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###############################################
## Extraction of correlation and mean via FACE
## Author: Luo Xiao
## Email: lxiao5@ncsu.edu
## Date: Sep 1, 2015
#############################################
cor.face <- function(object,argvals.new,option="raw"){
## arguments
# "object": fitted object from function ''face.sparse''
# "argvals.new": at which time points to evaluate correlations,
## a correlation matrix evaulated at these time points will be the output
##
## check inputs
if(!inherits(object, "face.sparse")) stop("'fit' has to be a face_sparse object")
if(is.null(argvals.new)) stop("'argvals.new' needs to be specified")
if(length(argvals.new)<2) stop("'argvals.new' needs to contain at least two time points")
if(!is.numeric(argvals.new[1])) stop("'argvals.new' need to be numeric")
## create a hypothetical data at time points 'argvals.new'
## and then make prediction and estimation
k <- length(argvals.new)
newdata_pred <- data.frame(
subj=rep(1,k),
argvals = argvals.new,
y = rep(1,k)
)
yhat <- predict.face.sparse(object,newdata_pred)
## extract correlation and mean
Chat <- yhat$Chat.pred
if(option=="raw") Chat <- Chat + diag(yhat$var.error.pred)
Cov_diag <- diag(Chat)
Cor <- diag(sqrt(1/Cov_diag))%*%Chat%*%diag(sqrt(1/Cov_diag))
mu <- yhat$mu.pred
## ouptut
res <- list("argvals.new" = argvals.new,
"option" = option,
"Cor" = Cor, #estimated correlation matrix at argvals.new
"mu" = mu #estimated group/population mean at argvals.new
)
return(res)
}
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