How to get p values for odds ratios from an ordinal regression in r

Question

I am trying to get the p values for my odds ratio from an ordinal regression using r.

I previously constructed my p values on the log odds like this

scm <- polr(finaloutcome ~ Size_no + Hegemony + Committee, data = data3, Hess = TRUE)
(ctable <- coef(summary(scm)))

Calculate and store p value

p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
(ctable <- cbind(ctable, "p value" = p))

I created by odds ratios like this:

ci <- confint.default(scm) 
exp(coef(scm))
## OR and CI 
exp(cbind(OR = coef(scm), ci))

However, I am now unsure how to create the p values for the odds ratio. Using the previous method I got:

(ctable1 <- exp(coef(scm)))
p1 <- pnorm(abs(ctable1[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p1))

However i get the error: Error in ctable1[, "t value"] : incorrect number of dimensions

Odds ratio output sample:

Size	Hegem	Committee
9.992240e-01	6.957805e-02	1.204437e-01

Data sample:

	finaloutcome	Size_no	Committee	Hegemony
1	3	54	2	0
2	2	127	3	0
3	2	127	3	0
4	2	22	1	1
5	2	193	4	1
6	2	54	2	0
7	NA	11	1	1
8	3	54	2	0
9	3	22	1	1
10	2	53	3	1
11	2	53	3	1
12	2	53	3	1
13	2	53	3	1
14	2	53	3	1
15	2	53	3	1
16	2	120	3	0
17	2	120	3	0
18	1	22	1	1
19	1	22	1	1
20	2	193	4	1
21	2	193	4	1
22	2	193	4	1
23	2	12	4	1
24	2	35	1	1
25	1	193	4	1
26	1	164	4	1
27	1	12	4	1
28	2	12	4	1
29	2	193	4	1
30	2	54	2	0
31	2	193	4	1
32	2	193	4	1
33	2	54	2	0
34	2	12	4	1
35	2	22	1	1
36	4	53	3	1
37	2	35	1	1
38	1	193	4	1
39	5	54	2	0
40	7	164	4	1
41	5	54	2	0
42	1	12	4	1
43	7	193	4	1
44	2	193	4	1
45	2	193	4	1
46	2	193	4	1
47	2	193	4	1
48	2	193	4	1
49	2	12	4	1
50	2	22	1	1
51	2	12	4	1
52	2	12	4	1
53	6	13	1	1
54	6	13	1	1
55	6	13	1	1
56	6	12	4	1
57	2	193	4	1
58	3	12	4	1
59	1	12	4	1
60	1	12	4	1
61	8	35	1	1
62	2	193	4	1
63	8	35	1	1
64	6	30	2	1
65	8	12	4	1
66	4	12	4	1
67	5	30	2	1
68	5	54	2	0
69	7	12	4	1
70	5	12	4	1
71	5	54	2	0
72	5	193	4	1
73	5	193	4	1
74	5	54	2	0
75	5	54	2	0
76	1	11	1	1
77	3	22	1	1
78	3	12	4	1
79	6	12	4	1
80	2	22	1	1
81	8	193	4	1
82	8	193	4	1
83	4	193	4	1
84	2	193	4	1
85	2	193	4	1
86	2	193	4	1
87	2	193	4	1
88	2	193	4	1
89	2	193	4	1
90	2	193	4	1
91	2	193	4	1
92	2	193	4	1
93	8	193	4	1
94	6	12	4	1
95	5	12	4	1
96	5	12	4	1
97	5	12	4	1
98	5	12	4	1
99	5	12	4	1
100	5	12	4	1

Answer 1

I usually use lm or glm to create my model (mdl <- lm(…) or mdl <- glm(…)). Then I use summary on the object to see these values. More than this, you can use the Yardstick and Broom. I recommend the book R for Data Science. There is a great explanation about modeling and using the Tidymodels packages.