weightit.md
In parameters: Processing of Model Parameters

weightit, multinom

Code
  print(model_parameters(fit4, exponentiate = TRUE), zap_small = TRUE)
Output
  # Response level: 2

  Parameter   | Odds Ratio |   SE |       95% CI |     z |     p
  --------------------------------------------------------------
  (Intercept) |       1.00 | 0.62 | [0.30, 3.39] |  0.00 | 0.998
  treat       |       1.08 | 0.25 | [0.68, 1.71] |  0.31 | 0.755
  age         |       0.97 | 0.01 | [0.95, 0.99] | -2.38 | 0.018
  educ        |       0.98 | 0.05 | [0.89, 1.08] | -0.33 | 0.738

  # Response level: 3

  Parameter   | Odds Ratio |   SE |       95% CI |     z |      p
  ---------------------------------------------------------------
  (Intercept) |       0.05 | 0.04 | [0.01, 0.20] | -4.23 | < .001
  treat       |       1.18 | 0.29 | [0.73, 1.91] |  0.67 | 0.502 
  age         |       1.00 | 0.01 | [0.98, 1.02] | -0.01 | 0.989 
  educ        |       1.20 | 0.06 | [1.08, 1.33] |  3.51 | < .001
Message

  Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
    using a Wald z-distribution approximation.

weightit, ordinal

Code
  print(model_parameters(fit5, exponentiate = TRUE), zap_small = TRUE)
Output
  # Fixed Effects

  Parameter | Odds Ratio |   SE |       95% CI |     z |     p
  ------------------------------------------------------------
  treat     |       1.12 | 0.21 | [0.78, 1.61] |  0.60 | 0.549
  age       |       0.99 | 0.01 | [0.97, 1.01] | -0.78 | 0.436
  educ      |       1.11 | 0.04 | [1.03, 1.20] |  2.70 | 0.007

  # Intercept

  Parameter | Odds Ratio |   SE |        95% CI |    z |      p
  -------------------------------------------------------------
  1|2       |       3.28 | 1.70 | [1.19,  9.04] | 2.30 | 0.022 
  2|3       |       9.84 | 5.03 | [3.61, 26.81] | 4.47 | < .001
Message

  Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
    using a Wald z-distribution approximation.