In mixed effect models, how account for grouped random effects?Group level random effectAllowed comparisons...
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In mixed effect models, how account for grouped random effects?
Group level random effectAllowed comparisons of mixed effects models (random effects primarily)Mixed effect logistic regression in R: choosing random effectsNested data analysis using nlme: Analysis leaves out factor levelsNested mixed effects with lme4Formula of Linear Mixed Effect ModelTrouble setting up a linear mixed effects modelMixed-effects model for response dataMixed Effects Model, Levels of grouping factor < observationsInterpreting main effect that is significant in some models and not others - Mixed Effects ModelsLinear Mixed Model in R: Rep. Measures, Nested, Random Effects
$begingroup$
In my experiment, each subject sees 10 different stimuli and provides one measure for each stimulus. All subjects saw the same stimuli. Among the 10 stimuli, 3 are of category A, 3 of category B, and 4 of category C. I want to model this with lme4 so I wrote:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
I am not comfortable with this model because the stimuli are random in the model, and the category is an intrinsic property of them. So it seems that I put twice some information here. How should I model the setting?
This question did not help me:
Group level random effect
r mixed-model lme4-nlme
$endgroup$
add a comment |
$begingroup$
In my experiment, each subject sees 10 different stimuli and provides one measure for each stimulus. All subjects saw the same stimuli. Among the 10 stimuli, 3 are of category A, 3 of category B, and 4 of category C. I want to model this with lme4 so I wrote:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
I am not comfortable with this model because the stimuli are random in the model, and the category is an intrinsic property of them. So it seems that I put twice some information here. How should I model the setting?
This question did not help me:
Group level random effect
r mixed-model lme4-nlme
$endgroup$
1
$begingroup$
What is your research question ? Are you interested in the treatment effects ofstimulus
? Please edit your question to include the output ofstr(My_data)
and the output ofxtabs(~ stimulus + subject, My_data)
andxtabs(~ category + subject, My_data)
andxtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago
add a comment |
$begingroup$
In my experiment, each subject sees 10 different stimuli and provides one measure for each stimulus. All subjects saw the same stimuli. Among the 10 stimuli, 3 are of category A, 3 of category B, and 4 of category C. I want to model this with lme4 so I wrote:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
I am not comfortable with this model because the stimuli are random in the model, and the category is an intrinsic property of them. So it seems that I put twice some information here. How should I model the setting?
This question did not help me:
Group level random effect
r mixed-model lme4-nlme
$endgroup$
In my experiment, each subject sees 10 different stimuli and provides one measure for each stimulus. All subjects saw the same stimuli. Among the 10 stimuli, 3 are of category A, 3 of category B, and 4 of category C. I want to model this with lme4 so I wrote:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
I am not comfortable with this model because the stimuli are random in the model, and the category is an intrinsic property of them. So it seems that I put twice some information here. How should I model the setting?
This question did not help me:
Group level random effect
r mixed-model lme4-nlme
r mixed-model lme4-nlme
asked 2 hours ago
RtistRtist
1384
1384
1
$begingroup$
What is your research question ? Are you interested in the treatment effects ofstimulus
? Please edit your question to include the output ofstr(My_data)
and the output ofxtabs(~ stimulus + subject, My_data)
andxtabs(~ category + subject, My_data)
andxtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago
add a comment |
1
$begingroup$
What is your research question ? Are you interested in the treatment effects ofstimulus
? Please edit your question to include the output ofstr(My_data)
and the output ofxtabs(~ stimulus + subject, My_data)
andxtabs(~ category + subject, My_data)
andxtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago
1
1
$begingroup$
What is your research question ? Are you interested in the treatment effects of
stimulus
? Please edit your question to include the output of str(My_data)
and the output of xtabs(~ stimulus + subject, My_data)
and xtabs(~ category + subject, My_data)
and xtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago
$begingroup$
What is your research question ? Are you interested in the treatment effects of
stimulus
? Please edit your question to include the output of str(My_data)
and the output of xtabs(~ stimulus + subject, My_data)
and xtabs(~ category + subject, My_data)
and xtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
If you believe the different categories vary in terms of their measurement and you want to test that, you need to model the category as a random intercept with (1|category).
However, I think we need more information as to what you are actually looking to decide. For example, are you wondering if each person measures them different? Or if each stimulus is measured differently?
$endgroup$
add a comment |
$begingroup$
It seems that in the model:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
category
is being used to denote the levels of stimulus
. As such, this does not make sense, since category
is not an actual variable.
Even though stimulus
is random in the sense that it has (presumably) been randomly assigned to each subject
, this does not mean that is should be included as a random effect - unless there is no interest in the treatment effect of stimulus
. In that case, you would simply be partitioning variance into the subject
level and the stimulus
level.
It seems more likely that you are in fact interested in the associations between each level of the stimulus
and the outcome - that is, you are interested in the treatment effect and therefore the model should be of the form:
measure ~ stimulus + (1|subject)
$endgroup$
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels ofstimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.
$endgroup$
– Robert Long
58 mins ago
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it thatstimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist
$endgroup$
– Robert Long
36 mins ago
add a comment |
$begingroup$
In your setting, subject and stimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
If you believe the different categories vary in terms of their measurement and you want to test that, you need to model the category as a random intercept with (1|category).
However, I think we need more information as to what you are actually looking to decide. For example, are you wondering if each person measures them different? Or if each stimulus is measured differently?
$endgroup$
add a comment |
$begingroup$
If you believe the different categories vary in terms of their measurement and you want to test that, you need to model the category as a random intercept with (1|category).
However, I think we need more information as to what you are actually looking to decide. For example, are you wondering if each person measures them different? Or if each stimulus is measured differently?
$endgroup$
add a comment |
$begingroup$
If you believe the different categories vary in terms of their measurement and you want to test that, you need to model the category as a random intercept with (1|category).
However, I think we need more information as to what you are actually looking to decide. For example, are you wondering if each person measures them different? Or if each stimulus is measured differently?
$endgroup$
If you believe the different categories vary in terms of their measurement and you want to test that, you need to model the category as a random intercept with (1|category).
However, I think we need more information as to what you are actually looking to decide. For example, are you wondering if each person measures them different? Or if each stimulus is measured differently?
answered 2 hours ago
Craig K. Van PayCraig K. Van Pay
587
587
add a comment |
add a comment |
$begingroup$
It seems that in the model:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
category
is being used to denote the levels of stimulus
. As such, this does not make sense, since category
is not an actual variable.
Even though stimulus
is random in the sense that it has (presumably) been randomly assigned to each subject
, this does not mean that is should be included as a random effect - unless there is no interest in the treatment effect of stimulus
. In that case, you would simply be partitioning variance into the subject
level and the stimulus
level.
It seems more likely that you are in fact interested in the associations between each level of the stimulus
and the outcome - that is, you are interested in the treatment effect and therefore the model should be of the form:
measure ~ stimulus + (1|subject)
$endgroup$
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels ofstimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.
$endgroup$
– Robert Long
58 mins ago
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it thatstimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist
$endgroup$
– Robert Long
36 mins ago
add a comment |
$begingroup$
It seems that in the model:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
category
is being used to denote the levels of stimulus
. As such, this does not make sense, since category
is not an actual variable.
Even though stimulus
is random in the sense that it has (presumably) been randomly assigned to each subject
, this does not mean that is should be included as a random effect - unless there is no interest in the treatment effect of stimulus
. In that case, you would simply be partitioning variance into the subject
level and the stimulus
level.
It seems more likely that you are in fact interested in the associations between each level of the stimulus
and the outcome - that is, you are interested in the treatment effect and therefore the model should be of the form:
measure ~ stimulus + (1|subject)
$endgroup$
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels ofstimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.
$endgroup$
– Robert Long
58 mins ago
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it thatstimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist
$endgroup$
– Robert Long
36 mins ago
add a comment |
$begingroup$
It seems that in the model:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
category
is being used to denote the levels of stimulus
. As such, this does not make sense, since category
is not an actual variable.
Even though stimulus
is random in the sense that it has (presumably) been randomly assigned to each subject
, this does not mean that is should be included as a random effect - unless there is no interest in the treatment effect of stimulus
. In that case, you would simply be partitioning variance into the subject
level and the stimulus
level.
It seems more likely that you are in fact interested in the associations between each level of the stimulus
and the outcome - that is, you are interested in the treatment effect and therefore the model should be of the form:
measure ~ stimulus + (1|subject)
$endgroup$
It seems that in the model:
lmer(measure ~ category + (1|subject) + (1|stimulus), data = My_data)
category
is being used to denote the levels of stimulus
. As such, this does not make sense, since category
is not an actual variable.
Even though stimulus
is random in the sense that it has (presumably) been randomly assigned to each subject
, this does not mean that is should be included as a random effect - unless there is no interest in the treatment effect of stimulus
. In that case, you would simply be partitioning variance into the subject
level and the stimulus
level.
It seems more likely that you are in fact interested in the associations between each level of the stimulus
and the outcome - that is, you are interested in the treatment effect and therefore the model should be of the form:
measure ~ stimulus + (1|subject)
answered 1 hour ago
Robert LongRobert Long
10.5k22549
10.5k22549
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels ofstimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.
$endgroup$
– Robert Long
58 mins ago
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it thatstimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist
$endgroup$
– Robert Long
36 mins ago
add a comment |
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels ofstimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.
$endgroup$
– Robert Long
58 mins ago
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it thatstimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist
$endgroup$
– Robert Long
36 mins ago
2
2
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
Interesting perspective, Robert! If the 10 stimuli are the only ones @Rtist is interested in, then your suggested approach makes sense. But if he only included the 10 stimuli in the study because he is interested in generalizing the findings of the study to all stimuli represented by these 10 items, then stimulus should be treated as a random grouping factor (assuming there are multiple observations for at least some of all the subject by stimulus combinations). It really all depends on the purpose of the study - if we don't know the purpose, we can only speculate about the model set up.
$endgroup$
– Isabella Ghement
1 hour ago
$begingroup$
@IsabellaGhement there are only 3 levels of
stimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.$endgroup$
– Robert Long
58 mins ago
$begingroup$
@IsabellaGhement there are only 3 levels of
stimulus
, so fitting random effects for it and estimating a variance would not be advisable in my opinion.$endgroup$
– Robert Long
58 mins ago
1
1
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
$begingroup$
But the question says each subject sees 10 stimuli and that these 10 stimuli can be categorized into 3 different categories? I am not suggesting stimulus category (3 levels) could be treated as a random grouping factor, but rather stimulus (10 levels). However, if there aren't repeated values of measure for each subject by stimulus combination OR if the research question calls for it (i.e., the 10 stimuli are the only ones @Rtist wants to learn something about), then the option you suggested would work.
$endgroup$
– Isabella Ghement
43 mins ago
1
1
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it that
stimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist$endgroup$
– Robert Long
36 mins ago
$begingroup$
@IsabellaGhement perhaps I misunderstood the question. I read it that
stimulus
has 3 levels, not 10. Anyway, we can wait for clarification from @Rtist$endgroup$
– Robert Long
36 mins ago
add a comment |
$begingroup$
In your setting, subject and stimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).
$endgroup$
add a comment |
$begingroup$
In your setting, subject and stimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).
$endgroup$
add a comment |
$begingroup$
In your setting, subject and stimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).
$endgroup$
In your setting, subject and stimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).
edited 1 hour ago
answered 1 hour ago
Isabella GhementIsabella Ghement
6,966320
6,966320
add a comment |
add a comment |
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$begingroup$
What is your research question ? Are you interested in the treatment effects of
stimulus
? Please edit your question to include the output ofstr(My_data)
and the output ofxtabs(~ stimulus + subject, My_data)
andxtabs(~ category + subject, My_data)
andxtabs(~ category + stimulus, My_data)
$endgroup$
– Robert Long
32 mins ago