Mixing
Planned contrasts using ezANOVA output in R
I've been looking into using planned contrasts as opposed to post-hoc t-tests. I typically use ezANOVA (Type III ANOVA) but it seems that conducting planned contrasts using ezANOVA is not currently catered for.
aov() on the other hand is a Type I ANOVA (I don't want to get into a debate about which type is best for which type of design). It is straight forward to conduct planned contrasts using aov() (for between group designs) but I want to conduct a Type III ANOVA in a repeated measures and to be frank ezANOVA has a much more user friendly output.
Bearing in mind ezANOVA has the option to include return_aov = TRUE does anyone know of a way to use the information provided by ezANOVA to conduct planned contrasts?
Note: return_aov = TRUE allows access to the output of aov via something along these lines:
summary.lm(ModelName$aov$'Participant:IndependentVariable1')
Participant above is an example variable added to wid in ezANOVA:
wid = .(Participant)
summary.lm() is typically used when presenting the results of planned contrasts in aov, granted between groups ANOVAs as opposed to repeated measures.
I'm particularly interested in using the output to conduct planned contrasts for repeated measures ANOVA.
BOUNTY GOALS
Goals I would like to achieve from this bounty:
1) Use the output of ezANOVA to conduct planned contrasts in a repeated measures ANOVA.
1A) Use the output of ezANOVA to conduct planned contrasts on between subjects ANOVA (this one should be relatively easy & therefore not a requisite to claim the bounty.)
Any dummy data should suffice but here is a reminder of the format for ezANOVA repeated measures ANOVA:
ModelName <- ezANOVA(
data = DataSet,
dv = .(DependentVariable),
wid = .(Participant),
within = .(IndependentVariable1, IndependentVariable2),
type=3,
detailed = TRUE,
return_aov = TRUE)
This is a Related Question with reproducible data and code which can be used in relation to this problem.
You can find a PDF here giving some background on planned contrasts and what they do.
r anova posthoc
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
add a comment |
I've been looking into using planned contrasts as opposed to post-hoc t-tests. I typically use ezANOVA (Type III ANOVA) but it seems that conducting planned contrasts using ezANOVA is not currently catered for.
aov() on the other hand is a Type I ANOVA (I don't want to get into a debate about which type is best for which type of design). It is straight forward to conduct planned contrasts using aov() (for between group designs) but I want to conduct a Type III ANOVA in a repeated measures and to be frank ezANOVA has a much more user friendly output.
Bearing in mind ezANOVA has the option to include return_aov = TRUE does anyone know of a way to use the information provided by ezANOVA to conduct planned contrasts?
Note: return_aov = TRUE allows access to the output of aov via something along these lines:
summary.lm(ModelName$aov$'Participant:IndependentVariable1')
Participant above is an example variable added to wid in ezANOVA:
wid = .(Participant)
summary.lm() is typically used when presenting the results of planned contrasts in aov, granted between groups ANOVAs as opposed to repeated measures.
I'm particularly interested in using the output to conduct planned contrasts for repeated measures ANOVA.
BOUNTY GOALS
Goals I would like to achieve from this bounty:
1) Use the output of ezANOVA to conduct planned contrasts in a repeated measures ANOVA.
1A) Use the output of ezANOVA to conduct planned contrasts on between subjects ANOVA (this one should be relatively easy & therefore not a requisite to claim the bounty.)
Any dummy data should suffice but here is a reminder of the format for ezANOVA repeated measures ANOVA:
ModelName <- ezANOVA(
data = DataSet,
dv = .(DependentVariable),
wid = .(Participant),
within = .(IndependentVariable1, IndependentVariable2),
type=3,
detailed = TRUE,
return_aov = TRUE)
This is a Related Question with reproducible data and code which can be used in relation to this problem.
You can find a PDF here giving some background on planned contrasts and what they do.
r anova posthoc
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
According to a statement by the package developer made in 2012 or so, there is no way of usingezfor planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...
– coffeinjunky
Mar 11 '16 at 18:00
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06
add a comment |
I've been looking into using planned contrasts as opposed to post-hoc t-tests. I typically use ezANOVA (Type III ANOVA) but it seems that conducting planned contrasts using ezANOVA is not currently catered for.
aov() on the other hand is a Type I ANOVA (I don't want to get into a debate about which type is best for which type of design). It is straight forward to conduct planned contrasts using aov() (for between group designs) but I want to conduct a Type III ANOVA in a repeated measures and to be frank ezANOVA has a much more user friendly output.
Bearing in mind ezANOVA has the option to include return_aov = TRUE does anyone know of a way to use the information provided by ezANOVA to conduct planned contrasts?
Note: return_aov = TRUE allows access to the output of aov via something along these lines:
summary.lm(ModelName$aov$'Participant:IndependentVariable1')
Participant above is an example variable added to wid in ezANOVA:
wid = .(Participant)
summary.lm() is typically used when presenting the results of planned contrasts in aov, granted between groups ANOVAs as opposed to repeated measures.
I'm particularly interested in using the output to conduct planned contrasts for repeated measures ANOVA.
BOUNTY GOALS
Goals I would like to achieve from this bounty:
1) Use the output of ezANOVA to conduct planned contrasts in a repeated measures ANOVA.
1A) Use the output of ezANOVA to conduct planned contrasts on between subjects ANOVA (this one should be relatively easy & therefore not a requisite to claim the bounty.)
Any dummy data should suffice but here is a reminder of the format for ezANOVA repeated measures ANOVA:
ModelName <- ezANOVA(
data = DataSet,
dv = .(DependentVariable),
wid = .(Participant),
within = .(IndependentVariable1, IndependentVariable2),
type=3,
detailed = TRUE,
return_aov = TRUE)
This is a Related Question with reproducible data and code which can be used in relation to this problem.
You can find a PDF here giving some background on planned contrasts and what they do.
r anova posthoc
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
I've been looking into using planned contrasts as opposed to post-hoc t-tests. I typically use ezANOVA (Type III ANOVA) but it seems that conducting planned contrasts using ezANOVA is not currently catered for.
aov() on the other hand is a Type I ANOVA (I don't want to get into a debate about which type is best for which type of design). It is straight forward to conduct planned contrasts using aov() (for between group designs) but I want to conduct a Type III ANOVA in a repeated measures and to be frank ezANOVA has a much more user friendly output.
Bearing in mind ezANOVA has the option to include return_aov = TRUE does anyone know of a way to use the information provided by ezANOVA to conduct planned contrasts?
Note: return_aov = TRUE allows access to the output of aov via something along these lines:
summary.lm(ModelName$aov$'Participant:IndependentVariable1')
Participant above is an example variable added to wid in ezANOVA:
wid = .(Participant)
summary.lm() is typically used when presenting the results of planned contrasts in aov, granted between groups ANOVAs as opposed to repeated measures.
I'm particularly interested in using the output to conduct planned contrasts for repeated measures ANOVA.
BOUNTY GOALS
Goals I would like to achieve from this bounty:
1) Use the output of ezANOVA to conduct planned contrasts in a repeated measures ANOVA.
1A) Use the output of ezANOVA to conduct planned contrasts on between subjects ANOVA (this one should be relatively easy & therefore not a requisite to claim the bounty.)
Any dummy data should suffice but here is a reminder of the format for ezANOVA repeated measures ANOVA:
ModelName <- ezANOVA(
data = DataSet,
dv = .(DependentVariable),
wid = .(Participant),
within = .(IndependentVariable1, IndependentVariable2),
type=3,
detailed = TRUE,
return_aov = TRUE)
This is a Related Question with reproducible data and code which can be used in relation to this problem.
You can find a PDF here giving some background on planned contrasts and what they do.
r anova posthoc
r anova posthoc
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
edited Dec 13 '17 at 5:04
Cœur
19.1k9114155
19.1k9114155
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
asked Mar 9 '16 at 12:02
DocconcoctDocconcoct
1,41721735
1,41721735
According to a statement by the package developer made in 2012 or so, there is no way of usingezfor planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...
– coffeinjunky
Mar 11 '16 at 18:00
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06
add a comment |
According to a statement by the package developer made in 2012 or so, there is no way of usingezfor planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...
– coffeinjunky
Mar 11 '16 at 18:00
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06
According to a statement by the package developer made in 2012 or so, there is no way of using
ez for planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...– coffeinjunky
Mar 11 '16 at 18:00
According to a statement by the package developer made in 2012 or so, there is no way of using
ez for planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...– coffeinjunky
Mar 11 '16 at 18:00
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06
add a comment |
1 Answer
1
active
oldest
votes
The emmeans package provides the appropriate functionality to calculate custom contrasts/arbitrary linear functions of the estimates marginal means (EMMs) for aov and aovlist objects (see here for a full list of supported models).
In the following I use the ANT data set which comes with the ez package.
First we set up a mixed factorial ANOVA using ezANOVA. Note that one needs to set orthogonal contrasts in order to get meaningful Type-III tests (see, e.g., John Fox' answer here).
library("ez")
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
data(ANT)
rt_anova <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
We can then calculate the EMMs for, say all group-flank combinations.
emm <- emmeans(rt_anova$aov, ~ group * flank)
emm
## group flank emmean SE df lower.CL upper.CL
## Control Neutral 381.5546 1.735392 53.97 378.0753 385.0339
## Treatment Neutral 379.9286 1.735392 53.97 376.4493 383.4079
## Control Congruent 381.6363 1.735392 53.97 378.1570 385.1155
## Treatment Congruent 379.7520 1.735392 53.97 376.2727 383.2313
## Control Incongruent 466.6770 1.735392 53.97 463.1977 470.1563
## Treatment Incongruent 452.2352 1.735392 53.97 448.7559 455.7145
Now it is easy to calculate all pairwise comparisons or any desired contrast on these EMMs.
See also this book chapter and my answer here if you need a bit more detail on how one can derive the contrasts weights from the hypotheses.
# all pairwise comparisons
pairs(emm, adjust = "Holm")
## contrast estimate SE df t.ratio p.value
## Control,Neutral - Treatment,Neutral 1.62594836 2.454215 53.97 0.663 1.0000
## Control,Neutral - Control,Congruent -0.08167403 2.473955 36.00 -0.033 1.0000
## Control,Neutral - Treatment,Congruent 1.80259257 2.454215 53.97 0.734 1.0000
## Control,Neutral - Control,Incongruent -85.12239797 2.473955 36.00 -34.407 <.0001
## Control,Neutral - Treatment,Incongruent -70.68062093 2.454215 53.97 -28.800 <.0001
## Treatment,Neutral - Control,Congruent -1.70762239 2.454215 53.97 -0.696 1.0000
## Treatment,Neutral - Treatment,Congruent 0.17664421 2.473955 36.00 0.071 1.0000
## Treatment,Neutral - Control,Incongruent -86.74834633 2.454215 53.97 -35.347 <.0001
## Treatment,Neutral - Treatment,Incongruent -72.30656929 2.473955 36.00 -29.227 <.0001
## Control,Congruent - Treatment,Congruent 1.88426660 2.454215 53.97 0.768 1.0000
## Control,Congruent - Control,Incongruent -85.04072394 2.473955 36.00 -34.374 <.0001
## Control,Congruent - Treatment,Incongruent -70.59894690 2.454215 53.97 -28.766 <.0001
## Treatment,Congruent - Control,Incongruent -86.92499054 2.454215 53.97 -35.419 <.0001
## Treatment,Congruent - Treatment,Incongruent -72.48321351 2.473955 36.00 -29.299 <.0001
## Control,Incongruent - Treatment,Incongruent 14.44177704 2.454215 53.97 5.884 <.0001
##
## Results are averaged over the levels of: cue
## P value adjustment: holm method for 15 tests
# custom contrasts
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0), # reproduces first pairwise comparison
# emmean of row 1 - (emmean of row 1 + emmean of row 2) / 2; see EMMs table
# 381.5546 - (379.9286 + 381.6363) / 2
c2 = c(1, -0.5, -0.5, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 1.6259484 2.454215 53.97 0.663 0.5105
## c2 0.7721372 2.136825 43.84 0.361 0.7196
The same applies for purely within-subjects ANOVAs or between-subjects ANOVAs.
# within-subjects ANOVA
rt_anova_wi <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
type = 3,
return_aov = TRUE)
emm <- emmeans(rt_anova_wi$aov, ~ cue * flank)
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
c2 = c(1, -0.5, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 47.31005 3.802857 170.34 12.441 <.0001
## c2 50.35320 3.293371 170.34 15.289 <.0001
# between-subjects ANOVA
rt_anova_bw <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within_full = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
emm_bw <- emmeans(rt_anova_bw$aov, ~ group)
# custom linear function
contrast(
emm_bw,
list(c1 = c(2/3, 1/2))
)
## contrast estimate SE df t.ratio p.value
## c1 475.2899 0.8213448 18 578.673 <.0001
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
add a comment |
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The emmeans package provides the appropriate functionality to calculate custom contrasts/arbitrary linear functions of the estimates marginal means (EMMs) for aov and aovlist objects (see here for a full list of supported models).
In the following I use the ANT data set which comes with the ez package.
First we set up a mixed factorial ANOVA using ezANOVA. Note that one needs to set orthogonal contrasts in order to get meaningful Type-III tests (see, e.g., John Fox' answer here).
library("ez")
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
data(ANT)
rt_anova <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
We can then calculate the EMMs for, say all group-flank combinations.
emm <- emmeans(rt_anova$aov, ~ group * flank)
emm
## group flank emmean SE df lower.CL upper.CL
## Control Neutral 381.5546 1.735392 53.97 378.0753 385.0339
## Treatment Neutral 379.9286 1.735392 53.97 376.4493 383.4079
## Control Congruent 381.6363 1.735392 53.97 378.1570 385.1155
## Treatment Congruent 379.7520 1.735392 53.97 376.2727 383.2313
## Control Incongruent 466.6770 1.735392 53.97 463.1977 470.1563
## Treatment Incongruent 452.2352 1.735392 53.97 448.7559 455.7145
Now it is easy to calculate all pairwise comparisons or any desired contrast on these EMMs.
See also this book chapter and my answer here if you need a bit more detail on how one can derive the contrasts weights from the hypotheses.
# all pairwise comparisons
pairs(emm, adjust = "Holm")
## contrast estimate SE df t.ratio p.value
## Control,Neutral - Treatment,Neutral 1.62594836 2.454215 53.97 0.663 1.0000
## Control,Neutral - Control,Congruent -0.08167403 2.473955 36.00 -0.033 1.0000
## Control,Neutral - Treatment,Congruent 1.80259257 2.454215 53.97 0.734 1.0000
## Control,Neutral - Control,Incongruent -85.12239797 2.473955 36.00 -34.407 <.0001
## Control,Neutral - Treatment,Incongruent -70.68062093 2.454215 53.97 -28.800 <.0001
## Treatment,Neutral - Control,Congruent -1.70762239 2.454215 53.97 -0.696 1.0000
## Treatment,Neutral - Treatment,Congruent 0.17664421 2.473955 36.00 0.071 1.0000
## Treatment,Neutral - Control,Incongruent -86.74834633 2.454215 53.97 -35.347 <.0001
## Treatment,Neutral - Treatment,Incongruent -72.30656929 2.473955 36.00 -29.227 <.0001
## Control,Congruent - Treatment,Congruent 1.88426660 2.454215 53.97 0.768 1.0000
## Control,Congruent - Control,Incongruent -85.04072394 2.473955 36.00 -34.374 <.0001
## Control,Congruent - Treatment,Incongruent -70.59894690 2.454215 53.97 -28.766 <.0001
## Treatment,Congruent - Control,Incongruent -86.92499054 2.454215 53.97 -35.419 <.0001
## Treatment,Congruent - Treatment,Incongruent -72.48321351 2.473955 36.00 -29.299 <.0001
## Control,Incongruent - Treatment,Incongruent 14.44177704 2.454215 53.97 5.884 <.0001
##
## Results are averaged over the levels of: cue
## P value adjustment: holm method for 15 tests
# custom contrasts
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0), # reproduces first pairwise comparison
# emmean of row 1 - (emmean of row 1 + emmean of row 2) / 2; see EMMs table
# 381.5546 - (379.9286 + 381.6363) / 2
c2 = c(1, -0.5, -0.5, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 1.6259484 2.454215 53.97 0.663 0.5105
## c2 0.7721372 2.136825 43.84 0.361 0.7196
The same applies for purely within-subjects ANOVAs or between-subjects ANOVAs.
# within-subjects ANOVA
rt_anova_wi <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
type = 3,
return_aov = TRUE)
emm <- emmeans(rt_anova_wi$aov, ~ cue * flank)
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
c2 = c(1, -0.5, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 47.31005 3.802857 170.34 12.441 <.0001
## c2 50.35320 3.293371 170.34 15.289 <.0001
# between-subjects ANOVA
rt_anova_bw <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within_full = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
emm_bw <- emmeans(rt_anova_bw$aov, ~ group)
# custom linear function
contrast(
emm_bw,
list(c1 = c(2/3, 1/2))
)
## contrast estimate SE df t.ratio p.value
## c1 475.2899 0.8213448 18 578.673 <.0001
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
add a comment |
The emmeans package provides the appropriate functionality to calculate custom contrasts/arbitrary linear functions of the estimates marginal means (EMMs) for aov and aovlist objects (see here for a full list of supported models).
In the following I use the ANT data set which comes with the ez package.
First we set up a mixed factorial ANOVA using ezANOVA. Note that one needs to set orthogonal contrasts in order to get meaningful Type-III tests (see, e.g., John Fox' answer here).
library("ez")
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
data(ANT)
rt_anova <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
We can then calculate the EMMs for, say all group-flank combinations.
emm <- emmeans(rt_anova$aov, ~ group * flank)
emm
## group flank emmean SE df lower.CL upper.CL
## Control Neutral 381.5546 1.735392 53.97 378.0753 385.0339
## Treatment Neutral 379.9286 1.735392 53.97 376.4493 383.4079
## Control Congruent 381.6363 1.735392 53.97 378.1570 385.1155
## Treatment Congruent 379.7520 1.735392 53.97 376.2727 383.2313
## Control Incongruent 466.6770 1.735392 53.97 463.1977 470.1563
## Treatment Incongruent 452.2352 1.735392 53.97 448.7559 455.7145
Now it is easy to calculate all pairwise comparisons or any desired contrast on these EMMs.
See also this book chapter and my answer here if you need a bit more detail on how one can derive the contrasts weights from the hypotheses.
# all pairwise comparisons
pairs(emm, adjust = "Holm")
## contrast estimate SE df t.ratio p.value
## Control,Neutral - Treatment,Neutral 1.62594836 2.454215 53.97 0.663 1.0000
## Control,Neutral - Control,Congruent -0.08167403 2.473955 36.00 -0.033 1.0000
## Control,Neutral - Treatment,Congruent 1.80259257 2.454215 53.97 0.734 1.0000
## Control,Neutral - Control,Incongruent -85.12239797 2.473955 36.00 -34.407 <.0001
## Control,Neutral - Treatment,Incongruent -70.68062093 2.454215 53.97 -28.800 <.0001
## Treatment,Neutral - Control,Congruent -1.70762239 2.454215 53.97 -0.696 1.0000
## Treatment,Neutral - Treatment,Congruent 0.17664421 2.473955 36.00 0.071 1.0000
## Treatment,Neutral - Control,Incongruent -86.74834633 2.454215 53.97 -35.347 <.0001
## Treatment,Neutral - Treatment,Incongruent -72.30656929 2.473955 36.00 -29.227 <.0001
## Control,Congruent - Treatment,Congruent 1.88426660 2.454215 53.97 0.768 1.0000
## Control,Congruent - Control,Incongruent -85.04072394 2.473955 36.00 -34.374 <.0001
## Control,Congruent - Treatment,Incongruent -70.59894690 2.454215 53.97 -28.766 <.0001
## Treatment,Congruent - Control,Incongruent -86.92499054 2.454215 53.97 -35.419 <.0001
## Treatment,Congruent - Treatment,Incongruent -72.48321351 2.473955 36.00 -29.299 <.0001
## Control,Incongruent - Treatment,Incongruent 14.44177704 2.454215 53.97 5.884 <.0001
##
## Results are averaged over the levels of: cue
## P value adjustment: holm method for 15 tests
# custom contrasts
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0), # reproduces first pairwise comparison
# emmean of row 1 - (emmean of row 1 + emmean of row 2) / 2; see EMMs table
# 381.5546 - (379.9286 + 381.6363) / 2
c2 = c(1, -0.5, -0.5, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 1.6259484 2.454215 53.97 0.663 0.5105
## c2 0.7721372 2.136825 43.84 0.361 0.7196
The same applies for purely within-subjects ANOVAs or between-subjects ANOVAs.
# within-subjects ANOVA
rt_anova_wi <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
type = 3,
return_aov = TRUE)
emm <- emmeans(rt_anova_wi$aov, ~ cue * flank)
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
c2 = c(1, -0.5, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 47.31005 3.802857 170.34 12.441 <.0001
## c2 50.35320 3.293371 170.34 15.289 <.0001
# between-subjects ANOVA
rt_anova_bw <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within_full = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
emm_bw <- emmeans(rt_anova_bw$aov, ~ group)
# custom linear function
contrast(
emm_bw,
list(c1 = c(2/3, 1/2))
)
## contrast estimate SE df t.ratio p.value
## c1 475.2899 0.8213448 18 578.673 <.0001
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
add a comment |
The emmeans package provides the appropriate functionality to calculate custom contrasts/arbitrary linear functions of the estimates marginal means (EMMs) for aov and aovlist objects (see here for a full list of supported models).
In the following I use the ANT data set which comes with the ez package.
First we set up a mixed factorial ANOVA using ezANOVA. Note that one needs to set orthogonal contrasts in order to get meaningful Type-III tests (see, e.g., John Fox' answer here).
library("ez")
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
data(ANT)
rt_anova <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
We can then calculate the EMMs for, say all group-flank combinations.
emm <- emmeans(rt_anova$aov, ~ group * flank)
emm
## group flank emmean SE df lower.CL upper.CL
## Control Neutral 381.5546 1.735392 53.97 378.0753 385.0339
## Treatment Neutral 379.9286 1.735392 53.97 376.4493 383.4079
## Control Congruent 381.6363 1.735392 53.97 378.1570 385.1155
## Treatment Congruent 379.7520 1.735392 53.97 376.2727 383.2313
## Control Incongruent 466.6770 1.735392 53.97 463.1977 470.1563
## Treatment Incongruent 452.2352 1.735392 53.97 448.7559 455.7145
Now it is easy to calculate all pairwise comparisons or any desired contrast on these EMMs.
See also this book chapter and my answer here if you need a bit more detail on how one can derive the contrasts weights from the hypotheses.
# all pairwise comparisons
pairs(emm, adjust = "Holm")
## contrast estimate SE df t.ratio p.value
## Control,Neutral - Treatment,Neutral 1.62594836 2.454215 53.97 0.663 1.0000
## Control,Neutral - Control,Congruent -0.08167403 2.473955 36.00 -0.033 1.0000
## Control,Neutral - Treatment,Congruent 1.80259257 2.454215 53.97 0.734 1.0000
## Control,Neutral - Control,Incongruent -85.12239797 2.473955 36.00 -34.407 <.0001
## Control,Neutral - Treatment,Incongruent -70.68062093 2.454215 53.97 -28.800 <.0001
## Treatment,Neutral - Control,Congruent -1.70762239 2.454215 53.97 -0.696 1.0000
## Treatment,Neutral - Treatment,Congruent 0.17664421 2.473955 36.00 0.071 1.0000
## Treatment,Neutral - Control,Incongruent -86.74834633 2.454215 53.97 -35.347 <.0001
## Treatment,Neutral - Treatment,Incongruent -72.30656929 2.473955 36.00 -29.227 <.0001
## Control,Congruent - Treatment,Congruent 1.88426660 2.454215 53.97 0.768 1.0000
## Control,Congruent - Control,Incongruent -85.04072394 2.473955 36.00 -34.374 <.0001
## Control,Congruent - Treatment,Incongruent -70.59894690 2.454215 53.97 -28.766 <.0001
## Treatment,Congruent - Control,Incongruent -86.92499054 2.454215 53.97 -35.419 <.0001
## Treatment,Congruent - Treatment,Incongruent -72.48321351 2.473955 36.00 -29.299 <.0001
## Control,Incongruent - Treatment,Incongruent 14.44177704 2.454215 53.97 5.884 <.0001
##
## Results are averaged over the levels of: cue
## P value adjustment: holm method for 15 tests
# custom contrasts
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0), # reproduces first pairwise comparison
# emmean of row 1 - (emmean of row 1 + emmean of row 2) / 2; see EMMs table
# 381.5546 - (379.9286 + 381.6363) / 2
c2 = c(1, -0.5, -0.5, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 1.6259484 2.454215 53.97 0.663 0.5105
## c2 0.7721372 2.136825 43.84 0.361 0.7196
The same applies for purely within-subjects ANOVAs or between-subjects ANOVAs.
# within-subjects ANOVA
rt_anova_wi <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
type = 3,
return_aov = TRUE)
emm <- emmeans(rt_anova_wi$aov, ~ cue * flank)
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
c2 = c(1, -0.5, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 47.31005 3.802857 170.34 12.441 <.0001
## c2 50.35320 3.293371 170.34 15.289 <.0001
# between-subjects ANOVA
rt_anova_bw <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within_full = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
emm_bw <- emmeans(rt_anova_bw$aov, ~ group)
# custom linear function
contrast(
emm_bw,
list(c1 = c(2/3, 1/2))
)
## contrast estimate SE df t.ratio p.value
## c1 475.2899 0.8213448 18 578.673 <.0001
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
The emmeans package provides the appropriate functionality to calculate custom contrasts/arbitrary linear functions of the estimates marginal means (EMMs) for aov and aovlist objects (see here for a full list of supported models).
In the following I use the ANT data set which comes with the ez package.
First we set up a mixed factorial ANOVA using ezANOVA. Note that one needs to set orthogonal contrasts in order to get meaningful Type-III tests (see, e.g., John Fox' answer here).
library("ez")
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
data(ANT)
rt_anova <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
We can then calculate the EMMs for, say all group-flank combinations.
emm <- emmeans(rt_anova$aov, ~ group * flank)
emm
## group flank emmean SE df lower.CL upper.CL
## Control Neutral 381.5546 1.735392 53.97 378.0753 385.0339
## Treatment Neutral 379.9286 1.735392 53.97 376.4493 383.4079
## Control Congruent 381.6363 1.735392 53.97 378.1570 385.1155
## Treatment Congruent 379.7520 1.735392 53.97 376.2727 383.2313
## Control Incongruent 466.6770 1.735392 53.97 463.1977 470.1563
## Treatment Incongruent 452.2352 1.735392 53.97 448.7559 455.7145
Now it is easy to calculate all pairwise comparisons or any desired contrast on these EMMs.
See also this book chapter and my answer here if you need a bit more detail on how one can derive the contrasts weights from the hypotheses.
# all pairwise comparisons
pairs(emm, adjust = "Holm")
## contrast estimate SE df t.ratio p.value
## Control,Neutral - Treatment,Neutral 1.62594836 2.454215 53.97 0.663 1.0000
## Control,Neutral - Control,Congruent -0.08167403 2.473955 36.00 -0.033 1.0000
## Control,Neutral - Treatment,Congruent 1.80259257 2.454215 53.97 0.734 1.0000
## Control,Neutral - Control,Incongruent -85.12239797 2.473955 36.00 -34.407 <.0001
## Control,Neutral - Treatment,Incongruent -70.68062093 2.454215 53.97 -28.800 <.0001
## Treatment,Neutral - Control,Congruent -1.70762239 2.454215 53.97 -0.696 1.0000
## Treatment,Neutral - Treatment,Congruent 0.17664421 2.473955 36.00 0.071 1.0000
## Treatment,Neutral - Control,Incongruent -86.74834633 2.454215 53.97 -35.347 <.0001
## Treatment,Neutral - Treatment,Incongruent -72.30656929 2.473955 36.00 -29.227 <.0001
## Control,Congruent - Treatment,Congruent 1.88426660 2.454215 53.97 0.768 1.0000
## Control,Congruent - Control,Incongruent -85.04072394 2.473955 36.00 -34.374 <.0001
## Control,Congruent - Treatment,Incongruent -70.59894690 2.454215 53.97 -28.766 <.0001
## Treatment,Congruent - Control,Incongruent -86.92499054 2.454215 53.97 -35.419 <.0001
## Treatment,Congruent - Treatment,Incongruent -72.48321351 2.473955 36.00 -29.299 <.0001
## Control,Incongruent - Treatment,Incongruent 14.44177704 2.454215 53.97 5.884 <.0001
##
## Results are averaged over the levels of: cue
## P value adjustment: holm method for 15 tests
# custom contrasts
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0), # reproduces first pairwise comparison
# emmean of row 1 - (emmean of row 1 + emmean of row 2) / 2; see EMMs table
# 381.5546 - (379.9286 + 381.6363) / 2
c2 = c(1, -0.5, -0.5, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 1.6259484 2.454215 53.97 0.663 0.5105
## c2 0.7721372 2.136825 43.84 0.361 0.7196
The same applies for purely within-subjects ANOVAs or between-subjects ANOVAs.
# within-subjects ANOVA
rt_anova_wi <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within = .(cue, flank),
type = 3,
return_aov = TRUE)
emm <- emmeans(rt_anova_wi$aov, ~ cue * flank)
contrast(
emm,
list(c1 = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
c2 = c(1, -0.5, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0))
)
## contrast estimate SE df t.ratio p.value
## c1 47.31005 3.802857 170.34 12.441 <.0001
## c2 50.35320 3.293371 170.34 15.289 <.0001
# between-subjects ANOVA
rt_anova_bw <- ezANOVA(data = ANT[ANT$error == 0, ],
dv = rt,
wid = subnum,
within_full = .(cue, flank),
between = group,
type = 3,
return_aov = TRUE)
emm_bw <- emmeans(rt_anova_bw$aov, ~ group)
# custom linear function
contrast(
emm_bw,
list(c1 = c(2/3, 1/2))
)
## contrast estimate SE df t.ratio p.value
## c1 475.2899 0.8213448 18 578.673 <.0001
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
edited Jan 4 at 0:08
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
answered Jan 2 at 17:11
statmerkurstatmerkur
19818
19818
add a comment |
add a comment |
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According to a statement by the package developer made in 2012 or so, there is no way of using
ezfor planned contrasts. See groups.google.com/forum/#!topic/ez4r/RpwYT6pEva0 . However, if you follow this link, alternatives are being discussed that might be helpful. It might also be useful to see if he has updated something since then...– coffeinjunky
Mar 11 '16 at 18:00
@coffeinjunky I suspect that the author is referring to a lack of direct functionality written in ezANOVA but it may be possible to use the information it produces to work with a new function. For example, I provided a 'hack' for ggplot 2 using the available functionality in a way it was not intended here: stackoverflow.com/a/20426482/2653210 This is what I'm hoping this bounty will encourage.
– Docconcoct
Mar 11 '16 at 18:06