tests/testthat/_snaps/estimate_contrasts_methods.md
In easystats/estimate: Estimation of Model-Based Predictions, Contrasts and Means
estimate_contrasts - Random Effects Levels, pairwise
Code
print(estimate_contrasts(estim, contrast = c("gender", "employed", "age")),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | Statistic | p
-------------------------------------------------------------------------------------------------
Male, no, -40 | Female, no, -40 | 0.07 | 1.11 | [-2.11, 2.25] | 0.06 | 0.951
Male, no, -40 | Male, yes, -40 | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
Female, no, -40 | Male, yes, -40 | -0.95 | 1.04 | [-2.99, 1.10] | -0.91 | 0.364
Male, no, -40 | Female, yes, -40 | -0.79 | 1.07 | [-2.89, 1.30] | -0.74 | 0.457
Female, no, -40 | Female, yes, -40 | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
Male, yes, -40 | Female, yes, -40 | 0.08 | 0.99 | [-1.86, 2.03] | 0.08 | 0.932
Male, no, -40 | Male, no, 41-64 | 0.30 | 1.10 | [-1.84, 2.45] | 0.28 | 0.781
Female, no, -40 | Male, no, 41-64 | 0.24 | 0.95 | [-1.63, 2.10] | 0.25 | 0.805
Male, yes, -40 | Male, no, 41-64 | 1.18 | 1.02 | [-0.83, 3.19] | 1.15 | 0.248
Female, yes, -40 | Male, no, 41-64 | 1.10 | 0.90 | [-0.66, 2.86] | 1.22 | 0.221
Male, no, -40 | Female, no, 41-64 | 1.49 | 0.94 | [-0.35, 3.32] | 1.59 | 0.112
Female, no, -40 | Female, no, 41-64 | 1.42 | 0.76 | [-0.08, 2.91] | 1.86 | 0.063
Male, yes, -40 | Female, no, 41-64 | 2.37 | 0.85 | [ 0.70, 4.04] | 2.78 | 0.005
Female, yes, -40 | Female, no, 41-64 | 2.28 | 0.69 | [ 0.92, 3.64] | 3.29 | 0.001
Male, no, 41-64 | Female, no, 41-64 | 1.18 | 0.74 | [-0.26, 2.63] | 1.60 | 0.109
Male, no, -40 | Male, yes, 41-64 | -0.23 | 1.04 | [-2.26, 1.81] | -0.22 | 0.827
Female, no, -40 | Male, yes, 41-64 | -0.30 | 0.88 | [-2.03, 1.44] | -0.33 | 0.738
Male, yes, -40 | Male, yes, 41-64 | 0.65 | 0.96 | [-1.23, 2.54] | 0.68 | 0.498
Female, yes, -40 | Male, yes, 41-64 | 0.57 | 0.83 | [-1.05, 2.19] | 0.69 | 0.492
Male, no, 41-64 | Male, yes, 41-64 | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
Female, no, 41-64 | Male, yes, 41-64 | -1.71 | 0.65 | [-2.98, -0.44] | -2.65 | 0.008
Male, no, -40 | Female, yes, 41-64 | 1.15 | 0.94 | [-0.70, 2.99] | 1.22 | 0.224
Female, no, -40 | Female, yes, 41-64 | 1.08 | 0.77 | [-0.43, 2.59] | 1.40 | 0.162
Male, yes, -40 | Female, yes, 41-64 | 2.02 | 0.86 | [ 0.34, 3.71] | 2.36 | 0.018
Female, yes, -40 | Female, yes, 41-64 | 1.94 | 0.70 | [ 0.56, 3.32] | 2.76 | 0.006
Male, no, 41-64 | Female, yes, 41-64 | 0.84 | 0.74 | [-0.62, 2.30] | 1.13 | 0.258
Female, no, 41-64 | Female, yes, 41-64 | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
Male, yes, 41-64 | Female, yes, 41-64 | 1.37 | 0.66 | [ 0.09, 2.66] | 2.09 | 0.036
Male, no, -40 | Male, no, 65++ | 0.83 | 1.08 | [-1.29, 2.94] | 0.77 | 0.443
Female, no, -40 | Male, no, 65++ | 0.76 | 0.93 | [-1.07, 2.58] | 0.82 | 0.415
Male, yes, -40 | Male, no, 65++ | 1.71 | 1.01 | [-0.26, 3.68] | 1.70 | 0.090
Female, yes, -40 | Male, no, 65++ | 1.62 | 0.88 | [-0.10, 3.34] | 1.85 | 0.064
Male, no, 41-64 | Male, no, 65++ | 0.52 | 0.91 | [-1.26, 2.31] | 0.57 | 0.565
Female, no, 41-64 | Male, no, 65++ | -0.66 | 0.71 | [-2.05, 0.73] | -0.93 | 0.354
Male, yes, 41-64 | Male, no, 65++ | 1.06 | 0.84 | [-0.59, 2.70] | 1.26 | 0.209
Female, yes, 41-64 | Male, no, 65++ | -0.32 | 0.72 | [-1.72, 1.09] | -0.44 | 0.658
Male, no, -40 | Female, no, 65++ | 1.71 | 0.98 | [-0.21, 3.63] | 1.75 | 0.081
Female, no, -40 | Female, no, 65++ | 1.64 | 0.81 | [ 0.04, 3.23] | 2.01 | 0.044
Male, yes, -40 | Female, no, 65++ | 2.59 | 0.90 | [ 0.83, 4.35] | 2.88 | 0.004
Female, yes, -40 | Female, no, 65++ | 2.50 | 0.75 | [ 1.03, 3.97] | 3.34 | < .001
Male, no, 41-64 | Female, no, 65++ | 1.40 | 0.79 | [-0.14, 2.95] | 1.78 | 0.076
Female, no, 41-64 | Female, no, 65++ | 0.22 | 0.55 | [-0.85, 1.29] | 0.40 | 0.686
Male, yes, 41-64 | Female, no, 65++ | 1.94 | 0.71 | [ 0.55, 3.32] | 2.74 | 0.006
Female, yes, 41-64 | Female, no, 65++ | 0.56 | 0.56 | [-0.53, 1.65] | 1.01 | 0.312
Male, no, 65++ | Female, no, 65++ | 0.88 | 0.77 | [-0.62, 2.38] | 1.15 | 0.250
Male, no, -40 | Male, yes, 65++ | -0.10 | 1.39 | [-2.83, 2.63] | -0.07 | 0.942
Female, no, -40 | Male, yes, 65++ | -0.17 | 1.28 | [-2.69, 2.35] | -0.13 | 0.895
Male, yes, -40 | Male, yes, 65++ | 0.78 | 1.34 | [-1.85, 3.40] | 0.58 | 0.561
Female, yes, -40 | Male, yes, 65++ | 0.69 | 1.25 | [-1.75, 3.13] | 0.56 | 0.577
Male, no, 41-64 | Male, yes, 65++ | -0.41 | 1.27 | [-2.89, 2.08] | -0.32 | 0.750
Female, no, 41-64 | Male, yes, 65++ | -1.59 | 1.13 | [-3.81, 0.64] | -1.40 | 0.162
Male, yes, 41-64 | Male, yes, 65++ | 0.13 | 1.22 | [-2.26, 2.52] | 0.10 | 0.917
Female, yes, 41-64 | Male, yes, 65++ | -1.25 | 1.14 | [-3.48, 0.99] | -1.09 | 0.274
Male, no, 65++ | Male, yes, 65++ | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
Female, no, 65++ | Male, yes, 65++ | -1.81 | 1.17 | [-4.10, 0.48] | -1.55 | 0.122
Male, no, -40 | Female, yes, 65++ | 0.39 | 1.36 | [-2.27, 3.05] | 0.29 | 0.775
Female, no, -40 | Female, yes, 65++ | 0.32 | 1.25 | [-2.12, 2.76] | 0.26 | 0.798
Male, yes, -40 | Female, yes, 65++ | 1.27 | 1.30 | [-1.28, 3.82] | 0.97 | 0.330
Female, yes, -40 | Female, yes, 65++ | 1.18 | 1.20 | [-1.18, 3.54] | 0.98 | 0.326
Male, no, 41-64 | Female, yes, 65++ | 0.08 | 1.23 | [-2.33, 2.49] | 0.07 | 0.946
Female, no, 41-64 | Female, yes, 65++ | -1.10 | 1.09 | [-3.23, 1.04] | -1.01 | 0.313
Male, yes, 41-64 | Female, yes, 65++ | 0.62 | 1.18 | [-1.69, 2.92] | 0.52 | 0.601
Female, yes, 41-64 | Female, yes, 65++ | -0.76 | 1.09 | [-2.90, 1.39] | -0.69 | 0.489
Male, no, 65++ | Female, yes, 65++ | -0.44 | 1.21 | [-2.82, 1.94] | -0.36 | 0.717
Female, no, 65++ | Female, yes, 65++ | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
Male, yes, 65++ | Female, yes, 65++ | 0.49 | 1.50 | [-2.45, 3.43] | 0.33 | 0.745
Variable predicted: qol
Predictors contrasted: gender, employed, age
Code
print(estimate_contrasts(estim, contrast = c("gender", "employed"), by = "age"),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
age | Parameter | Difference | SE | 95% CI | Statistic | p
--------------------------------------------------------------------------------------
-40 | Male no -Female no | 0.07 | 1.11 | [-2.11, 2.25] | 0.06 | 0.951
-40 | Male no -Male yes | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
-40 | Female no -Male yes | -0.95 | 1.04 | [-2.99, 1.10] | -0.91 | 0.364
-40 | Male no -Female yes | -0.79 | 1.07 | [-2.89, 1.30] | -0.74 | 0.457
-40 | Female no -Female yes | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
-40 | Male yes -Female yes | 0.08 | 0.99 | [-1.86, 2.03] | 0.08 | 0.932
41-64 | Male no -Female no | 1.18 | 0.74 | [-0.26, 2.63] | 1.60 | 0.109
41-64 | Male no -Male yes | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
41-64 | Female no -Male yes | -1.71 | 0.65 | [-2.98, -0.44] | -2.65 | 0.008
41-64 | Male no -Female yes | 0.84 | 0.74 | [-0.62, 2.30] | 1.13 | 0.258
41-64 | Female no -Female yes | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
41-64 | Male yes -Female yes | 1.37 | 0.66 | [ 0.09, 2.66] | 2.09 | 0.036
65+ | Male no -Female no | 0.88 | 0.77 | [-0.62, 2.38] | 1.15 | 0.250
65+ | Male no -Male yes | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
65+ | Female no -Male yes | -1.81 | 1.17 | [-4.10, 0.48] | -1.55 | 0.122
65+ | Male no -Female yes | -0.44 | 1.21 | [-2.82, 1.94] | -0.36 | 0.717
65+ | Female no -Female yes | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
65+ | Male yes -Female yes | 0.49 | 1.50 | [-2.45, 3.43] | 0.33 | 0.745
Variable predicted: qol
Predictors contrasted: gender, employed
Code
print(estimate_contrasts(estim, contrast = "employed", by = c("age", "gender")),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
Level1 | Level2 | age | gender | Difference | SE | 95% CI | Statistic | p
----------------------------------------------------------------------------------------
no | yes | -40 | Female | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
no | yes | -40 | Male | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
no | yes | 41-64 | Female | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
no | yes | 41-64 | Male | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
no | yes | 65+ | Female | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
no | yes | 65+ | Male | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
Variable predicted: qol
Predictors contrasted: employed
estimate_contrasts - Random Effects Levels, interaction
Code
print(estimate_contrasts(estim, contrast = c("age", "employed"), comparison = "interaction"),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
age | employed | Difference | SE | 95% CI | Statistic | p
-------------------------------------------------------------------------------
-40-41-64 | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
-40-65+ | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
41-64-65+ | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
Variable predicted: qol
Predictors contrasted: age, employed
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estimate_contrasts - Random Effects Levels, pairwise
Code
print(estimate_contrasts(estim, contrast = c("gender", "employed", "age")),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
Level1 | Level2 | Difference | SE | 95% CI | Statistic | p
-------------------------------------------------------------------------------------------------
Male, no, -40 | Female, no, -40 | 0.07 | 1.11 | [-2.11, 2.25] | 0.06 | 0.951
Male, no, -40 | Male, yes, -40 | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
Female, no, -40 | Male, yes, -40 | -0.95 | 1.04 | [-2.99, 1.10] | -0.91 | 0.364
Male, no, -40 | Female, yes, -40 | -0.79 | 1.07 | [-2.89, 1.30] | -0.74 | 0.457
Female, no, -40 | Female, yes, -40 | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
Male, yes, -40 | Female, yes, -40 | 0.08 | 0.99 | [-1.86, 2.03] | 0.08 | 0.932
Male, no, -40 | Male, no, 41-64 | 0.30 | 1.10 | [-1.84, 2.45] | 0.28 | 0.781
Female, no, -40 | Male, no, 41-64 | 0.24 | 0.95 | [-1.63, 2.10] | 0.25 | 0.805
Male, yes, -40 | Male, no, 41-64 | 1.18 | 1.02 | [-0.83, 3.19] | 1.15 | 0.248
Female, yes, -40 | Male, no, 41-64 | 1.10 | 0.90 | [-0.66, 2.86] | 1.22 | 0.221
Male, no, -40 | Female, no, 41-64 | 1.49 | 0.94 | [-0.35, 3.32] | 1.59 | 0.112
Female, no, -40 | Female, no, 41-64 | 1.42 | 0.76 | [-0.08, 2.91] | 1.86 | 0.063
Male, yes, -40 | Female, no, 41-64 | 2.37 | 0.85 | [ 0.70, 4.04] | 2.78 | 0.005
Female, yes, -40 | Female, no, 41-64 | 2.28 | 0.69 | [ 0.92, 3.64] | 3.29 | 0.001
Male, no, 41-64 | Female, no, 41-64 | 1.18 | 0.74 | [-0.26, 2.63] | 1.60 | 0.109
Male, no, -40 | Male, yes, 41-64 | -0.23 | 1.04 | [-2.26, 1.81] | -0.22 | 0.827
Female, no, -40 | Male, yes, 41-64 | -0.30 | 0.88 | [-2.03, 1.44] | -0.33 | 0.738
Male, yes, -40 | Male, yes, 41-64 | 0.65 | 0.96 | [-1.23, 2.54] | 0.68 | 0.498
Female, yes, -40 | Male, yes, 41-64 | 0.57 | 0.83 | [-1.05, 2.19] | 0.69 | 0.492
Male, no, 41-64 | Male, yes, 41-64 | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
Female, no, 41-64 | Male, yes, 41-64 | -1.71 | 0.65 | [-2.98, -0.44] | -2.65 | 0.008
Male, no, -40 | Female, yes, 41-64 | 1.15 | 0.94 | [-0.70, 2.99] | 1.22 | 0.224
Female, no, -40 | Female, yes, 41-64 | 1.08 | 0.77 | [-0.43, 2.59] | 1.40 | 0.162
Male, yes, -40 | Female, yes, 41-64 | 2.02 | 0.86 | [ 0.34, 3.71] | 2.36 | 0.018
Female, yes, -40 | Female, yes, 41-64 | 1.94 | 0.70 | [ 0.56, 3.32] | 2.76 | 0.006
Male, no, 41-64 | Female, yes, 41-64 | 0.84 | 0.74 | [-0.62, 2.30] | 1.13 | 0.258
Female, no, 41-64 | Female, yes, 41-64 | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
Male, yes, 41-64 | Female, yes, 41-64 | 1.37 | 0.66 | [ 0.09, 2.66] | 2.09 | 0.036
Male, no, -40 | Male, no, 65++ | 0.83 | 1.08 | [-1.29, 2.94] | 0.77 | 0.443
Female, no, -40 | Male, no, 65++ | 0.76 | 0.93 | [-1.07, 2.58] | 0.82 | 0.415
Male, yes, -40 | Male, no, 65++ | 1.71 | 1.01 | [-0.26, 3.68] | 1.70 | 0.090
Female, yes, -40 | Male, no, 65++ | 1.62 | 0.88 | [-0.10, 3.34] | 1.85 | 0.064
Male, no, 41-64 | Male, no, 65++ | 0.52 | 0.91 | [-1.26, 2.31] | 0.57 | 0.565
Female, no, 41-64 | Male, no, 65++ | -0.66 | 0.71 | [-2.05, 0.73] | -0.93 | 0.354
Male, yes, 41-64 | Male, no, 65++ | 1.06 | 0.84 | [-0.59, 2.70] | 1.26 | 0.209
Female, yes, 41-64 | Male, no, 65++ | -0.32 | 0.72 | [-1.72, 1.09] | -0.44 | 0.658
Male, no, -40 | Female, no, 65++ | 1.71 | 0.98 | [-0.21, 3.63] | 1.75 | 0.081
Female, no, -40 | Female, no, 65++ | 1.64 | 0.81 | [ 0.04, 3.23] | 2.01 | 0.044
Male, yes, -40 | Female, no, 65++ | 2.59 | 0.90 | [ 0.83, 4.35] | 2.88 | 0.004
Female, yes, -40 | Female, no, 65++ | 2.50 | 0.75 | [ 1.03, 3.97] | 3.34 | < .001
Male, no, 41-64 | Female, no, 65++ | 1.40 | 0.79 | [-0.14, 2.95] | 1.78 | 0.076
Female, no, 41-64 | Female, no, 65++ | 0.22 | 0.55 | [-0.85, 1.29] | 0.40 | 0.686
Male, yes, 41-64 | Female, no, 65++ | 1.94 | 0.71 | [ 0.55, 3.32] | 2.74 | 0.006
Female, yes, 41-64 | Female, no, 65++ | 0.56 | 0.56 | [-0.53, 1.65] | 1.01 | 0.312
Male, no, 65++ | Female, no, 65++ | 0.88 | 0.77 | [-0.62, 2.38] | 1.15 | 0.250
Male, no, -40 | Male, yes, 65++ | -0.10 | 1.39 | [-2.83, 2.63] | -0.07 | 0.942
Female, no, -40 | Male, yes, 65++ | -0.17 | 1.28 | [-2.69, 2.35] | -0.13 | 0.895
Male, yes, -40 | Male, yes, 65++ | 0.78 | 1.34 | [-1.85, 3.40] | 0.58 | 0.561
Female, yes, -40 | Male, yes, 65++ | 0.69 | 1.25 | [-1.75, 3.13] | 0.56 | 0.577
Male, no, 41-64 | Male, yes, 65++ | -0.41 | 1.27 | [-2.89, 2.08] | -0.32 | 0.750
Female, no, 41-64 | Male, yes, 65++ | -1.59 | 1.13 | [-3.81, 0.64] | -1.40 | 0.162
Male, yes, 41-64 | Male, yes, 65++ | 0.13 | 1.22 | [-2.26, 2.52] | 0.10 | 0.917
Female, yes, 41-64 | Male, yes, 65++ | -1.25 | 1.14 | [-3.48, 0.99] | -1.09 | 0.274
Male, no, 65++ | Male, yes, 65++ | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
Female, no, 65++ | Male, yes, 65++ | -1.81 | 1.17 | [-4.10, 0.48] | -1.55 | 0.122
Male, no, -40 | Female, yes, 65++ | 0.39 | 1.36 | [-2.27, 3.05] | 0.29 | 0.775
Female, no, -40 | Female, yes, 65++ | 0.32 | 1.25 | [-2.12, 2.76] | 0.26 | 0.798
Male, yes, -40 | Female, yes, 65++ | 1.27 | 1.30 | [-1.28, 3.82] | 0.97 | 0.330
Female, yes, -40 | Female, yes, 65++ | 1.18 | 1.20 | [-1.18, 3.54] | 0.98 | 0.326
Male, no, 41-64 | Female, yes, 65++ | 0.08 | 1.23 | [-2.33, 2.49] | 0.07 | 0.946
Female, no, 41-64 | Female, yes, 65++ | -1.10 | 1.09 | [-3.23, 1.04] | -1.01 | 0.313
Male, yes, 41-64 | Female, yes, 65++ | 0.62 | 1.18 | [-1.69, 2.92] | 0.52 | 0.601
Female, yes, 41-64 | Female, yes, 65++ | -0.76 | 1.09 | [-2.90, 1.39] | -0.69 | 0.489
Male, no, 65++ | Female, yes, 65++ | -0.44 | 1.21 | [-2.82, 1.94] | -0.36 | 0.717
Female, no, 65++ | Female, yes, 65++ | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
Male, yes, 65++ | Female, yes, 65++ | 0.49 | 1.50 | [-2.45, 3.43] | 0.33 | 0.745
Variable predicted: qol
Predictors contrasted: gender, employed, age
Code
print(estimate_contrasts(estim, contrast = c("gender", "employed"), by = "age"),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
age | Parameter | Difference | SE | 95% CI | Statistic | p
--------------------------------------------------------------------------------------
-40 | Male no -Female no | 0.07 | 1.11 | [-2.11, 2.25] | 0.06 | 0.951
-40 | Male no -Male yes | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
-40 | Female no -Male yes | -0.95 | 1.04 | [-2.99, 1.10] | -0.91 | 0.364
-40 | Male no -Female yes | -0.79 | 1.07 | [-2.89, 1.30] | -0.74 | 0.457
-40 | Female no -Female yes | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
-40 | Male yes -Female yes | 0.08 | 0.99 | [-1.86, 2.03] | 0.08 | 0.932
41-64 | Male no -Female no | 1.18 | 0.74 | [-0.26, 2.63] | 1.60 | 0.109
41-64 | Male no -Male yes | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
41-64 | Female no -Male yes | -1.71 | 0.65 | [-2.98, -0.44] | -2.65 | 0.008
41-64 | Male no -Female yes | 0.84 | 0.74 | [-0.62, 2.30] | 1.13 | 0.258
41-64 | Female no -Female yes | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
41-64 | Male yes -Female yes | 1.37 | 0.66 | [ 0.09, 2.66] | 2.09 | 0.036
65+ | Male no -Female no | 0.88 | 0.77 | [-0.62, 2.38] | 1.15 | 0.250
65+ | Male no -Male yes | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
65+ | Female no -Male yes | -1.81 | 1.17 | [-4.10, 0.48] | -1.55 | 0.122
65+ | Male no -Female yes | -0.44 | 1.21 | [-2.82, 1.94] | -0.36 | 0.717
65+ | Female no -Female yes | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
65+ | Male yes -Female yes | 0.49 | 1.50 | [-2.45, 3.43] | 0.33 | 0.745
Variable predicted: qol
Predictors contrasted: gender, employed
Code
print(estimate_contrasts(estim, contrast = "employed", by = c("age", "gender")),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
Level1 | Level2 | age | gender | Difference | SE | 95% CI | Statistic | p
----------------------------------------------------------------------------------------
no | yes | -40 | Female | -0.86 | 0.92 | [-2.67, 0.94] | -0.94 | 0.348
no | yes | -40 | Male | -0.88 | 1.18 | [-3.18, 1.43] | -0.75 | 0.455
no | yes | 41-64 | Female | -0.34 | 0.48 | [-1.28, 0.60] | -0.71 | 0.476
no | yes | 41-64 | Male | -0.53 | 0.86 | [-2.22, 1.16] | -0.62 | 0.538
no | yes | 65+ | Female | -1.32 | 1.13 | [-3.53, 0.89] | -1.17 | 0.241
no | yes | 65+ | Male | -0.93 | 1.25 | [-3.39, 1.53] | -0.74 | 0.459
Variable predicted: qol
Predictors contrasted: employed
estimate_contrasts - Random Effects Levels, interaction
Code
print(estimate_contrasts(estim, contrast = c("age", "employed"), comparison = "interaction"),
zap_small = TRUE, table_width = Inf)
Output
Model-based Contrasts Analysis
age | employed | Difference | SE | 95% CI | Statistic | p
-------------------------------------------------------------------------------
-40-41-64 | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
-40-65+ | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
41-64-65+ | no and yes | 0.00 | 2.21 | [-4.34, 4.34] | 0.00 | > .999
Variable predicted: qol
Predictors contrasted: age, employed
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