Nothing
par.ancova <- function(data=data, time.series=FALSE, Var.Cov.eps=NULL, p.max=3,
q.max=3, ic="BIC", num.lb=10, alpha=0.05)
{
if (!is.array(data)) stop("data must be an array")
if (ncol(data) < 2) stop("data must have at least 2 columns")
if (!is.logical(time.series)) stop("time.series must be logical")
if ( (!is.null(Var.Cov.eps)) && (sum(is.na(Var.Cov.eps)) != 0) ) stop("Var.Cov.eps must have numeric values")
if ( (!is.null(Var.Cov.eps)) && (!is.array(Var.Cov.eps)) ) stop("Var.Cov.eps must be an array")
if ( (!is.null(Var.Cov.eps)) && ( (ncol(Var.Cov.eps) != nrow(data)) | (nrow(Var.Cov.eps) != nrow(data)) | (dim(Var.Cov.eps)[3] != dim(data)[3]) ) ) stop("Var.Cov.eps must have dimension n x n x L")
if (!is.null(Var.Cov.eps)) {for (k in 1:(dim(Var.Cov.eps)[3]) ) if (any(t(Var.Cov.eps[,,k]) != Var.Cov.eps[,,k])) stop("Var.Cov.eps must be symmetric") }
if (is.null(p.max)) stop ("p.max must not be NULL")
if (length(p.max) !=1) stop ("p.max must be an only value")
if (!is.numeric(p.max)) stop ("p.max must be numeric")
if (p.max<0) stop ("p.max must be a positive value")
if (is.null(q.max)) stop ("q.max must not be NULL")
if (length(q.max) !=1) stop ("q.max must be an only value")
if (!is.numeric(q.max)) stop ("q.max must be numeric")
if (q.max<0) stop ("q.max must be a positive value")
if ( (ic != "BIC") & (ic != "AIC") & (ic != "AICC") ) stop("ic=BIC or ic=AIC or ic=AICC is required")
if (is.null(num.lb)) stop ("num.lb must not be NULL")
if (length(num.lb) !=1) stop ("num.lb must be an only value")
if (!is.numeric(num.lb)) stop ("num.lb must be numeric")
if (num.lb<=0) stop ("num.lb must be a positive value")
if (is.null(alpha)) stop ("alpha must not be NULL")
if (length(alpha) !=1) stop ("alpha must be an only value")
if (!is.numeric(alpha)) stop ("alpha must be numeric")
if ( (alpha<0) | (alpha>1) ) stop ("alpha must be between 0 and 1")
L <- dim(data)[3]
n <- nrow(data)
p <- ncol(data)-1
BETA <- 1:(L*p)
A <- matrix(0,p*L,p*L)
beta.est <- array(0,c(p,1,L))
B <- matrix(0, p*(L-1), p*L)
B[row(B)==col(B)] <- 1
I <- matrix(0,p,p)
I[row(I)==col(I)] <- 1
for (k in 1:(L-1)) B[(k-1)*p+(1:p),(L-1)*p+(1:p)] <- -I
for (k in 1:L) {
Y <- data[, 1, k]
X <- data[, -1, k]
if (!is.matrix(X)) X <- as.matrix(X)
X.X <- t(X)%*%X
X.Y <- t(X)%*%Y
beta.est[,1,k] <- symsolve(X.X, X.Y)
BETA[(k-1)*p+(1:p)] <- beta.est[,1,k]
X.X.1 <- solve(X.X)
if (!is.null(Var.Cov.eps)) V.eps <- Var.Cov.eps[,,k]
else {
eps <- Y - X%*%beta.est[,1,k]
if (!time.series) {
var.eps <- var(eps)
var.eps <- as.numeric(var.eps)
V.eps <- diag(var.eps,n,n)
}
else {
V.eps <- matrix(NA, n, n)
Var.Cov.mat <- var.cov.matrix(x=eps, n=n, p.max=p.max, q.max=q.max, ic=ic, alpha=alpha, num.lb=num.lb)
V.eps <- Var.Cov.mat[[1]]
pv.Box.test <- Var.Cov.mat[[2]]
pv.t.test <- Var.Cov.mat[[3]]
ar.ma <- Var.Cov.mat[[4]]
}
}
A.k <- n*X.X.1%*%t(X)%*%V.eps%*%X%*%X.X.1
A[(k-1)*p+(1:p) , (k-1)*p+(1:p)] <- A.k
} # for k
Q.beta <- n*t(B%*%BETA)%*%solve(B%*%A%*%t(B))%*%(B%*%BETA)
p.v <- 1-pchisq(Q.beta, df=p*(L-1), ncp=0)
if ((is.null(Var.Cov.eps)) && (time.series)) list(par.ancova=data.frame(Q.beta=Q.beta, p.value=p.v), pv.Box.test=pv.Box.test, pv.t.test=pv.t.test, ar.ma=ar.ma)
else list(par.ancova=data.frame(Q.beta=Q.beta, p.value=p.v))
}
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