Nothing
curvetest.raw <-
function (fits1, fits2, equal.var,
conf.level , plotit) {
if(missing(plotit)) plotit=F
if(missing(conf.level)) conf.level=0.05
if(missing(equal.var)) equal.var=TRUE
ww=fits1$kernel
myx=fits1$myx
n1 <- length(fits1$data.model$x)
if (!missing(fits2)) {
n2 <- length(fits2$data.model$x)
myx=sort(unique(c(myx, fits2$myx)))
}
nn<-length(myx)
if (missing(fits2)) {
sx<-fits1$sx; y=fits1$data.model$y;res<-fits1$res; delta<-fits1$delta
sigma.square <- sum(res^2)/delta; k0=fits1$k0; vv<-fits1$vv;
CC <- max(abs(sx%*%y)/(sqrt(sigma.square) * sqrt(rowSums(sx^2))))
p <- k0/3.1415926 * (1 + (CC * CC)/vv)^(-vv/2) + pt(-CC, vv) * 2
out<-list(Statistic=CC, p=ifelse(p>1, 1, p), eDF=vv, sigma.square= sigma.square, k0=k0, fits=fits1)
} else if (!missing(fits2))
if(equal.var) { ##Fits2 and equal.var
delta11<-fits1$delta; delta12=fits1$delta2
delta21<-fits2$delta; delta22<-fits2$delta2
resid1 <- fits1$res
resid2 <- fits2$res
sx<-fits1$sx; y1<-fits1$data.model$y
tx<-fits2$sx; y2<-fits2$data.model$y
stx<-sqrt(rowSums(sx^2)+rowSums(tx^2))
T1X <- sweep(sx, 1, stx, FUN = "/")
T2X <- sweep(tx, 1, stx, FUN = "/")
k0 <- 0
for (ii in 2:nn) k0 <- k0 + sum(sqrt(distance(T1X[ii,
], T1X[ii - 1, ])^2 + distance(T2X[ii, ], T2X[ii-1, ])^2))
vv<-(delta11+delta21)^2/(delta12+delta22)
sigma.square <- (sum(resid1^2) + sum(resid2^2))/(delta11 +delta21)
CC <- max(abs(sx %*% y1 - tx %*% y2)/(sqrt(sigma.square)*stx))
p <- k0/3.1415926 * (1 + (CC * CC)/vv)^(-vv/2) + pt(-CC, vv) * 2
out<-list(Statistic=CC, p=ifelse(p>1, 1, p), eDF=vv, equal.var=equal.var,
sigma.square= sigma.square, k0=k0, fits1=fits1, fits2=fits2)
}else if (!equal.var) {
delta11<-fits1$delta; delta12=fits1$delta2; v1 <- delta11^2/delta12
delta21<-fits2$delta; delta22<-fits2$delta2; v2 <- delta21^2/delta22
resid1 <- fits1$res
resid2 <- fits2$res
esigma1 <- sqrt((sum(resid1^2))/delta11)
esigma2 <- sqrt(sum(resid2^2)/delta21)
sx<-fits1$sx; y1<-fits1$data.model$y
tx<-fits2$sx; y2<-fits2$data.model$y
vv <- (n1 + n2)^2 * v1 * v2/(n2^2 * v1 + n1^2 * v2)
stx <- sqrt(esigma1^2 * rowSums(sx^2) + esigma2^2 * rowSums(tx^2))
T1X <- esigma1 * sweep(sx, 1, stx, FUN = "/")
T2X <- esigma2 * sweep(tx, 1, stx, FUN = "/")
k0 <- 0
for (ii in 2:nn) k0 <- k0 + sum(sqrt(distance(T1X[ii,
], T1X[ii - 1, ])^2 + distance(T2X[ii, ], T2X[ii -
1, ])^2))
CC <- max(abs(sx %*% y1 - tx %*% y2)/stx)
p <- k0/3.1415926 * exp(-CC^2/2) + pnorm(-CC) * 2
out<-list(Statistic=CC, p=ifelse(p>1, 1, p), eDF=vv, equal.var=equal.var,
esigma1=esigma1,esigma2=esigma2, k0=k0, fits1=fits1, fits2=fits2)
}
class(out)<-"curvetest"
invisible(out)
}
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