Artificial Data from a Generalized Linear Regression Mixture
Generate random data mixed from k generalized linear regressions (GLMs).
ExLinear(beta, n, xdist = "runif", xdist.args = NULL,
family = c("gaussian","poisson"), sd = 1, ...)beta |
A matrix of regression coefficients. Each row corresponds to a variable, each column to a mixture component. The first row is used as intercept. |
n |
Integer, the number of observations per component. |
xdist |
Character, name of a random number function for the explanatory variables. |
xdist.args |
List, arguments for the random number functions. |
family |
A character string naming a GLM
family.Only |
sd |
Numeric, the error standard deviation for each component for Gaussian responses. |
... |
Used as default for |
First, arguments n (and sd for Gaussian response) are
recycled to the number of mixture components ncol(beta), and
arguments xdist and xdist.args are recycled to the
number of explanatory variables nrow(beta)-1. Then a design
matrix is created for each mixture component by drawing random numbers
from xdist. For each component, the design matrix is multiplied
by the regression coefficients to form the linear predictor. For
Gaussian responses the identity link is used, for Poisson responses
the log link.
The true cluster memberships are returned as attribute "clusters".
## simple example in 2d
beta <- matrix(c(1, 2, 3, -1), ncol = 2)
sigma <- c(.5, 1)
df1 <- ExLinear(beta, 100, sd = sigma, min = -1, max = 2)
plot(y~x1, df1, col = attr(df1, "clusters"))
## add a second explanatory variable with exponential distribution
beta2 <- rbind(beta, c(-2, 2))
df2 <- ExLinear(beta2, 100, sd = c(.5, 1),
xdist = c("runif", "rexp"),
xdist.args = list(list(min = -1, max = 2),
list(rate = 3)))
summary(df2)
opar = par("mfrow")
par(mfrow = 1:2)
hist(df2$x1)
hist(df2$x2)
par(opar)
f1 <- flexmix(y ~ ., data = df2, k = 2)
## sort components on Intercept
f1 <- relabel(f1, "model", "Intercept")
## the parameters should be close to the true beta and sigma
round(parameters(f1), 3)
rbind(beta2, sigma)
### A simple Poisson GLM
df3 <- ExLinear(beta/2, 100, min = -1, max = 2, family = "poisson")
plot(y ~ x1, df3, col = attr(df3, "clusters"))
f3 <- flexmix(y ~ ., data = df3, k = 2,
model = FLXMRglm(family = "poisson"))
round(parameters(relabel(f3, "model", "Intercept")), 3)
beta/2Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.