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