| Type: | Package |
| Version: | 0.1.1 |
| Title: | Distributed Laplace Factor Model |
| Description: | Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | stats, FarmTest, MASS, LaplacesDemon, matrixcalc, relliptical, LFM |
| NeedsCompilation: | no |
| Language: | en-US |
| Author: | Guangbao Guo [aut, cre], Siqi Liu [aut] |
| Depends: | R (≥ 3.5.0) |
| BuildManual: | yes |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Maintainer: | Guangbao Guo <ggb11111111@163.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-14 12:40:22 UTC |
| Packaged: | 2025-11-14 08:54:42 UTC; R7000 |
Distributed general unilateral loading principal component
Description
Distributed general unilateral loading principal component
Usage
DGulPC(data, m, n1, K)
Arguments
data |
is a total data set |
m |
is the number of principal component |
n1 |
is the length of each data subset |
K |
is the number of nodes |
Value
AU1,AU2,DU3,Shat
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
DGulPC(data_a,m=3,n1=128,K=2)
Distributed Incremental Principal Component Analysis (DIPC)
Description
Apply IPC in a distributed manner across K nodes.
Usage
DIPC(data, m, eta, K)
Arguments
data |
Matrix of input data (n × p). |
m |
Number of principal components. |
eta |
Proportion of initial batch to total data within each node. |
K |
Number of nodes (distributed splits). |
Value
List with per-node results and aggregated averages.
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
m=5
eta=0.8
K=2
results <- DIPC(data_a, m, eta, K)
Distributed principal component
Description
Distributed principal component
Usage
DPC(data, m, n1, K)
Arguments
data |
is a total data set |
m |
is the number of principal component |
n1 |
is the length of each data subset |
K |
is the number of nodes |
Value
Ahat,Dhat,Sigmahathat
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
DPC(data_a,m=3,n1=128,K=2)
Distributed projection principal component
Description
Distributed projection principal component
Usage
DPPC(data, m, n1, K)
Arguments
data |
is a total data set |
m |
is the number of principal component |
n1 |
is the length of each data subset |
K |
is the number of nodes |
Value
Apro,pro,Sigmahathatpro
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
DPPC(data_a,m=3,n1=128,K=2)
Apply the FanPC method to the Laplace factor model
Description
This function performs Factor Analysis via Principal Component (FanPC) on a given data set. It calculates the estimated factor loading matrix (AF), specific variance matrix (DF), and the mean squared errors.
Usage
FanPC(data, m)
Arguments
data |
A matrix of input data. |
m |
is the number of principal component |
Value
AF,DF,SigmahatF
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
results <- FanPC(data, m)
print(results)
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)
Arguments
data |
A matrix or data frame of simulated or observed data from a Laplace factor model. |
p1 |
The proportion of non-zero hypotheses. |
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. |
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)
General unilateral loading principal component
Description
General unilateral loading principal component
Usage
GulPC(data, m)
Arguments
data |
is a total data set |
m |
is the number of first layer principal component |
Value
AU1,AU2,DU3,SigmaUhat
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
GulPC(data=data_a,m=5)
Generate Laplace factor models
Description
The function is to generate Laplace factor model data. The function supports various distribution types for generating the data, including: - 'truncated_laplace': Truncated Laplace distribution - 'log_laplace': Univariate Symmetric Log-Laplace distribution - 'Asymmetric Log_Laplace': Log-Laplace distribution - 'Skew-Laplace': Skew-Laplace distribution
Usage
LFM(n, p, m, distribution_type)
Arguments
n |
An integer specifying the sample size. |
p |
An integer specifying the sample dimensionality or the number of variables. |
m |
An integer specifying the number of factors in the model. |
distribution_type |
A character string indicating the type of distribution to use for generating the data. |
Value
A list containing the following elements:
data |
A numeric matrix of the generated data. |
A |
A numeric matrix representing the factor loadings. |
D |
A numeric matrix representing the uniquenesses, which is a diagonal matrix. |
Examples
library(MASS)
library(matrixcalc)
library(relliptical)
n <- 1000
p <- 10
m <- 5
sigma1 <- 1
sigma2 <- matrix(c(1,0.7,0.7,1), 2, 2)
distribution_type <- "truncated_laplace"
results <- LFM(n, p, m, distribution_type)
print(results)
Principal component
Description
Principal component
Usage
PC(data, m)
Arguments
data |
is a total data set |
m |
is the number of principal component |
Value
Ahat, Dhat, Sigmahat
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
PC(data_a,m=5)
Projection principal component
Description
Projection principal component
Usage
PPC(data, m)
Arguments
data |
is a total data set |
m |
is the number of principal component |
Value
Apro, Dpro, Sigmahatpro
Examples
library(LFM)
data_from_package <- Wine
data_a <- Wine
PPC(data=data_a,m=5)
Factor Model Testing with Wald, GRS, PY tests and FDR control
Description
Performs comprehensive factor model testing including joint tests (Wald, GRS, PY), individual asset t-tests, and False Discovery Rate control.
Usage
factor.tests(ret, fac, q.fdr = 0.05)
Arguments
ret |
A T × N matrix representing the excess returns of N assets at T time points. |
fac |
A T × K matrix representing the returns of K factors at T time points. |
q.fdr |
The significance level for FDR (False Discovery Rate) testing, defaulting to 5%. |
Value
A list containing the following components:
alpha |
N-vector of estimated alphas for each asset |
tstat |
N-vector of t-statistics for testing individual alphas |
pval |
N-vector of p-values for individual alpha tests |
Wald |
Wald test statistic for joint alpha significance |
p_Wald |
p-value for Wald test |
GRS |
GRS test statistic (finite-sample F-test) |
p_GRS |
p-value for GRS test |
PY |
Pesaran and Yamagata test statistic |
p_PY |
p-value for PY test |
reject_fdr |
Logical vector indicating which assets have significant alphas after FDR correction |
fdr_p |
Adjusted p-values using Benjamini-Hochberg procedure |
power_proxy |
Number of significant assets after FDR correction |
Examples
set.seed(42)
T <- 120
N <- 25
K <- 3
fac <- matrix(rnorm(T * K), T, K)
beta <- matrix(rnorm(N * K), N, K)
alpha <- rep(0, N)
alpha[1:3] <- 0.4 / 100 # 3 non-zero alphas
eps <- matrix(rnorm(T * N, sd = 0.02), T, N)
ret <- alpha + fac %*% t(beta) + eps
results <- factor.tests(ret, fac, q.fdr = 0.05)
# View results
cat("Wald test p-value:", results$p_Wald, "\n")
cat("GRS test p-value:", results$p_GRS, "\n")
cat("PY test p-value:", results$p_PY, "\n")
cat("Significant assets after FDR:", results$power_proxy, "\n")