y <- matrix(c(1,5,4,5,5,7, 1,2,2,0,0,0), byrow = TRUE, 2) x <- c(0,1) library(VGAM) #> Warning: package 'VGAM' was built under R version 4.3.3 #> Loading required package: stats4 #> Loading required package: splines summary(vglm(y ~ x, family=cumulative(parallel = TRUE, link="logitlink"))) #> #> Call: #> vglm(formula = y ~ x, family = cumulative(parallel = TRUE, link = "logitlink")) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept):1 -3.3900 0.8595 -3.944 8e-05 *** #> (Intercept):2 -1.4291 0.4722 -3.027 0.00247 ** #> (Intercept):3 -0.4724 0.3915 -1.206 0.22766 #> (Intercept):4 0.2624 0.3849 0.682 0.49550 #> (Intercept):5 1.0794 0.4383 2.463 0.01379 * #> x 2.1203 0.9628 2.202 0.02765 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Number of linear predictors: 5 #> #> Names of linear predictors: logitlink(P[Y<=1]), logitlink(P[Y<=2]), #> logitlink(P[Y<=3]), logitlink(P[Y<=4]), logitlink(P[Y<=5]) #> #> Residual deviance: 3.1386 on 4 degrees of freedom #> #> Log-likelihood: -10.6303 on 4 degrees of freedom #> #> Number of Fisher scoring iterations: 5 #> #> Warning: Hauck-Donner effect detected in the following estimate(s): #> '(Intercept):1' #> #> #> Exponentiated coefficients: #> x #> 8.333441