.6907     0.1255   5.505
> 
> ## Another example:
> data(soup, package = "ordinal")
Error in find.package(package, lib.loc, verbose = verbose) : 
  there is no package called 'ordinal'
Calls: data -> find.package
Execution halted
** running examples for arch 'x64' ... ERROR
Running examples in 'ordinal2-Ex.R' failed
The error most likely occurred in:

> ### Name: clm
> ### Title: Cumulative Link Models
> ### Aliases: clm
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> data(wine)
> fm1 <- clm(rating ~ temp * contact, data = wine)
> fm1 ## print method
formula: rating ~ temp * contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad
 logit flexible  72   -86.42 186.83 6(0)  5.20e-12
           tempwarm          contactyes tempwarm:contactyes 
             2.3212              1.3475              0.3595 

Threshold coefficients:
   1|2    2|3    3|4    4|5 
-1.411  1.144  3.377  4.942 
> summary(fm1)
formula: rating ~ temp * contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.42 186.83 6(0)  5.20e-12 5.1e+01

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)    
tempwarm              2.3212     0.7009   3.311 0.000928 ***
contactyes            1.3475     0.6604   2.041 0.041300 *  
tempwarm:contactyes   0.3595     0.9238   0.389 0.697129    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.4113     0.5454  -2.588
2|3   1.1436     0.5097   2.244
3|4   3.3771     0.6382   5.292
4|5   4.9420     0.7509   6.581
> anova(fm1, update(fm1, ~.-temp:contact))
Likelihood ratio tests of cumulative link models:
 
                               formula:                link: threshold:
update(fm1, ~. - temp:contact) rating ~ temp + contact logit flexible  
fm1                            rating ~ temp * contact logit flexible  

                               no.par    AIC  logLik LR.stat df Pr(>Chisq)
update(fm1, ~. - temp:contact)      6 184.98 -86.492                      
fm1                                 7 186.83 -86.416  0.1514  1     0.6972
> 
> dropterm(fm1, test = "Chi")
Single term deletions

Model:
rating ~ temp * contact
             Df    AIC     LRT Pr(Chi)
          186.83                
temp:contact  1 184.98 0.15145  0.6972
> drop1(fm1, test = "Chi")
Single term deletions

Model:
rating ~ temp * contact
             Df    AIC     LRT Pr(>Chi)
          186.83                 
temp:contact  1 184.98 0.15145   0.6972
> add1(fm1, ~.+judge, test = "Chi")
Single term additions

Model:
rating ~ temp * contact
       Df    AIC    LRT  Pr(>Chi)    
    186.83                     
judge   8 171.80 31.036 0.0001384 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> addterm(fm1, ~.+judge, test = "Chi")
Single term additions

Model:
rating ~ temp * contact
       Df    AIC    LRT   Pr(Chi)    
    186.83                     
judge   8 171.80 31.036 0.0001384 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> fm2 <- step(fm1)
Start:  AIC=186.83
rating ~ temp * contact

               Df    AIC
- temp:contact  1 184.98
            186.83

Step:  AIC=184.98
rating ~ temp + contact

          Df    AIC
       184.98
- contact  1 194.03
- temp     1 209.91
> summary(fm2)
formula: rating ~ temp + contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.49 184.98 6(0)  4.01e-12 2.7e+01

Coefficients:
           Estimate Std. Error z value Pr(>|z|)    
tempwarm     2.5031     0.5287   4.735 2.19e-06 ***
contactyes   1.5278     0.4766   3.205  0.00135 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.3444     0.5171  -2.600
2|3   1.2508     0.4379   2.857
3|4   3.4669     0.5978   5.800
4|5   5.0064     0.7309   6.850
> fm3 <- stepAIC(fm1)
Start:  AIC=186.83
rating ~ temp * contact

               Df    AIC
- temp:contact  1 184.98
            186.83

Step:  AIC=184.98
rating ~ temp + contact

          Df    AIC
       184.98
- contact  1 194.03
- temp     1 209.91
> summary(fm3)
formula: rating ~ temp + contact
data:    wine

 link  threshold nobs logLik AIC    niter max.grad cond.H 
 logit flexible  72   -86.49 184.98 6(0)  4.01e-12 2.7e+01

Coefficients:
           Estimate Std. Error z value Pr(>|z|)    
tempwarm     2.5031     0.5287   4.735 2.19e-06 ***
contactyes   1.5278     0.4766   3.205  0.00135 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
    Estimate Std. Error z value
1|2  -1.3444     0.5171  -2.600
2|3   1.2508     0.4379   2.857
3|4   3.4669     0.5978   5.800
4|5   5.0064     0.7309   6.850
> 
> coef(fm1)
                1|2                 2|3                 3|4                 4|5 
         -1.4112620           1.1435537           3.3770825           4.9419823 
           tempwarm          contactyes tempwarm:contactyes 
          2.3211843           1.3474604           0.3595489 
> vcov(fm1)
                           1|2        2|3        3|4        4|5   tempwarm
1|2                  0.2974102  0.1433319  0.1434281  0.1437944  0.1470096
2|3                  0.1433319  0.2597488  0.2498799  0.2501820  0.2521417
3|4                  0.1434281  0.2498799  0.4072504  0.3976946  0.3249357
4|5                  0.1437944  0.2501820  0.3976946  0.5638677  0.3317317
tempwarm             0.1470096  0.2521417  0.3249357  0.3317317  0.4913280
contactyes           0.1565436  0.2477552  0.2730741  0.2730982  0.2581980
tempwarm:contactyes -0.1598445 -0.2494219 -0.2039255 -0.1408440 -0.4256882
                    contactyes tempwarm:contactyes
1|2                  0.1565436          -0.1598445
2|3                  0.2477552          -0.2494219
3|4                  0.2730741          -0.2039255
4|5                  0.2730982          -0.1408440
tempwarm             0.2581980          -0.4256882
contactyes           0.4360696          -0.4226690
tempwarm:contactyes -0.4226690           0.8534413
> AIC(fm1)
[1] 186.8324
> extractAIC(fm1)
[1]   7.0000 186.8324
> logLik(fm1)
'log Lik.' -86.4162 (df=7)
> fitted(fm1)
 [1] 0.56229641 0.20864908 0.43467309 0.08938852 0.19028226 0.19028226
 [7] 0.28622518 0.28622518 0.19603509 0.56229641 0.05959593 0.43467309
[13] 0.21210373 0.50642742 0.28622518 0.37103562 0.56229641 0.20864908
[19] 0.43467309 0.38960327 0.06781183 0.06781183 0.37103562 0.37103562
[25] 0.20864908 0.56229641 0.43467309 0.38960327 0.50642742 0.21210373
[31] 0.28622518 0.28982109 0.56229641 0.20864908 0.08938852 0.43467309
[37] 0.50642742 0.50642742 0.28982109 0.28982109 0.20864908 0.56229641
[43] 0.43467309 0.38960327 0.21210373 0.19028226 0.28622518 0.37103562
[49] 0.19603509 0.19603509 0.38960327 0.38960327 0.21210373 0.50642742
[55] 0.04859504 0.28982109 0.56229641 0.56229641 0.38960327 0.43467309
[61] 0.50642742 0.50642742 0.28982109 0.37103562 0.19603509 0.56229641
[67] 0.43467309 0.38960327 0.50642742 0.21210373 0.37103562 0.37103562
> 
> confint(fm1) ## type = "profile"
                          2.5 %   97.5 %
tempwarm             0.99435182 3.761793
contactyes           0.08378091 2.694828
tempwarm:contactyes -1.45985126 2.180286
> confint(fm1, type = "Wald")
                          2.5 %     97.5 %
1|2                 -2.48013466 -0.3423893
2|3                  0.14464718  2.1424601
3|4                  2.12630850  4.6278565
4|5                  3.47022323  6.4137413
tempwarm             0.94735154  3.6950170
contactyes           0.05318714  2.6417337
tempwarm:contactyes -1.45110279  2.1702006
> pr1 <- profile(fm1)
> confint(pr1)
                          2.5 %   97.5 %
tempwarm             0.99438454 3.761828
contactyes           0.08379044 2.694864
tempwarm:contactyes -1.45984555 2.180280
> 
> ## plotting the profiles:
> par(mfrow = c(2, 2))
> plot(pr1, root = TRUE) ## check for linearity 
> par(mfrow = c(2, 2))
> plot(pr1)
> par(mfrow = c(2, 2))
> plot(pr1, approx = TRUE)
> par(mfrow = c(2, 2))
> plot(pr1, Log = TRUE)
> par(mfrow = c(2, 2))
> plot(pr1, Log = TRUE, relative = FALSE)
> 
> ## other link functions:
> fm4.lgt <- update(fm1, link = "logit") ## default
> fm4.prt <- update(fm1, link = "probit")
> fm4.ll <- update(fm1, link = "loglog")
> fm4.cll <- update(fm1, link = "cloglog")
> fm4.cct <- update(fm1, link = "cauchit")
> anova(fm4.lgt, fm4.prt, fm4.ll, fm4.cll, fm4.cct)
Likelihood ratio tests of cumulative link models:
 
        formula:                link:   threshold:
fm4.lgt rating ~ temp * contact logit   flexible  
fm4.prt rating ~ temp * contact probit  flexible  
fm4.ll  rating ~ temp * contact loglog  flexible  
fm4.cll rating ~ temp * contact cloglog flexible  
fm4.cct rating ~ temp * contact cauchit flexible  

        no.par    AIC  logLik  LR.stat df Pr(>Chisq)
fm4.lgt      7 186.83 -86.416                       
fm4.prt      7 185.45 -85.723   1.3864  0           
fm4.ll       7 189.14 -87.569  -3.6923  0           
fm4.cll      7 187.22 -86.610   1.9175  0           
fm4.cct      7 198.05 -92.027 -10.8323  0           
> 
> ## structured thresholds:
> fm5 <- update(fm1, threshold = "symmetric")
> fm6 <- update(fm1, threshold = "equidistant")
> anova(fm1, fm5, fm6)
Likelihood ratio tests of cumulative link models:
 
    formula:                link: threshold: 
fm6 rating ~ temp * contact logit equidistant
fm5 rating ~ temp * contact logit symmetric  
fm1 rating ~ temp * contact logit flexible   

    no.par    AIC  logLik LR.stat df Pr(>Chisq)
fm6      5 185.14 -87.570                      
fm5      6 187.05 -87.527  0.0864  1     0.7688
fm1      7 186.83 -86.416  2.2220  1     0.1361
> 
> ## the slice methods:
> slice.fm1 <- slice(fm1)
> par(mfrow = c(3, 3))
> plot(slice.fm1)
> ## see more at '?slice.clm'
> 
> ## Example from MASS::polr:
> data(housing, package = "MASS")
> fm1 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
> summary(fm1)
formula: Sat ~ Infl + Type + Cont
data:    housing

 link  threshold nobs logLik   AIC     niter max.grad cond.H 
 logit flexible  1681 -1739.57 3495.15 4(0)  6.60e-09 4.7e+01

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
InflMedium     0.56639    0.10465   5.412 6.23e-08 ***
InflHigh       1.28882    0.12716  10.136  < 2e-16 ***
TypeApartment -0.57235    0.11924  -4.800 1.59e-06 ***
TypeAtrium    -0.36619    0.15517  -2.360 0.018282 *  
TypeTerrace   -1.09101    0.15149  -7.202 5.93e-13 ***
ContHigh       0.36028    0.09554   3.771 0.000162 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Threshold coefficients:
            Estimate Std. Error z value
Low|Medium   -0.4961     0.1248  -3.974
Medium|High   0.6907     0.1255   5.505
> 
> ## Another example:
> data(soup, package = "ordinal")
Error in find.package(package, lib.loc, verbose = verbose) : 
  there is no package called 'ordinal'
Calls: data -> find.package
Execution halted
Run time: 103.3 seconds.
Additional Logs:   00install.out
Thanks to:
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