Displaying 3 results from an estimated 3 matches for "observarion".
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2011 Jan 17
1
Problem about for loop
Hi everyones, my function like;
e <- rnorm(n=50, mean=0, sd=sqrt(0.5625))
x0 <- c(rep(1,50))
x1 <- rnorm(n=50,mean=2,sd=1)
x2 <- rnorm(n=50,mean=2,sd=1)
x3 <- rnorm(n=50,mean=2,sd=1)
x4 <- rnorm(n=50,mean=2,sd=1)
y <- 1+ 2*x1+4*x2+3*x3+2*x4+e
x2[1] = 10 #influential observarion
y[1] = 10 #influential observarion
data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5)
data.y <- matrix(y,ncol=1)
data.k <- cbind(data.x,data.y)
dataX <- data.k[,1:5]
dataY <- data.k[,6]
theta <- function(data) {
B.cap <- solve(crossprod(dataX)) %*% crossprod(dataX,dataY)
P <...
2011 Mar 20
2
Why unique(sample) decreases the performance ?
...nts than full sample. Code
as follows;
e <- rnorm(n=50, mean=0, sd=sqrt(0.5625))
x0 <- c(rep(1,50))
x1 <- rnorm(n=50,mean=2,sd=1)
x2 <- rnorm(n=50,mean=2,sd=1)
x3 <- rnorm(n=50,mean=2,sd=1)
x4 <- rnorm(n=50,mean=2,sd=1)
y <- 1+ 2*x1+4*x2+3*x3+2*x4+e
x2[1] = 10 #influential observarion
y[1] = 10 #influential observarion
X <- matrix(c(x0,x1,x2,x3,x4),ncol=5)
Y <- matrix(y,ncol=1)
Design.data <- cbind(X, Y)
for (j in 1:nrow(X)) {
result <- vector("list", )
for( i in 1: 3100) {
data <- Design.data[sample(50,50,replace=TRUE),] ##### and
unique(Desi...
2010 Apr 08
0
selected observations based several variables
...o
need to consider the values of "prior". The rough ideas are as follows.
# Check whether there are observations which have same x and y values,e.g.1
and 3. If same, their date difference need to be checked further. If their
date difference is <=8days, we only need to keep the earliest observarion;
#During this selection, variable of "prior" is also needed to be
considered. For "prior", the priority is C>A>N. For the same several
observations, if the earliest observation has a value of "N" and other later
observations have "A" or "C", t...