| Title: | Penalized Generalized Estimating Equations in High-Dimension |
|---|---|
| Description: | Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates. |
| Authors: | Gul Inan (Lecturer, Middle East Technical University), Jianhui Zhou (Associate Professor, University of Virginia) and Lan Wang (Professor, University of Minnesota) |
| Maintainer: | Gul Inan <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.5 |
| Built: | 2024-11-13 06:26:40 UTC |
| Source: | CRAN |
This package fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates through accommodating SCAD-penalty function into generalized estimating equations.
This package consists of three functions. The function
PGEE fits penalized generalized estimating equations to the data. But, before that,
the tuning parameter should be estimated through the function CVfit.
On the other hand, the function MGEE fits unpenalized generalized estimating
equations to the data.
Gul Inan, Jianhui Zhou and Lan Wang
Maintainer: Gul Inan
Wang, L., Zhou, J., and Qu, A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics, 68, 353–360.
This function computes cross-validated tuning parameter value for longitudinal data with working independence structure.
CVfit(formula, id, data, family, scale.fix, scale.value, fold, lambda.vec, pindex, eps, maxiter, tol)CVfit(formula, id, data, family, scale.fix, scale.value, fold, lambda.vec, pindex, eps, maxiter, tol)
formula |
A formula expression in the form of |
id |
A vector for identifying subjects/clusters. |
data |
A data frame which stores the variables in |
scale.fix |
A logical variable; if true, the scale parameter is fixed at the value of |
scale.value |
If |
family |
A |
fold |
The number of folds used in cross-validation. |
lambda.vec |
A vector of tuning parameters that will be used in the cross-validation. |
pindex |
An index vector showing the parameters which are not subject to penalization. The default value
is |
eps |
A numerical value for the epsilon used in minorization-maximization algorithm. The default value is
|
maxiter |
The number of iterations that is used in the estimation algorithm. The default value is |
tol |
The tolerance level that is used in the estimation algorithm. The default value is |
An object class of CVfit.
Wang, L., Zhou, J., and Qu, A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics, 68, 353–360.
This function fits a generalized estimating equation model to longitudinal data.
MGEE(formula, id, data, na.action = NULL, family = gaussian(link = "identity"), corstr = "independence", Mv = NULL, beta_int = NULL, R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 25, tol = 10^-3, silent = TRUE)MGEE(formula, id, data, na.action = NULL, family = gaussian(link = "identity"), corstr = "independence", Mv = NULL, beta_int = NULL, R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 25, tol = 10^-3, silent = TRUE)
formula |
A formula expression in the form of |
id |
A vector for identifying subjects/clusters. |
data |
A data frame which stores the variables in |
na.action |
A function to remove missing values from the data. Only |
family |
A |
corstr |
A character string, which specifies the type of correlation structure.
Structures supported in |
Mv |
If either |
beta_int |
User specified initial values for regression parameters. The default value is |
R |
If |
scale.fix |
A logical variable; if true, the scale parameter is fixed at the value of |
scale.value |
If |
maxiter |
The number of iterations that is used in the estimation algorithm. The default value is |
tol |
The tolerance level that is used in the estimation algorithm. The default value is |
silent |
A logical variable; if false, the regression parameter estimates at each iteration are
printed. The default value is |
An object class of MGEE representing the fit.
The structures "non_stat_M_dep" and "unstructured" are valid only when the data is balanced.
Liang, K.Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.
Zeger, S.L. and Liang, K.Y. (1986) . Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121–130.
This function fits a penalized generalized estimating equation model to longitudinal data.
PGEE(formula, id, data, na.action = NULL, family = gaussian(link = "identity"), corstr = "independence", Mv = NULL, beta_int = NULL, R = NULL, scale.fix = TRUE, scale.value = 1, lambda, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3, silent = TRUE)PGEE(formula, id, data, na.action = NULL, family = gaussian(link = "identity"), corstr = "independence", Mv = NULL, beta_int = NULL, R = NULL, scale.fix = TRUE, scale.value = 1, lambda, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3, silent = TRUE)
formula |
A formula expression in the form of |
id |
A vector for identifying subjects/clusters. |
data |
A data frame which stores the variables in |
na.action |
A function to remove missing values from the data. Only |
family |
A |
corstr |
A character string, which specifies the type of correlation structure.
Structures supported in |
Mv |
If either |
beta_int |
User specified initial values for regression parameters. The default value is |
R |
If |
scale.fix |
A logical variable; if true, the scale parameter is fixed at the value of |
scale.value |
If |
lambda |
A numerical value for the penalization parameter of the scad function, which is estimated via cross-validation. |
pindex |
An index vector showing the parameters which are not subject to penalization. The default value
is |
eps |
A numerical value for the epsilon used in minorization-maximization algorithm. The default value is
|
maxiter |
The number of iterations that is used in the estimation algorithm. The default value is |
tol |
The tolerance level that is used in the estimation algorithm. The default value is |
silent |
A logical variable; if false, the regression parameter estimates at each iteration are
printed. The default value is |
An object class of PGEE representing the fit.
Wang, L., Zhou, J., and Qu, A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics, 68, 353–360.
# Consider an example similar to example 1 # in Wang et al. (2012). # required R package library(mvtnorm) # number of subjects n <- 200 # number of covariates pn <- 10 # number of time points m <- 4 # vector if subject ids id.vect <- rep(1:n, each = m) # covariance matrix of (pn-1) number of continuous covariates X.sigma <- matrix(0,(pn-1),(pn-1)) { for (i in 1:(pn-1)) X.sigma[i,] <- 0.5^(abs((1:(pn-1))-i)) } # generate matrix of covariates x.mat <- as.matrix(rmvnorm(n*m, mean = rep(0,(pn-1)), X.sigma)) x.mat <- cbind(rbinom(n*m,1, 0.5), x.mat) # true values beta.true <- c(2,3,1.5,2,rep(0,6)) sigma2 <- 1 rho <- 0.5 R <- matrix(rho,m,m)+diag(rep(1-rho,m)) # covariance matrix of error SIGMA <- sigma2*R error <- rmvnorm(n, mean = rep(0,m),SIGMA) # generate longitudinal data with continuous outcomes y.temp <- x.mat%*%beta.true y.vect <- y.temp+as.vector(t(error)) mydata <- data.frame(id.vect,y.vect,x.mat) colnames(mydata) <- c("id","y",paste("x",1:length(beta.true),sep = "")) ###Input Arguments for CVfit fitting### library(PGEE) formula <- "y ~.-id-1" data <- mydata family <- gaussian(link = "identity") lambda.vec <- seq(0.1,1,0.1) ## Not run: cv <- CVfit(formula = formula, id = id, data = data, family = family, fold = 4, lambda.vec = lambda.vec, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3) names(cv) cv$lam.opt ## End(Not run) lambda <- 0.1 #this value obtained through CVfit # analyze the data through penalized generalized estimating equations myfit1 <- PGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "exchangeable", Mv = NULL, beta_int = c(rep(0,length(beta.true))), R = NULL, scale.fix = TRUE, scale.value = 1, lambda = lambda, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3, silent = TRUE) summary(myfit1) # analyze the data through unpenalized generalized estimating equations myfit2 <- MGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "exchangeable", Mv = NULL, beta_int = c(rep(0,length(beta.true))), R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 30, tol = 10^-3, silent = TRUE) summary(myfit2)# Consider an example similar to example 1 # in Wang et al. (2012). # required R package library(mvtnorm) # number of subjects n <- 200 # number of covariates pn <- 10 # number of time points m <- 4 # vector if subject ids id.vect <- rep(1:n, each = m) # covariance matrix of (pn-1) number of continuous covariates X.sigma <- matrix(0,(pn-1),(pn-1)) { for (i in 1:(pn-1)) X.sigma[i,] <- 0.5^(abs((1:(pn-1))-i)) } # generate matrix of covariates x.mat <- as.matrix(rmvnorm(n*m, mean = rep(0,(pn-1)), X.sigma)) x.mat <- cbind(rbinom(n*m,1, 0.5), x.mat) # true values beta.true <- c(2,3,1.5,2,rep(0,6)) sigma2 <- 1 rho <- 0.5 R <- matrix(rho,m,m)+diag(rep(1-rho,m)) # covariance matrix of error SIGMA <- sigma2*R error <- rmvnorm(n, mean = rep(0,m),SIGMA) # generate longitudinal data with continuous outcomes y.temp <- x.mat%*%beta.true y.vect <- y.temp+as.vector(t(error)) mydata <- data.frame(id.vect,y.vect,x.mat) colnames(mydata) <- c("id","y",paste("x",1:length(beta.true),sep = "")) ###Input Arguments for CVfit fitting### library(PGEE) formula <- "y ~.-id-1" data <- mydata family <- gaussian(link = "identity") lambda.vec <- seq(0.1,1,0.1) ## Not run: cv <- CVfit(formula = formula, id = id, data = data, family = family, fold = 4, lambda.vec = lambda.vec, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3) names(cv) cv$lam.opt ## End(Not run) lambda <- 0.1 #this value obtained through CVfit # analyze the data through penalized generalized estimating equations myfit1 <- PGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "exchangeable", Mv = NULL, beta_int = c(rep(0,length(beta.true))), R = NULL, scale.fix = TRUE, scale.value = 1, lambda = lambda, pindex = NULL, eps = 10^-6, maxiter = 30, tol = 10^-3, silent = TRUE) summary(myfit1) # analyze the data through unpenalized generalized estimating equations myfit2 <- MGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "exchangeable", Mv = NULL, beta_int = c(rep(0,length(beta.true))), R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 30, tol = 10^-3, silent = TRUE) summary(myfit2)
A yeast cell-cycle gene expression data set collected in the CDC15 experiment of Spellman et al. (1998) where genome-wide mRNA levels of 6178 yeast open reading frames (ORFs) in a two cell-cycle period were measured at M/G1-G1-S-G2-M stages. However, to better understand the phenomenon underlying cell-cycle process, it is important to identify transcription factors (TFs) that regulate the gene expression levels of cell cycle-regulated genes. In this study, we presented a subset of 283 cell-cycled-regularized genes observed over 4 time points at G1 stage and the standardized binding probabilities of a total of 96 TFs obtained from a mixture model approach of Wang et al. (2007) based on the ChIP data of Lee et al. (2002).
data("yeastG1")data("yeastG1")
A data frame with 1132 observations (283 cell-cycled-regularized genes observed over 4 time points) with 99 variables (e.g., id, y, time, and 96 TFs).
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., et al. (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799–804.
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., and Futcher, B. (1998). Comprehensive identification of cell cycle regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of Cell, 9, 3273–3297.
Wang, L., Chen, G., and Li, H. (2007). Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics, 23, 1486–1494.
Wang, L., Zhou, J., and Qu, A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data anaysis. Biometrics, 68, 353–360.
## Not run: library(PGEE) # load data data(yeastG1) data <- yeastG1 # get the column names colnames(data)[1:9] # see some portion of yeast G1 data head(data,5)[1:9] # define the input arguments formula <- "y ~.-id" family <- gaussian(link = "identity") lambda.vec <- seq(0.01,0.2,0.01) # find the optimum lambda cv <- CVfit(formula = formula, id = id, data = data, family = family, scale.fix = TRUE, scale.value = 1, fold = 4, lambda.vec = lambda.vec, pindex = c(1,2), eps = 10^-6, maxiter = 30, tol = 10^-6) # print the results print(cv) # see the returned values by CVfit names(cv) # get the optimum lambda cv$lam.opt #fit the PGEE model myfit1 <- PGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "independence", Mv = NULL, beta_int = c(rep(0,dim(data)[2]-1)), R = NULL, scale.fix = TRUE, scale.value = 1, lambda = cv$lam.opt, pindex = c(1,2), eps = 10^-6, maxiter = 30, tol = 10^-6, silent = TRUE) # get the values returned by myfit object names(myfit1) # get the values returned by summary(myfit) object names(summary(myfit1)) # see a portion of the results returned by coef(summary(myfit1)) head(coef(summary(myfit1)),7) # see the variables which have non-zero coefficients index1 <- which(abs(coef(summary(myfit1))[,"Estimate"]) > 10^-3) names(abs(coef(summary(myfit1))[index1,"Estimate"])) # see the PGEE summary statistics of these non-zero variables coef(summary(myfit1))[index1,] # fit the GEE model myfit2 <- MGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "independence", Mv = NULL, beta_int = c(rep(0,dim(data)[2]-1)), R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 30, tol = 10^-6, silent = TRUE) # get the GEE summary statistics of the variables that turned out to be # non-zero in PGEE analysis coef(summary(myfit2))[index1,] # see the significantly associated TFs in PGEE analysis names(which(abs(coef(summary(myfit1))[index1,"Robust z"]) > 1.96)) # see the significantly associated TFs in GEE analysis names(which(abs(coef(summary(myfit2))[,"Robust z"]) > 1.96)) ## End(Not run)## Not run: library(PGEE) # load data data(yeastG1) data <- yeastG1 # get the column names colnames(data)[1:9] # see some portion of yeast G1 data head(data,5)[1:9] # define the input arguments formula <- "y ~.-id" family <- gaussian(link = "identity") lambda.vec <- seq(0.01,0.2,0.01) # find the optimum lambda cv <- CVfit(formula = formula, id = id, data = data, family = family, scale.fix = TRUE, scale.value = 1, fold = 4, lambda.vec = lambda.vec, pindex = c(1,2), eps = 10^-6, maxiter = 30, tol = 10^-6) # print the results print(cv) # see the returned values by CVfit names(cv) # get the optimum lambda cv$lam.opt #fit the PGEE model myfit1 <- PGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "independence", Mv = NULL, beta_int = c(rep(0,dim(data)[2]-1)), R = NULL, scale.fix = TRUE, scale.value = 1, lambda = cv$lam.opt, pindex = c(1,2), eps = 10^-6, maxiter = 30, tol = 10^-6, silent = TRUE) # get the values returned by myfit object names(myfit1) # get the values returned by summary(myfit) object names(summary(myfit1)) # see a portion of the results returned by coef(summary(myfit1)) head(coef(summary(myfit1)),7) # see the variables which have non-zero coefficients index1 <- which(abs(coef(summary(myfit1))[,"Estimate"]) > 10^-3) names(abs(coef(summary(myfit1))[index1,"Estimate"])) # see the PGEE summary statistics of these non-zero variables coef(summary(myfit1))[index1,] # fit the GEE model myfit2 <- MGEE(formula = formula, id = id, data = data, na.action = NULL, family = family, corstr = "independence", Mv = NULL, beta_int = c(rep(0,dim(data)[2]-1)), R = NULL, scale.fix = TRUE, scale.value = 1, maxiter = 30, tol = 10^-6, silent = TRUE) # get the GEE summary statistics of the variables that turned out to be # non-zero in PGEE analysis coef(summary(myfit2))[index1,] # see the significantly associated TFs in PGEE analysis names(which(abs(coef(summary(myfit1))[index1,"Robust z"]) > 1.96)) # see the significantly associated TFs in GEE analysis names(which(abs(coef(summary(myfit2))[,"Robust z"]) > 1.96)) ## End(Not run)