Displaying 5 results from an estimated 5 matches for "comb1".
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2012 Apr 05
1
integrate function - error -integration not occurring with last few rows
...d(data1$ID) , c(1,3)]
ed$base=1
ed$drop=1
ed$bshz<-1
ed$up<-1
ed
set.seed(5234123)
k<-0
for (i in 1:length(ed$ID))
{
k<-k+1
ed$base[k]<-basescore*exp(rnorm(1,0,basescore_sd))
ed$drop[k]<-fall*exp(rnorm(1,0,fall_sd))
ed$up[k]<-slope*exp(rnorm(1,0,rise_sd))
ed$bshz<-beta0
}
comb1<-merge(data1[, c("ID","TIME")], ed)
comb1$disprog<-1
comb1$beta1<-0.035
comb1$beta21<-0.02
comb1$beta22<-0.45
comb1$beta23<-0085
comb1$beta31<-0.7
comb1$beta32<-0.05
comb1$exphz<-1
comb2<-comb1
p<-0
for(l in 1:length(comb2$ID))
{
p<-p+1
comb...
2012 Apr 08
1
Avoid loop with the integrate function
...DOSE")
data1<-data1[order(data1$ID,data1$TIME),]
ed<-data1[!duplicated(data1$ID) , c("ID","DOSE")]
set.seed(5324123)
for (k in 1:length(ed$ID))
{
ed$base[k]<-100*exp(rnorm(1,0,0.05))
ed$drop[k]<-0.2*exp(rnorm(1,0,0.01))
ed$frac[k]<-0.5*exp(rnorm(1,0,0.1))
}
comb1<-merge(data1[, c("ID","TIME")], ed)
comb2<-comb1
comb2$score<-comb2$base*exp(-comb2$drop*comb2$TIME)
func1<-function(t,cov1,beta1, change,other)
{
ifelse(t==0,cov1, cov1*exp(beta1*change+other))
}
comb3<-comb2
comb3$cmhz=0
comb3<-comb3[order(comb3$ID, comb3$TI...
2012 Aug 27
1
interpret the importance output?
> importance(rfor.pdp11_t25.comb1,type=1)
%IncMSE
v1 -0.28956401263
v2 1.92865561147
v3 -0.63443929130
v4 1.58949137047
v5 0.03190940065
I wasn't entirely confident with interpreting these results based on the
documentation.
Could you please interpret?
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2005 Oct 04
1
"Survey" package and NAMCS data... unsure of specification
...command as
follows:
svyset pweight PATWT
svyset strata CSTRATM
svyset psu CPSUM
They provide similar instructions for SUDAAN: as
SUDAAN 1-stage WR Option
The program below provides a with replacement ultimate cluster (1-stage)
estimate of standard errors for a
cross-tabulation.
PROC CROSSTAB DATA=COMB1 DESIGN=WR FILETYPE=SAS;
NEST CSTRATM CPSUM/MISSUNIT;
In R, the svydesign command is used to set the sampling scheme, but as
follows (example from the documentation):
dstrat <- svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
stratified on stype, with sampling weights pw....
2012 Mar 25
2
avoiding for loops
I have data that looks like this:
> df1
group id
1 red A
2 red B
3 red C
4 blue D
5 blue E
6 blue F
I want a list of the groups containing vectors with the ids. I am
avoiding subset(), as it is
only recommended for interactive use. Here's what I have so far:
df1 <- data.frame(group=c("red", "red", "red", "blue",