Margarida Soares writes:
> library(pls)
> plsrcue<- plsr(cue~fb+cn+n+ph+fung+bact+resp, data = cue, ncomp=7,
> na.action = NULL, method = "kernelpls", scale=FALSE, validation = "LOO",
> model = TRUE, x = FALSE, y = FALSE)
> summary(plsrcue)
>
> and I got this output, where I think I can choose th
Your code generates an error that has nothing to do with dopar. I have
no idea what your function stack is supposed to do; you may be
inadvertently calling utils::stack which would produce this kind of
error:
> stack(1:25, RAT = FALSE)
Error in data.frame(values = unlist(unname(x)), ind, stringsAs
I have used foreach() for parallel computing but in the current problem, it
is not working. Given the volume and type of the data involved in the
analysis, I will try to give below the complete code without reproducible
example.
In short, each R environment will draw a set of separate files, perfo
> On Dec 6, 2017, at 4:27 PM, Ashta wrote:
>
> Thank you Ista! Worked fine.
Here's another (possibly more direct in its logic?):
DM[ !ave(DM$x, DM$GR, FUN= function(x) {!length(unique(x))==1}), ]
GR x y
5 B 25 321
6 B 25 512
7 B 25 123
8 B 25 451
--
David
> On Wed, Dec 6, 2017 at
Thank you Ista! Worked fine.
On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn wrote:
> Hi Ashta,
>
> There are many ways to do it. Here is one:
>
> vars <- sapply(split(DM$x, DM$GR), var)
> DM[DM$GR %in% names(vars[vars > 0]), ]
>
> Best
> Ista
>
> On Wed, Dec 6, 2017 at 6:58 PM, Ashta wrote:
>> Thank
Hi Ashta,
There are many ways to do it. Here is one:
vars <- sapply(split(DM$x, DM$GR), var)
DM[DM$GR %in% names(vars[vars > 0]), ]
Best
Ista
On Wed, Dec 6, 2017 at 6:58 PM, Ashta wrote:
> Thank you Jeff,
>
> subset( DM, "B" != x ), this works if I know the group only.
> But if I don't know th
Thank you Jeff,
subset( DM, "B" != x ), this works if I know the group only.
But if I don't know that group in this case "B", how do I identify
group(s) that all elements of x have the same value?
On Wed, Dec 6, 2017 at 5:48 PM, Jeff Newmiller wrote:
> subset( DM, "B" != x )
>
> This is covered
subset( DM, "B" != x )
This is covered in the Introduction to R document that comes with R.
--
Sent from my phone. Please excuse my brevity.
On December 6, 2017 3:21:12 PM PST, David Winsemius
wrote:
>
>> On Dec 6, 2017, at 3:15 PM, Ashta wrote:
>>
>> Hi all,
>> In a data set I have group(GR
Thank you David.
This will not work. Tthis removes only duplicate records.
DM[ !duplicated(DM$x) , ]
My goal is to remove the group if all elements of x in that group have
the same value.
On Wed, Dec 6, 2017 at 5:21 PM, David Winsemius wrote:
>
>> On Dec 6, 2017, at 3:15 PM, Ashta wrote:
>>
> On Dec 6, 2017, at 3:15 PM, Ashta wrote:
>
> Hi all,
> In a data set I have group(GR) and two variables x and y. I want to
> remove a group that have the same record for the x variable in each
> row.
>
> DM <- read.table( text='GR x y
> A 25 125
> A 23 135
> A 14 145
> A 12 230
> B 25 321
Hi all,
In a data set I have group(GR) and two variables x and y. I want to
remove a group that have the same record for the x variable in each
row.
DM <- read.table( text='GR x y
A 25 125
A 23 135
A 14 145
A 12 230
B 25 321
B 25 512
B 25 123
B 25 451
C 11 521
C 14 235
C 15 258
C 10 654',heade
Hi Sergio,
You seem to be aiming for a univariate repeated measures analysis.
Maybe this will help:
subno<-rep(1:6,2)
dat <- data.frame(subno=rep(1:6,2),,vals = c(ctrl, ttd),
cond = c(rep("ctrl", 6), rep("ttd", 6)), ind = factor(rep(1:6, 2)))
fit<-aov(vals~ind+cond+Error(subno),data=dat)
fit
su
> On Dec 6, 2017, at 11:09 AM, Paul Bernal wrote:
>
> Thank you very much David. As a matter of fact, I solved it by doing the
> following:
>
> MyTimeSeriesObj <- ts(MyData, freq=365.25/7,
> start=decimal_date(mdy("01-04-2003")))
>
> After doing that adjustment, my forecasts dates started fr
Thank you very much David. As a matter of fact, I solved it by doing the
following:
MyTimeSeriesObj <- ts(MyData, freq=365.25/7,
start=decimal_date(mdy("01-04-2003")))
After doing that adjustment, my forecasts dates started from 2017 on.
Cheers,
Paul
2017-12-06 12:03 GMT-05:00 David Winsemius
Sergio:
1. You do not have a "repeated measures linear model" .
2. This list is not designed to replace your own efforts to learn the
necessary R background, in this case, factor coding and contrasts in linear
models. I would suggest you spend some time with any of the many fine R
linear model tu
> On Dec 6, 2017, at 5:07 AM, Paul Bernal wrote:
>
> Dear friends,
>
> I have a weekly time series which starts on Jan 4th, 2003 and ends on
> december 31st, 2016.
>
> I set up my ts object as follows:
>
> MyTseries <- ts(mydataset, start=2003, end=2016, frequency=52)
>
> MyModel <- auto.ari
Dear Users,
I am trying to understand the inner workings of a repeated measures linear
model. Take for example a situation with 6 individuals sampled twice for
two conditions (control and treated).
set.seed(12)
ctrl <- rnorm(n = 6, mean = 2)
ttd <- rnorm(n = 6, mean = 10)
dat <- data.frame(vals =
Dear friends,
I have a weekly time series which starts on Jan 4th, 2003 and ends on
december 31st, 2016.
I set up my ts object as follows:
MyTseries <- ts(mydataset, start=2003, end=2016, frequency=52)
MyModel <- auto.arima(MyTseries, d=1, D=1)
MyModelForecast <- forecast (MyModel, h=12)
Sinc
18 matches
Mail list logo
