tests/testthat/_snaps/plot-ordinal-latent.md

In strengejacke/ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

ggeffect, polr, latent = FALSE

Code
  print(out)
Output
  # Predicted probabilities of Sat

  Sat: Low
  Type: Tower

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.34 | 0.29, 0.39
  Medium |      0.22 | 0.19, 0.27
  High   |      0.12 | 0.10, 0.16

  Sat: Low
  Type: Apartment

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.47 | 0.43, 0.52
  Medium |      0.34 | 0.30, 0.38
  High   |      0.20 | 0.17, 0.24

  Sat: Low
  Type: Atrium

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.42 | 0.36, 0.49
  Medium |      0.29 | 0.24, 0.35
  High   |      0.17 | 0.13, 0.21

  Sat: Low
  Type: Terrace

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.60 | 0.54, 0.66
  Medium |      0.46 | 0.40, 0.53
  High   |      0.29 | 0.24, 0.36

  Sat: Medium
  Type: Tower

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.29 | 0.27, 0.31
  Medium |      0.26 | 0.24, 0.29
  High   |      0.19 | 0.16, 0.22

  Sat: Medium
  Type: Apartment

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.27 | 0.25, 0.30
  Medium |      0.29 | 0.27, 0.31
  High   |      0.25 | 0.22, 0.28

  Sat: Medium
  Type: Atrium

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.28 | 0.26, 0.31
  Medium |      0.28 | 0.26, 0.31
  High   |      0.23 | 0.20, 0.27

  Sat: Medium
  Type: Terrace

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.23 | 0.20, 0.26
  Medium |      0.28 | 0.25, 0.30
  High   |      0.28 | 0.26, 0.31

  Sat: High
  Type: Tower

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.38 | 0.32, 0.43
  Medium |      0.51 | 0.46, 0.57
  High   |      0.69 | 0.63, 0.74

  Sat: High
  Type: Apartment

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.25 | 0.22, 0.29
  Medium |      0.37 | 0.33, 0.42
  High   |      0.55 | 0.50, 0.60

  Sat: High
  Type: Atrium

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.29 | 0.24, 0.35
  Medium |      0.42 | 0.36, 0.49
  High   |      0.60 | 0.53, 0.67

  Sat: High
  Type: Terrace

  Infl   | Predicted |     95% CI
  -------------------------------
  Low    |      0.17 | 0.13, 0.21
  Medium |      0.26 | 0.22, 0.32
  High   |      0.42 | 0.35, 0.49

Code
  print(out)
Output
  # Predicted probabilities of rating

  rating: X1
  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.20 | 0.08, 0.42
  warm |      0.02 | 0.01, 0.09

  rating: X1
  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.06 | 0.02, 0.18
  warm |      0.00 | 0.00, 0.02

  rating: X2
  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.56 | 0.39, 0.72
  warm |      0.21 | 0.10, 0.41

  rating: X2
  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.39 | 0.23, 0.58
  warm |      0.05 | 0.02, 0.14

  rating: X3
  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.21 | 0.09, 0.40
  warm |      0.51 | 0.36, 0.65

  rating: X3
  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.43 | 0.28, 0.60
  warm |      0.29 | 0.15, 0.48

  rating: X4
  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.03 | 0.01, 0.09
  warm |      0.19 | 0.09, 0.37

  rating: X4
  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.09 | 0.03, 0.22
  warm |      0.37 | 0.22, 0.56

  rating: X5
  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.01 | 0.00, 0.03
  warm |      0.07 | 0.02, 0.20

  rating: X5
  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.03 | 0.01, 0.09
  warm |      0.29 | 0.13, 0.51

ggeffect, polr, latent = TRUE

Code
  print(out)
Output
  # Predicted log-odds of Sat

  Type: Tower

  Infl   | Predicted |       95% CI
  ---------------------------------
  Low    |      0.18 |  0.09,  0.27
  Medium |      0.75 |  0.52,  0.98
  High   |      1.47 |  1.19,  1.75

  Type: Apartment

  Infl   | Predicted |       95% CI
  ---------------------------------
  Low    |     -0.39 | -0.63, -0.15
  Medium |      0.17 | -0.14,  0.49
  High   |      0.90 |  0.55,  1.24

  Type: Atrium

  Infl   | Predicted |       95% CI
  ---------------------------------
  Low    |     -0.19 | -0.49,  0.12
  Medium |      0.38 |  0.01,  0.76
  High   |      1.10 |  0.71,  1.50

  Type: Terrace

  Infl   | Predicted |       95% CI
  ---------------------------------
  Low    |     -0.91 | -1.21, -0.61
  Medium |     -0.34 | -0.71,  0.02
  High   |      0.38 | -0.02,  0.77

Code
  print(out)
Output
  # Predicted log-odds of rating

  contact: no

  temp | Predicted |     95% CI
  -----------------------------
  cold |      0.00 | 0.00, 0.00
  warm |      2.32 | 0.95, 3.70

  contact: yes

  temp | Predicted |     95% CI
  -----------------------------
  cold |      1.35 | 0.05, 2.64
  warm |      4.03 | 2.51, 5.55

strengejacke/ggeffects documentation built on Dec. 24, 2024, 3:27 a.m.