a matrix (or vector) of predictor(s), with dimensions of N*p, where N is the total sample size of all studies
y
a vector of response, with length N, the total sample size of all studies
sid
study id, numbered from 1 to K
fuse.which
a vector of a subset of integers from 0 to p, indicating which covariates to be considered for fusion; 0 corresponds to intercept
family
"gaussian" for continuous response, "binomial" for binary response, "poisson" for count response
intercept
if TRUE, intercept will be included in the model
alpha
the ratio of sparsity penalty to fusion penalty, default is 0 (no penalty on sparsity)
criterion
"AIC" for AIC, "BIC" for BIC, "EBIC" for extended BIC
verbose
if TRUE, output fusion events and tuning parameter lambda
plots
if TRUE, create plots of solution paths and clustering trees
loglambda
if TRUE, lambda will be plot in log-10 transformed scale
Details
Adaptive lasso penalty is used. See Zou (2006) for detail.
Value
a list containing the following items will be returned:
family
the model type
criterion
model selection criterion used
alpha
the ratio of sparsity penalty to fusion penalty
if.fuse
whether the covariate is fused (1) or not (0)
betahat
the estimated coefficients
betainfo
additional information about the fit, including degree of freedom, lambda optimal, lambda fuse, friction of fusion for each covariate
Examples
n <- 200 # sample size in each study
K <- 10 # number of studies
p <- 3 # number of covariates in X (including intercept)
N <- n*K # total sample size
# the coefficient matrix, used this to set desired heterogeneous pattern (depends on p and K)
beta0 <- matrix(c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, # intercept
0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0, # beta_1, etc.
0.0,0.0,0.0,0.0,0.5,0.5,0.5,1.0,1.0,1.0), K, p)
# generate a data set, family=c("gaussian", "binomial", "poisson")
data <- datagenerator(n=n, beta0=beta0, family="gaussian", seed=123)
# prepare the input (y, X, studyID)
y <- data$y
sid <- data$group
X <- data[,-c(1,ncol(data))]
# fuse slopes of X1 (it is heterogeneous with 2 groups)
metafuse(X=X, y=y, sid=sid, fuse.which=c(1), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse slopes of X2 (it is heterogeneous with 3 groups)
metafuse(X=X, y=y, sid=sid, fuse.which=c(2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates, with sparsity penalty
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=1,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
providing 
.