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> ## Yamaguchi (1990), Table 5, p. 202, and Table 6B, p. 205
> 
> library(logmult)
Loading required package: gnm

Attaching package: 'logmult'

The following object is masked from 'package:gnm':

    se

> data(ocg1973)
> 
> # Simple symmetric RC(1) model ("Null skew-symmetry")
> rc.model <- rc(ocg1973, diagonal=TRUE, symmetric=TRUE, weighting="none")
Initialising
Running start-up iterations..
Running main iterations.......
Done
> 
> # Reported phi is slightly different, coefficients agree
> rc.model
Call:
rc(tab = ocg1973, symmetric = TRUE, diagonal = TRUE, weighting = "none")

Intrinsic association coefficients:
Dim1 
1.94 

Normalized row and column scores:
   UNM    LNM     UM     LM      F 
 0.690  0.303 -0.103 -0.332 -0.558 

Diagonal coefficients:
    UNM     LNM      UM      LM       F 
2.07446 0.28852 0.12524 0.35374 0.00674 

Normalization weights: none
Deviance:              46.64896
Pearson chi-squared:   51.69522
Dissimilarity index:   1.049807%
Residual df:           7
BIC:                   -22.0295
AIC:                   32.64896
> stopifnot(isTRUE(all.equal(round(rc.model$assoc$row[,1,1], d=2),
+                            c(0.69, 0.3, -0.1, -0.33, -0.56),
+                            check.attributes=FALSE)))
> 
> 
> # Note model does not always converge, several attempts may be needed
> # Here we set known starting values to be sure it works
> set.seed(5)
> model <- yrcskew(ocg1973, nd.symm=1, nd.skew=1, diagonal=TRUE, weighting="none")
Running base model to find starting values...
Initialising
Running start-up iterations..
Running main iterations....
Done
Running real model...
Initialising
Running main iterations.................
Done
> 
> # We do not get the same results as the author, but the smaller deviance
> # indicates a better fit in our version (!)
> model
Call:
yrcskew(tab = ocg1973, nd.symm = 1, nd.skew = 1, diagonal = TRUE, 
    weighting = "none")

Intrinsic symmetric association coefficients:
Dim1 
2.29 

Normalized symmetric association scores:
    UNM     LNM      UM      LM       F 
 0.8506  0.0489 -0.2461 -0.3313 -0.3222 


Intrinsic skew association coefficients:
Dim1 
-1.8 

Normalized skew association scores:
    UNM     LNM      UM      LM       F 
-0.4207 -0.3320 -0.1662  0.0968  0.8221 

Diagonal coefficients:
   UNM    LNM     UM     LM      F 
 2.416  0.198  0.318  0.592 -1.390 

Normalization weights: none
Deviance:              1.187443
Pearson chi-squared:   1.189223
Dissimilarity index:   0.1792143%
Residual df:           3
BIC:                   -28.24618
AIC:                   -4.812557
> 
> 
> summary(model)
Call:
yrcskew(tab = ocg1973, nd.symm = 1, nd.skew = 1, diagonal = TRUE, 
    weighting = "none")

Deviance Residuals:
     D
O            UNM        LNM         UM         LM          F
  UNM   0.00e+00  -3.73e-01   4.02e-01   1.64e-01  -4.99e-01
  LNM   2.21e-01   0.00e+00  -1.97e-01  -2.96e-01   5.62e-01
  UM   -2.02e-01   1.55e-01   0.00e+00   9.94e-02  -3.91e-02
  LM   -6.44e-02   1.55e-01  -4.21e-02  -4.71e-08  -8.47e-02
  F     6.07e-02  -4.10e-02  -3.56e-02   1.22e-02   0.00e+00

Association coefficients:
             Normalized  Adjusted
Dim1           2.286413        NA
Dim1:O|DUNM    0.850616    1.2862
Dim1:O|DLNM    0.048866    0.0739
Dim1:O|DUM    -0.246076   -0.3721
Dim1:O|DLM    -0.331254   -0.5009
Dim1:O|DF     -0.322152   -0.4871
Dim1          -1.796569        NA
Dim1:O|DUNM   -0.420702   -0.5639
Dim1:O|DLNM   -0.332003   -0.4450
Dim1:O|DUM    -0.166162   -0.2227
Dim1:O|DLM     0.096794    0.1297
Dim1:O|DF      0.822073    1.1019

No standard errors available
(jackknife and bootstrap disabled, or weighting changed since fitting).

Diagonal coefficients:
   UNM    LNM     UM     LM      F 
 2.416  0.198  0.318  0.592 -1.390 

Normalization weights: none
Deviance:              1.187443
Pearson chi-squared:   1.189223
Dissimilarity index:   0.1792143%
Residual df:           3
BIC:                   -28.24618
AIC:                   -4.812557
> 
> # These scores cannot be checked since the author reports a higher deviance:
> # they are simply here to ensure no regressions appear
> stopifnot(isTRUE(all.equal(c(round(model$assoc.yrcskew$row, d=3)),
+                            c(-0.421, -0.332, -0.166, 0.097, 0.822))))
> stopifnot(isTRUE(all.equal(c(round(model$assoc$row, d=3)),
+                              c(0.851, 0.049, -0.246, -0.331, -0.322))))
> 
> proc.time()
   user  system elapsed 
   0.85    0.12    0.98