Displaying 7 results from an estimated 7 matches for "gdat".
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2007 Jun 21
1
Result depends on order of factors in unbalanced designs (lme, anova)?
...gender<-factor(rep(rep(1:2, each=20),4))
visit<-factor(rep(1:4, each=NSubj))
y<-runif(4*NSubj) # Results
# Add effects
y<-y+0.01*as.integer(visit)
y<-y+0.02*as.integer(gender)
y<-y+0.024*as.integer(treat)
df<-data.frame(id, treat, gender, visit, y)
# groupedData object for lme
gdat<-groupedData(y ~ visit|id, data=df)
# fits - different ordering of factors
fit1<-lme(y ~ visit*treat*gender, data=gdat, random = ~visit|id)
anova(fit1)
fit2<-lme(y ~ gender*treat*visit, data=gdat, random = ~visit|id)
anova(fit2)
# Result: identical (balanced design so far), ok
# Now change...
2002 Apr 08
1
Problem(?) in strptime() -- short version
...to
either of the POSIX datetime classes with the OS timezone set to
US/Pacific. And I've tried everything I could think of. The bottom
line appears to be the fact that strptime() always uses the local
timezone.
> Sys.getenv('TZ')
TZ
"US/Pacific"
>
> gdat <- c('2002-4-7 1:30:00 GMT',
+ '2002-4-7 2:30:00 GMT',
+ '2002-4-7 3:30:00 GMT')
>
>
> as.POSIXct(gdat)
[1] "2002-04-07 01:30:00 PST" "2002-04-07 01:30:00 PST" "2002-04-07
03:30:00 PDT"
>
> as.POSIXct(gda...
2002 May 03
1
Daylight savings time and conversion to POSIXt (arghh!)
...IXt functions (as.POSIXct,
as.POSIXlt, strptime, in particular).
I have environmental data collected once per minute. Here is a subset
of 3 input character strings; I have not found a way to correctly
convert all three to POSIXt using POSIXt functions. I would
appreciate help with this.
> gdat <- c('2002-4-7 1:30:00',
+ '2002-4-7 2:30:00',
+ '2002-4-7 3:30:00')
The times are recorded with a fixed 8 hour offset from GMT
(equivalently, they are recorded in "pacific standard time"
year-round, even when the local convention is to sw...
2006 Mar 07
0
rsync huge files from cygwin to linux stalls - bug?
...83)
rsync: connection unexpectedly closed (1412433 bytes received so far)
[generator]
rsync error: error in rsync protocol data stream (code 12) at io.c(434)
--- /snip ---
--- snip --- (-vvv)
renaming .ANTRAG.TXT.gUq0RW to ANTRAG.TXT
false_alarms=0 tag_hits=1 matches=1
sender finished /cygdrive/d/GDAT/ANTRAG.TXT
send_files(91, /cygdrive/d/GDAT/BARTEMP.DBF)
send_files mapped /cygdrive/d/GDAT/BARTEMP.DBF of size 576
recv_files(BARTEMP.DBF)
BARTEMP.DBF
recv mapped BARTEMP.DBF of size 576
calling match_sums /cygdrive/d/GDAT/BARTEMP.DBF
built hash table
hash search b=700 len=576
match at 0 last_match...
2006 Mar 07
0
runaway process in 3.0.21b
...IT TIME CPU COMMAND
30693 Guest 2 0 342M 342M sleep select 0:29 1.03% smbd
-------------
It can reach over 1G.
# smbstatus |grep 30675
AB 30675 recepcija Mon Mar 6 18:09:06 2006
30675 DENY_NONE 0x20089 RDONLY EXCLUSIVE+BATCH
/var/shared/AB gdat/ini/G_dat.ini Mon Mar 6 18:30:50 2006
# ls -lrt /var/shared/AB/gdat/ini/G_dat.ini
-rwxr--r-- 1 Guest wheel 2493 Mar 6 18:31
/var/shared/AB/gdat/ini/G_dat.ini
I have a debug 10 log file as well as a kernel trace, if it is of any help.
Can anyone give me a hint on where to start looking?...
2002 Jan 25
0
nested versus crossed random effects
...ave two within subjects factors A, B both with
two levels. Using aov I can do:
aov.1 <- aov(y ~ A*B + Error(S/(A+B))
following Pinheiro and Bates I can acheive the analagous mixed-effects
model with:
lme.1 <- lme(y~A*B, random=pdBlocked(list(pdIdent(~1),pdIdent(~A-1),
pdIdent(~B-1))), data=gdat)
But what if we add an additional level of nesting such that each of
the conditions within A and B are repeated multiple times within a
subject. This would then be a trial factor, call it "T". So we'd have
something like this:
s1:t1:a1:b1, s1:t1:a2:b2, s1:t1:a1:b2, s1:t1:a2:b1, s1:t...
2007 Jun 24
2
matlab/gauss code in R
...j))
> > y<-runif(4*NSubj) # Results
> > # Add effects
> > y<-y+0.01*as.integer(visit)
> > y<-y+0.02*as.integer(gender)
> > y<-y+0.024*as.integer(treat)
> > df<-data.frame(id, treat, gender, visit, y)
> > # groupedData object for lme
> > gdat<-groupedData(y ~ visit|id, data=df)
> > # fits - different ordering of factors
> > fit1<-lme(y ~ visit*treat*gender, data=gdat, random = ~visit|id)
> > anova(fit1)
> > fit2<-lme(y ~ gender*treat*visit, data=gdat, random = ~visit|id)
> > anova(fit2)
> > #...