summary.multistep: Summary statistics for multistep objects In liso: Fitting lasso penalised additive isotone models

Description

Calculates a variety of summary statistics for `multistep` (multidimensional step function) objects.

Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## S3 method for class 'multistep' max(x, ..., na.rm) ## S3 method for class 'multistep' min(x, ..., na.rm) ## S3 method for class 'multistep' dim(x) ## S3 method for class 'multistep' abs(x) ## S3 method for class 'multistep' summary(object, ...) ```

Arguments

 `x` A `multistep` object. `object` A `multistep` object.

Dummy variables for compatibility:

 `...` Unused. `na.rm` Unused.

Details

'max' and 'min' returns the maximum or minimum respectively of each covariate component.

'dim' returns the number of non-zero steps in each covariate component.

'abs' returns the total variation of each covariate component.

'summary' returns a list containing all of the above.

Value

For 'max', 'min', 'abs', 'dim', a vector with length equal to the number of covariates.

For 'summary', a list containing 'max', 'min', 'totalvar', 'dim', each being a vector of length equal to the number of covariates.

Zhou Fang

References

Zhou Fang and Nicolai Meinshausen (2009), Liso for High Dimensional Additive Isotonic Regression, available at http://blah.com

`multistep`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```## Use the method on a simulated data set set.seed(79) n <- 100; p <- 50 ## Simulate design matrix and response x <- matrix(runif(n * p, min = -2.5, max = 2.5), nrow = n, ncol = p) y <- scale(3 * (x[,1]> 0), scale=FALSE) + x[,2]^3 + rnorm(n) ## try lambda = 2 fits <- liso.backfit(x,y, 2) ## Plot some diagnostics plot(max(fits)) plot(min(fits)) plot(abs(fits)) plot(dim(fits)) print(summary(fits)) ```