Cfrgtkky

How do I get average marginal effects (AMEs) for each category/threshold in a partial proportional odds model (PPOM)?

This is my first post in this forum. I hope that I have heeded the most essential recommendations for asking good questions.

This sample dataset consists of an ordinal outcome variable (Y1) and three independent variables (VAR1, VAR2, VAR3).

set.seed(3)

sampleData <- data.frame(id = 1:1000, Y1 = sample(c("1", "2", "3", "4"), 

    1000, replace=TRUE), Var1 = rnorm(1000, 40, 10), 

    Var2 = rnorm(1000, 60, 10), Var3 = rnorm(1000, 80, 5))

Assuming proportional odds assumption is violated, one could carry out a partial proportional odds model (PPOM) using package ordinal to predict Y1 by the three independent variables (Var1, Var2, Var3).

library(ordinal)  

PPOM <- clm(as.factor(Y1) ~ Var1 + Var2 + Var3, 

        nominal = ~ Var1 + Var2 + Var3, data = sampleData)

We get the following output with coefficients for each category:

summary(PPOM)



formula: as.factor(Y1) ~ Var1 + Var2 + Var3

nominal: ~Var1 + Var2 + Var3

data:    sampleData



 link  threshold nobs logLik   AIC     niter max.grad cond.H 

 logit flexible  1000 -1381.17 2786.34 4(0)  2.82e-10 2.2e+07



Coefficients: (3 not defined because of singularities)

     Estimate Std. Error z value Pr(>|z|)

Var1       NA         NA      NA       NA

Var2       NA         NA      NA       NA

Var3       NA         NA      NA       NA



Threshold coefficients:

                  Estimate Std. Error z value

1|2.(Intercept)  0.4952642  1.2260010   0.404

2|3.(Intercept)  1.9790234  1.0724982   1.845

3|4.(Intercept)  2.0892425  1.2550636   1.665

1|2.Var1         0.0026194  0.0075920   0.345

2|3.Var1        -0.0077578  0.0065845  -1.178

3|4.Var1        -0.0064243  0.0075364  -0.852

1|2.Var2        -0.0001089  0.0074568  -0.015

2|3.Var2        -0.0082836  0.0063447  -1.306

3|4.Var2        -0.0073638  0.0071008  -1.037

1|2.Var3        -0.0219767  0.0140701  -1.562

2|3.Var3        -0.0157235  0.0121943  -1.289

3|4.Var3        -0.0047098  0.0141844  -0.332

I am interested in AMEs for each predictor for each category. By using margins I get only AMEs for all of the thresholds together.

library(margins) 

summary(margins(PPOM))

Output:

 factor    AME     SE      z      p   lower  upper

   Var1 0.0000 0.0000 1.1365 0.2557 -0.0000 0.0001

   Var2 0.0000 0.0000 1.3056 0.1917 -0.0000 0.0001

   Var3 0.0001 0.0001 0.9990 0.3178 -0.0001 0.0002

Does anyone know hot to calculate AMEs for each category?

Any help would be greatly appreciated!