Displaying 6 results from an estimated 6 matches for "dinv".
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dinh
2005 Nov 03
0
problems with pan(): Indizierung ausserhalb der Grenzen = subscript out of bounds
...dim(pred)[2]
> xcol
[1] 1
#xcol = 1 , using all number of cols of pred[]
> zcol <- c(1) # = 1 , number of cols to use
> y.ncol <- dim(y)[2]
> n.zcol <- length(zcol)
> prior <- list(a=y.ncol,
+ Binv=diag(y.ncol),
+ c=n.zcol,
+ Dinv=diag(n.zcol))
> prior
$a
[1] 15
$Binv
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[,13] [,14] [,15]
[1,] 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0
[2,] 0 1 0 0 0 0 0 0 0 0 0 0
0...
2004 Feb 06
1
Savitzky-Golay smoothing -- an R implementation
...pe="o") # convolve(...)
T2 <- T2[(fc+1):(length(T2)-fc)]
}
#-----------------------------------------------------------------------
# *** PseudoInvers of a Matrix ***
# using singular value decomposition
#
pinv <- function (A)
{
s <- svd(A)
# D <- diag(s$d); Dinv <- diag(1/s$d)
# U <- s$u; V <- s$v
# A = U D V'
# X = V Dinv U'
s$v %*% diag(1/s$d) %*% t(s$u)
}
#-----------------------------------------------------------------------
2005 May 26
1
PAN: Need Help for Multiple Imputation Package
...ed
out at end.
> sim <- read.xport('c:\\xptds.dat')
>
> int <- rep(1,1200)
> y <- cbind(sim$MIY1,sim$TCOV1)
> subj <- sim$ID
> pred <- cbind(int, sim$TIME, sim$GROUP)
>
> xcol <- 1:3
> zcol <- 1
> prior <- list(a=2,Binv=4,c=2,Dinv=4)
> result <-
pan(y,subj,pred,xcol,zcol,prior,seed=13579,iter=1000)
Error: subscript out of bounds
By the way, I also received the same error message
when I tried to include intercept and time in Zcol, a
matrix for random effect specification. I used
command “ zcol <- 1:2”. Does anyb...
2005 Aug 31
0
Imputation using Pan in R
...10,10,10,10)
> pred <- cbind(int=rep(1,40),
+ dummy1=rep(c(1,0,0,0),10),
+ dummy2=rep(c(0,1,0,0),10),
+ dummy3=rep(c(0,0,1,0),10),
+ dummy4=rep(c(0,2,4,6),10))
> xcol <- 1:4
> zcol <- c(1,5)
> a <- array (2,dim=c(2,2))
> prior <- list(a=1,Binv=1,c=2,Dinv=a)
> result <- pan(y,subj,pred,xcol,zcol,prior,seed=25679,iter=5000)
> result$y
[1] 5 8 NaN 10 2 NaN NaN NaN 10 10 8 NaN NaN 10 4 NaN 2 8
NaN
[20] 9 NaN NaN NaN NaN 8 6 NaN 5 6 NaN NaN NaN 10 2 NaN NaN 6
8
[39] NaN 2
>
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2007 Sep 24
0
longitudinal imputation with PAN
...quot;Girls"))
impht.data$visit <- factor (impht.data$visit)
impht.data$code <- factor (impht.data$code)
y <- impht.data$htmiss
subj <- impht.data$code
pred <- cbind (impht.data$age, impht.data$sex, impht.data$visit)
xcol <- 1:3
zcol <- 1
prior <- list(a=1, Binv=1, c=1, Dinv=1)
ht1 <- pan(y, subj, pred, xcol, zcol, prior, seed=13579, iter=1000)
code sex visit age ht htmiss
1 2 1 4.87 105 105
1 2 2 5.86 109.6
1 2 3 6.88 116.4 116.4
1 2 4 7.72 121.2 121.2
1...
2011 Jun 21
0
R crash when using pan for multiple imputation
... #col with random effect in pred
prior <- list( a=ncol(y), #non-informative prior
Binv= diag( rep(1,ncol(y) ) ) ,
c= ncol(y) * length(zcol) ,
Dinv= diag( rep(1 ,ncol(y)*length(zcol) ) )
)
imput1 <- pan(y,subj,pred,xcol,zcol,prior,seed= 13579 ,iter= 1000) #run first imputation
...after 30 minutes: CRASH (R ends automatically, Rgui is closed without log)
Is there a way to identify the sourc...