View source: R/mvnorm.l2boost.R
mvnorm.l2boost | R Documentation |
Create simulated dataset from a multivariate normal. Used to recreate data simulations from Ehrlinger and Ishwaran (2012).
mvnorm.l2boost(n = 100, p = 100, beta = NULL, which.beta = NULL, rho = 0)
n |
number of observations |
p |
number of coordinate directions in the design matrix |
beta |
a "true" beta vector of length p (default=NULL) See details. |
which.beta |
indicator vector for which beta coefficients to include as signal in simulation (default=NULL) see details |
rho |
correlation coefficient between coordinate directions |
By default, mvnorm.l2boost creates a data set of n multivariate normal random observations of p covariates (see MASS:mvrnorm). The correlation matrix is constructed with 1 on the diagonals and the correlation coefficient rho on the off diagonals.
The response is constructed as follows: If a true beta vector is not supplied, the first 10 beta coefficients carry the signal with a value of 5, and the remaining p-10 values are set to zero. Given a beta.true vector, all values are used as specified. The coefficent vector is truncated to have p signal terms if length(beta.true) > p, and noise coordinates are added if length(beta.true) < p.
It is possible to pass an indicator vector which.beta to select specific signal elements from the full vector beta.true.
call Matched function call
x design matrix of size n x p
y response vector of length n
Ehrlinger J., and Ishwaran H. (2012). "Characterizing l2boosting" Ann. Statist., 40 (2), 1074-1101
#-------------------------------------------------------------------------- # Example: Multivariate normal data simulation # Create a (reproducable) data set of size 100 x 100 set.seed(1024) n<- 100 p<- 100 # Set 10 signal variables using a uniform beta=5, the remaining (p-10)=90 are # set to zero indicating random noise. beta <- c(rep(5,10), rep(0,p-10)) # Example with orthogonal design matrix columns (orthogonal + noise) ortho.data <- mvnorm.l2boost(n, p, beta) cbind(ortho.data$y[1:5],ortho.data$x[1:5,]) # Example with correlation between design matrix columns corr.data <- mvnorm.l2boost(n, p, beta, rho=0.65) cbind(corr.data$y[1:5],corr.data$x[1:5,])
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