This is a wrapper for emmeans::joint_tests() that provides an easy way to
specify which simple effects we wish to test, and within what variable(s).
Arguments
- model
The model.
- effect
The name of the required simple effect. e.g.,
"A"for a simple effect of A, or"A:B"for a simple A-by-B interaction.- inside
A vector of the name(s) of the variable(s) within whose levels the
effectwill be tested. Can also be the name of an interaction (e.g.,"B:C"). If not specified, will use all the terms not ineffect.- ...
Passed to
emmeans::joint_tests(), e.g.,cov.reduce,at, etc.
Examples
library(afex)
#> ************
#> Welcome to afex. For support visit: http://afex.singmann.science/
#> - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
#> - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
#> - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
#> - NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
#> - Get and set global package options with: afex_options()
#> - Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
#> - For example analyses see: browseVignettes("afex")
#> ************
#>
#> Attaching package: ‘afex’
#> The following object is masked from ‘package:lme4’:
#>
#> lmer
data(obk.long, package = "afex")
A <- aov_car(value ~ treatment * gender + Error(id / (phase * hour)),
data = obk.long
)
#> Contrasts set to contr.sum for the following variables: treatment, gender
simple_effects(A, effect = "treatment")
#> phase = fup, hour = X1:
#> gender df1 df2 F.ratio p.value
#> F 2 10 2.510 0.1308
#> M 2 10 5.561 0.0238
#>
#> phase = post, hour = X1:
#> gender df1 df2 F.ratio p.value
#> F 2 10 1.627 0.2446
#> M 2 10 6.363 0.0165
#>
#> phase = pre, hour = X1:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.458 0.6454
#> M 2 10 1.737 0.2251
#>
#> phase = fup, hour = X2:
#> gender df1 df2 F.ratio p.value
#> F 2 10 9.720 0.0045
#> M 2 10 13.600 0.0014
#>
#> phase = post, hour = X2:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.770 0.4884
#> M 2 10 4.717 0.0361
#>
#> phase = pre, hour = X2:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.166 0.8495
#> M 2 10 1.278 0.3205
#>
#> phase = fup, hour = X3:
#> gender df1 df2 F.ratio p.value
#> F 2 10 2.051 0.1793
#> M 2 10 6.105 0.0185
#>
#> phase = post, hour = X3:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.417 0.6702
#> M 2 10 2.824 0.1066
#>
#> phase = pre, hour = X3:
#> gender df1 df2 F.ratio p.value
#> F 2 10 1.087 0.3741
#> M 2 10 0.883 0.4433
#>
#> phase = fup, hour = X4:
#> gender df1 df2 F.ratio p.value
#> F 2 10 5.994 0.0195
#> M 2 10 5.000 0.0312
#>
#> phase = post, hour = X4:
#> gender df1 df2 F.ratio p.value
#> F 2 10 1.234 0.3319
#> M 2 10 5.524 0.0242
#>
#> phase = pre, hour = X4:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.000 1.0000
#> M 2 10 0.831 0.4635
#>
#> phase = fup, hour = X5:
#> gender df1 df2 F.ratio p.value
#> F 2 10 1.401 0.2908
#> M 2 10 2.534 0.1287
#>
#> phase = post, hour = X5:
#> gender df1 df2 F.ratio p.value
#> F 2 10 1.761 0.2213
#> M 2 10 5.282 0.0272
#>
#> phase = pre, hour = X5:
#> gender df1 df2 F.ratio p.value
#> F 2 10 0.185 0.8339
#> M 2 10 2.332 0.1475
#>
#> Omnibus test for simple-simple-simple effect of treatment.
#>
simple_effects(A, effect = "treatment:phase")
#> hour = X1:
#> gender df1 df2 F.ratio p.value
#> F 4 10 3.403 0.0529
#> M 4 10 1.034 0.4363
#>
#> hour = X2:
#> gender df1 df2 F.ratio p.value
#> F 4 10 3.346 0.0552
#> M 4 10 2.443 0.1149
#>
#> hour = X3:
#> gender df1 df2 F.ratio p.value
#> F 4 10 2.687 0.0934
#> M 4 10 1.408 0.3001
#>
#> hour = X4:
#> gender df1 df2 F.ratio p.value
#> F 4 10 3.155 0.0640
#> M 4 10 0.758 0.5753
#>
#> hour = X5:
#> gender df1 df2 F.ratio p.value
#> F 4 10 0.686 0.6178
#> M 4 10 2.044 0.1638
#>
#> Omnibus test for simple-simple effect of treatment:phase.
#>
simple_effects(A, effect = "phase", inside = "treatment")
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 2.06567e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 3.67534e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.40043e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.03889e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 9.86756e-20
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.37789e-18
#> treatment df1 df2 F.ratio p.value
#> control 2 10 0.142 0.8693
#> A 2 10 9.119 0.0056
#> B 2 10 28.272 0.0001
#>
#> Omnibus test for simple effect of phase.
#>
simple_effects(A, effect = "phase", inside = c("treatment", "gender"))
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 5.36978e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.10019e-17
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.21249e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.43614e-17
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 8.60451e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 8.61359e-20
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.27793e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 7.11874e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 6.48829e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 2.66086e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.14801e-17
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.44236e-18
#> gender = F:
#> treatment df1 df2 F.ratio p.value
#> control 2 10 0.416 0.6708
#> A 2 10 4.079 0.0507
#> B 2 10 18.728 0.0004
#>
#> gender = M:
#> treatment df1 df2 F.ratio p.value
#> control 2 10 1.823 0.2114
#> A 2 10 5.248 0.0276
#> B 2 10 11.015 0.0030
#>
#> Omnibus test for simple-simple effect of phase.
#>
# simple_effects(A, effect = "phase", inside = "treatment:gender") # same
simple_effects(A,
effect = "phase", inside = c("treatment", "gender"),
at = list(gender = "F")
)
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 5.36978e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.10019e-17
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.21249e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.27793e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 7.11874e-18
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 6.48829e-18
#> gender = F:
#> treatment df1 df2 F.ratio p.value
#> control 2 10 0.416 0.6708
#> A 2 10 4.079 0.0507
#> B 2 10 18.728 0.0004
#>
#> Omnibus test for simple-simple effect of phase.
#>
simple_effects(A, effect = "phase:treatment", inside = "gender")
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 2.29602e-20
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 1.48356e-19
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 4.20904e-20
#> Error in solve.default(zcov, z) :
#> system is computationally singular: reciprocal condition number = 4.86218e-20
#> gender df1 df2 F.ratio p.value
#> F 4 10 4.498 0.0245
#> M 4 10 1.930 0.1819
#>
#> Omnibus test for simple effect of phase:treatment.
#>