What does Deviance mean in lmerIf the log-likelihood is negative, should we choose it? - model comparisonWhat to conclude about these models? Random intercept + Fixed Slope vs. Random intercept and SlopeInterpreting output of analysis of deviance table from anova() model comparisonHow can mixed-effect and fixed-effect generalised linear models be compared using BIC?Output interpretation of mixed anova with lme4 package in RHow to interpret the output of lmerTest::ranova in R?Why are the coefficients of REML and ML estimation the same? What does that mean?
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What does Deviance mean in lmer
If the log-likelihood is negative, should we choose it? - model comparisonWhat to conclude about these models? Random intercept + Fixed Slope vs. Random intercept and SlopeInterpreting output of analysis of deviance table from anova() model comparisonHow can mixed-effect and fixed-effect generalised linear models be compared using BIC?Output interpretation of mixed anova with lme4 package in RHow to interpret the output of lmerTest::ranova in R?Why are the coefficients of REML and ML estimation the same? What does that mean?
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$begingroup$
Probably a rather silly question, but I would like to have a clear explanation of what deviance in linear mixed models (using lmer) is.
For instance, how do I interpret it along AIC, BIC, and LOgLik in the anova output below, when conducting a model comparison.
Thanks in advance!
r mixed-model lme4-nlme
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Probably a rather silly question, but I would like to have a clear explanation of what deviance in linear mixed models (using lmer) is.
For instance, how do I interpret it along AIC, BIC, and LOgLik in the anova output below, when conducting a model comparison.
Thanks in advance!
r mixed-model lme4-nlme
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Probably a rather silly question, but I would like to have a clear explanation of what deviance in linear mixed models (using lmer) is.
For instance, how do I interpret it along AIC, BIC, and LOgLik in the anova output below, when conducting a model comparison.
Thanks in advance!
r mixed-model lme4-nlme
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Probably a rather silly question, but I would like to have a clear explanation of what deviance in linear mixed models (using lmer) is.
For instance, how do I interpret it along AIC, BIC, and LOgLik in the anova output below, when conducting a model comparison.
Thanks in advance!
r mixed-model lme4-nlme
r mixed-model lme4-nlme
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 8 hours ago
Helio Cuve
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
asked 9 hours ago
Helio CuveHelio Cuve
83 bronze badges
83 bronze badges
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Helio Cuve is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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$begingroup$
The deviance for a linear mixed model is defined as follows:
deviance=−2∗log likelihood
The deviance is an index of model fit: a model with a higher deviance provides a poorer model fit to the data than a model with a lower deviance.
When comparing two linear mixed effects models such as yours, you are essentially asking whether introducing additional fixed effects into the model (which will utilize additional degrees of freedom) will significantly improve the model fit.
From your R output, you can see that adding the terms tasc and tasc:condition to your simpler model which includes only the term condition in its fixed effects part lead to an improvement in model fit (as captured by the smaller deviance). This improvement is statistically significant given the reported p-value is statistically significant. Here, I used that tasc*condition is the same as tasc + condition + tasc:condition.
See here for more examples on interpreting deviance: https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html.
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
$endgroup$
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
add a comment |
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$begingroup$
The deviance for a linear mixed model is defined as follows:
deviance=−2∗log likelihood
The deviance is an index of model fit: a model with a higher deviance provides a poorer model fit to the data than a model with a lower deviance.
When comparing two linear mixed effects models such as yours, you are essentially asking whether introducing additional fixed effects into the model (which will utilize additional degrees of freedom) will significantly improve the model fit.
From your R output, you can see that adding the terms tasc and tasc:condition to your simpler model which includes only the term condition in its fixed effects part lead to an improvement in model fit (as captured by the smaller deviance). This improvement is statistically significant given the reported p-value is statistically significant. Here, I used that tasc*condition is the same as tasc + condition + tasc:condition.
See here for more examples on interpreting deviance: https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html.
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
$endgroup$
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
add a comment |
$begingroup$
The deviance for a linear mixed model is defined as follows:
deviance=−2∗log likelihood
The deviance is an index of model fit: a model with a higher deviance provides a poorer model fit to the data than a model with a lower deviance.
When comparing two linear mixed effects models such as yours, you are essentially asking whether introducing additional fixed effects into the model (which will utilize additional degrees of freedom) will significantly improve the model fit.
From your R output, you can see that adding the terms tasc and tasc:condition to your simpler model which includes only the term condition in its fixed effects part lead to an improvement in model fit (as captured by the smaller deviance). This improvement is statistically significant given the reported p-value is statistically significant. Here, I used that tasc*condition is the same as tasc + condition + tasc:condition.
See here for more examples on interpreting deviance: https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html.
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
$endgroup$
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
add a comment |
$begingroup$
The deviance for a linear mixed model is defined as follows:
deviance=−2∗log likelihood
The deviance is an index of model fit: a model with a higher deviance provides a poorer model fit to the data than a model with a lower deviance.
When comparing two linear mixed effects models such as yours, you are essentially asking whether introducing additional fixed effects into the model (which will utilize additional degrees of freedom) will significantly improve the model fit.
From your R output, you can see that adding the terms tasc and tasc:condition to your simpler model which includes only the term condition in its fixed effects part lead to an improvement in model fit (as captured by the smaller deviance). This improvement is statistically significant given the reported p-value is statistically significant. Here, I used that tasc*condition is the same as tasc + condition + tasc:condition.
See here for more examples on interpreting deviance: https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html.
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
$endgroup$
The deviance for a linear mixed model is defined as follows:
deviance=−2∗log likelihood
The deviance is an index of model fit: a model with a higher deviance provides a poorer model fit to the data than a model with a lower deviance.
When comparing two linear mixed effects models such as yours, you are essentially asking whether introducing additional fixed effects into the model (which will utilize additional degrees of freedom) will significantly improve the model fit.
From your R output, you can see that adding the terms tasc and tasc:condition to your simpler model which includes only the term condition in its fixed effects part lead to an improvement in model fit (as captured by the smaller deviance). This improvement is statistically significant given the reported p-value is statistically significant. Here, I used that tasc*condition is the same as tasc + condition + tasc:condition.
See here for more examples on interpreting deviance: https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html.
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
edited 5 hours ago
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
answered 6 hours ago
Isabella GhementIsabella Ghement
10.1k2 gold badges8 silver badges25 bronze badges
10.1k2 gold badges8 silver badges25 bronze badges
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
add a comment |
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
1
1
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
$begingroup$
thank you for the explanation @Isabella Ghement!
$endgroup$
– Helio Cuve
6 hours ago
add a comment |
Helio Cuve is a new contributor. Be nice, and check out our Code of Conduct.
Helio Cuve is a new contributor. Be nice, and check out our Code of Conduct.
Helio Cuve is a new contributor. Be nice, and check out our Code of Conduct.
Helio Cuve is a new contributor. Be nice, and check out our Code of Conduct.
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