tests/testthat/_snaps/printing.md
In easystats/parameters: Processing of Model Parameters
print model with multiple components
Code
print(out)
Output
# Fixed Effects (Count Model)
Parameter | IRR | SE | 95% CI | z | p
---------------------------------------------------------
(Intercept) | 0.54 | 0.22 | [0.25, 1.20] | -1.51 | 0.132
spp [PR] | 0.38 | 0.25 | [0.11, 1.35] | -1.50 | 0.134
spp [DM] | 1.19 | 0.28 | [0.75, 1.88] | 0.73 | 0.468
spp [EC-A] | 0.68 | 0.23 | [0.35, 1.33] | -1.13 | 0.258
spp [EC-L] | 1.63 | 0.39 | [1.02, 2.60] | 2.05 | 0.041
spp [DES-L] | 1.80 | 0.41 | [1.15, 2.82] | 2.59 | 0.010
spp [DF] | 0.89 | 0.22 | [0.55, 1.44] | -0.46 | 0.642
mined [no] | 4.18 | 1.53 | [2.04, 8.57] | 3.90 | < .001
# Fixed Effects (Zero-Inflation Component)
Parameter | Odds Ratio | SE | 95% CI | z | p
----------------------------------------------------------------
(Intercept) | 2.48 | 1.56 | [0.73, 8.51] | 1.45 | 0.147
spp [PR] | 3.19 | 4.26 | [0.23, 43.70] | 0.87 | 0.384
spp [DM] | 0.39 | 0.31 | [0.08, 1.88] | -1.17 | 0.241
spp [EC-A] | 2.84 | 2.02 | [0.70, 11.49] | 1.46 | 0.144
spp [EC-L] | 0.57 | 0.41 | [0.14, 2.37] | -0.77 | 0.439
spp [DES-L] | 0.41 | 0.31 | [0.09, 1.79] | -1.19 | 0.236
spp [DF] | 0.08 | 0.17 | [0.00, 5.68] | -1.16 | 0.244
mined [no] | 0.08 | 0.05 | [0.02, 0.25] | -4.24 | < .001
# Dispersion
Parameter | Coefficient | 95% CI
----------------------------------------
(Intercept) | 1.51 | [0.93, 2.46]
# Random Effects Variances
Parameter | Coefficient | 95% CI
-------------------------------------------------
SD (Intercept: site) | 0.38 | [0.17, 0.87]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald z-distribution approximation.
Code
print(out, split_component = FALSE)
Output
# Fixed Effects
Parameter | Coefficient | SE | 95% CI | z | p | Effects | Component
----------------------------------------------------------------------------------------------------
(Intercept) | 0.54 | 0.22 | [0.25, 1.20] | -1.51 | 0.132 | fixed | conditional
spp [PR] | 0.38 | 0.25 | [0.11, 1.35] | -1.50 | 0.134 | fixed | conditional
spp [DM] | 1.19 | 0.28 | [0.75, 1.88] | 0.73 | 0.468 | fixed | conditional
spp [EC-A] | 0.68 | 0.23 | [0.35, 1.33] | -1.13 | 0.258 | fixed | conditional
spp [EC-L] | 1.63 | 0.39 | [1.02, 2.60] | 2.05 | 0.041 | fixed | conditional
spp [DES-L] | 1.80 | 0.41 | [1.15, 2.82] | 2.59 | 0.010 | fixed | conditional
spp [DF] | 0.89 | 0.22 | [0.55, 1.44] | -0.46 | 0.642 | fixed | conditional
mined [no] | 4.18 | 1.53 | [2.04, 8.57] | 3.90 | < .001 | fixed | conditional
(Intercept) | 2.48 | 1.56 | [0.73, 8.51] | 1.45 | 0.147 | fixed | zero_inflated
sppPR | 3.19 | 4.26 | [0.23, 43.70] | 0.87 | 0.384 | fixed | zero_inflated
sppDM | 0.39 | 0.31 | [0.08, 1.88] | -1.17 | 0.241 | fixed | zero_inflated
sppEC-A | 2.84 | 2.02 | [0.70, 11.49] | 1.46 | 0.144 | fixed | zero_inflated
sppEC-L | 0.57 | 0.41 | [0.14, 2.37] | -0.77 | 0.439 | fixed | zero_inflated
sppDES-L | 0.41 | 0.31 | [0.09, 1.79] | -1.19 | 0.236 | fixed | zero_inflated
sppDF | 0.08 | 0.17 | [0.00, 5.68] | -1.16 | 0.244 | fixed | zero_inflated
minedno | 0.08 | 0.05 | [0.02, 0.25] | -4.24 | < .001 | fixed | zero_inflated
(Intercept) | 1.51 | | [0.93, 2.46] | | | fixed | dispersion
SD (Intercept: site) | 0.38 | | [0.17, 0.87] | | | random | conditional
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald z-distribution approximation.
adding model summaries
Code
print(out)
Output
Parameter | Coefficient | SE | 95% CI | t(144) | p
-------------------------------------------------------------------------------------------
(Intercept) | 4.21 | 0.41 | [ 3.41, 5.02] | 10.34 | < .001
Species [versicolor] | -1.81 | 0.60 | [-2.99, -0.62] | -3.02 | 0.003
Species [virginica] | -3.15 | 0.63 | [-4.41, -1.90] | -4.97 | < .001
Petal Length | 0.54 | 0.28 | [ 0.00, 1.09] | 1.96 | 0.052
Species [versicolor] * Petal Length | 0.29 | 0.30 | [-0.30, 0.87] | 0.97 | 0.334
Species [virginica] * Petal Length | 0.45 | 0.29 | [-0.12, 1.03] | 1.56 | 0.120
Model: Sepal.Length ~ Species * Petal.Length (150 Observations)
Sigma: 0.336 (df = 144)
RMSE : 0.330
R2: 0.840; adjusted R2: 0.835
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
grouped parameters
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter | Coefficient | SE | 95% CI | t(22) | p
-----------------------------------------------------------------------
Engine | | | | |
cyl [6] | -2.47 | 2.21 | [ -7.05, 2.12] | -1.12 | 0.276
cyl [8] | 1.97 | 5.11 | [ -8.63, 12.58] | 0.39 | 0.703
vs | 3.18 | 3.79 | [ -4.68, 11.04] | 0.84 | 0.410
hp | -0.06 | 0.02 | [ -0.11, -0.02] | -2.91 | 0.008
Interactions | | | | |
gear [4] * vs | -2.90 | 4.67 | [-12.57, 6.78] | -0.62 | 0.541
gear [5] * vs | 2.59 | 4.54 | [ -6.82, 12.00] | 0.57 | 0.574
Controls | | | | |
gear [4] | 3.10 | 4.34 | [ -5.90, 12.10] | 0.71 | 0.482
gear [5] | 4.80 | 3.48 | [ -2.42, 12.01] | 1.38 | 0.182
drat | 2.70 | 2.03 | [ -1.52, 6.91] | 1.33 | 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, sep = " ", groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"),
Interactions = c("gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter Coefficient SE 95% CI t(22) p
------------------------------------------------------------------
Engine
cyl [6] -2.47 2.21 [ -7.05, 2.12] -1.12 0.276
cyl [8] 1.97 5.11 [ -8.63, 12.58] 0.39 0.703
vs 3.18 3.79 [ -4.68, 11.04] 0.84 0.410
hp -0.06 0.02 [ -0.11, -0.02] -2.91 0.008
Interactions
gear [4] * vs -2.90 4.67 [-12.57, 6.78] -0.62 0.541
gear [5] * vs 2.59 4.54 [ -6.82, 12.00] 0.57 0.574
Controls
gear [4] 3.10 4.34 [ -5.90, 12.10] 0.71 0.482
gear [5] 4.80 3.48 [ -2.42, 12.01] 1.38 0.182
drat 2.70 2.03 [ -1.52, 6.91] 1.33 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
digits and ci_digits
Code
model_parameters(model, digits = 4)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.5648, 34.4112] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1038, -0.0217] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9084, 5.5530] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3373, 10.7051] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.3593, 6.2759] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7262, 2.1148] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1490, 10.0049] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7313, 6.4172] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
model_parameters(model, digits = 4, ci_digits = 1)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.6, 34.4] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1, 0.0] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9, 5.6] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3, 10.7] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.4, 6.3] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7, 2.1] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1, 10.0] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7, 6.4] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, digits = 4)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.5648, 34.4112] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1038, -0.0217] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9084, 5.5530] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3373, 10.7051] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.3593, 6.2759] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7262, 2.1148] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1490, 10.0049] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7313, 6.4172] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, digits = 4, ci_digits = 1)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.6, 34.4] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1, 0.0] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9, 5.6] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3, 10.7] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.4, 6.3] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7, 2.1] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1, 10.0] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7, 6.4] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
select pattern
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter | Coefficient | SE | 95% CI | t(22) | p
-----------------------------------------------------------------------
Engine | | | | |
cyl [6] | -2.47 | 2.21 | [ -7.05, 2.12] | -1.12 | 0.276
cyl [8] | 1.97 | 5.11 | [ -8.63, 12.58] | 0.39 | 0.703
vs | 3.18 | 3.79 | [ -4.68, 11.04] | 0.84 | 0.410
hp | -0.06 | 0.02 | [ -0.11, -0.02] | -2.91 | 0.008
Interactions | | | | |
gear [4] * vs | -2.90 | 4.67 | [-12.57, 6.78] | -0.62 | 0.541
gear [5] * vs | 2.59 | 4.54 | [ -6.82, 12.00] | 0.57 | 0.574
Controls | | | | |
gear [4] | 3.10 | 4.34 | [ -5.90, 12.10] | 0.71 | 0.482
gear [5] | 4.80 | 3.48 | [ -2.42, 12.01] | 1.38 | 0.182
drat | 2.70 | 2.03 | [ -1.52, 6.91] | 1.33 | 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, select = "{coef} ({se})")
Output
Parameter | Estimate (SE)
-----------------------------
hp | -0.06 (0.02)
gear [4] | 3.10 (4.34)
gear [5] | 4.80 (3.48)
vs | 3.18 (3.79)
cyl [6] | -2.47 (2.21)
cyl [8] | 1.97 (5.11)
drat | 2.70 (2.03)
gear [4] * vs | -2.90 (4.67)
gear [5] * vs | 2.59 (4.54)
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, select = "{coef}{stars}|[{ci}]")
Output
Parameter | Estimate | [ci]
------------------------------------------
hp | -0.06** | [ -0.11, -0.02]
gear [4] | 3.10 | [ -5.90, 12.10]
gear [5] | 4.80 | [ -2.42, 12.01]
vs | 3.18 | [ -4.68, 11.04]
cyl [6] | -2.47 | [ -7.05, 2.12]
cyl [8] | 1.97 | [ -8.63, 12.58]
drat | 2.70 | [ -1.52, 6.91]
gear [4] * vs | -2.90 | [-12.57, 6.78]
gear [5] * vs | 2.59 | [ -6.82, 12.00]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)), select = "{coef}{stars}|[{ci}]")
Output
Parameter | Estimate | [ci]
---------------------------------------------
Engine | |
cyl [6] | -2.47 | [ -7.05, 2.12]
cyl [8] | 1.97 | [ -8.63, 12.58]
vs | 3.18 | [ -4.68, 11.04]
hp | -0.06** | [ -0.11, -0.02]
Interactions | |
gear [4] * vs | -2.90 | [-12.57, 6.78]
gear [5] * vs | 2.59 | [ -6.82, 12.00]
Controls | |
gear [4] | 3.10 | [ -5.90, 12.10]
gear [5] | 4.80 | [ -2.42, 12.01]
drat | 2.70 | [ -1.52, 6.91]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, sep = " ", groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"),
Interactions = c("gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)), select = "{coef}{stars}|[{ci}]")
Output
Parameter Estimate [ci]
-------------------------------------------
Engine
cyl [6] -2.47 [ -7.05, 2.12]
cyl [8] 1.97 [ -8.63, 12.58]
vs 3.18 [ -4.68, 11.04]
hp -0.06** [ -0.11, -0.02]
Interactions
gear [4] * vs -2.90 [-12.57, 6.78]
gear [5] * vs 2.59 [ -6.82, 12.00]
Controls
gear [4] 3.10 [ -5.90, 12.10]
gear [5] 4.80 [ -2.42, 12.01]
drat 2.70 [ -1.52, 6.91]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
no more message about interpretation of log-resoponse
Code
print(out)
Output
Parameter | Coefficient | SE | 95% CI | t(30) | p
-----------------------------------------------------------------
(Intercept) | 9.29 | 2.24 | [5.67, 15.21] | 9.23 | < .001
gear | 1.22 | 0.08 | [1.07, 1.39] | 3.08 | 0.004
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
no fail for mgcv-binomial
Code
print(out)
Output
# Fixed Effects
Parameter | Log-Odds | SE | 95% CI | z | df | p
---------------------------------------------------------------------
(Intercept) | -0.20 | 0.50 | [-1.18, 0.79] | -0.39 | 29.98 | 0.695
# Smooth Terms
Parameter | z | df | p
---------------------------------------
Smooth term (mpg) | 7.24 | 1.02 | 0.007
Message
The model has a log- or logit-link. Consider using `exponentiate =
TRUE` to interpret coefficients as ratios.
easystats/parameters documentation built on June 9, 2025, 9:51 a.m.
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print model with multiple components
Code
print(out)
Output
# Fixed Effects (Count Model)
Parameter | IRR | SE | 95% CI | z | p
---------------------------------------------------------
(Intercept) | 0.54 | 0.22 | [0.25, 1.20] | -1.51 | 0.132
spp [PR] | 0.38 | 0.25 | [0.11, 1.35] | -1.50 | 0.134
spp [DM] | 1.19 | 0.28 | [0.75, 1.88] | 0.73 | 0.468
spp [EC-A] | 0.68 | 0.23 | [0.35, 1.33] | -1.13 | 0.258
spp [EC-L] | 1.63 | 0.39 | [1.02, 2.60] | 2.05 | 0.041
spp [DES-L] | 1.80 | 0.41 | [1.15, 2.82] | 2.59 | 0.010
spp [DF] | 0.89 | 0.22 | [0.55, 1.44] | -0.46 | 0.642
mined [no] | 4.18 | 1.53 | [2.04, 8.57] | 3.90 | < .001
# Fixed Effects (Zero-Inflation Component)
Parameter | Odds Ratio | SE | 95% CI | z | p
----------------------------------------------------------------
(Intercept) | 2.48 | 1.56 | [0.73, 8.51] | 1.45 | 0.147
spp [PR] | 3.19 | 4.26 | [0.23, 43.70] | 0.87 | 0.384
spp [DM] | 0.39 | 0.31 | [0.08, 1.88] | -1.17 | 0.241
spp [EC-A] | 2.84 | 2.02 | [0.70, 11.49] | 1.46 | 0.144
spp [EC-L] | 0.57 | 0.41 | [0.14, 2.37] | -0.77 | 0.439
spp [DES-L] | 0.41 | 0.31 | [0.09, 1.79] | -1.19 | 0.236
spp [DF] | 0.08 | 0.17 | [0.00, 5.68] | -1.16 | 0.244
mined [no] | 0.08 | 0.05 | [0.02, 0.25] | -4.24 | < .001
# Dispersion
Parameter | Coefficient | 95% CI
----------------------------------------
(Intercept) | 1.51 | [0.93, 2.46]
# Random Effects Variances
Parameter | Coefficient | 95% CI
-------------------------------------------------
SD (Intercept: site) | 0.38 | [0.17, 0.87]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald z-distribution approximation.
Code
print(out, split_component = FALSE)
Output
# Fixed Effects
Parameter | Coefficient | SE | 95% CI | z | p | Effects | Component
----------------------------------------------------------------------------------------------------
(Intercept) | 0.54 | 0.22 | [0.25, 1.20] | -1.51 | 0.132 | fixed | conditional
spp [PR] | 0.38 | 0.25 | [0.11, 1.35] | -1.50 | 0.134 | fixed | conditional
spp [DM] | 1.19 | 0.28 | [0.75, 1.88] | 0.73 | 0.468 | fixed | conditional
spp [EC-A] | 0.68 | 0.23 | [0.35, 1.33] | -1.13 | 0.258 | fixed | conditional
spp [EC-L] | 1.63 | 0.39 | [1.02, 2.60] | 2.05 | 0.041 | fixed | conditional
spp [DES-L] | 1.80 | 0.41 | [1.15, 2.82] | 2.59 | 0.010 | fixed | conditional
spp [DF] | 0.89 | 0.22 | [0.55, 1.44] | -0.46 | 0.642 | fixed | conditional
mined [no] | 4.18 | 1.53 | [2.04, 8.57] | 3.90 | < .001 | fixed | conditional
(Intercept) | 2.48 | 1.56 | [0.73, 8.51] | 1.45 | 0.147 | fixed | zero_inflated
sppPR | 3.19 | 4.26 | [0.23, 43.70] | 0.87 | 0.384 | fixed | zero_inflated
sppDM | 0.39 | 0.31 | [0.08, 1.88] | -1.17 | 0.241 | fixed | zero_inflated
sppEC-A | 2.84 | 2.02 | [0.70, 11.49] | 1.46 | 0.144 | fixed | zero_inflated
sppEC-L | 0.57 | 0.41 | [0.14, 2.37] | -0.77 | 0.439 | fixed | zero_inflated
sppDES-L | 0.41 | 0.31 | [0.09, 1.79] | -1.19 | 0.236 | fixed | zero_inflated
sppDF | 0.08 | 0.17 | [0.00, 5.68] | -1.16 | 0.244 | fixed | zero_inflated
minedno | 0.08 | 0.05 | [0.02, 0.25] | -4.24 | < .001 | fixed | zero_inflated
(Intercept) | 1.51 | | [0.93, 2.46] | | | fixed | dispersion
SD (Intercept: site) | 0.38 | | [0.17, 0.87] | | | random | conditional
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald z-distribution approximation.
adding model summaries
Code
print(out)
Output
Parameter | Coefficient | SE | 95% CI | t(144) | p
-------------------------------------------------------------------------------------------
(Intercept) | 4.21 | 0.41 | [ 3.41, 5.02] | 10.34 | < .001
Species [versicolor] | -1.81 | 0.60 | [-2.99, -0.62] | -3.02 | 0.003
Species [virginica] | -3.15 | 0.63 | [-4.41, -1.90] | -4.97 | < .001
Petal Length | 0.54 | 0.28 | [ 0.00, 1.09] | 1.96 | 0.052
Species [versicolor] * Petal Length | 0.29 | 0.30 | [-0.30, 0.87] | 0.97 | 0.334
Species [virginica] * Petal Length | 0.45 | 0.29 | [-0.12, 1.03] | 1.56 | 0.120
Model: Sepal.Length ~ Species * Petal.Length (150 Observations)
Sigma: 0.336 (df = 144)
RMSE : 0.330
R2: 0.840; adjusted R2: 0.835
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
grouped parameters
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter | Coefficient | SE | 95% CI | t(22) | p
-----------------------------------------------------------------------
Engine | | | | |
cyl [6] | -2.47 | 2.21 | [ -7.05, 2.12] | -1.12 | 0.276
cyl [8] | 1.97 | 5.11 | [ -8.63, 12.58] | 0.39 | 0.703
vs | 3.18 | 3.79 | [ -4.68, 11.04] | 0.84 | 0.410
hp | -0.06 | 0.02 | [ -0.11, -0.02] | -2.91 | 0.008
Interactions | | | | |
gear [4] * vs | -2.90 | 4.67 | [-12.57, 6.78] | -0.62 | 0.541
gear [5] * vs | 2.59 | 4.54 | [ -6.82, 12.00] | 0.57 | 0.574
Controls | | | | |
gear [4] | 3.10 | 4.34 | [ -5.90, 12.10] | 0.71 | 0.482
gear [5] | 4.80 | 3.48 | [ -2.42, 12.01] | 1.38 | 0.182
drat | 2.70 | 2.03 | [ -1.52, 6.91] | 1.33 | 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, sep = " ", groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"),
Interactions = c("gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter Coefficient SE 95% CI t(22) p
------------------------------------------------------------------
Engine
cyl [6] -2.47 2.21 [ -7.05, 2.12] -1.12 0.276
cyl [8] 1.97 5.11 [ -8.63, 12.58] 0.39 0.703
vs 3.18 3.79 [ -4.68, 11.04] 0.84 0.410
hp -0.06 0.02 [ -0.11, -0.02] -2.91 0.008
Interactions
gear [4] * vs -2.90 4.67 [-12.57, 6.78] -0.62 0.541
gear [5] * vs 2.59 4.54 [ -6.82, 12.00] 0.57 0.574
Controls
gear [4] 3.10 4.34 [ -5.90, 12.10] 0.71 0.482
gear [5] 4.80 3.48 [ -2.42, 12.01] 1.38 0.182
drat 2.70 2.03 [ -1.52, 6.91] 1.33 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
digits and ci_digits
Code
model_parameters(model, digits = 4)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.5648, 34.4112] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1038, -0.0217] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9084, 5.5530] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3373, 10.7051] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.3593, 6.2759] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7262, 2.1148] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1490, 10.0049] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7313, 6.4172] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
model_parameters(model, digits = 4, ci_digits = 1)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.6, 34.4] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1, 0.0] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9, 5.6] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3, 10.7] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.4, 6.3] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7, 2.1] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1, 10.0] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7, 6.4] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, digits = 4)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.5648, 34.4112] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1038, -0.0217] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9084, 5.5530] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3373, 10.7051] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.3593, 6.2759] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7262, 2.1148] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1490, 10.0049] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7313, 6.4172] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, digits = 4, ci_digits = 1)
Output
Parameter | Coefficient | SE | 95% CI | t(24) | p
-------------------------------------------------------------------
(Intercept) | 18.9880 | 7.4728 | [ 3.6, 34.4] | 2.5409 | 0.018
hp | -0.0627 | 0.0199 | [-0.1, 0.0] | -3.1541 | 0.004
gear [4] | 0.8223 | 2.2921 | [-3.9, 5.6] | 0.3587 | 0.723
gear [5] | 5.1839 | 2.6751 | [-0.3, 10.7] | 1.9378 | 0.064
vs | 1.9583 | 2.0920 | [-2.4, 6.3] | 0.9361 | 0.359
cyl [6] | -2.3057 | 2.1418 | [-6.7, 2.1] | -1.0765 | 0.292
cyl [8] | 0.9279 | 4.3980 | [-8.1, 10.0] | 0.2110 | 0.835
drat | 2.3430 | 1.9741 | [-1.7, 6.4] | 1.1869 | 0.247
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
select pattern
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)))
Output
Parameter | Coefficient | SE | 95% CI | t(22) | p
-----------------------------------------------------------------------
Engine | | | | |
cyl [6] | -2.47 | 2.21 | [ -7.05, 2.12] | -1.12 | 0.276
cyl [8] | 1.97 | 5.11 | [ -8.63, 12.58] | 0.39 | 0.703
vs | 3.18 | 3.79 | [ -4.68, 11.04] | 0.84 | 0.410
hp | -0.06 | 0.02 | [ -0.11, -0.02] | -2.91 | 0.008
Interactions | | | | |
gear [4] * vs | -2.90 | 4.67 | [-12.57, 6.78] | -0.62 | 0.541
gear [5] * vs | 2.59 | 4.54 | [ -6.82, 12.00] | 0.57 | 0.574
Controls | | | | |
gear [4] | 3.10 | 4.34 | [ -5.90, 12.10] | 0.71 | 0.482
gear [5] | 4.80 | 3.48 | [ -2.42, 12.01] | 1.38 | 0.182
drat | 2.70 | 2.03 | [ -1.52, 6.91] | 1.33 | 0.198
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, select = "{coef} ({se})")
Output
Parameter | Estimate (SE)
-----------------------------
hp | -0.06 (0.02)
gear [4] | 3.10 (4.34)
gear [5] | 4.80 (3.48)
vs | 3.18 (3.79)
cyl [6] | -2.47 (2.21)
cyl [8] | 1.97 (5.11)
drat | 2.70 (2.03)
gear [4] * vs | -2.90 (4.67)
gear [5] * vs | 2.59 (4.54)
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, select = "{coef}{stars}|[{ci}]")
Output
Parameter | Estimate | [ci]
------------------------------------------
hp | -0.06** | [ -0.11, -0.02]
gear [4] | 3.10 | [ -5.90, 12.10]
gear [5] | 4.80 | [ -2.42, 12.01]
vs | 3.18 | [ -4.68, 11.04]
cyl [6] | -2.47 | [ -7.05, 2.12]
cyl [8] | 1.97 | [ -8.63, 12.58]
drat | 2.70 | [ -1.52, 6.91]
gear [4] * vs | -2.90 | [-12.57, 6.78]
gear [5] * vs | 2.59 | [ -6.82, 12.00]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"), Interactions = c(
"gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)), select = "{coef}{stars}|[{ci}]")
Output
Parameter | Estimate | [ci]
---------------------------------------------
Engine | |
cyl [6] | -2.47 | [ -7.05, 2.12]
cyl [8] | 1.97 | [ -8.63, 12.58]
vs | 3.18 | [ -4.68, 11.04]
hp | -0.06** | [ -0.11, -0.02]
Interactions | |
gear [4] * vs | -2.90 | [-12.57, 6.78]
gear [5] * vs | 2.59 | [ -6.82, 12.00]
Controls | |
gear [4] | 3.10 | [ -5.90, 12.10]
gear [5] | 4.80 | [ -2.42, 12.01]
drat | 2.70 | [ -1.52, 6.91]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
Code
print(out, sep = " ", groups = list(Engine = c("cyl6", "cyl8", "vs", "hp"),
Interactions = c("gear4:vs", "gear5:vs"), Controls = c(2, 3, 7)), select = "{coef}{stars}|[{ci}]")
Output
Parameter Estimate [ci]
-------------------------------------------
Engine
cyl [6] -2.47 [ -7.05, 2.12]
cyl [8] 1.97 [ -8.63, 12.58]
vs 3.18 [ -4.68, 11.04]
hp -0.06** [ -0.11, -0.02]
Interactions
gear [4] * vs -2.90 [-12.57, 6.78]
gear [5] * vs 2.59 [ -6.82, 12.00]
Controls
gear [4] 3.10 [ -5.90, 12.10]
gear [5] 4.80 [ -2.42, 12.01]
drat 2.70 [ -1.52, 6.91]
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
no more message about interpretation of log-resoponse
Code
print(out)
Output
Parameter | Coefficient | SE | 95% CI | t(30) | p
-----------------------------------------------------------------
(Intercept) | 9.29 | 2.24 | [5.67, 15.21] | 9.23 | < .001
gear | 1.22 | 0.08 | [1.07, 1.39] | 3.08 | 0.004
Message
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
no fail for mgcv-binomial
Code
print(out)
Output
# Fixed Effects
Parameter | Log-Odds | SE | 95% CI | z | df | p
---------------------------------------------------------------------
(Intercept) | -0.20 | 0.50 | [-1.18, 0.79] | -0.39 | 29.98 | 0.695
# Smooth Terms
Parameter | z | df | p
---------------------------------------
Smooth term (mpg) | 7.24 | 1.02 | 0.007
Message
The model has a log- or logit-link. Consider using `exponentiate =
TRUE` to interpret coefficients as ratios.
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