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How to run two-way repeated within subject comparisons in R?
Post Hoc tests for ANOVARepeated-Measures ANOVA: ezANOVA vs. aov vs. lme syntaxRepeated-measures ANOVA in R: +Error(subject) or + Error(subject/ VI1 * VI2)?How to exclude unwanted comparisons in two-way ANOVA in RHow to set up a 2 way repeated measures ANCOVA using R?post-hoc test for one-way ANOVA with random effectHow to perform Tukey HSD unconfounded comparisons with adjusted p-value in R or PythonRepeated Measures Two-Way Anova using Two Different ListsSpecify within-subjects and between-subjects ANOVA model using lme or lmer, as fixed-effectsR - post-hoc t-tests in mixed ANOVA design and triple interaction
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I have this following data (3x3 within-subject design):
subj phase cope mean
1 A a ...
1 A b ...
1 A c ...
1 B a ...
1 B b ...
1 B c ...
1 C a ...
1 C b ...
1 C c ...
2 A a ...
2 A b ...
2 A c ...
2 B a ...
2 B b ...
2 B c ...
2 C a ...
2 C b ...
2 C c ...
...
Both phase and cope are categorical and the DV (mean) is continuous.
How do I run a 3-by-3 (ABCxabc) within subject ANOVA/lme with this data and what should I do for the post-hoc test?
After searching on the web, I have found the following 3 potential options:
1. baselinemodel <- lme(m ~ 1, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
copemodel <- lme(m ~ cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
phasemodel <- lme(m ~ phase, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
fullmodel <- lme(m ~ phase + cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
interactionmodel <- lme(m ~ phase * cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
anova(baselinemodel, copemodel, phasemodel, fullmodel, interactionmodel)
2. m.lme <- lme(m ~ cope * phase, data = csv_temp, random = ~1 | subj)
3. m.lme <- lme(m ~ cope * phase, random = list(subj = pdBlocked(list(~1, pdIdent(~ cope - 1), pdIdent(~ phase - 1)))), data = csv_temp)
anova(m.lme)
They produced different results. Which way is the correct way? And how do I followup with post-hoc tests (at this time, neither method yield significant interactions)?
r
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
add a comment |
I have this following data (3x3 within-subject design):
subj phase cope mean
1 A a ...
1 A b ...
1 A c ...
1 B a ...
1 B b ...
1 B c ...
1 C a ...
1 C b ...
1 C c ...
2 A a ...
2 A b ...
2 A c ...
2 B a ...
2 B b ...
2 B c ...
2 C a ...
2 C b ...
2 C c ...
...
Both phase and cope are categorical and the DV (mean) is continuous.
How do I run a 3-by-3 (ABCxabc) within subject ANOVA/lme with this data and what should I do for the post-hoc test?
After searching on the web, I have found the following 3 potential options:
1. baselinemodel <- lme(m ~ 1, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
copemodel <- lme(m ~ cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
phasemodel <- lme(m ~ phase, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
fullmodel <- lme(m ~ phase + cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
interactionmodel <- lme(m ~ phase * cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
anova(baselinemodel, copemodel, phasemodel, fullmodel, interactionmodel)
2. m.lme <- lme(m ~ cope * phase, data = csv_temp, random = ~1 | subj)
3. m.lme <- lme(m ~ cope * phase, random = list(subj = pdBlocked(list(~1, pdIdent(~ cope - 1), pdIdent(~ phase - 1)))), data = csv_temp)
anova(m.lme)
They produced different results. Which way is the correct way? And how do I followup with post-hoc tests (at this time, neither method yield significant interactions)?
r
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17
add a comment |
I have this following data (3x3 within-subject design):
subj phase cope mean
1 A a ...
1 A b ...
1 A c ...
1 B a ...
1 B b ...
1 B c ...
1 C a ...
1 C b ...
1 C c ...
2 A a ...
2 A b ...
2 A c ...
2 B a ...
2 B b ...
2 B c ...
2 C a ...
2 C b ...
2 C c ...
...
Both phase and cope are categorical and the DV (mean) is continuous.
How do I run a 3-by-3 (ABCxabc) within subject ANOVA/lme with this data and what should I do for the post-hoc test?
After searching on the web, I have found the following 3 potential options:
1. baselinemodel <- lme(m ~ 1, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
copemodel <- lme(m ~ cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
phasemodel <- lme(m ~ phase, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
fullmodel <- lme(m ~ phase + cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
interactionmodel <- lme(m ~ phase * cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
anova(baselinemodel, copemodel, phasemodel, fullmodel, interactionmodel)
2. m.lme <- lme(m ~ cope * phase, data = csv_temp, random = ~1 | subj)
3. m.lme <- lme(m ~ cope * phase, random = list(subj = pdBlocked(list(~1, pdIdent(~ cope - 1), pdIdent(~ phase - 1)))), data = csv_temp)
anova(m.lme)
They produced different results. Which way is the correct way? And how do I followup with post-hoc tests (at this time, neither method yield significant interactions)?
r
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
I have this following data (3x3 within-subject design):
subj phase cope mean
1 A a ...
1 A b ...
1 A c ...
1 B a ...
1 B b ...
1 B c ...
1 C a ...
1 C b ...
1 C c ...
2 A a ...
2 A b ...
2 A c ...
2 B a ...
2 B b ...
2 B c ...
2 C a ...
2 C b ...
2 C c ...
...
Both phase and cope are categorical and the DV (mean) is continuous.
How do I run a 3-by-3 (ABCxabc) within subject ANOVA/lme with this data and what should I do for the post-hoc test?
After searching on the web, I have found the following 3 potential options:
1. baselinemodel <- lme(m ~ 1, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
copemodel <- lme(m ~ cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
phasemodel <- lme(m ~ phase, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
fullmodel <- lme(m ~ phase + cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
interactionmodel <- lme(m ~ phase * cope, random = ~1 | subj/phase/cope, data = csv_temp, method = "ML")
anova(baselinemodel, copemodel, phasemodel, fullmodel, interactionmodel)
2. m.lme <- lme(m ~ cope * phase, data = csv_temp, random = ~1 | subj)
3. m.lme <- lme(m ~ cope * phase, random = list(subj = pdBlocked(list(~1, pdIdent(~ cope - 1), pdIdent(~ phase - 1)))), data = csv_temp)
anova(m.lme)
They produced different results. Which way is the correct way? And how do I followup with post-hoc tests (at this time, neither method yield significant interactions)?
r
r
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
edited Mar 26 at 22:01
Jiang
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
asked Mar 26 at 21:20
JiangJiang
62 bronze badges
62 bronze badges
This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17
add a comment |
This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17
This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17
This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17
add a comment |
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This might get a better response on stats.stackexchange.com
– Stedy
Mar 26 at 22:17