Print a Cross-Validated Parity Regression Model Object

Prints a summarized output of a fitted cross-validated parity regression model object. It clearly displays the optimal tuning parameters and the resulting estimated coefficients.

Usage

# S3 method for class 'cv.savvyPR'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

A fitted model object of class "cv.savvyPR" returned by cv.savvyPR.

digits

Significant digits to be used in the printout.

...

Additional arguments passed to the generic print function.

Value

Invisibly returns a data frame summarizing the cross-validation results, including the parameterization method, number of non-zero coefficients, and optimal tuning parameters.

Details

Print a Cross-Validated Parity Regression Model Object

This function is an S3 method for the generic print function. It formats and prints the matched call that produced the cv.savvyPR object, followed by a summary data frame. This summary includes:

• The parameterization method used ("budget" or "target").

• The number of non-zero coefficients.

• Whether an intercept was included.

• The optimal tuning value (val) and/or lambda parameter, depending on the model_type (PR1, PR2, or PR3).

Finally, it prints a data frame of the optimally tuned estimated coefficients.

See also

cv.savvyPR

Author

Ziwei Chen, Vali Asimit and Pietro Millossovich
Maintainer: Ziwei Chen <ziwei.chen.3@citystgeorges.ac.uk>

Examples

# \donttest{
# Generate synthetic data
set.seed(123)
n <- 100
p <- 10
x <- matrix(rnorm(n * p), n, p)
beta <- matrix(rnorm(p), p, 1)
y <- x %*% beta + rnorm(n, sd = 0.5)

# Fit and print a cross-validated budget-based parity regression model
cv_fit_budget <- cv.savvyPR(x, y, method = "budget", model_type = "PR3")
print(cv_fit_budget)
#> 
#> Call:  cv.savvyPR(x = x, y = y, method = "budget", model_type = "PR3") 
#> 
#>  Method Number of Non-Zero Coefficients Intercept Included Fixed Val
#>  budget                              11                Yes         0
#>  Optimal Lambda Value
#>                     0
#> 
#> Coefficients:
#>  Coefficient Estimate
#>  (Intercept)   0.0659
#>           X1  -0.9956
#>           X2  -1.0854
#>           X3   0.0103
#>           X4  -0.0654
#>           X5  -2.6262
#>           X6   1.0722
#>           X7   0.2621
#>           X8   2.3303
#>           X9   0.6715
#>          X10  -0.4369

# Fit and print a cross-validated target-based parity regression model
cv_fit_target <- cv.savvyPR(x, y, method = "target", model_type = "PR1")
print(cv_fit_target)
#> 
#> Call:  cv.savvyPR(x = x, y = y, method = "target", model_type = "PR1") 
#> 
#>  Method Number of Non-Zero Coefficients Intercept Included Optimal Val
#>  target                              11                Yes           0
#>  Fixed Lambda Value
#>                   0
#> 
#> Coefficients:
#>  Coefficient Estimate
#>  (Intercept)   0.0659
#>           X1  -0.9956
#>           X2  -1.0854
#>           X3   0.0103
#>           X4  -0.0654
#>           X5  -2.6262
#>           X6   1.0722
#>           X7   0.2621
#>           X8   2.3303
#>           X9   0.6715
#>          X10  -0.4369
# }