tests/testthat/_snaps/estimate_contrasts.md
In easystats/estimate: Estimation of Model-Based Predictions, Contrasts and Means
estimate_contrasts - Frequentist, Three factors 1
Code
print(estimate_contrasts(model, contrast = c("vs", "am"), by = "gear", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | gear | Difference | SE | 95% CI | t(25) | p
------------------------------------------------------------------------------------
vs 0, am 1 | vs 0, am 0 | 3 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 3 | 5.28 | 2.33 | [ 0.47, 10.09] | 2.26 | 0.033
vs 1, am 1 | vs 0, am 0 | 3 | 12.27 | 3.30 | [ 5.47, 19.07] | 3.71 | 0.001
vs 1, am 0 | vs 0, am 1 | 3 | -1.70 | 3.30 | [-8.50, 5.10] | -0.51 | 0.611
vs 1, am 1 | vs 0, am 1 | 3 | 5.28 | 2.33 | [ 0.47, 10.09] | 2.26 | 0.033
vs 1, am 1 | vs 1, am 0 | 3 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 0, am 1 | vs 0, am 0 | 4 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 4 | 7.03 | 2.95 | [ 0.95, 13.12] | 2.38 | 0.025
vs 1, am 1 | vs 0, am 0 | 4 | 14.02 | 4.31 | [ 5.15, 22.88] | 3.26 | 0.003
vs 1, am 0 | vs 0, am 1 | 4 | 0.05 | 3.13 | [-6.40, 6.50] | 0.02 | 0.987
vs 1, am 1 | vs 0, am 1 | 4 | 7.03 | 2.95 | [ 0.95, 13.12] | 2.38 | 0.025
vs 1, am 1 | vs 1, am 0 | 4 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 0, am 1 | vs 0, am 0 | 5 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 5 | 11.27 | 4.04 | [ 2.95, 19.60] | 2.79 | 0.010
vs 1, am 1 | vs 0, am 0 | 5 | 18.26 | 4.67 | [ 8.64, 27.88] | 3.91 | < .001
vs 1, am 0 | vs 0, am 1 | 5 | 4.29 | 4.67 | [-5.33, 13.91] | 0.92 | 0.367
vs 1, am 1 | vs 0, am 1 | 5 | 11.27 | 4.04 | [ 2.95, 19.60] | 2.79 | 0.010
vs 1, am 1 | vs 1, am 0 | 5 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
Variable predicted: mpg
Predictors contrasted: vs, am
p-values are uncorrected.
estimate_contrasts - marginaleffects, comparisons, validate against predict
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none"), zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(113) | p
---------------------------------------------------------------------------------------------
morning, control | morning, coffee | -5.78 | 1.99 | [-9.73, -1.83] | -2.90 | 0.004
noon, coffee | morning, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
noon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, coffee | morning, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
noon, coffee | morning, control | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon, coffee | morning, control | 7.71 | 1.99 | [ 3.76, 11.66] | 3.87 | < .001
afternoon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
noon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
afternoon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, control | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | noon, control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, control | afternoon, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = ratio ~ reference | coffee), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | coffee | Ratio | SE | 95% CI | t(113) | p
-----------------------------------------------------------------------------
noon | morning | coffee | 0.89 | 0.11 | [0.67, 1.11] | 8.08 | < .001
afternoon | morning | coffee | 1.11 | 0.12 | [0.87, 1.36] | 9.05 | < .001
noon | morning | control | 1.51 | 0.22 | [1.06, 1.95] | 6.73 | < .001
afternoon | morning | control | 1.51 | 0.22 | [1.06, 1.95] | 6.73 | < .001
Variable predicted: alertness
Predictors contrasted: time
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = "(b2-b1)=(b4-b3)"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(113) | p
----------------------------------------------------------------
b2-b1=b4-b3 | 5.78 | 2.82 | [0.20, 11.37] | 2.05 | 0.043
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Parameters:
b2 = time [noon], coffee [coffee]
b1 = time [morning], coffee [coffee]
b4 = time [morning], coffee [control]
b3 = time [afternoon], coffee [coffee]
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = "b5=b3"), zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(113) | p
--------------------------------------------------------------
b5=b3 | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Parameters:
b5 = time [noon], coffee [control]
b3 = time [afternoon], coffee [coffee]
Code
estimate_contrasts(fit, c("e42dep", "c172code"), comparison = "b6-b3=0",
backend = "marginaleffects")
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(800) | p
--------------------------------------------------------------
b6-b3=0 | -0.34 | 0.56 | [-1.44, 0.77] | -0.60 | 0.552
Variable predicted: neg_c_7
Predictors contrasted: e42dep, c172code
Predictors averaged: c12hour (42), barthtot (65), c161sex
p-values are uncorrected.
Parameters:
b6 = e42dep [slightly dependent], c172code [intermediate level of education]
b3 = e42dep [moderately dependent], c172code [low level of education]
estimate_contrasts - marginaleffects vs emmeans
Code
estimate_contrasts(model, backend = "emmeans")
Message
No variable was specified for contrast estimation. Selecting `contrast =
"Species"`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | SE | df | z | p
-----------------------------------------------------------------------------------
setosa | versicolor | -0.68 | [-0.82, -0.54] | 0.07 | Inf | -9.27 | < .001
setosa | virginica | -0.50 | [-0.67, -0.33] | 0.08 | Inf | -5.90 | < .001
versicolor | virginica | 0.18 | [ 0.01, 0.35] | 0.08 | Inf | 2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-values are uncorrected.
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "marginaleffects")
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
versicolor | setosa | 0.68 | 0.07 | [ 0.54, 0.82] | 9.27 | < .001
virginica | setosa | 0.50 | 0.08 | [ 0.33, 0.67] | 5.90 | < .001
virginica | versicolor | -0.18 | 0.08 | [-0.35, -0.01] | -2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-values are uncorrected.
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "emmeans", p_adjust = "holm")
Message
No variable was specified for contrast estimation. Selecting `contrast =
"Species"`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | SE | df | z | p
-----------------------------------------------------------------------------------
setosa | versicolor | -0.68 | [-0.86, -0.50] | 0.07 | Inf | -9.27 | < .001
setosa | virginica | -0.50 | [-0.70, -0.30] | 0.08 | Inf | -5.90 | < .001
versicolor | virginica | 0.18 | [-0.02, 0.38] | 0.08 | Inf | 2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-value adjustment method: Holm (1979)
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "marginaleffects", p_adjust = "holm")
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
versicolor | setosa | 0.68 | 0.07 | [ 0.54, 0.82] | 9.27 | < .001
virginica | setosa | 0.50 | 0.08 | [ 0.33, 0.67] | 5.90 | < .001
virginica | versicolor | -0.18 | 0.08 | [-0.35, -0.01] | -2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-value adjustment method: Holm (1979)
Contrasts are on the response-scale (in %-points).
estimate_contrasts - filtering works
Code
print(estimate_contrasts(fit, "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(827) | p
--------------------------------------------------------------------
mid | low | -0.09 | 0.34 | [-0.76, 0.58] | -0.25 | 0.802
high | low | 0.61 | 0.43 | [-0.24, 1.45] | 1.40 | 0.162
high | mid | 0.69 | 0.36 | [-0.02, 1.40] | 1.92 | 0.055
Variable predicted: neg_c_7
Predictors contrasted: c172code
Predictors averaged: e16sex, c161sex
p-values are uncorrected.
Code
print(estimate_contrasts(fit, c("c161sex", "c172code"), backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(825) | p
-------------------------------------------------------------------------------
Male, mid | Male, low | 0.29 | 0.71 | [-1.10, 1.68] | 0.41 | 0.684
Male, high | Male, low | 0.69 | 0.83 | [-0.95, 2.32] | 0.82 | 0.410
Female, low | Male, low | 1.05 | 0.69 | [-0.31, 2.40] | 1.51 | 0.131
Female, mid | Male, low | 0.85 | 0.64 | [-0.40, 2.10] | 1.33 | 0.183
Female, high | Male, low | 1.65 | 0.71 | [ 0.24, 3.05] | 2.30 | 0.022
Male, high | Male, mid | 0.40 | 0.68 | [-0.94, 1.73] | 0.59 | 0.558
Female, low | Male, mid | 0.76 | 0.50 | [-0.22, 1.73] | 1.52 | 0.129
Female, mid | Male, mid | 0.56 | 0.42 | [-0.26, 1.38] | 1.35 | 0.178
Female, high | Male, mid | 1.36 | 0.53 | [ 0.32, 2.39] | 2.58 | 0.010
Female, low | Male, high | 0.36 | 0.66 | [-0.95, 1.66] | 0.54 | 0.589
Female, mid | Male, high | 0.16 | 0.61 | [-1.03, 1.35] | 0.27 | 0.789
Female, high | Male, high | 0.96 | 0.69 | [-0.39, 2.30] | 1.40 | 0.163
Female, mid | Female, low | -0.20 | 0.39 | [-0.96, 0.57] | -0.51 | 0.613
Female, high | Female, low | 0.60 | 0.51 | [-0.39, 1.59] | 1.18 | 0.236
Female, high | Female, mid | 0.80 | 0.43 | [-0.04, 1.63] | 1.87 | 0.061
Variable predicted: neg_c_7
Predictors contrasted: c161sex, c172code
Predictors averaged: e16sex
p-values are uncorrected.
Code
print(estimate_contrasts(fit, "c161sex", "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | c172code | Difference | SE | 95% CI | t(825) | p
-------------------------------------------------------------------------------
Female | Male | low | 1.05 | 0.69 | [-0.31, 2.40] | 1.51 | 0.131
Female | Male | mid | 0.56 | 0.42 | [-0.26, 1.38] | 1.35 | 0.178
Female | Male | high | 0.96 | 0.69 | [-0.39, 2.30] | 1.40 | 0.163
Variable predicted: neg_c_7
Predictors contrasted: c161sex
Predictors averaged: e16sex
p-values are uncorrected.
Code
print(estimate_slopes(fit, "barthtot", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Estimated Marginal Effects
Slope | SE | 95% CI | t | p
-----------------------------------------------
-0.05 | 0.00 | [-0.06, -0.05] | -12.77 | < .001
Marginal effects estimated for barthtot
Type of slope was dY/dX
Code
print(estimate_slopes(fit, "barthtot", by = "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Estimated Marginal Effects
c172code | Slope | SE | 95% CI | t | p
---------------------------------------------------------
low | -0.06 | 0.01 | [-0.08, -0.05] | -7.08 | < .001
mid | -0.05 | 0.01 | [-0.06, -0.04] | -9.82 | < .001
high | -0.05 | 0.01 | [-0.07, -0.03] | -4.51 | < .001
Marginal effects estimated for barthtot
Type of slope was dY/dX
Code
print(estimate_contrasts(fit, "barthtot", "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
------------------------------------------------------------------
mid | low | 0.01 | 0.01 | [-0.01, 0.03] | 1.17 | 0.243
high | low | 0.02 | 0.01 | [-0.01, 0.04] | 1.10 | 0.271
high | mid | 0.00 | 0.01 | [-0.02, 0.03] | 0.27 | 0.786
Variable predicted: neg_c_7
Predictors contrasted: barthtot
Predictors averaged: e16sex, barthtot (65)
p-values are uncorrected.
Code
print(estimate_contrasts(fit, "barthtot", c("c172code", "e16sex"), backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
------------------------------------------------------------------------------
low, female | low, male | -0.01 | 0.02 | [-0.04, 0.03] | -0.35 | 0.729
mid, male | low, male | -0.01 | 0.02 | [-0.04, 0.02] | -0.46 | 0.648
mid, female | low, male | 0.02 | 0.01 | [-0.01, 0.05] | 1.24 | 0.216
high, male | low, male | 0.00 | 0.02 | [-0.05, 0.04] | -0.16 | 0.876
high, female | low, male | 0.02 | 0.02 | [-0.01, 0.06] | 1.19 | 0.236
mid, male | low, female | 0.00 | 0.02 | [-0.03, 0.03] | -0.08 | 0.940
mid, female | low, female | 0.02 | 0.01 | [ 0.00, 0.05] | 1.76 | 0.079
high, male | low, female | 0.00 | 0.02 | [-0.04, 0.05] | 0.12 | 0.908
high, female | low, female | 0.03 | 0.02 | [-0.01, 0.06] | 1.58 | 0.115
mid, female | mid, male | 0.03 | 0.01 | [ 0.00, 0.05] | 2.24 | 0.025
high, male | mid, male | 0.00 | 0.02 | [-0.04, 0.05] | 0.18 | 0.859
high, female | mid, male | 0.03 | 0.02 | [ 0.00, 0.06] | 1.83 | 0.067
high, male | mid, female | -0.02 | 0.02 | [-0.06, 0.02] | -1.08 | 0.280
high, female | mid, female | 0.00 | 0.01 | [-0.02, 0.03] | 0.26 | 0.792
high, female | high, male | 0.03 | 0.02 | [-0.02, 0.07] | 1.11 | 0.268
Variable predicted: neg_c_7
Predictors contrasted: barthtot
Predictors averaged: barthtot (65)
p-values are uncorrected.
estimate_contrasts - simple contrasts and with - in levels works
Code
print(estimate_contrasts(model, "Species", backend = "marginaleffects"),
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(146) | p
----------------------------------------------------------------------------
versicolor | setosa | 1.46 | 0.11 | [1.24, 1.68] | 13.01 | < .001
virginica | setosa | 1.95 | 0.10 | [1.75, 2.14] | 19.47 | < .001
virginica | versicolor | 0.49 | 0.09 | [0.31, 0.67] | 5.41 | < .001
Variable predicted: Sepal.Length
Predictors contrasted: Species
Predictors averaged: Sepal.Width (3.1)
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(113) | p
---------------------------------------------------------------------------------------------
morning, control | morning, coffee | -5.78 | 1.99 | [-9.73, -1.83] | -2.90 | 0.004
noon, coffee | morning, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
noon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, coffee | morning, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
noon, coffee | morning, control | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon, coffee | morning, control | 7.71 | 1.99 | [ 3.76, 11.66] | 3.87 | < .001
afternoon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
noon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
afternoon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, control | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | noon, control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, control | afternoon, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, contrast = "time", by = "coffee", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | coffee | Difference | SE | 95% CI | t(113) | p
-----------------------------------------------------------------------------------
noon | morning | coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
afternoon | morning | coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon | noon | coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon | morning | control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon | morning | control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon | noon | control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
Variable predicted: alertness
Predictors contrasted: time
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("mined", "spp"), backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
yes, PR | yes, GP | 0.06 | 0.05 | [-0.04, 0.16] | 1.10 | 0.271
yes, DM | yes, GP | 0.32 | 0.11 | [ 0.10, 0.54] | 2.89 | 0.004
yes, EC-A | yes, GP | 0.04 | 0.05 | [-0.05, 0.13] | 0.80 | 0.421
yes, EC-L | yes, GP | 0.17 | 0.08 | [ 0.02, 0.32] | 2.19 | 0.028
yes, DES-L | yes, GP | 0.43 | 0.14 | [ 0.17, 0.70] | 3.19 | 0.001
yes, DF | yes, GP | 0.43 | 0.14 | [ 0.17, 0.70] | 3.19 | 0.001
no, GP | yes, GP | 2.59 | 0.55 | [ 1.50, 3.67] | 4.67 | < .001
no, PR | yes, GP | 0.51 | 0.16 | [ 0.20, 0.82] | 3.21 | 0.001
no, DM | yes, GP | 2.86 | 0.61 | [ 1.68, 4.05] | 4.73 | < .001
no, EC-A | yes, GP | 1.10 | 0.27 | [ 0.57, 1.64] | 4.03 | < .001
no, EC-L | yes, GP | 4.67 | 0.94 | [ 2.82, 6.52] | 4.95 | < .001
no, DES-L | yes, GP | 4.62 | 0.93 | [ 2.79, 6.45] | 4.95 | < .001
no, DF | yes, GP | 2.24 | 0.49 | [ 1.28, 3.20] | 4.58 | < .001
yes, DM | yes, PR | 0.26 | 0.11 | [ 0.05, 0.48] | 2.43 | 0.015
yes, EC-A | yes, PR | -0.02 | 0.06 | [-0.13, 0.09] | -0.33 | 0.740
yes, EC-L | yes, PR | 0.11 | 0.08 | [-0.04, 0.27] | 1.43 | 0.154
yes, DES-L | yes, PR | 0.38 | 0.13 | [ 0.12, 0.64] | 2.86 | 0.004
yes, DF | yes, PR | 0.38 | 0.13 | [ 0.12, 0.64] | 2.86 | 0.004
no, GP | yes, PR | 2.53 | 0.56 | [ 1.44, 3.62] | 4.54 | < .001
no, PR | yes, PR | 0.45 | 0.16 | [ 0.13, 0.77] | 2.75 | 0.006
no, DM | yes, PR | 2.80 | 0.61 | [ 1.61, 4.00] | 4.61 | < .001
no, EC-A | yes, PR | 1.05 | 0.28 | [ 0.50, 1.59] | 3.76 | < .001
no, EC-L | yes, PR | 4.61 | 0.95 | [ 2.76, 6.47] | 4.87 | < .001
no, DES-L | yes, PR | 4.56 | 0.94 | [ 2.73, 6.40] | 4.87 | < .001
no, DF | yes, PR | 2.18 | 0.49 | [ 1.22, 3.15] | 4.44 | < .001
yes, EC-A | yes, DM | -0.28 | 0.11 | [-0.50, -0.07] | -2.59 | 0.010
yes, EC-L | yes, DM | -0.15 | 0.11 | [-0.36, 0.06] | -1.39 | 0.164
yes, DES-L | yes, DM | 0.11 | 0.13 | [-0.14, 0.36] | 0.89 | 0.375
yes, DF | yes, DM | 0.11 | 0.13 | [-0.14, 0.36] | 0.89 | 0.375
no, GP | yes, DM | 2.27 | 0.58 | [ 1.14, 3.40] | 3.93 | < .001
no, PR | yes, DM | 0.19 | 0.20 | [-0.21, 0.58] | 0.93 | 0.352
no, DM | yes, DM | 2.54 | 0.63 | [ 1.31, 3.77] | 4.04 | < .001
no, EC-A | yes, DM | 0.78 | 0.31 | [ 0.18, 1.38] | 2.56 | 0.011
no, EC-L | yes, DM | 4.35 | 0.96 | [ 2.46, 6.24] | 4.51 | < .001
no, DES-L | yes, DM | 4.30 | 0.95 | [ 2.43, 6.17] | 4.51 | < .001
no, DF | yes, DM | 1.92 | 0.51 | [ 0.91, 2.93] | 3.74 | < .001
yes, EC-L | yes, EC-A | 0.13 | 0.08 | [-0.02, 0.29] | 1.68 | 0.093
yes, DES-L | yes, EC-A | 0.40 | 0.13 | [ 0.14, 0.66] | 2.97 | 0.003
yes, DF | yes, EC-A | 0.40 | 0.13 | [ 0.14, 0.66] | 2.97 | 0.003
no, GP | yes, EC-A | 2.55 | 0.56 | [ 1.46, 3.64] | 4.58 | < .001
no, PR | yes, EC-A | 0.47 | 0.16 | [ 0.15, 0.79] | 2.90 | 0.004
no, DM | yes, EC-A | 2.82 | 0.61 | [ 1.63, 4.01] | 4.65 | < .001
no, EC-A | yes, EC-A | 1.06 | 0.28 | [ 0.52, 1.61] | 3.85 | < .001
no, EC-L | yes, EC-A | 4.63 | 0.95 | [ 2.78, 6.48] | 4.90 | < .001
no, DES-L | yes, EC-A | 4.58 | 0.94 | [ 2.75, 6.42] | 4.90 | < .001
no, DF | yes, EC-A | 2.20 | 0.49 | [ 1.24, 3.17] | 4.49 | < .001
yes, DES-L | yes, EC-L | 0.26 | 0.13 | [ 0.02, 0.51] | 2.08 | 0.037
yes, DF | yes, EC-L | 0.26 | 0.13 | [ 0.02, 0.51] | 2.08 | 0.037
no, GP | yes, EC-L | 2.42 | 0.57 | [ 1.31, 3.53] | 4.28 | < .001
no, PR | yes, EC-L | 0.34 | 0.18 | [-0.01, 0.69] | 1.89 | 0.058
no, DM | yes, EC-L | 2.69 | 0.62 | [ 1.48, 3.90] | 4.37 | < .001
no, EC-A | yes, EC-L | 0.93 | 0.29 | [ 0.37, 1.50] | 3.23 | 0.001
no, EC-L | yes, EC-L | 4.50 | 0.95 | [ 2.63, 6.37] | 4.72 | < .001
no, DES-L | yes, EC-L | 4.45 | 0.94 | [ 2.60, 6.30] | 4.71 | < .001
no, DF | yes, EC-L | 2.07 | 0.50 | [ 1.09, 3.05] | 4.14 | < .001
yes, DF | yes, DES-L | 0.00 | 0.13 | [-0.26, 0.26] | 0.00 | > .999
no, GP | yes, DES-L | 2.15 | 0.59 | [ 1.00, 3.31] | 3.67 | < .001
no, PR | yes, DES-L | 0.07 | 0.22 | [-0.36, 0.50] | 0.33 | 0.738
no, DM | yes, DES-L | 2.43 | 0.64 | [ 1.18, 3.68] | 3.81 | < .001
no, EC-A | yes, DES-L | 0.67 | 0.32 | [ 0.04, 1.29] | 2.09 | 0.037
no, EC-L | yes, DES-L | 4.24 | 0.97 | [ 2.33, 6.14] | 4.36 | < .001
no, DES-L | yes, DES-L | 4.19 | 0.96 | [ 2.30, 6.07] | 4.35 | < .001
no, DF | yes, DES-L | 1.81 | 0.52 | [ 0.78, 2.83] | 3.45 | < .001
no, GP | yes, DF | 2.15 | 0.59 | [ 1.00, 3.31] | 3.67 | < .001
no, PR | yes, DF | 0.07 | 0.22 | [-0.36, 0.50] | 0.33 | 0.738
no, DM | yes, DF | 2.43 | 0.64 | [ 1.18, 3.68] | 3.81 | < .001
no, EC-A | yes, DF | 0.67 | 0.32 | [ 0.04, 1.29] | 2.09 | 0.037
no, EC-L | yes, DF | 4.24 | 0.97 | [ 2.33, 6.14] | 4.36 | < .001
no, DES-L | yes, DF | 4.19 | 0.96 | [ 2.30, 6.07] | 4.35 | < .001
no, DF | yes, DF | 1.81 | 0.52 | [ 0.78, 2.83] | 3.45 | < .001
no, PR | no, GP | -2.08 | 0.48 | [-3.02, -1.14] | -4.35 | < .001
no, DM | no, GP | 0.27 | 0.37 | [-0.46, 1.00] | 0.73 | 0.466
no, EC-A | no, GP | -1.49 | 0.41 | [-2.29, -0.68] | -3.61 | < .001
no, EC-L | no, GP | 2.08 | 0.58 | [ 0.95, 3.21] | 3.61 | < .001
no, DES-L | no, GP | 2.03 | 0.57 | [ 0.92, 3.15] | 3.57 | < .001
no, DF | no, GP | -0.35 | 0.35 | [-1.04, 0.35] | -0.98 | 0.328
no, DM | no, PR | 2.35 | 0.53 | [ 1.32, 3.39] | 4.47 | < .001
no, EC-A | no, PR | 0.59 | 0.23 | [ 0.14, 1.05] | 2.56 | 0.011
no, EC-L | no, PR | 4.16 | 0.86 | [ 2.49, 5.84] | 4.87 | < .001
no, DES-L | no, PR | 4.11 | 0.85 | [ 2.45, 5.77] | 4.86 | < .001
no, DF | no, PR | 1.73 | 0.42 | [ 0.92, 2.55] | 4.15 | < .001
no, EC-A | no, DM | -1.76 | 0.46 | [-2.65, -0.87] | -3.86 | < .001
no, EC-L | no, DM | 1.81 | 0.55 | [ 0.73, 2.89] | 3.29 | < .001
no, DES-L | no, DM | 1.76 | 0.54 | [ 0.70, 2.82] | 3.24 | 0.001
no, DF | no, DM | -0.62 | 0.38 | [-1.36, 0.12] | -1.65 | 0.100
no, EC-L | no, EC-A | 3.57 | 0.77 | [ 2.07, 5.07] | 4.66 | < .001
no, DES-L | no, EC-A | 3.52 | 0.76 | [ 2.03, 5.00] | 4.65 | < .001
no, DF | no, EC-A | 1.14 | 0.36 | [ 0.43, 1.85] | 3.16 | 0.002
no, DES-L | no, EC-L | -0.05 | 0.48 | [-0.99, 0.89] | -0.10 | 0.918
no, DF | no, EC-L | -2.43 | 0.61 | [-3.63, -1.22] | -3.95 | < .001
no, DF | no, DES-L | -2.38 | 0.61 | [-3.57, -1.19] | -3.92 | < .001
Variable predicted: count
Predictors contrasted: mined, spp
Predictors averaged: cover (8.7e-11), site
p-values are uncorrected.
Contrasts are on the response-scale.
Code
print(estimate_contrasts(model, contrast = "mined", by = "spp", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | spp | Difference | SE | 95% CI | z | p
--------------------------------------------------------------------------
no | yes | GP | 2.59 | 0.55 | [1.50, 3.67] | 4.67 | < .001
no | yes | PR | 0.45 | 0.16 | [0.13, 0.77] | 2.75 | 0.006
no | yes | DM | 2.54 | 0.63 | [1.31, 3.77] | 4.04 | < .001
no | yes | EC-A | 1.06 | 0.28 | [0.52, 1.61] | 3.85 | < .001
no | yes | EC-L | 4.50 | 0.95 | [2.63, 6.37] | 4.72 | < .001
no | yes | DES-L | 4.19 | 0.96 | [2.30, 6.07] | 4.35 | < .001
no | yes | DF | 1.81 | 0.52 | [0.78, 2.83] | 3.45 | < .001
Variable predicted: count
Predictors contrasted: mined
Predictors averaged: cover (8.7e-11), site
p-values are uncorrected.
Contrasts are on the response-scale.
estimate_contrasts - examples from docs work as intendec
Code
print(estimate_contrasts(model, contrast = "Petal.Width", by = "Species"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
---------------------------------------------------------------------------
versicolor | setosa | 0.22 | 0.46 | [-0.69, 1.12] | 0.47 | 0.639
virginica | setosa | -0.21 | 0.44 | [-1.06, 0.65] | -0.47 | 0.637
virginica | versicolor | -0.42 | 0.27 | [-0.95, 0.10] | -1.58 | 0.114
Variable predicted: Sepal.Width
Predictors contrasted: Petal.Width
Predictors averaged: Petal.Width (1.2)
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("Species", "Petal.Width"), length = 2),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(144) | p
----------------------------------------------------------------------------------------
setosa, 2.5 | setosa, 0.1 | 2.01 | 0.98 | [ 0.08, 3.94] | 2.06 | 0.041
versicolor, 0.1 | setosa, 0.1 | -1.83 | 0.28 | [-2.38, -1.28] | -6.55 | < .001
versicolor, 2.5 | setosa, 0.1 | 0.70 | 0.27 | [ 0.17, 1.23] | 2.61 | 0.010
virginica, 0.1 | setosa, 0.1 | -1.55 | 0.31 | [-2.17, -0.93] | -4.95 | < .001
virginica, 2.5 | setosa, 0.1 | -0.03 | 0.11 | [-0.25, 0.19] | -0.29 | 0.773
versicolor, 0.1 | setosa, 2.5 | -3.84 | 0.96 | [-5.73, -1.95] | -4.01 | < .001
versicolor, 2.5 | setosa, 2.5 | -1.31 | 0.95 | [-3.19, 0.58] | -1.37 | 0.172
virginica, 0.1 | setosa, 2.5 | -3.56 | 0.97 | [-5.47, -1.65] | -3.68 | < .001
virginica, 2.5 | setosa, 2.5 | -2.04 | 0.92 | [-3.86, -0.22] | -2.21 | 0.028
versicolor, 2.5 | versicolor, 0.1 | 2.53 | 0.52 | [ 1.50, 3.56] | 4.86 | < .001
virginica, 0.1 | versicolor, 0.1 | 0.28 | 0.41 | [-0.52, 1.08] | 0.69 | 0.492
virginica, 2.5 | versicolor, 0.1 | 1.80 | 0.28 | [ 1.24, 2.35] | 6.35 | < .001
virginica, 0.1 | versicolor, 2.5 | -2.25 | 0.40 | [-3.04, -1.46] | -5.64 | < .001
virginica, 2.5 | versicolor, 2.5 | -0.73 | 0.27 | [-1.27, -0.20] | -2.70 | 0.008
virginica, 2.5 | virginica, 0.1 | 1.52 | 0.37 | [ 0.77, 2.26] | 4.04 | < .001
Variable predicted: Sepal.Width
Predictors contrasted: Species, Petal.Width
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)")),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(144) | p
------------------------------------------------------------------------------------
setosa, 2 | setosa, 1 | 0.84 | 0.41 | [ 0.03, 1.64] | 2.06 | 0.041
versicolor, 1 | setosa, 1 | -1.63 | 0.32 | [-2.27, -1.00] | -5.09 | < .001
versicolor, 2 | setosa, 1 | -0.58 | 0.35 | [-1.26, 0.10] | -1.68 | 0.096
virginica, 1 | setosa, 1 | -1.73 | 0.35 | [-2.43, -1.04] | -4.93 | < .001
virginica, 2 | setosa, 1 | -1.10 | 0.31 | [-1.72, -0.48] | -3.52 | < .001
versicolor, 1 | setosa, 2 | -2.47 | 0.72 | [-3.89, -1.05] | -3.43 | < .001
versicolor, 2 | setosa, 2 | -1.42 | 0.73 | [-2.86, 0.03] | -1.94 | 0.055
virginica, 1 | setosa, 2 | -2.57 | 0.73 | [-4.02, -1.12] | -3.50 | < .001
virginica, 2 | setosa, 2 | -1.94 | 0.72 | [-3.35, -0.52] | -2.71 | 0.008
versicolor, 2 | versicolor, 1 | 1.05 | 0.22 | [ 0.62, 1.48] | 4.86 | < .001
virginica, 1 | versicolor, 1 | -0.10 | 0.19 | [-0.47, 0.27] | -0.54 | 0.589
virginica, 2 | versicolor, 1 | 0.53 | 0.09 | [ 0.35, 0.71] | 5.72 | < .001
virginica, 1 | versicolor, 2 | -1.15 | 0.23 | [-1.60, -0.71] | -5.13 | < .001
virginica, 2 | versicolor, 2 | -0.52 | 0.16 | [-0.84, -0.21] | -3.31 | 0.001
virginica, 2 | virginica, 1 | 0.63 | 0.16 | [ 0.32, 0.94] | 4.04 | < .001
Variable predicted: Sepal.Width
Predictors contrasted: Species, Petal.Width=c(1, 2)
p-values are uncorrected.
Code
print(estimate_contrasts(model, by = "Petal.Width", length = 4), zap_small = TRUE,
table_width = Inf)
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Petal.Width | Difference | SE | 95% CI | t(144) | p
--------------------------------------------------------------------------------------------
versicolor | setosa | 0.10 | -1.83 | 0.28 | [-2.38, -1.28] | -6.55 | < .001
virginica | setosa | 0.10 | -1.55 | 0.31 | [-2.17, -0.93] | -4.95 | < .001
virginica | versicolor | 0.10 | 0.28 | 0.41 | [-0.52, 1.08] | 0.69 | 0.492
versicolor | setosa | 0.90 | -1.65 | 0.29 | [-2.22, -1.08] | -5.74 | < .001
virginica | setosa | 0.90 | -1.71 | 0.32 | [-2.35, -1.07] | -5.28 | < .001
virginica | versicolor | 0.90 | -0.06 | 0.21 | [-0.47, 0.35] | -0.28 | 0.780
versicolor | setosa | 1.70 | -1.48 | 0.60 | [-2.67, -0.29] | -2.47 | 0.015
virginica | setosa | 1.70 | -1.88 | 0.60 | [-3.06, -0.70] | -3.14 | 0.002
virginica | versicolor | 1.70 | -0.40 | 0.11 | [-0.62, -0.17] | -3.50 | < .001
versicolor | setosa | 2.50 | -1.31 | 0.95 | [-3.19, 0.58] | -1.37 | 0.172
virginica | setosa | 2.50 | -2.04 | 0.92 | [-3.86, -0.22] | -2.21 | 0.028
virginica | versicolor | 2.50 | -0.73 | 0.27 | [-1.27, -0.20] | -2.70 | 0.008
Variable predicted: Sepal.Width
Predictors contrasted: Species
p-values are uncorrected.
estimate_contrasts - test all combinations of contrast and by, with filtering
Code
print(estimate_contrasts(model2, c("grp", "time", "x")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(992) | p
--------------------------------------------------------------------------------------
control, 1, b | control, 1, a | 0.15 | 0.13 | [-0.10, 0.40] | 1.20 | 0.231
control, 2, a | control, 1, a | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
control, 2, b | control, 1, a | 0.09 | 0.13 | [-0.16, 0.34] | 0.73 | 0.465
treatment, 1, a | control, 1, a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 1, b | control, 1, a | -0.03 | 0.13 | [-0.28, 0.22] | -0.22 | 0.824
treatment, 2, a | control, 1, a | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 2, b | control, 1, a | 0.05 | 0.12 | [-0.19, 0.28] | 0.37 | 0.712
control, 2, a | control, 1, b | -0.12 | 0.13 | [-0.38, 0.13] | -0.97 | 0.332
control, 2, b | control, 1, b | -0.06 | 0.12 | [-0.30, 0.19] | -0.46 | 0.648
treatment, 1, a | control, 1, b | 0.04 | 0.13 | [-0.20, 0.29] | 0.34 | 0.730
treatment, 1, b | control, 1, b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment, 2, a | control, 1, b | -0.13 | 0.12 | [-0.37, 0.12] | -1.02 | 0.307
treatment, 2, b | control, 1, b | -0.10 | 0.12 | [-0.34, 0.13] | -0.89 | 0.376
control, 2, b | control, 2, a | 0.07 | 0.13 | [-0.19, 0.32] | 0.52 | 0.604
treatment, 1, a | control, 2, a | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 1, b | control, 2, a | -0.05 | 0.13 | [-0.31, 0.20] | -0.41 | 0.680
treatment, 2, a | control, 2, a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2, b | control, 2, a | 0.02 | 0.13 | [-0.23, 0.27] | 0.16 | 0.876
treatment, 1, a | control, 2, b | 0.10 | 0.13 | [-0.15, 0.35] | 0.78 | 0.437
treatment, 1, b | control, 2, b | -0.12 | 0.13 | [-0.37, 0.12] | -0.97 | 0.333
treatment, 2, a | control, 2, b | -0.07 | 0.13 | [-0.32, 0.18] | -0.55 | 0.582
treatment, 2, b | control, 2, b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
treatment, 1, b | treatment, 1, a | -0.22 | 0.13 | [-0.47, 0.03] | -1.73 | 0.083
treatment, 2, a | treatment, 1, a | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
treatment, 2, b | treatment, 1, a | -0.15 | 0.12 | [-0.39, 0.09] | -1.20 | 0.230
treatment, 2, a | treatment, 1, b | 0.05 | 0.12 | [-0.19, 0.30] | 0.42 | 0.676
treatment, 2, b | treatment, 1, b | 0.07 | 0.12 | [-0.16, 0.31] | 0.61 | 0.541
treatment, 2, b | treatment, 2, a | 0.02 | 0.12 | [-0.21, 0.26] | 0.18 | 0.861
Variable predicted: score
Predictors contrasted: grp, time, x
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time"), by = "x"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | x | Difference | SE | 95% CI | t(992) | p
------------------------------------------------------------------------------------
control, 2 | control, 1 | a | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
treatment, 1 | control, 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 2 | control, 1 | a | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 1 | control, 2 | a | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 2 | control, 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2 | treatment, 1 | a | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
control, 2 | control, 1 | b | -0.06 | 0.12 | [-0.30, 0.19] | -0.46 | 0.648
treatment, 1 | control, 1 | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment, 2 | control, 1 | b | -0.10 | 0.12 | [-0.34, 0.13] | -0.89 | 0.376
treatment, 1 | control, 2 | b | -0.12 | 0.13 | [-0.37, 0.12] | -0.97 | 0.333
treatment, 2 | control, 2 | b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
treatment, 2 | treatment, 1 | b | 0.07 | 0.12 | [-0.16, 0.31] | 0.61 | 0.541
Variable predicted: score
Predictors contrasted: grp, time
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = c("time", "x")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | x | Difference | SE | 95% CI | t(992) | p
-----------------------------------------------------------------------------------
treatment | control | 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment | control | 1 | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment | control | 2 | b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
Variable predicted: score
Predictors contrasted: grp
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = "time"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | Difference | SE | 95% CI | t(992) | p
-------------------------------------------------------------------------------
treatment | control | 1 | 0.01 | 0.09 | [-0.17, 0.18] | 0.09 | 0.931
treatment | control | 2 | -0.02 | 0.09 | [-0.20, 0.15] | -0.28 | 0.780
Variable predicted: score
Predictors contrasted: grp
Predictors averaged: x
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time", "x='a'")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(992) | p
--------------------------------------------------------------------------------
control, 2 | control, 1 | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
treatment, 1 | control, 1 | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 2 | control, 1 | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 1 | control, 2 | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 2 | control, 2 | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2 | treatment, 1 | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
Variable predicted: score
Predictors contrasted: grp, time, x='a'
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time=1"), by = "x"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | x | Difference | SE | 95% CI | t(992) | p
----------------------------------------------------------------------------
treatment | control | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
Variable predicted: score
Predictors contrasted: grp, time=1
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = c("time", "x='a'")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | x | Difference | SE | 95% CI | t(992) | p
-----------------------------------------------------------------------------------
treatment | control | 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
Variable predicted: score
Predictors contrasted: grp
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "time=c(1,2)", by = "grp"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | grp | Difference | SE | 95% CI | t(994) | p
--------------------------------------------------------------------------------
2 | 1 | control | 0.14 | 0.11 | [-0.08, 0.36] | 1.24 | 0.216
2 | 1 | treatment | -0.07 | 0.11 | [-0.28, 0.14] | -0.63 | 0.529
Variable predicted: score
Predictors contrasted: time=c(1,2)
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time=2")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(994) | p
------------------------------------------------------------------------
treatment | control | -0.15 | 0.11 | [-0.36, 0.05] | -1.47 | 0.143
Variable predicted: score
Predictors contrasted: grp, time=2
p-values are uncorrected.
estimate_contrast, slopes with emmeans
Code
print(out, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | p
----------------------------------------------------------
0, 0.725 | 0, -2.012 | 1.12 | [0.37, 3.43] | 0.839
0, 3.463 | 0, -2.012 | 1.26 | [0.14, 11.76] | 0.839
1, -2.012 | 0, -2.012 | 1.00 | [0.35, 2.82] | 0.999
1, 0.725 | 0, -2.012 | 1.12 | [0.23, 5.43] | 0.887
1, 3.463 | 0, -2.012 | 1.26 | [0.10, 15.76] | 0.858
0, 3.463 | 0, 0.725 | 1.12 | [0.37, 3.43] | 0.839
1, -2.012 | 0, 0.725 | 0.89 | [0.20, 3.87] | 0.877
1, 0.725 | 0, 0.725 | 1.00 | [0.35, 2.82] | 0.999
1, 3.463 | 0, 0.725 | 1.12 | [0.23, 5.43] | 0.887
1, -2.012 | 0, 3.463 | 0.79 | [0.07, 8.71] | 0.849
1, 0.725 | 0, 3.463 | 0.89 | [0.20, 3.87] | 0.877
1, 3.463 | 0, 3.463 | 1.00 | [0.35, 2.82] | 0.999
1, 0.725 | 1, -2.012 | 1.12 | [0.37, 3.43] | 0.839
1, 3.463 | 1, -2.012 | 1.26 | [0.14, 11.76] | 0.839
1, 3.463 | 1, 0.725 | 1.12 | [0.37, 3.43] | 0.839
Variable predicted: outcome
Predictors contrasted: var_binom, var_cont
p-values are uncorrected.
Contrasts are on the link-scale.
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estimate_contrasts - Frequentist, Three factors 1
Code
print(estimate_contrasts(model, contrast = c("vs", "am"), by = "gear", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | gear | Difference | SE | 95% CI | t(25) | p
------------------------------------------------------------------------------------
vs 0, am 1 | vs 0, am 0 | 3 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 3 | 5.28 | 2.33 | [ 0.47, 10.09] | 2.26 | 0.033
vs 1, am 1 | vs 0, am 0 | 3 | 12.27 | 3.30 | [ 5.47, 19.07] | 3.71 | 0.001
vs 1, am 0 | vs 0, am 1 | 3 | -1.70 | 3.30 | [-8.50, 5.10] | -0.51 | 0.611
vs 1, am 1 | vs 0, am 1 | 3 | 5.28 | 2.33 | [ 0.47, 10.09] | 2.26 | 0.033
vs 1, am 1 | vs 1, am 0 | 3 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 0, am 1 | vs 0, am 0 | 4 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 4 | 7.03 | 2.95 | [ 0.95, 13.12] | 2.38 | 0.025
vs 1, am 1 | vs 0, am 0 | 4 | 14.02 | 4.31 | [ 5.15, 22.88] | 3.26 | 0.003
vs 1, am 0 | vs 0, am 1 | 4 | 0.05 | 3.13 | [-6.40, 6.50] | 0.02 | 0.987
vs 1, am 1 | vs 0, am 1 | 4 | 7.03 | 2.95 | [ 0.95, 13.12] | 2.38 | 0.025
vs 1, am 1 | vs 1, am 0 | 4 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 0, am 1 | vs 0, am 0 | 5 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
vs 1, am 0 | vs 0, am 0 | 5 | 11.27 | 4.04 | [ 2.95, 19.60] | 2.79 | 0.010
vs 1, am 1 | vs 0, am 0 | 5 | 18.26 | 4.67 | [ 8.64, 27.88] | 3.91 | < .001
vs 1, am 0 | vs 0, am 1 | 5 | 4.29 | 4.67 | [-5.33, 13.91] | 0.92 | 0.367
vs 1, am 1 | vs 0, am 1 | 5 | 11.27 | 4.04 | [ 2.95, 19.60] | 2.79 | 0.010
vs 1, am 1 | vs 1, am 0 | 5 | 6.98 | 2.33 | [ 2.17, 11.79] | 2.99 | 0.006
Variable predicted: mpg
Predictors contrasted: vs, am
p-values are uncorrected.
estimate_contrasts - marginaleffects, comparisons, validate against predict
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none"), zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(113) | p
---------------------------------------------------------------------------------------------
morning, control | morning, coffee | -5.78 | 1.99 | [-9.73, -1.83] | -2.90 | 0.004
noon, coffee | morning, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
noon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, coffee | morning, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
noon, coffee | morning, control | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon, coffee | morning, control | 7.71 | 1.99 | [ 3.76, 11.66] | 3.87 | < .001
afternoon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
noon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
afternoon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, control | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | noon, control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, control | afternoon, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = ratio ~ reference | coffee), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | coffee | Ratio | SE | 95% CI | t(113) | p
-----------------------------------------------------------------------------
noon | morning | coffee | 0.89 | 0.11 | [0.67, 1.11] | 8.08 | < .001
afternoon | morning | coffee | 1.11 | 0.12 | [0.87, 1.36] | 9.05 | < .001
noon | morning | control | 1.51 | 0.22 | [1.06, 1.95] | 6.73 | < .001
afternoon | morning | control | 1.51 | 0.22 | [1.06, 1.95] | 6.73 | < .001
Variable predicted: alertness
Predictors contrasted: time
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = "(b2-b1)=(b4-b3)"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(113) | p
----------------------------------------------------------------
b2-b1=b4-b3 | 5.78 | 2.82 | [0.20, 11.37] | 2.05 | 0.043
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Parameters:
b2 = time [noon], coffee [coffee]
b1 = time [morning], coffee [coffee]
b4 = time [morning], coffee [control]
b3 = time [afternoon], coffee [coffee]
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects",
p_adjust = "none", comparison = "b5=b3"), zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(113) | p
--------------------------------------------------------------
b5=b3 | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Parameters:
b5 = time [noon], coffee [control]
b3 = time [afternoon], coffee [coffee]
Code
estimate_contrasts(fit, c("e42dep", "c172code"), comparison = "b6-b3=0",
backend = "marginaleffects")
Output
Marginal Contrasts Analysis
Parameter | Difference | SE | 95% CI | t(800) | p
--------------------------------------------------------------
b6-b3=0 | -0.34 | 0.56 | [-1.44, 0.77] | -0.60 | 0.552
Variable predicted: neg_c_7
Predictors contrasted: e42dep, c172code
Predictors averaged: c12hour (42), barthtot (65), c161sex
p-values are uncorrected.
Parameters:
b6 = e42dep [slightly dependent], c172code [intermediate level of education]
b3 = e42dep [moderately dependent], c172code [low level of education]
estimate_contrasts - marginaleffects vs emmeans
Code
estimate_contrasts(model, backend = "emmeans")
Message
No variable was specified for contrast estimation. Selecting `contrast =
"Species"`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | SE | df | z | p
-----------------------------------------------------------------------------------
setosa | versicolor | -0.68 | [-0.82, -0.54] | 0.07 | Inf | -9.27 | < .001
setosa | virginica | -0.50 | [-0.67, -0.33] | 0.08 | Inf | -5.90 | < .001
versicolor | virginica | 0.18 | [ 0.01, 0.35] | 0.08 | Inf | 2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-values are uncorrected.
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "marginaleffects")
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
versicolor | setosa | 0.68 | 0.07 | [ 0.54, 0.82] | 9.27 | < .001
virginica | setosa | 0.50 | 0.08 | [ 0.33, 0.67] | 5.90 | < .001
virginica | versicolor | -0.18 | 0.08 | [-0.35, -0.01] | -2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-values are uncorrected.
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "emmeans", p_adjust = "holm")
Message
No variable was specified for contrast estimation. Selecting `contrast =
"Species"`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | SE | df | z | p
-----------------------------------------------------------------------------------
setosa | versicolor | -0.68 | [-0.86, -0.50] | 0.07 | Inf | -9.27 | < .001
setosa | virginica | -0.50 | [-0.70, -0.30] | 0.08 | Inf | -5.90 | < .001
versicolor | virginica | 0.18 | [-0.02, 0.38] | 0.08 | Inf | 2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-value adjustment method: Holm (1979)
Contrasts are on the response-scale (in %-points).
Code
estimate_contrasts(model, backend = "marginaleffects", p_adjust = "holm")
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
versicolor | setosa | 0.68 | 0.07 | [ 0.54, 0.82] | 9.27 | < .001
virginica | setosa | 0.50 | 0.08 | [ 0.33, 0.67] | 5.90 | < .001
virginica | versicolor | -0.18 | 0.08 | [-0.35, -0.01] | -2.12 | 0.034
Variable predicted: y
Predictors contrasted: Species
p-value adjustment method: Holm (1979)
Contrasts are on the response-scale (in %-points).
estimate_contrasts - filtering works
Code
print(estimate_contrasts(fit, "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(827) | p
--------------------------------------------------------------------
mid | low | -0.09 | 0.34 | [-0.76, 0.58] | -0.25 | 0.802
high | low | 0.61 | 0.43 | [-0.24, 1.45] | 1.40 | 0.162
high | mid | 0.69 | 0.36 | [-0.02, 1.40] | 1.92 | 0.055
Variable predicted: neg_c_7
Predictors contrasted: c172code
Predictors averaged: e16sex, c161sex
p-values are uncorrected.
Code
print(estimate_contrasts(fit, c("c161sex", "c172code"), backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(825) | p
-------------------------------------------------------------------------------
Male, mid | Male, low | 0.29 | 0.71 | [-1.10, 1.68] | 0.41 | 0.684
Male, high | Male, low | 0.69 | 0.83 | [-0.95, 2.32] | 0.82 | 0.410
Female, low | Male, low | 1.05 | 0.69 | [-0.31, 2.40] | 1.51 | 0.131
Female, mid | Male, low | 0.85 | 0.64 | [-0.40, 2.10] | 1.33 | 0.183
Female, high | Male, low | 1.65 | 0.71 | [ 0.24, 3.05] | 2.30 | 0.022
Male, high | Male, mid | 0.40 | 0.68 | [-0.94, 1.73] | 0.59 | 0.558
Female, low | Male, mid | 0.76 | 0.50 | [-0.22, 1.73] | 1.52 | 0.129
Female, mid | Male, mid | 0.56 | 0.42 | [-0.26, 1.38] | 1.35 | 0.178
Female, high | Male, mid | 1.36 | 0.53 | [ 0.32, 2.39] | 2.58 | 0.010
Female, low | Male, high | 0.36 | 0.66 | [-0.95, 1.66] | 0.54 | 0.589
Female, mid | Male, high | 0.16 | 0.61 | [-1.03, 1.35] | 0.27 | 0.789
Female, high | Male, high | 0.96 | 0.69 | [-0.39, 2.30] | 1.40 | 0.163
Female, mid | Female, low | -0.20 | 0.39 | [-0.96, 0.57] | -0.51 | 0.613
Female, high | Female, low | 0.60 | 0.51 | [-0.39, 1.59] | 1.18 | 0.236
Female, high | Female, mid | 0.80 | 0.43 | [-0.04, 1.63] | 1.87 | 0.061
Variable predicted: neg_c_7
Predictors contrasted: c161sex, c172code
Predictors averaged: e16sex
p-values are uncorrected.
Code
print(estimate_contrasts(fit, "c161sex", "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | c172code | Difference | SE | 95% CI | t(825) | p
-------------------------------------------------------------------------------
Female | Male | low | 1.05 | 0.69 | [-0.31, 2.40] | 1.51 | 0.131
Female | Male | mid | 0.56 | 0.42 | [-0.26, 1.38] | 1.35 | 0.178
Female | Male | high | 0.96 | 0.69 | [-0.39, 2.30] | 1.40 | 0.163
Variable predicted: neg_c_7
Predictors contrasted: c161sex
Predictors averaged: e16sex
p-values are uncorrected.
Code
print(estimate_slopes(fit, "barthtot", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Estimated Marginal Effects
Slope | SE | 95% CI | t | p
-----------------------------------------------
-0.05 | 0.00 | [-0.06, -0.05] | -12.77 | < .001
Marginal effects estimated for barthtot
Type of slope was dY/dX
Code
print(estimate_slopes(fit, "barthtot", by = "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Estimated Marginal Effects
c172code | Slope | SE | 95% CI | t | p
---------------------------------------------------------
low | -0.06 | 0.01 | [-0.08, -0.05] | -7.08 | < .001
mid | -0.05 | 0.01 | [-0.06, -0.04] | -9.82 | < .001
high | -0.05 | 0.01 | [-0.07, -0.03] | -4.51 | < .001
Marginal effects estimated for barthtot
Type of slope was dY/dX
Code
print(estimate_contrasts(fit, "barthtot", "c172code", backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
------------------------------------------------------------------
mid | low | 0.01 | 0.01 | [-0.01, 0.03] | 1.17 | 0.243
high | low | 0.02 | 0.01 | [-0.01, 0.04] | 1.10 | 0.271
high | mid | 0.00 | 0.01 | [-0.02, 0.03] | 0.27 | 0.786
Variable predicted: neg_c_7
Predictors contrasted: barthtot
Predictors averaged: e16sex, barthtot (65)
p-values are uncorrected.
Code
print(estimate_contrasts(fit, "barthtot", c("c172code", "e16sex"), backend = "marginaleffects"),
table_width = Inf, zap_small = TRUE)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
------------------------------------------------------------------------------
low, female | low, male | -0.01 | 0.02 | [-0.04, 0.03] | -0.35 | 0.729
mid, male | low, male | -0.01 | 0.02 | [-0.04, 0.02] | -0.46 | 0.648
mid, female | low, male | 0.02 | 0.01 | [-0.01, 0.05] | 1.24 | 0.216
high, male | low, male | 0.00 | 0.02 | [-0.05, 0.04] | -0.16 | 0.876
high, female | low, male | 0.02 | 0.02 | [-0.01, 0.06] | 1.19 | 0.236
mid, male | low, female | 0.00 | 0.02 | [-0.03, 0.03] | -0.08 | 0.940
mid, female | low, female | 0.02 | 0.01 | [ 0.00, 0.05] | 1.76 | 0.079
high, male | low, female | 0.00 | 0.02 | [-0.04, 0.05] | 0.12 | 0.908
high, female | low, female | 0.03 | 0.02 | [-0.01, 0.06] | 1.58 | 0.115
mid, female | mid, male | 0.03 | 0.01 | [ 0.00, 0.05] | 2.24 | 0.025
high, male | mid, male | 0.00 | 0.02 | [-0.04, 0.05] | 0.18 | 0.859
high, female | mid, male | 0.03 | 0.02 | [ 0.00, 0.06] | 1.83 | 0.067
high, male | mid, female | -0.02 | 0.02 | [-0.06, 0.02] | -1.08 | 0.280
high, female | mid, female | 0.00 | 0.01 | [-0.02, 0.03] | 0.26 | 0.792
high, female | high, male | 0.03 | 0.02 | [-0.02, 0.07] | 1.11 | 0.268
Variable predicted: neg_c_7
Predictors contrasted: barthtot
Predictors averaged: barthtot (65)
p-values are uncorrected.
estimate_contrasts - simple contrasts and with - in levels works
Code
print(estimate_contrasts(model, "Species", backend = "marginaleffects"),
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(146) | p
----------------------------------------------------------------------------
versicolor | setosa | 1.46 | 0.11 | [1.24, 1.68] | 13.01 | < .001
virginica | setosa | 1.95 | 0.10 | [1.75, 2.14] | 19.47 | < .001
virginica | versicolor | 0.49 | 0.09 | [0.31, 0.67] | 5.41 | < .001
Variable predicted: Sepal.Length
Predictors contrasted: Species
Predictors averaged: Sepal.Width (3.1)
p-values are uncorrected.
Code
print(estimate_contrasts(m, c("time", "coffee"), backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(113) | p
---------------------------------------------------------------------------------------------
morning, control | morning, coffee | -5.78 | 1.99 | [-9.73, -1.83] | -2.90 | 0.004
noon, coffee | morning, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
noon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, coffee | morning, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | morning, coffee | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
noon, coffee | morning, control | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon, coffee | morning, control | 7.71 | 1.99 | [ 3.76, 11.66] | 3.87 | < .001
afternoon, control | morning, control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
noon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
afternoon, control | noon, coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, coffee | noon, control | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon, control | noon, control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
afternoon, control | afternoon, coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
Variable predicted: alertness
Predictors contrasted: time, coffee
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(m, contrast = "time", by = "coffee", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | coffee | Difference | SE | 95% CI | t(113) | p
-----------------------------------------------------------------------------------
noon | morning | coffee | -1.93 | 1.99 | [-5.88, 2.02] | -0.97 | 0.336
afternoon | morning | coffee | 1.93 | 1.99 | [-2.02, 5.88] | 0.97 | 0.336
afternoon | noon | coffee | 3.86 | 1.99 | [-0.09, 7.81] | 1.93 | 0.056
noon | morning | control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon | morning | control | 5.78 | 1.99 | [ 1.83, 9.73] | 2.90 | 0.004
afternoon | noon | control | 0.00 | 1.99 | [-3.95, 3.95] | 0.00 | > .999
Variable predicted: alertness
Predictors contrasted: time
Predictors averaged: sex
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("mined", "spp"), backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | z | p
-----------------------------------------------------------------------------
yes, PR | yes, GP | 0.06 | 0.05 | [-0.04, 0.16] | 1.10 | 0.271
yes, DM | yes, GP | 0.32 | 0.11 | [ 0.10, 0.54] | 2.89 | 0.004
yes, EC-A | yes, GP | 0.04 | 0.05 | [-0.05, 0.13] | 0.80 | 0.421
yes, EC-L | yes, GP | 0.17 | 0.08 | [ 0.02, 0.32] | 2.19 | 0.028
yes, DES-L | yes, GP | 0.43 | 0.14 | [ 0.17, 0.70] | 3.19 | 0.001
yes, DF | yes, GP | 0.43 | 0.14 | [ 0.17, 0.70] | 3.19 | 0.001
no, GP | yes, GP | 2.59 | 0.55 | [ 1.50, 3.67] | 4.67 | < .001
no, PR | yes, GP | 0.51 | 0.16 | [ 0.20, 0.82] | 3.21 | 0.001
no, DM | yes, GP | 2.86 | 0.61 | [ 1.68, 4.05] | 4.73 | < .001
no, EC-A | yes, GP | 1.10 | 0.27 | [ 0.57, 1.64] | 4.03 | < .001
no, EC-L | yes, GP | 4.67 | 0.94 | [ 2.82, 6.52] | 4.95 | < .001
no, DES-L | yes, GP | 4.62 | 0.93 | [ 2.79, 6.45] | 4.95 | < .001
no, DF | yes, GP | 2.24 | 0.49 | [ 1.28, 3.20] | 4.58 | < .001
yes, DM | yes, PR | 0.26 | 0.11 | [ 0.05, 0.48] | 2.43 | 0.015
yes, EC-A | yes, PR | -0.02 | 0.06 | [-0.13, 0.09] | -0.33 | 0.740
yes, EC-L | yes, PR | 0.11 | 0.08 | [-0.04, 0.27] | 1.43 | 0.154
yes, DES-L | yes, PR | 0.38 | 0.13 | [ 0.12, 0.64] | 2.86 | 0.004
yes, DF | yes, PR | 0.38 | 0.13 | [ 0.12, 0.64] | 2.86 | 0.004
no, GP | yes, PR | 2.53 | 0.56 | [ 1.44, 3.62] | 4.54 | < .001
no, PR | yes, PR | 0.45 | 0.16 | [ 0.13, 0.77] | 2.75 | 0.006
no, DM | yes, PR | 2.80 | 0.61 | [ 1.61, 4.00] | 4.61 | < .001
no, EC-A | yes, PR | 1.05 | 0.28 | [ 0.50, 1.59] | 3.76 | < .001
no, EC-L | yes, PR | 4.61 | 0.95 | [ 2.76, 6.47] | 4.87 | < .001
no, DES-L | yes, PR | 4.56 | 0.94 | [ 2.73, 6.40] | 4.87 | < .001
no, DF | yes, PR | 2.18 | 0.49 | [ 1.22, 3.15] | 4.44 | < .001
yes, EC-A | yes, DM | -0.28 | 0.11 | [-0.50, -0.07] | -2.59 | 0.010
yes, EC-L | yes, DM | -0.15 | 0.11 | [-0.36, 0.06] | -1.39 | 0.164
yes, DES-L | yes, DM | 0.11 | 0.13 | [-0.14, 0.36] | 0.89 | 0.375
yes, DF | yes, DM | 0.11 | 0.13 | [-0.14, 0.36] | 0.89 | 0.375
no, GP | yes, DM | 2.27 | 0.58 | [ 1.14, 3.40] | 3.93 | < .001
no, PR | yes, DM | 0.19 | 0.20 | [-0.21, 0.58] | 0.93 | 0.352
no, DM | yes, DM | 2.54 | 0.63 | [ 1.31, 3.77] | 4.04 | < .001
no, EC-A | yes, DM | 0.78 | 0.31 | [ 0.18, 1.38] | 2.56 | 0.011
no, EC-L | yes, DM | 4.35 | 0.96 | [ 2.46, 6.24] | 4.51 | < .001
no, DES-L | yes, DM | 4.30 | 0.95 | [ 2.43, 6.17] | 4.51 | < .001
no, DF | yes, DM | 1.92 | 0.51 | [ 0.91, 2.93] | 3.74 | < .001
yes, EC-L | yes, EC-A | 0.13 | 0.08 | [-0.02, 0.29] | 1.68 | 0.093
yes, DES-L | yes, EC-A | 0.40 | 0.13 | [ 0.14, 0.66] | 2.97 | 0.003
yes, DF | yes, EC-A | 0.40 | 0.13 | [ 0.14, 0.66] | 2.97 | 0.003
no, GP | yes, EC-A | 2.55 | 0.56 | [ 1.46, 3.64] | 4.58 | < .001
no, PR | yes, EC-A | 0.47 | 0.16 | [ 0.15, 0.79] | 2.90 | 0.004
no, DM | yes, EC-A | 2.82 | 0.61 | [ 1.63, 4.01] | 4.65 | < .001
no, EC-A | yes, EC-A | 1.06 | 0.28 | [ 0.52, 1.61] | 3.85 | < .001
no, EC-L | yes, EC-A | 4.63 | 0.95 | [ 2.78, 6.48] | 4.90 | < .001
no, DES-L | yes, EC-A | 4.58 | 0.94 | [ 2.75, 6.42] | 4.90 | < .001
no, DF | yes, EC-A | 2.20 | 0.49 | [ 1.24, 3.17] | 4.49 | < .001
yes, DES-L | yes, EC-L | 0.26 | 0.13 | [ 0.02, 0.51] | 2.08 | 0.037
yes, DF | yes, EC-L | 0.26 | 0.13 | [ 0.02, 0.51] | 2.08 | 0.037
no, GP | yes, EC-L | 2.42 | 0.57 | [ 1.31, 3.53] | 4.28 | < .001
no, PR | yes, EC-L | 0.34 | 0.18 | [-0.01, 0.69] | 1.89 | 0.058
no, DM | yes, EC-L | 2.69 | 0.62 | [ 1.48, 3.90] | 4.37 | < .001
no, EC-A | yes, EC-L | 0.93 | 0.29 | [ 0.37, 1.50] | 3.23 | 0.001
no, EC-L | yes, EC-L | 4.50 | 0.95 | [ 2.63, 6.37] | 4.72 | < .001
no, DES-L | yes, EC-L | 4.45 | 0.94 | [ 2.60, 6.30] | 4.71 | < .001
no, DF | yes, EC-L | 2.07 | 0.50 | [ 1.09, 3.05] | 4.14 | < .001
yes, DF | yes, DES-L | 0.00 | 0.13 | [-0.26, 0.26] | 0.00 | > .999
no, GP | yes, DES-L | 2.15 | 0.59 | [ 1.00, 3.31] | 3.67 | < .001
no, PR | yes, DES-L | 0.07 | 0.22 | [-0.36, 0.50] | 0.33 | 0.738
no, DM | yes, DES-L | 2.43 | 0.64 | [ 1.18, 3.68] | 3.81 | < .001
no, EC-A | yes, DES-L | 0.67 | 0.32 | [ 0.04, 1.29] | 2.09 | 0.037
no, EC-L | yes, DES-L | 4.24 | 0.97 | [ 2.33, 6.14] | 4.36 | < .001
no, DES-L | yes, DES-L | 4.19 | 0.96 | [ 2.30, 6.07] | 4.35 | < .001
no, DF | yes, DES-L | 1.81 | 0.52 | [ 0.78, 2.83] | 3.45 | < .001
no, GP | yes, DF | 2.15 | 0.59 | [ 1.00, 3.31] | 3.67 | < .001
no, PR | yes, DF | 0.07 | 0.22 | [-0.36, 0.50] | 0.33 | 0.738
no, DM | yes, DF | 2.43 | 0.64 | [ 1.18, 3.68] | 3.81 | < .001
no, EC-A | yes, DF | 0.67 | 0.32 | [ 0.04, 1.29] | 2.09 | 0.037
no, EC-L | yes, DF | 4.24 | 0.97 | [ 2.33, 6.14] | 4.36 | < .001
no, DES-L | yes, DF | 4.19 | 0.96 | [ 2.30, 6.07] | 4.35 | < .001
no, DF | yes, DF | 1.81 | 0.52 | [ 0.78, 2.83] | 3.45 | < .001
no, PR | no, GP | -2.08 | 0.48 | [-3.02, -1.14] | -4.35 | < .001
no, DM | no, GP | 0.27 | 0.37 | [-0.46, 1.00] | 0.73 | 0.466
no, EC-A | no, GP | -1.49 | 0.41 | [-2.29, -0.68] | -3.61 | < .001
no, EC-L | no, GP | 2.08 | 0.58 | [ 0.95, 3.21] | 3.61 | < .001
no, DES-L | no, GP | 2.03 | 0.57 | [ 0.92, 3.15] | 3.57 | < .001
no, DF | no, GP | -0.35 | 0.35 | [-1.04, 0.35] | -0.98 | 0.328
no, DM | no, PR | 2.35 | 0.53 | [ 1.32, 3.39] | 4.47 | < .001
no, EC-A | no, PR | 0.59 | 0.23 | [ 0.14, 1.05] | 2.56 | 0.011
no, EC-L | no, PR | 4.16 | 0.86 | [ 2.49, 5.84] | 4.87 | < .001
no, DES-L | no, PR | 4.11 | 0.85 | [ 2.45, 5.77] | 4.86 | < .001
no, DF | no, PR | 1.73 | 0.42 | [ 0.92, 2.55] | 4.15 | < .001
no, EC-A | no, DM | -1.76 | 0.46 | [-2.65, -0.87] | -3.86 | < .001
no, EC-L | no, DM | 1.81 | 0.55 | [ 0.73, 2.89] | 3.29 | < .001
no, DES-L | no, DM | 1.76 | 0.54 | [ 0.70, 2.82] | 3.24 | 0.001
no, DF | no, DM | -0.62 | 0.38 | [-1.36, 0.12] | -1.65 | 0.100
no, EC-L | no, EC-A | 3.57 | 0.77 | [ 2.07, 5.07] | 4.66 | < .001
no, DES-L | no, EC-A | 3.52 | 0.76 | [ 2.03, 5.00] | 4.65 | < .001
no, DF | no, EC-A | 1.14 | 0.36 | [ 0.43, 1.85] | 3.16 | 0.002
no, DES-L | no, EC-L | -0.05 | 0.48 | [-0.99, 0.89] | -0.10 | 0.918
no, DF | no, EC-L | -2.43 | 0.61 | [-3.63, -1.22] | -3.95 | < .001
no, DF | no, DES-L | -2.38 | 0.61 | [-3.57, -1.19] | -3.92 | < .001
Variable predicted: count
Predictors contrasted: mined, spp
Predictors averaged: cover (8.7e-11), site
p-values are uncorrected.
Contrasts are on the response-scale.
Code
print(estimate_contrasts(model, contrast = "mined", by = "spp", backend = "marginaleffects"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | spp | Difference | SE | 95% CI | z | p
--------------------------------------------------------------------------
no | yes | GP | 2.59 | 0.55 | [1.50, 3.67] | 4.67 | < .001
no | yes | PR | 0.45 | 0.16 | [0.13, 0.77] | 2.75 | 0.006
no | yes | DM | 2.54 | 0.63 | [1.31, 3.77] | 4.04 | < .001
no | yes | EC-A | 1.06 | 0.28 | [0.52, 1.61] | 3.85 | < .001
no | yes | EC-L | 4.50 | 0.95 | [2.63, 6.37] | 4.72 | < .001
no | yes | DES-L | 4.19 | 0.96 | [2.30, 6.07] | 4.35 | < .001
no | yes | DF | 1.81 | 0.52 | [0.78, 2.83] | 3.45 | < .001
Variable predicted: count
Predictors contrasted: mined
Predictors averaged: cover (8.7e-11), site
p-values are uncorrected.
Contrasts are on the response-scale.
estimate_contrasts - examples from docs work as intendec
Code
print(estimate_contrasts(model, contrast = "Petal.Width", by = "Species"),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t | p
---------------------------------------------------------------------------
versicolor | setosa | 0.22 | 0.46 | [-0.69, 1.12] | 0.47 | 0.639
virginica | setosa | -0.21 | 0.44 | [-1.06, 0.65] | -0.47 | 0.637
virginica | versicolor | -0.42 | 0.27 | [-0.95, 0.10] | -1.58 | 0.114
Variable predicted: Sepal.Width
Predictors contrasted: Petal.Width
Predictors averaged: Petal.Width (1.2)
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("Species", "Petal.Width"), length = 2),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(144) | p
----------------------------------------------------------------------------------------
setosa, 2.5 | setosa, 0.1 | 2.01 | 0.98 | [ 0.08, 3.94] | 2.06 | 0.041
versicolor, 0.1 | setosa, 0.1 | -1.83 | 0.28 | [-2.38, -1.28] | -6.55 | < .001
versicolor, 2.5 | setosa, 0.1 | 0.70 | 0.27 | [ 0.17, 1.23] | 2.61 | 0.010
virginica, 0.1 | setosa, 0.1 | -1.55 | 0.31 | [-2.17, -0.93] | -4.95 | < .001
virginica, 2.5 | setosa, 0.1 | -0.03 | 0.11 | [-0.25, 0.19] | -0.29 | 0.773
versicolor, 0.1 | setosa, 2.5 | -3.84 | 0.96 | [-5.73, -1.95] | -4.01 | < .001
versicolor, 2.5 | setosa, 2.5 | -1.31 | 0.95 | [-3.19, 0.58] | -1.37 | 0.172
virginica, 0.1 | setosa, 2.5 | -3.56 | 0.97 | [-5.47, -1.65] | -3.68 | < .001
virginica, 2.5 | setosa, 2.5 | -2.04 | 0.92 | [-3.86, -0.22] | -2.21 | 0.028
versicolor, 2.5 | versicolor, 0.1 | 2.53 | 0.52 | [ 1.50, 3.56] | 4.86 | < .001
virginica, 0.1 | versicolor, 0.1 | 0.28 | 0.41 | [-0.52, 1.08] | 0.69 | 0.492
virginica, 2.5 | versicolor, 0.1 | 1.80 | 0.28 | [ 1.24, 2.35] | 6.35 | < .001
virginica, 0.1 | versicolor, 2.5 | -2.25 | 0.40 | [-3.04, -1.46] | -5.64 | < .001
virginica, 2.5 | versicolor, 2.5 | -0.73 | 0.27 | [-1.27, -0.20] | -2.70 | 0.008
virginica, 2.5 | virginica, 0.1 | 1.52 | 0.37 | [ 0.77, 2.26] | 4.04 | < .001
Variable predicted: Sepal.Width
Predictors contrasted: Species, Petal.Width
p-values are uncorrected.
Code
print(estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)")),
zap_small = TRUE, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(144) | p
------------------------------------------------------------------------------------
setosa, 2 | setosa, 1 | 0.84 | 0.41 | [ 0.03, 1.64] | 2.06 | 0.041
versicolor, 1 | setosa, 1 | -1.63 | 0.32 | [-2.27, -1.00] | -5.09 | < .001
versicolor, 2 | setosa, 1 | -0.58 | 0.35 | [-1.26, 0.10] | -1.68 | 0.096
virginica, 1 | setosa, 1 | -1.73 | 0.35 | [-2.43, -1.04] | -4.93 | < .001
virginica, 2 | setosa, 1 | -1.10 | 0.31 | [-1.72, -0.48] | -3.52 | < .001
versicolor, 1 | setosa, 2 | -2.47 | 0.72 | [-3.89, -1.05] | -3.43 | < .001
versicolor, 2 | setosa, 2 | -1.42 | 0.73 | [-2.86, 0.03] | -1.94 | 0.055
virginica, 1 | setosa, 2 | -2.57 | 0.73 | [-4.02, -1.12] | -3.50 | < .001
virginica, 2 | setosa, 2 | -1.94 | 0.72 | [-3.35, -0.52] | -2.71 | 0.008
versicolor, 2 | versicolor, 1 | 1.05 | 0.22 | [ 0.62, 1.48] | 4.86 | < .001
virginica, 1 | versicolor, 1 | -0.10 | 0.19 | [-0.47, 0.27] | -0.54 | 0.589
virginica, 2 | versicolor, 1 | 0.53 | 0.09 | [ 0.35, 0.71] | 5.72 | < .001
virginica, 1 | versicolor, 2 | -1.15 | 0.23 | [-1.60, -0.71] | -5.13 | < .001
virginica, 2 | versicolor, 2 | -0.52 | 0.16 | [-0.84, -0.21] | -3.31 | 0.001
virginica, 2 | virginica, 1 | 0.63 | 0.16 | [ 0.32, 0.94] | 4.04 | < .001
Variable predicted: Sepal.Width
Predictors contrasted: Species, Petal.Width=c(1, 2)
p-values are uncorrected.
Code
print(estimate_contrasts(model, by = "Petal.Width", length = 4), zap_small = TRUE,
table_width = Inf)
Message
We selected `contrast=c("Species")`.
Output
Marginal Contrasts Analysis
Level1 | Level2 | Petal.Width | Difference | SE | 95% CI | t(144) | p
--------------------------------------------------------------------------------------------
versicolor | setosa | 0.10 | -1.83 | 0.28 | [-2.38, -1.28] | -6.55 | < .001
virginica | setosa | 0.10 | -1.55 | 0.31 | [-2.17, -0.93] | -4.95 | < .001
virginica | versicolor | 0.10 | 0.28 | 0.41 | [-0.52, 1.08] | 0.69 | 0.492
versicolor | setosa | 0.90 | -1.65 | 0.29 | [-2.22, -1.08] | -5.74 | < .001
virginica | setosa | 0.90 | -1.71 | 0.32 | [-2.35, -1.07] | -5.28 | < .001
virginica | versicolor | 0.90 | -0.06 | 0.21 | [-0.47, 0.35] | -0.28 | 0.780
versicolor | setosa | 1.70 | -1.48 | 0.60 | [-2.67, -0.29] | -2.47 | 0.015
virginica | setosa | 1.70 | -1.88 | 0.60 | [-3.06, -0.70] | -3.14 | 0.002
virginica | versicolor | 1.70 | -0.40 | 0.11 | [-0.62, -0.17] | -3.50 | < .001
versicolor | setosa | 2.50 | -1.31 | 0.95 | [-3.19, 0.58] | -1.37 | 0.172
virginica | setosa | 2.50 | -2.04 | 0.92 | [-3.86, -0.22] | -2.21 | 0.028
virginica | versicolor | 2.50 | -0.73 | 0.27 | [-1.27, -0.20] | -2.70 | 0.008
Variable predicted: Sepal.Width
Predictors contrasted: Species
p-values are uncorrected.
estimate_contrasts - test all combinations of contrast and by, with filtering
Code
print(estimate_contrasts(model2, c("grp", "time", "x")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(992) | p
--------------------------------------------------------------------------------------
control, 1, b | control, 1, a | 0.15 | 0.13 | [-0.10, 0.40] | 1.20 | 0.231
control, 2, a | control, 1, a | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
control, 2, b | control, 1, a | 0.09 | 0.13 | [-0.16, 0.34] | 0.73 | 0.465
treatment, 1, a | control, 1, a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 1, b | control, 1, a | -0.03 | 0.13 | [-0.28, 0.22] | -0.22 | 0.824
treatment, 2, a | control, 1, a | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 2, b | control, 1, a | 0.05 | 0.12 | [-0.19, 0.28] | 0.37 | 0.712
control, 2, a | control, 1, b | -0.12 | 0.13 | [-0.38, 0.13] | -0.97 | 0.332
control, 2, b | control, 1, b | -0.06 | 0.12 | [-0.30, 0.19] | -0.46 | 0.648
treatment, 1, a | control, 1, b | 0.04 | 0.13 | [-0.20, 0.29] | 0.34 | 0.730
treatment, 1, b | control, 1, b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment, 2, a | control, 1, b | -0.13 | 0.12 | [-0.37, 0.12] | -1.02 | 0.307
treatment, 2, b | control, 1, b | -0.10 | 0.12 | [-0.34, 0.13] | -0.89 | 0.376
control, 2, b | control, 2, a | 0.07 | 0.13 | [-0.19, 0.32] | 0.52 | 0.604
treatment, 1, a | control, 2, a | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 1, b | control, 2, a | -0.05 | 0.13 | [-0.31, 0.20] | -0.41 | 0.680
treatment, 2, a | control, 2, a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2, b | control, 2, a | 0.02 | 0.13 | [-0.23, 0.27] | 0.16 | 0.876
treatment, 1, a | control, 2, b | 0.10 | 0.13 | [-0.15, 0.35] | 0.78 | 0.437
treatment, 1, b | control, 2, b | -0.12 | 0.13 | [-0.37, 0.12] | -0.97 | 0.333
treatment, 2, a | control, 2, b | -0.07 | 0.13 | [-0.32, 0.18] | -0.55 | 0.582
treatment, 2, b | control, 2, b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
treatment, 1, b | treatment, 1, a | -0.22 | 0.13 | [-0.47, 0.03] | -1.73 | 0.083
treatment, 2, a | treatment, 1, a | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
treatment, 2, b | treatment, 1, a | -0.15 | 0.12 | [-0.39, 0.09] | -1.20 | 0.230
treatment, 2, a | treatment, 1, b | 0.05 | 0.12 | [-0.19, 0.30] | 0.42 | 0.676
treatment, 2, b | treatment, 1, b | 0.07 | 0.12 | [-0.16, 0.31] | 0.61 | 0.541
treatment, 2, b | treatment, 2, a | 0.02 | 0.12 | [-0.21, 0.26] | 0.18 | 0.861
Variable predicted: score
Predictors contrasted: grp, time, x
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time"), by = "x"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | x | Difference | SE | 95% CI | t(992) | p
------------------------------------------------------------------------------------
control, 2 | control, 1 | a | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
treatment, 1 | control, 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 2 | control, 1 | a | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 1 | control, 2 | a | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 2 | control, 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2 | treatment, 1 | a | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
control, 2 | control, 1 | b | -0.06 | 0.12 | [-0.30, 0.19] | -0.46 | 0.648
treatment, 1 | control, 1 | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment, 2 | control, 1 | b | -0.10 | 0.12 | [-0.34, 0.13] | -0.89 | 0.376
treatment, 1 | control, 2 | b | -0.12 | 0.13 | [-0.37, 0.12] | -0.97 | 0.333
treatment, 2 | control, 2 | b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
treatment, 2 | treatment, 1 | b | 0.07 | 0.12 | [-0.16, 0.31] | 0.61 | 0.541
Variable predicted: score
Predictors contrasted: grp, time
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = c("time", "x")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | x | Difference | SE | 95% CI | t(992) | p
-----------------------------------------------------------------------------------
treatment | control | 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment | control | 1 | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
treatment | control | 2 | b | -0.05 | 0.12 | [-0.29, 0.19] | -0.40 | 0.691
Variable predicted: score
Predictors contrasted: grp
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = "time"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | Difference | SE | 95% CI | t(992) | p
-------------------------------------------------------------------------------
treatment | control | 1 | 0.01 | 0.09 | [-0.17, 0.18] | 0.09 | 0.931
treatment | control | 2 | -0.02 | 0.09 | [-0.20, 0.15] | -0.28 | 0.780
Variable predicted: score
Predictors contrasted: grp
Predictors averaged: x
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time", "x='a'")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(992) | p
--------------------------------------------------------------------------------
control, 2 | control, 1 | 0.03 | 0.13 | [-0.23, 0.28] | 0.19 | 0.847
treatment, 1 | control, 1 | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment, 2 | control, 1 | 0.02 | 0.13 | [-0.23, 0.27] | 0.19 | 0.850
treatment, 1 | control, 2 | 0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.207
treatment, 2 | control, 2 | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
treatment, 2 | treatment, 1 | -0.17 | 0.13 | [-0.42, 0.08] | -1.32 | 0.187
Variable predicted: score
Predictors contrasted: grp, time, x='a'
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time=1"), by = "x"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | x | Difference | SE | 95% CI | t(992) | p
----------------------------------------------------------------------------
treatment | control | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | b | -0.18 | 0.12 | [-0.42, 0.06] | -1.45 | 0.147
Variable predicted: score
Predictors contrasted: grp, time=1
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "grp", by = c("time", "x='a'")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | time | x | Difference | SE | 95% CI | t(992) | p
-----------------------------------------------------------------------------------
treatment | control | 1 | a | 0.19 | 0.13 | [-0.06, 0.45] | 1.49 | 0.137
treatment | control | 2 | a | 0.00 | 0.13 | [-0.26, 0.25] | -0.01 | 0.991
Variable predicted: score
Predictors contrasted: grp
p-values are uncorrected.
Code
print(estimate_contrasts(model2, "time=c(1,2)", by = "grp"), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | grp | Difference | SE | 95% CI | t(994) | p
--------------------------------------------------------------------------------
2 | 1 | control | 0.14 | 0.11 | [-0.08, 0.36] | 1.24 | 0.216
2 | 1 | treatment | -0.07 | 0.11 | [-0.28, 0.14] | -0.63 | 0.529
Variable predicted: score
Predictors contrasted: time=c(1,2)
p-values are uncorrected.
Code
print(estimate_contrasts(model2, c("grp", "time=2")), zap_small = TRUE,
table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | t(994) | p
------------------------------------------------------------------------
treatment | control | -0.15 | 0.11 | [-0.36, 0.05] | -1.47 | 0.143
Variable predicted: score
Predictors contrasted: grp, time=2
p-values are uncorrected.
estimate_contrast, slopes with emmeans
Code
print(out, table_width = Inf)
Output
Marginal Contrasts Analysis
Level1 | Level2 | Difference | 95% CI | p
----------------------------------------------------------
0, 0.725 | 0, -2.012 | 1.12 | [0.37, 3.43] | 0.839
0, 3.463 | 0, -2.012 | 1.26 | [0.14, 11.76] | 0.839
1, -2.012 | 0, -2.012 | 1.00 | [0.35, 2.82] | 0.999
1, 0.725 | 0, -2.012 | 1.12 | [0.23, 5.43] | 0.887
1, 3.463 | 0, -2.012 | 1.26 | [0.10, 15.76] | 0.858
0, 3.463 | 0, 0.725 | 1.12 | [0.37, 3.43] | 0.839
1, -2.012 | 0, 0.725 | 0.89 | [0.20, 3.87] | 0.877
1, 0.725 | 0, 0.725 | 1.00 | [0.35, 2.82] | 0.999
1, 3.463 | 0, 0.725 | 1.12 | [0.23, 5.43] | 0.887
1, -2.012 | 0, 3.463 | 0.79 | [0.07, 8.71] | 0.849
1, 0.725 | 0, 3.463 | 0.89 | [0.20, 3.87] | 0.877
1, 3.463 | 0, 3.463 | 1.00 | [0.35, 2.82] | 0.999
1, 0.725 | 1, -2.012 | 1.12 | [0.37, 3.43] | 0.839
1, 3.463 | 1, -2.012 | 1.26 | [0.14, 11.76] | 0.839
1, 3.463 | 1, 0.725 | 1.12 | [0.37, 3.43] | 0.839
Variable predicted: outcome
Predictors contrasted: var_binom, var_cont
p-values are uncorrected.
Contrasts are on the link-scale.
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