Summary method for cv.ncvreg objects

# S3 method for cv.ncvreg
summary(object, ...)

# S3 method for summary.cv.ncvreg
print(x, digits, ...)

Arguments

object

A "cv.ncvreg" or "cv.ncvsurv" object.

...

Further arguments passed to or from other methods.

x

A "summary.cv.ncvreg" object.

digits

Number of digits past the decimal point to print out. Can be a vector specifying different display digits for each of the five non-integer printed values.

Value

summary.cv.ncvreg produces an object with S3 class "summary.cv.ncvreg". The class has its own print method and contains the following list elements:

penalty

The penalty used by ncvreg.

model

Either "linear" or "logistic", depending on the family option in ncvreg.

n

Number of observations

p

Number of regression coefficients (not including the intercept).

min

The index of lambda with the smallest cross-validation error.

lambda

The sequence of lambda values used by cv.ncvreg.

cve

Cross-validation error (deviance).

r.squared

Proportion of variance explained by the model, as estimated by cross-validation. For models outside of linear regression, the Cox-Snell approach to defining R-squared is used.

snr

Signal to noise ratio, as estimated by cross-validation.

sigma

For linear regression models, the scale parameter estimate.

pe

For logistic regression models, the prediction error (misclassification error).

References

Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. c("\Sexpr[results=rd]tools:::Rd_expr_doi(\"#1\")", "10.1214/10-AOAS388")doi:10.1214/10-AOAS388

Author

Patrick Breheny

Examples


# Linear regression --------------------------------------------------
data(Prostate)
cvfit <- cv.ncvreg(Prostate$X, Prostate$y)
summary(cvfit)
#> MCP-penalized linear regression with n=97, p=8
#> At minimum cross-validation error (lambda=0.0224):
#> -------------------------------------------------
#>   Nonzero coefficients: 7
#>   Cross-validation error (deviance): 0.54
#>   R-squared: 0.59
#>   Signal-to-noise ratio: 1.46
#>   Scale estimate (sigma): 0.732
#> MCP-penalized linear regression with n=97, p=8
#> At lambda=0.0224:
#> -------------------------------------------------
#>   Nonzero coefficients         :   7
#>   Expected nonzero coefficients:   2.50
#>   Average mfdr (7 features)    :   0.357
#> 
#>          Estimate      z       mfdr Selected
#> lcavol   0.569546  8.986    < 1e-04        *
#> svi      0.752398  4.170 0.00080455        *
#> lweight  0.614420  3.524 0.00822827        *
#> pgg45    0.005324  2.010 0.48176729        *
#> lbph     0.097353  1.891 0.61027217        *
#> age     -0.020913 -2.084 0.62413088        *
#> lcp     -0.104959 -1.965 0.77570294        *

# Logistic regression ------------------------------------------------
data(Heart)
cvfit <- cv.ncvreg(Heart$X, Heart$y, family="binomial")
summary(cvfit)
#> MCP-penalized logistic regression with n=462, p=9
#> At minimum cross-validation error (lambda=0.0270):
#> -------------------------------------------------
#>   Nonzero coefficients: 5
#>   Cross-validation error (deviance): 1.07
#>   R-squared: 0.20
#>   Signal-to-noise ratio: 0.25
#>   Prediction error: 0.279
#> MCP-penalized logistic regression with n=462, p=9
#> At lambda=0.0270:
#> -------------------------------------------------
#>   Nonzero coefficients         :   5
#>   Expected nonzero coefficients:   0.06
#>   Average mfdr (5 features)    :   0.011
#> 
#>         Estimate     z       mfdr Selected
#> age      0.05109 5.946    < 1e-04        *
#> famhist  0.90619 4.143 0.00059229        *
#> tobacco  0.07012 3.328 0.01113128        *
#> typea    0.03045 3.169 0.01861106        *
#> ldl      0.13459 3.062 0.02605639        *

# Cox regression -----------------------------------------------------
data(Lung)
cvfit <- cv.ncvsurv(Lung$X, Lung$y)
summary(cvfit)
#> MCP-penalized Cox regression with n=137, p=8
#> At minimum cross-validation error (lambda=0.1573):
#> -------------------------------------------------
#>   Nonzero coefficients: 3
#>   Cross-validation error (deviance): 7.53
#>   R-squared: 0.29
#>   Signal-to-noise ratio: 0.42
#> MCP-penalized Cox regression with n=137, p=8
#> At lambda=0.1573:
#> -------------------------------------------------
#>   Nonzero coefficients         :   3
#>   Expected nonzero coefficients:   0.26
#>   Average mfdr (3 features)    :   0.086
#> 
#>          Estimate      z    mfdr Selected
#> karno    -0.03322 -6.564 < 1e-04        *
#> squamous -0.31025 -2.872 0.10842        *
#> adeno     0.18197  2.689 0.14969        *