Ftest: Apply the Farmtest method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the Farmtest method to the Laplace factor model

Description

This function simulates data from a Lapalce factor model and applies the FarmTest for multiple hypothesis testing. It calculates the false discovery rate (FDR) and power of the test.

Usage

Ftest(
  data,
  p1,
  alpha = 0.05,
  K = -1,
  alternative = c("two.sided", "less", "greater")
)

Arguments

data	A matrix or data frame of simulated or observed data from a Laplace factor model.
p1	The number or proportion of non-zero hypotheses.
alpha	The significance level for controlling the false discovery rate (default: 0.05).
K	The number of factors to estimate (default: -1, meaning auto-detect).
alternative	The alternative hypothesis: "two.sided", "less", or "greater" (default: "two.sided").

Value

A list containing the following elements:

FDR	The false discovery rate, which is the proportion of false positives among all discoveries (rejected hypotheses).
Power	The statistical power of the test, which is the probability of correctly rejecting a false null hypothesis.
PValues	A vector of p-values associated with each hypothesis test.
RejectedHypotheses	The total number of hypotheses that were rejected by the FarmTest.
reject	Indices of rejected hypotheses.
means	Estimated means.

Examples

library(LaplacesDemon)
library(MASS)
n=1000
p=10
m=5
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
lanor <- rlaplace(n*p,0,1)
epsilon=matrix(lanor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
p1=40
results <- Ftest(data, p1)
print(results$FDR)
print(results$Power)

LFM documentation built on Dec. 6, 2025, 5:06 p.m.