Is this the type of thing you want to do? I used some built-in data to create a polr object.
library(MASS)
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
house.plr
#> Call:
#> polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq)
#>
#> Coefficients:
#> InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
#> 0.5663937 1.2888191 -0.5723501 -0.3661866 -1.0910149
#> ContHigh
#> 0.3602841
#>
#> Intercepts:
#> Low|Medium Medium|High
#> -0.4961353 0.6907083
#>
#> Residual Deviance: 3479.149
#> AIC: 3495.149
DF <- housing[1:6,] #this could be entirely new data
predict(object = house.plr, newdata = DF, type = "class")
#> [1] Low Low Low High High High
#> Levels: Low Medium High
predict(object = house.plr, newdata = DF, type = "probs")
#> Low Medium High
#> 1 0.3784493 0.2876752 0.3338755
#> 2 0.3784493 0.2876752 0.3338755
#> 3 0.3784493 0.2876752 0.3338755
#> 4 0.2568264 0.2742122 0.4689613
#> 5 0.2568264 0.2742122 0.4689613
#> 6 0.2568264 0.2742122 0.4689613
Created on 2020-08-15 by the reprex package (v0.3.0)