Displaying 5 results from an estimated 5 matches for "preda".
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2008 Jun 13
1
help with colsplit (reshape)
Dear list,
I'm trying to figure out how to use the reshape package to reshape
data from a "wide" format to a "long" format. I have data like this
pid <- c(1:10)
predA <- c(-1,-2,-1,-2,-1,-2,-1,-2,-1,-2)
predB.1 <- c(0,0,0,1,1,0,0,0,1,1)
predB.2 <- c(2,2,3,3,3,2,2,3,3,3)
predC.1 <- c(10,10,10,10,10,11,11,11,11,11)
predC.2 <- c(12,12,13,13,13,12,12,13,13,13)
out.1 <- c(100:109)
out.2 <- c(200:209)
Data <- data.frame(pid, predA, predB.1, pre...
2005 Jul 11
2
CIs in predict?
...xlab="Log area",ylab="Log volume")
areapred.a <- seq(min(vol$log.area[vol$lake=="a"]), max(vol$log.area[vol$lake=="a"]), length=100)
areapred.b <- seq(min(vol$log.area[vol$lake=="b"]), max(vol$log.area[vol$lake=="b"]), length=100)
preda <- predict(vol.mod3, data.frame(log.area=areapred.a,interval="confidence" ,lake=rep("a",100)))
#This gives the fitted values as predicted, but no CIs
> preda
1 2 3 4 5 6 7 8...
2005 Mar 30
1
[LLVMdev] Branch simplification
Hi,
I have a CFG built by LLVM with blocks that look like this:
myBlock: ; preds = %predA, %predB
%cond = phi bool [ false, %predA ], [ %otherCond, %predB ]
br bool %cond, %succA, %succB
Is there a pass or sequence of passes that will see the constant
'false' in the PHI instruction and change the target of %predA to
point directly to %succB? I tried -simplifycfg but it d...
2005 Sep 22
2
xenconsole: Could not open tty `/dev/pts/2'': No such file or directory
...8.9
derico 4 127 1 1 -b--- 6.2
fassanha 1 255 1 1 -b--- 91.6
hostwide 5 255 1 1 -b--- 16.7
kippona 6 127 1 1 -b--- 17.8
neimar 7 255 1 1 -b--- 8.0
preda 9 127 1 1 -b--- 1.7
torquato 12 256 1 1 ----- 12.4
vale 10 255 1 1 -b--- 41.6
[root@router torquato]# xm console bruno
xenconsole: Could not open tty `/dev/pts/2'': No such file or directory
[root@r...
2007 Jun 24
2
matlab/gauss code in R
...t; colID <- gl(nInd, nPts)
>
> mydata <- data.frame(Time = TimePts, Observed = Obs, Individuals = colID)
>
> fmA <- lm(Observed ~ Time, mydata)
> fmB <- lm(Observed ~ poly(Time, 2), mydata)
> fmC <- lm(Observed ~ poly(Time, 2) * Individuals, mydata)
>
> mydata$PredA <- predict(fmA)
> mydata$PredB <- predict(fmB)
> mydata$PredC <- predict(fmC)
>
> xyplot(Observed + PredA + PredB + PredC ~ Time | Individuals,
> data = mydata,
> type = c("p", "l", "l", "l"),
> distribute.type...