simulXy function

Simulate model matrix and response

Simulate model matrix and response from a specified distribution.

simulXy(n, p, interc = 0, beta, family = gaussian(), prop =
  0.1, lim.b = c(-3, 3), sigma = 1, size = 1, rho = 0,
  scale = TRUE, seed, X)

Arguments

• n: number of observations.
• p: total number of covariates in the model matrix.
• interc: the model intercept.
• beta: the vector of p coefficients in the linear predictor.
• family: a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. Only gaussian, binomial or poisson are allowed.
• prop: if beta is missing, prop represent the quote of non-null coefficients out of p. The default is 0.10 p.
• lim.b: if beta is missing, the coefficients come from uniform variates in lim.b. The default is (-3,3).
• sigma: if family is 'gaussian', the standard deviation of the response. The default is 1.
• size: if family is 'binomial', the number of trials to build the response vector. The default is 1.
• rho: correlation value to define the variance covariance matrix to build the model matrix, i.e., rho^|i-j| i,j = 1,...,p and i different from j. The default is 0.
• scale: Should the columns of the mdoel matrix be scaled? The default is TRUE
• seed: optional, the seed to generate the data.
• X: optional, the model matrix.

Examples

n <- 100
p <- 100
beta <- c(runif(10, -3, 3), rep(0, p-10))
dat <- simulXy(n, p,  beta = beta, seed=1234)