simrel (version 2.1.0)

bisimrel: Simulation of Multivariate Linear Model data with response

Description

Simulation of Multivariate Linear Model data with response

Usage

bisimrel(
  n = 50,
  p = 100,
  q = c(10, 10, 5),
  rho = c(0.8, 0.4),
  relpos = list(c(1, 2), c(2, 3)),
  gamma = 0.5,
  R2 = c(0.8, 0.8),
  ntest = NULL,
  muY = NULL,
  muX = NULL,
  sim = NULL
)

Arguments

n

Number of training samples

p

Number of x-variables

q

Vector of number of relevant predictor variables for first, second and common to both responses

rho

A 2-element vector, unconditional and conditional correlation between y_1 and y_2

relpos

A list of position of relevant component for predictor variables. The list contains vectors of position index, one vector or each response

gamma

A declining (decaying) factor of eigen value of predictors (X). Higher the value of gamma, the decrease of eigenvalues will be steeper

R2

Vector of coefficient of determination for each response

ntest

Number of test observation

muY

Vector of average (mean) for each response variable

muX

Vector of average (mean) for each predictor variable

sim

A simrel object for reusing parameters setting

Value

A simrel object with all the input arguments along with following additional items

X

Simulated predictors

Y

Simulated responses

beta

True regression coefficients

beta0

True regression intercept

relpred

Position of relevant predictors

testX

Test Predictors

testY

Test Response

minerror

Minimum model error

Rotation

Rotation matrix of predictor (R)

type

Type of simrel object, in this case bivariate

lambda

Eigenvalues of predictors

Sigma

Variance-Covariance matrix of response and predictors

References

S<U+00E6>b<U+00F8>, S., Alm<U+00F8>y, T., & Helland, I. S. (2015). simrel<U+2014>A versatile tool for linear model data simulation based on the concept of a relevant subspace and relevant predictors. Chemometrics and Intelligent Laboratory Systems, 146, 128-135.

Alm<U+00F8>y, T. (1996). A simulation study on comparison of prediction methods when only a few components are relevant. Computational statistics & data analysis, 21(1), 87-107.

Examples

# NOT RUN {
sobj <- bisimrel(
   n = 100,
   p = 10,
   q = c(5, 5, 3),
   rho = c(0.8, 0.4),
   relpos = list(c(1, 2, 3), c(2, 3, 4)),
   gamma = 0.7,
   R2 = c(0.8, 0.8)
)
# Regression Coefficients from this simulation
sobj$beta
# }