averaging.md
In parameters: Processing of Model Parameters

MuMIn link functions

Code
  print(mp)
Output
  Parameter     | Log-Odds |   SE |         95% CI |    z |      p
  ----------------------------------------------------------------
  (Intercept)   |    -1.01 | 0.26 | [-1.51, -0.50] | 3.91 | < .001
  var cont      |    -0.42 | 0.25 | [-0.90,  0.07] | 1.70 | 0.090 
  var binom [1] |    -0.71 | 0.62 | [-1.92,  0.50] | 1.15 | 0.250 
Message

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

  The model has a log- or logit-link. Consider using `exponentiate =
    TRUE` to interpret coefficients as ratios.

ggpredict, glmmTMB averaging

Code
  print(mp)
Output
  Parameter    | Coefficient |       SE |                95% CI |        z |      p
  ---------------------------------------------------------------------------------
  cond((Int))  |       -0.11 |     0.22 | [    -0.55,     0.32] |     0.52 | 0.605 
  cond(income) |       -0.01 | 3.20e-03 | [    -0.02,    -0.01] |     4.07 | < .001
  zi((Int))    |      -23.11 | 17557.33 | [-34434.85, 34388.63] | 1.32e-03 | 0.999 
Message

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

ggpredict, poly averaging

Code
  print(mp)
Output
  Parameter   | Coefficient |     SE |            95% CI |    z |      p
  ----------------------------------------------------------------------
  (Intercept) |      954.50 | 123.60 | [712.26, 1196.75] | 7.72 | < .001
  gear        |      -24.81 |  18.54 | [-61.14,   11.52] | 1.34 | 0.181 
  mpg         |      -51.21 |  11.60 | [-73.96,  -28.47] | 4.41 | < .001
  mpg^2       |        0.79 |   0.26 | [  0.29,    1.30] | 3.07 | 0.002 
  am [1]      |      -30.80 |  32.30 | [-94.11,   32.52] | 0.95 | 0.340 
Message

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