Understanding Spikeslab Output The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsUnderstanding output stepAICUnderstanding of minbucket function in CART model using RSpike Slab in r, bad outputFor every Bayesian Network, is there a Neural Network that gives the same output?Understanding ROCs in imbalanced data-setsstem() output order in RUnderstand output of pwr.t2n.test in RHow to adapt output with multiple columns for better presentation?Meaning of “TRUE” column in R RandomForest output for Importance()?
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Understanding Spikeslab Output
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsUnderstanding output stepAICUnderstanding of minbucket function in CART model using RSpike Slab in r, bad outputFor every Bayesian Network, is there a Neural Network that gives the same output?Understanding ROCs in imbalanced data-setsstem() output order in RUnderstand output of pwr.t2n.test in RHow to adapt output with multiple columns for better presentation?Meaning of “TRUE” column in R RandomForest output for Importance()?
$begingroup$
I'm using spikeslab for the first time, but don't understand what the output means. It was suggested to me that I use it to tell which variables my dependent variable is most correlated to, in a ranked order.
Particuarly, what is "bma" bma.scale" "gnet" and "gnet.scale"? I also don't understand how to read the corresponding plot to the model.Thanks for any help!
For example, this is one of the models I created using spikeslab, with its output:
model2_ss <-spikeslab(Risk_Pct ~ Race
+ +hispanic
+ +Born_In_US
+ +Highest_Education
+ +Marital_Status
+ , na.rm = TRUE, data = LabeledData)
> model2_ss
-------------------------------------------------------------------
Variable selection method : AIC
Big p small n : FALSE
Screen variables : FALSE
Fast processing : TRUE
Sample size : 26
No. predictors : 5
No. burn-in values : 500
No. sampled values : 500
Estimated mse : 0.4299
Model size : 3
---> Top variables:
bma gnet bma.scale gnet.scale
Marital_Status 0.516 0.516 0.319 0.319
Born_In_US -0.469 -0.447 -0.440 -0.419
Race 0.458 0.421 0.926 0.851
r bayesian-networks
edited Mar 25 '16 at 18:08
Spacedman
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
$endgroup$
bumped to the homepage by Community♦ 33 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm using spikeslab for the first time, but don't understand what the output means. It was suggested to me that I use it to tell which variables my dependent variable is most correlated to, in a ranked order.
Particuarly, what is "bma" bma.scale" "gnet" and "gnet.scale"? I also don't understand how to read the corresponding plot to the model.Thanks for any help!
For example, this is one of the models I created using spikeslab, with its output:
model2_ss <-spikeslab(Risk_Pct ~ Race
+ +hispanic
+ +Born_In_US
+ +Highest_Education
+ +Marital_Status
+ , na.rm = TRUE, data = LabeledData)
> model2_ss
-------------------------------------------------------------------
Variable selection method : AIC
Big p small n : FALSE
Screen variables : FALSE
Fast processing : TRUE
Sample size : 26
No. predictors : 5
No. burn-in values : 500
No. sampled values : 500
Estimated mse : 0.4299
Model size : 3
---> Top variables:
bma gnet bma.scale gnet.scale
Marital_Status 0.516 0.516 0.319 0.319
Born_In_US -0.469 -0.447 -0.440 -0.419
Race 0.458 0.421 0.926 0.851
r bayesian-networks
edited Mar 25 '16 at 18:08
Spacedman
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
$endgroup$
bumped to the homepage by Community♦ 33 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm using spikeslab for the first time, but don't understand what the output means. It was suggested to me that I use it to tell which variables my dependent variable is most correlated to, in a ranked order.
Particuarly, what is "bma" bma.scale" "gnet" and "gnet.scale"? I also don't understand how to read the corresponding plot to the model.Thanks for any help!
For example, this is one of the models I created using spikeslab, with its output:
model2_ss <-spikeslab(Risk_Pct ~ Race
+ +hispanic
+ +Born_In_US
+ +Highest_Education
+ +Marital_Status
+ , na.rm = TRUE, data = LabeledData)
> model2_ss
-------------------------------------------------------------------
Variable selection method : AIC
Big p small n : FALSE
Screen variables : FALSE
Fast processing : TRUE
Sample size : 26
No. predictors : 5
No. burn-in values : 500
No. sampled values : 500
Estimated mse : 0.4299
Model size : 3
---> Top variables:
bma gnet bma.scale gnet.scale
Marital_Status 0.516 0.516 0.319 0.319
Born_In_US -0.469 -0.447 -0.440 -0.419
Race 0.458 0.421 0.926 0.851
r bayesian-networks
edited Mar 25 '16 at 18:08
Spacedman
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
$endgroup$
I'm using spikeslab for the first time, but don't understand what the output means. It was suggested to me that I use it to tell which variables my dependent variable is most correlated to, in a ranked order.
Particuarly, what is "bma" bma.scale" "gnet" and "gnet.scale"? I also don't understand how to read the corresponding plot to the model.Thanks for any help!
For example, this is one of the models I created using spikeslab, with its output:
model2_ss <-spikeslab(Risk_Pct ~ Race
+ +hispanic
+ +Born_In_US
+ +Highest_Education
+ +Marital_Status
+ , na.rm = TRUE, data = LabeledData)
> model2_ss
-------------------------------------------------------------------
Variable selection method : AIC
Big p small n : FALSE
Screen variables : FALSE
Fast processing : TRUE
Sample size : 26
No. predictors : 5
No. burn-in values : 500
No. sampled values : 500
Estimated mse : 0.4299
Model size : 3
---> Top variables:
bma gnet bma.scale gnet.scale
Marital_Status 0.516 0.516 0.319 0.319
Born_In_US -0.469 -0.447 -0.440 -0.419
Race 0.458 0.421 0.926 0.851
r bayesian-networks
r bayesian-networks
edited Mar 25 '16 at 18:08
Spacedman
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
edited Mar 25 '16 at 18:08
Spacedman
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
edited Mar 25 '16 at 18:08
Spacedman
1,722616
edited Mar 25 '16 at 18:08
Spacedman
1,722616
edited Mar 25 '16 at 18:08
Spacedman
1,722616
1,722616
asked Mar 22 '16 at 16:46
JenniferJennifer
161
asked Mar 22 '16 at 16:46
JenniferJennifer
161
asked Mar 22 '16 at 16:46
JenniferJennifer
161
161
bumped to the homepage by Community♦ 33 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 33 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
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$begingroup$
BMA is "Bayesian Model Averaged". GNET is "Generalized Elastic Net".
Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
$endgroup$
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
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active
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$begingroup$
BMA is "Bayesian Model Averaged". GNET is "Generalized Elastic Net".
Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
$endgroup$
add a comment |
$begingroup$
BMA is "Bayesian Model Averaged". GNET is "Generalized Elastic Net".
Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
$endgroup$
add a comment |
$begingroup$
BMA is "Bayesian Model Averaged". GNET is "Generalized Elastic Net".
Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
$endgroup$
BMA is "Bayesian Model Averaged". GNET is "Generalized Elastic Net".
Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
answered Mar 24 '16 at 23:19
SpacedmanSpacedman
1,722616
1,722616
add a comment |
add a comment |
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