plot.spikeslab: Plots for Spike and Slab Analysis

Description Usage Arguments Author(s) References See Also Examples

View source: R/plot.spikeslab.R

Description

Plots either the gnet solution path or the cross-validated mean-squared-error (the latter only applies when cross-validation is used).

Usage

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## S3 method for class 'spikeslab'
plot(x, plot.type = c("path", "cv"), breaks = FALSE, ...)

Arguments

x

An object of class spikeslab.

plot.type

Choosing "path" produces a plot of the gnet solution path. The choice "cv" produces the mean-squared error plot. The latter applies only to objects from a cv.spikeslab call.

breaks

If TRUE, then vertical lines are drawn at each break point in the gnet solution path.

...

Further arguments passed to or from other methods.

Author(s)

Hemant Ishwaran ([email protected])

J. Sunil Rao ([email protected])

Udaya B. Kogalur ([email protected])

References

Efron B., Hastie T., Johnstone I., and Tibshirani R. (2004). Least angle regression (with discussion). Ann. Statist., 32:407-499.

Ishwaran H. and Rao J.S. (2010). Generalized ridge regression: geometry and computational solutions when p is larger than n.

See Also

spikeslab, cv.spikeslab.

Examples

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## Not run: 
#------------------------------------------------------------
# Example 1: diabetes data
#------------------------------------------------------------

data(diabetesI, package = "spikeslab")
obj <- spikeslab(Y ~ . , diabetesI, verbose = TRUE)
plot(obj, plot.type = "path")

## End(Not run)

Example output

Loading required package: lars
Loaded lars 1.2

Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: parallel

 spikeslab 1.1.5 
 
 Type spikeslab.news() to see new features, changes, and bug fixes. 
 


 	 pre-processing data... 
	 running spike and slab regression...
                                                                                    
                                                                                    
	 	 50 : 0.24061 
                                                                                    
	 	 100 : 0.2341 
                                                                                    
	 	 150 : 0.20017 
                                                                                    
	 	 200 : 0.21873 
                                                                                    
	 	 250 : 0.25119 
                                                                                    
	 	 300 : 0.13974 
                                                                                    
	 	 350 : 0.12523 
                                                                                    
	 	 400 : 0.2036 
                                                                                    
	 	 450 : 0.13918 
                                                                                    
	 	 500 : 0.18273 
                                                                                    
	 	 50 : 0.19749 
                                                                                    
	 	 100 : 0.19352 
                                                                                    
	 	 150 : 0.18651 
                                                                                    
	 	 200 : 0.26707 
                                                                                    
	 	 250 : 0.19609 
                                                                                    
	 	 300 : 0.14958 
                                                                                    
	 	 350 : 0.08339 
                                                                                    
	 	 400 : 0.32133 
                                                                                    
	 	 450 : 0.18844 
                                                                                    
	 	 500 : 0.13074 
	 primary loop completed... 
	 generalized elastic net (gnet) variable selection...                
------------------------------------------------------------------- 
Variable selection method     : AIC 
Big p small n                 : FALSE 
Screen variables              : FALSE 
Fast processing               : TRUE 
Sample size                   : 442 
No. predictors                : 64 
No. burn-in values            : 500 
No. sampled values            : 500 
Estimated mse                 : 2829.238 
Model size                    : 15 


---> Top variables:
            bma   gnet bma.scale gnet.scale
bmi      24.256 24.270   509.945    510.244
ltg      22.873 22.505   480.873    473.132
map      14.317 12.769   300.997    268.462
hdl     -11.485 -9.887  -241.462   -207.866
sex      -9.131 -6.749  -191.973   -141.890
age.sex   6.052  5.682   127.227    119.454
bmi.map   4.465  4.700    93.871     98.819
glu.2     2.278  3.460    47.887     72.743
age.ltg   1.098  0.777    23.088     16.335
sex.map   1.023  0.474    21.512      9.957
age.2     0.949  0.806    19.955     16.938
age.glu   0.728  0.498    15.303     10.479
glu       0.639  0.683    13.432     14.351
bmi.2     0.607  1.135    12.767     23.863
age.map   0.538  0.807    11.306     16.966
------------------------------------------------------------------- 

spikeslab documentation built on May 30, 2017, 6:36 a.m.