Summary method for cv.ncvreg objects
A "cv.ncvreg" or "cv.ncvsurv" object.
Further arguments passed to or from other methods.
A "summary.cv.ncvreg" object.
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.
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:
The penalty used by
ncvreg.
Either "linear" or "logistic",
depending on the family option in ncvreg.
Number of observations
Number of regression coefficients (not including the intercept).
The index of lambda with the smallest
cross-validation error.
The sequence of lambda values
used by cv.ncvreg.
Cross-validation error (deviance).
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.
Signal to noise ratio, as estimated by cross-validation.
For linear regression models, the scale parameter estimate.
For logistic regression models, the prediction error (misclassification error).
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
# 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 *