Understanding Spikeslab Output
$begingroup$
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
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bumped to the homepage by Community♦ 21 hours 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♦ 21 hours 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♦ 21 hours 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♦ 21 hours 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
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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
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add a comment |
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1 Answer
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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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