Package 'pgee.mixed'

Title: Penalized Generalized Estimating Equations for Bivariate Mixed Outcomes
Description: Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).
Authors: Ved Deshpande [aut, cre]
Maintainer: Ved Deshpande <[email protected]>
License: GPL-2
Version: 0.1.0.9002
Built: 2026-05-22 09:52:30 UTC
Source: https://github.com/kaos42/pgee.mixed

Help Index

Cross validation for Penalized Generalized Estimating Equations

Description

Performs k-fold cross-validation for Penalized Generalized Estimating Equations (PGEEs) over grid(s) of tuning parameters lambda. Linear and binary logistic models are supported. In particular, can handle the case of bivariate correlated mixed outcomes, in which each cluster consists of one continuous outcome and one binary outcome.

Usage

cv.pgee(N, m, X, Z = NULL, y = NULL, yc = NULL, yb = NULL, K = 5,
  grid1, grid2 = NULL, wctype = "Ind", family = "Gaussian", eps = 1e-06,
  maxiter = 1000, tol.coef = 0.001, tol.score = 0.001, init = NULL,
  standardize = TRUE, penalty = "SCAD", warm = TRUE, weights = rep(1,
  N), type_c = "square", type_b = "deviance", marginal = 0, FDR = FALSE,
  fdr.corr = NULL, fdr.type = "all", ridge.weight = switch(family, Mixed =
  rep(0, 2), 0))

Arguments

N

Number of clusters.
m

Cluster size. Assumed equal across all clusters. Should be set to 2 for family=="Mixed".
X

Design matrix. If family=="Mixed", then design matrix for continuous responses. For family!="Mixed", should have N*m rows. For family=="Mixed", should have N rows.
Z

Design matrix for binary responses for family=="Mixed". Should not be provided for other family types. If not provided for family=="Mixed", is set equal to X. For family!="Mixed", should have N*m rows. For family=="Mixed", should have N rows.
y

Response vector. Don't use this argument for family == "Mixed". Instead, use arguments yc and yb. Since the cluster size is assumed equal across clusters, the vector is assumed to have the form c(y_1, y_2,...,y_N), with y_i = c(y_i1,...,y_im).
yc

Continuous response vector. Use only for family=="Mixed".
yb

Binary (0/1) response vector. Use only for family=="Mixed".
K

Number of folds.
grid1

For family!="Mixed", the grid of tuning parameters. For family=="Mixed", the grid of tuning parameters for coefficients corresponding to the continuous outcomes.
grid2

For family=="Mixed", the grid of tuning parameters for coefficients corresponding to the binary outcomes. Not used for family!="Mixed".
wctype

Working correlation type; one of "Ind", "CS", or "AR1". For family=="Mixed", "CS" and "AR1" are equivalent.
family

"Gaussian", "Binomial", or "Mixed" (use the last for bivariate mixed outcomes). Note that for "Binomial", currently only binary outcomes are supported.
eps

Disturbance in the Linear Quadratic Approximation algorithm.
maxiter

The maximum number of iterations the Newton algorithm tries before declaring failure to converge.
tol.coef

Converge of the Newton algorithm is declared if two conditions are met: The L1-norm of the difference of successive iterates should be less than tol.coef AND the L1-norm of the penalized score should be less than tol.score.
tol.score

See tol.coef.
init

Vector of initial values for regression coefficients. For family=="Mixed", should be c(init_c, init_b). Defaults to glm values.
standardize

Standardize the design matrices prior to estimation?
penalty

"SCAD", "MCP", or "LASSO".
warm

Use warm starts?
weights

Vector of cluster weights. All observations in a cluster are assumed to have the same weight.
type_c

Loss function for continuous outcomes. "square" (square error loss, the default) or "absolute" (absolute error loss).
type_b

Loss function for binary outcomes. "deviance" (binomial deviance, the default) or "classification" (prediction error).
marginal

For the mixed outcomes case, set to 0 (the default) to account for both the continuous loss and the binary loss, set to 1 to only account for the continuous loss, and set to 2 to only account for the binary loss.
FDR

Should the false discovery rate be estimated for family=="Mixed"? Currently, FDR cannot be estimated for other family types.
fdr.corr

Association parameter to use in FDR estimation. The default is to use the association parameter estimated from the PGEEs.
fdr.type

Estimate the FDR for only the coefficients corresponding to the continuous outcomes ("continuous"), for only the coefficients corresponding to the binary outcomes ("binary"), or for all coefficients ("all", the default).
ridge.weight

Tuning parameter(s) which control the relative contributions from the MCP/SCAD/LASSO penalty and the ridge, or L2 penalty. For family!="Mixed", ridge.weight=0 is equivalent to MCP/SCAD/LASSO penalty, while ridge.weight=1 would be equivalent to a ridge penalty. However, ridge.weight=1 is not supported; ridge.weight may be arbitrarily close to 1, but not exactly equal to 1. For family=="Mixed", should be two-dimensional vector, with entries corresponding to those in lambda.

Details

The function calls pgee.fit K times, each time leaving out 1/K of the data. The cross-validation error is determined by the arguments type_c and type_b. For family=="Mixed", the cross-validation error is (by default) the sum of the continuous error and the binary error.

Value

A list

coefficients

Vector of estimated regression coefficients. For family=="Mixed", this takes the form c(coef_c, coef_b).
vcov

Sandwich formula based covariance matrix of estimated regression coefficients (other than the intercept(s)). The row/column names correspond to elements of coefficients.
phi

Estimated dispersion parameter.
alpha

Estimated association parameter.
num_iterations

Number of iterations the Newton algorithm ran.
converge

0=converged, 1=did not converge.
PenScore

Vector of penalized score functions at the estimated regression coefficients. If the algorithm converges, then these should be close to zero.
FDR

Estimated FDR for family=="Mixed", if requested.
ridge.weight

The ridge weight(s).
lambda.loss

Cross validation loss (error) for the optimal tuning parameter(s) lambda, averaged across folds.
LossMat

Matrix of cross validation losses. Rows denote tuning parameter values, columns denote folds.

Examples

## Not run: 
# Gaussian
N <- 100
m <- 10
p <- 50
y <- rnorm(N * m)
# If you want standardize = TRUE, you must provide an intercept.
X <- cbind(1, matrix(rnorm(N * m * (p - 1)), N * m, p - 1))
gr1 <- seq(0.001, 0.1, length.out = 100)
fit <- cv.pgee(X = X, y = y, N = N, m = m, grid1 = gr1, wctype = "CS",
            family = "Gaussian")

# Binary
y <- sample(0:1, N*m, replace = TRUE)
fit <- cv.pgee(X = X, y = y, N = N, m = m, grid1 = gr1, wctype = "CS",
            family = "Binomial")

# Bivariate mixed outcomes
# Generate some data
Bc <- c(2.0, 3.0, 1.5, 2.0, rep(0,times=p-4))
Bb <- c(0.7, -0.7, -0.4, rep(0,times=p-3))
dat <- gen_mixed_data(Bc, Bb, N, 0.5)
# We require two grids of tuning parameters
gr2 <- seq(0.0001, 0.01, length.out = 100)
# Estimate regression coefficients and false discovery rate
fit <- cv.pgee(X = dat$X, Z = dat$Z, yc = dat$yc, yb = dat$yb, N = N, m = 2,
               wctype = "CS", family = "Mixed", grid1 = gr1, grid2 = gr2,
               FDR = TRUE)

## End(Not run)

Generate correlated bivariate mixed outcome data

Description

gen_mixed_data returns randomly generated correlated bivariate mixed outcomes, and covariate matrices to model them, based on design parameters set in the function.

Usage

gen_mixed_data(Beta.cont, Beta.bin, N, rho, intercept = TRUE, cov = "same",
  xcor = 0.25, sigma_yc = 1)

Arguments

Beta.cont

Vector of true regression coefficients for the continuous outcome.
Beta.bin

Vector of true regression coefficients for the binary outcome.
N

Number of pairs of correlated outcomes.
rho

Gaussian copula parameter.
intercept

Assume an intercept (for both outcomes)? (default TRUE). If TRUE, then the first coefficient in Beta.cont and Beta.bin are assumed to correspond to intercepts.
cov

Specify if the covariate matrices for the continuous outcome and the binary outcome should share all covariates (set to "same"), share some covariates (set to "shared"), or share no covariates (set to "separate").
xcor

Correlation parameter for AR(1) correlation structure of covariate design matrices (assumed same for both).
sigma_yc

Marginal variance of continuous responses.

Details

A Gaussian copula is used to generate the correlated outcomes. Marginally, the continuous outcome follows a normal distribution with identity link to covariates, while the binary outcome follows a Bernoulli distribution with logit link to covariates. Covariates are generated from a zero-mean unit variance multivariate normal distribution, with an AR(1) correlation structure.

Value

A list of generated data

yc

Vector of continuous outcomes.
yb

Vector of binary outcomes.
X

Covariate matrix for the continuous outcomes.
Z

Covariate matrix for the binary outcomes.

Examples

# default settings
gen_mixed_data(rnorm(5), rnorm(5), 10, 0.5)
# separate covariate matrices, non-unit continuous variance
gen_mixed_data(rnorm(5), rnorm(5), 10, 0.5, cov = "separate", sigma_yc = 2)

Penalized Generalized Estimating Equations

Description

Estimate regression coefficients using Penalized Generalized Estimating Equations (PGEEs). Linear and binary logistic models are currently supported. In particular, can handle the case of bivariate correlated mixed outcomes, in which each cluster consists of one continuous outcome and one binary outcome.

Usage

pgee.fit(N, m, X, Z = NULL, y = NULL, yc = NULL, yb = NULL,
  wctype = "Ind", family = "Gaussian", lambda = switch(family, Mixed =
  rep(0, 2), 0), eps = 1e-06, maxiter = 1000, tol.coef = 0.001,
  tol.score = 0.001, init = NULL, standardize = TRUE, penalty = "SCAD",
  weights = rep(1, N), FDR = FALSE, fdr.corr = NULL, fdr.type = "all",
  ridge.weight = switch(family, Mixed = rep(0, 2), 0))

Arguments

N

Number of clusters.
m

Cluster size. Assumed equal across all clusters. Should be set to 2 for family=="Mixed".
X

Design matrix. If family=="Mixed", then design matrix for continuous responses. For family!="Mixed", should have N*m rows. For family=="Mixed", should have N rows. For standardize=TRUE, the first column should be a column vector of ones, corresponding to the intercept.
Z

Design matrix for binary responses for family=="Mixed". Should not be provided for other family types. If not provided for family=="Mixed", is set equal to X. For family!="Mixed", should have N*m rows. For family=="Mixed", should have N rows. For standardize=TRUE, the first column should be a column vector of ones, corresponding to the intercept.
y

Response vector. Don't use this argument for family == "Mixed". Instead, use arguments yc and yb. Since the cluster size is assumed equal across clusters, the vector is assumed to have the form c(y_1, y_2,...,y_N), with y_i = c(y_i1,...,y_im).
yc

Continuous response vector. Use only for family=="Mixed".
yb

Binary (0/1) response vector. Use only for family=="Mixed".
wctype

Working correlation type; one of "Ind", "CS", or "AR1". For family=="Mixed", "CS" and "AR1" are equivalent.
family

"Gaussian", "Binomial", or "Mixed" (use the last for bivariate mixed outcomes). Note that for "Binomial", currently only binary outcomes are supported.
lambda

Tuning parameter(s). A two-dimensional vector should be provided for family=="Mixed" (one for the continuous outcome coefficients, and one of the binary outcome coefficients). Otherwise, a single tuning parameter should be provided.
eps

Disturbance in the Linear Quadratic Approximation algorithm.
maxiter

The maximum number of iterations the Newton algorithm tries before declaring failure to converge.
tol.coef

Converge of the Newton algorithm is declared if two conditions are met: The L1-norm of the difference of successive iterates should be less than tol.coef AND the L1-norm of the penalized score should be less than tol.score.
tol.score

See tol.coef.
init

Vector of initial values for regression coefficients. For family=="Mixed", should be c(init_c, init_b). Defaults to glm values.
standardize

Standardize the design matrices prior to estimation?
penalty

"SCAD", "MCP", or "LASSO".
weights

Vector of cluster weights. All observations in a cluster are assumed to have the same weight.
FDR

Should the false discovery rate be estimated for family=="Mixed"? Currently, FDR cannot be estimated for other family types.
fdr.corr

Association parameter to use in FDR estimation. The default is to use the association parameter estimated from the PGEEs.
fdr.type

Estimate the FDR for only the coefficients corresponding to the continuous outcomes ("continuous"), for only the coefficients corresponding to the binary outcomes ("binary"), or for all coefficients ("all", the default).
ridge.weight

Tuning parameter(s) which control the relative contributions from the MCP/SCAD/LASSO penalty and the ridge, or L2 penalty. For family!="Mixed", ridge.weight=0 is equivalent to MCP/SCAD/LASSO penalty, while ridge.weight=1 would be equivalent to a ridge penalty. However, ridge.weight=1 is not supported; ridge.weight may be arbitrarily close to 1, but not exactly equal to 1. For family=="Mixed", should be two-dimensional vector, with entries corresponding to those in lambda.

Details

pgee.fit estimates the regression coefficients for a single value of the tuning paramter (or a single pair of tuning parameters in the mixed outcomes case). To select optimal tuning parameter(s) via k-fold cross validation, see cv.pgee.

For bivariate mixed outcomes, the false discovery rate can be estimated.

Value

A list

coefficients

Vector of estimated regression coefficients. For family=="Mixed", this takes the form c(coef_c, coef_b).
vcov

Sandwich formula based covariance matrix of estimated regression coefficients (other than the intercept(s)). The row/column names correspond to elements of coefficients.
phi

Estimated dispersion parameter.
alpha

Estimated association parameter.
num_iterations

Number of iterations the Newton algorithm ran.
converge

0=converged, 1=did not converge.
PenScore

Vector of penalized score functions at the estimated regression coefficients. If the algorithm converges, then these should be close to zero.
FDR

Estimated FDR for family=="Mixed", if requested.
ridge.weight

The ridge weight(s).

Examples

set.seed(100)
# Gaussian
N <- 100
m <- 10
p <- 10
y <- rnorm(N * m)
# If you want standardize = TRUE, you must provide an intercept.
X <- cbind(1, matrix(rnorm(N * m * (p - 1)), N * m, p - 1))
fit <- pgee.fit(X = X, y = y, N = N, m = m, lambda = 0.5, wctype = "CS",
            family = "Gaussian")
str(fit)
fit$coefficients
fit$vcov

# Binary
y <- sample(0:1, N*m, replace = TRUE)
fit <- pgee.fit(X = X, y = y, N = N, m = m, lambda = 0.1, wctype = "CS",
            family = "Binomial")
str(fit)
fit$coefficients
fit$vcov

# Bivariate mixed outcomes
# Generate some data
Bc <- c(2.0, 3.0, 1.5, 2.0, rep(0, times = p - 4))
Bb <- c(0.7, -0.7, -0.4, rep(0, times = p - 3))
dat <- gen_mixed_data(Bc, Bb, N, 0.5)
# Estimate regression coefficients and false discovery rate
fit <- pgee.fit(X = dat$X, yc = dat$yc, yb = dat$yb, N = N, m = 2,
            wctype = "CS", family = "Mixed", lambda = c(0.1, 0.05),
            FDR = TRUE)
str(fit)
fit$coefficients
fit$vcov

pgee.mixed: Penalized Generalized Estimating Equations for Bivariate Mixed Outcomes

Description

Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).

References

Deshpande, V., Dey, D. K., and Schifano, E. D. (2016). Variable selection for correlated bivariate mixed outcomes using penalized generalized estimating equations. Technical Report 16-23, Department of Statistics, University of Connecticut, Storrs, CT.

Wang, L., Zhou, J., and Qu, A. (2012). Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics, 68, 353–360.