IF.lmer: Invisible Fence model selection (Linear Mixed Model)

In fence: Using Fence Methods for Model Selection

Description

Invisible Fence model selection (Linear Mixed Model)

Usage

IF.lmer(full, data, B = 100, REML = TRUE, method = c("marginal",
  "conditional"), cpus = parallel::detectCores(), lftype = c("abscoef",
  "tvalue"))

Arguments

full	formula of full model
data	data
B	number of bootstrap sample, parametric for lmer
REML	Restricted maximum likelihood estimation
method	choose either marginal (e.g., GEE) or conditional model
cpus	Number of parallel computers
lftype	subtractive measure type, e.g., absolute value of coefficients, p-value, t-value, etc.

Details

This method (Jiang et. al, 2011) is motivated by computational expensive in complex and high dimensional problem. The idea of the method–there is the best model in each dimension (in model space). The boostrapping determines the coverage probability of the selected model in each dimensions. The parsimonious model is the selected model with the highest coverage probabily (except the one for the full model, always probability of 1.)

Value

full	list the full model
B	list the number of bootstrap samples that have been used
freq	list the coverage probabilities of the selected model for each dimension
size	list the number of variables in the parsimonious model
term	list variables included in the full model
model	list the variables selected in-the-order in the parsimonious model

@note The current Invisible Fence focuses on variable selection. The current routine is applicable to the case in which the subtractive measure is the absolute value of the coefficients, p-value, t-value. However, the method can be extended to other subtractive measures. See Jiang et. al (2011) for more details.

Author(s)

Jiming Jiang Jianyang Zhao J. Sunil Rao Thuan Nguyen

References

• Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692

• Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415.

Examples

require(fence)
library(snow)
library(MASS)
data("X.lmer")
data = data.frame(X.lmer)
# non-zero beta I.col.2, I.col.3a, I.col.3b, V5, V7, V8, V9
beta = matrix(c(0, 1, 1, 1, 1, 0, 0.1, 0.05, 0.25, 0), ncol = 1)
set.seed(1234)
alpha = rep(rnorm(100), each = 3)
mu = alpha + as.matrix(data[,-1]) %*% beta
data$id = as.factor(data$id)
data$y = mu + rnorm(300)
raw = "y ~ (1|id)+I.col.2+I.col.3a+I.col.3b"
for (i in 5:10) {
    raw = paste0(raw, "+V", i)
}
full = as.formula(raw)
# The following output takes more than 5 seconds (~70 seconds) to run. 

# obj1.lmer = IF.lmer(full = full, data = data, B = 100, method="conditional",lftype = "abscoef")
# sort(obj1.lmer$model) 

# obj2.lmer = IF.lmer(full = full, data = data, B = 100, method="conditional",lftype = "tvalue")
# sort(obj2.lmer$model)

# Similarly, the following scenarios can be run

# obj2.lmer = IF.lmer(full = full, data = data, B = 100, method="conditional",lftype = "tvalue")
# sort(obj2.lmer$model)
# obj1.lm = IF.lmer(full = full, data = data, B = 100, method="marginal", lftype = "abscoef")
# sort(names(obj1.lm$model$coefficients[-1]))
# obj2.lm = IF.lmer(full = full, data = data, B = 100, method="marginal", lftype = "tvalue")
# sort(names(obj2.lm$model$coefficients[-1]))

fence documentation built on May 1, 2019, 11:32 p.m.