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.
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 instances
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 descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. 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=0.0638):
#> -------------------------------------------------
#> Nonzero coefficients: 5
#> Cross-validation error (deviance): 0.53
#> R-squared: 0.60
#> Signal-to-noise ratio: 1.51
#> Scale estimate (sigma): 0.725
#> MCP-penalized linear regression with n=97, p=8
#> At lambda=0.0638:
#> -------------------------------------------------
#> Nonzero coefficients : 5
#> Expected nonzero coefficients: 1.87
#> Average mfdr (5 features) : 0.374
#>
#> Estimate z mfdr Selected
#> lcavol 0.533903 8.500 < 1e-04 *
#> svi 0.692676 3.873 0.015225 *
#> lweight 0.611121 3.537 0.050561 *
#> lbph 0.054944 1.584 0.885443 *
#> age -0.006941 -1.331 0.917808 *
# 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=0.0177):
#> -------------------------------------------------
#> Nonzero coefficients: 7
#> Cross-validation error (deviance): 1.07
#> R-squared: 0.20
#> Signal-to-noise ratio: 0.25
#> Prediction error: 0.262
#> MCP-penalized logistic regression with n=462, p=9
#> At lambda=0.0177:
#> -------------------------------------------------
#> Nonzero coefficients : 7
#> Expected nonzero coefficients: 1.86
#> Average mfdr (7 features) : 0.266
#>
#> Estimate z mfdr Selected
#> age 0.050443 5.812 < 1e-04 *
#> famhist 0.911639 4.124 0.0023397 *
#> tobacco 0.080099 3.301 0.0451490 *
#> typea 0.037675 3.208 0.0601656 *
#> ldl 0.170154 3.160 0.0693819 *
#> obesity -0.015959 -1.322 0.8199744 *
#> sbp 0.001333 1.054 0.8623941 *
# 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=0.1276):
#> -------------------------------------------------
#> Nonzero coefficients: 3
#> Cross-validation error (deviance): 7.50
#> R-squared: 0.31
#> Signal-to-noise ratio: 0.46
#> MCP-penalized Cox regression with n=137, p=8
#> At lambda=0.1276:
#> -------------------------------------------------
#> Nonzero coefficients : 3
#> Expected nonzero coefficients: 1.28
#> Average mfdr (3 features) : 0.426
#>
#> Estimate z mfdr Selected
#> karno -0.03318 -6.589 < 1e-04 *
#> squamous -0.38295 -2.732 0.59462 *
#> adeno 0.33542 2.585 0.68452 *