R: Summarize a nested.glmnetr() output object

summary.nested.glmnetr {glmnetr}

R Documentation

Summarize a nested.glmnetr() output object

Description

Summarize the model fit from a nested.glmnetr() output object, i.e. the fit of a cross-validation informed relaxed lasso model fit, inferred by nested cross validation. Else summarize the cross-validated model fit.

Usage

## S3 method for class 'nested.glmnetr'
summary(
  object,
  cvfit = FALSE,
  pow = 2,
  printg1 = FALSE,
  digits = 4,
  Call = NULL,
  onese = 0,
  table = 1,
  tuning = 0,
  width = 84,
  ...
)

Arguments

`object`	a nested.glmnetr() output object.
`cvfit`	default of FALSE to summarize fit of a cross validation informed relaxed lasso model fit, inferred by nested cross validation. Option of TRUE will describe the cross validation informed relaxed lasso model itself.
`pow`	the power to which the average of correlations is to be raised. Only applies to the "gaussian" model. Default is 2 to yield R-square but can be on to show correlations. Pow is ignored for the family of "cox" and "binomial".
`printg1`	TRUE to also print out the fully penalized lasso beta, else to suppress. Only applies to cvfit=TRUE.
`digits`	digits for printing of deviances, linear calibration coefficients and agreement (concordances and R-squares).
`Call`	1 to print call used in generation of the object, 0 or NULL to not print
`onese`	0 (default) to not include summary for 1se lasso fits in tables, 1 to include
`table`	1 to print table to console, 0 to output the tabled information to a data frame
`tuning`	1 to print tuning parameters, 0 (default) to not print
`width`	character width of the text body preceding the performance measures which can be adjusted between 60 and 120.
`...`	Additional arguments passed to the summary function.

Value

- a nested cross validation fit summary, or a cross validation model summary.

Examples


sim.data=glmnetr.simdata(nrows=1000, ncols=100, beta=NULL)
xs=sim.data$xs 
y_=sim.data$yt
event=sim.data$event
# for this example we use a small number for folds_n to shorten run time 
fit3 = nested.glmnetr(xs, NULL, y_, event, family="cox", folds_n=3)  
summary(fit3)