Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates a variety of summary statistics for multistep
(multidimensional step function) objects.
1 2 3 4 5 6 7 8 9 10 |
x |
A |
object |
A |
Dummy variables for compatibility:
... |
Unused. |
na.rm |
Unused. |
'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.
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
Zhou Fang and Nicolai Meinshausen (2009), Liso for High Dimensional Additive Isotonic Regression, available at http://blah.com
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))
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