Not sure what you mean by this:
"But this only works if the vectors xr* are longer than xs*."
The solution I gave doesn't care about this.
> a <- rbind(unique(z2),unique(z1))
> a[duplicated(a),]
xs1 xs2
## as before
Presumably you are referring to your use of match() (which is how %in%
is de
Hello,
When the confidence interval is the normal CI, the return matrix has
only 3 columns, all other CI's return a matrix with 5 columns. See
section value in ?boot.ci.
So just change
$norm[, 4:5]
to
$norm[, 2:3]
Hope this helps,
Rui Barradas
Às 22:24 de 16/12/21, varin sacha via R-hel
Wow! These are awesome! Thanks so much for the special cases!
Erin
On Thu, Dec 16, 2021 at 3:52 PM Rui Barradas wrote:
> Hello,
>
> And here is another solution, addressing the problem raised by Bert and
> avoiding unique.
>
>
> xr1 <- 8:0
> xr2 <- 0:8
> xs1 <- 9:3
> xs2 <- 4
> cbind(xr1, xr2
Hello,
And here is another solution, addressing the problem raised by Bert and
avoiding unique.
xr1 <- 8:0
xr2 <- 0:8
xs1 <- 9:3
xs2 <- 4
cbind(xr1, xr2)[(xr1 %in% xs1) & (xr2 %in% xs2),]
#xr1 xr2
# 4 4
xr1 <- c(1,2,1)
xr2 <- c(4,5,4)
xs1 <- c(6,6)
xs2 <- c(7,7)
cbind(xr1, xr2)[(xr1 %in%
Dear R-experts,
Here below my R code. Using "bca" in the boot.ci function at the end of my R
code works perfectly. Using "perc" and "basic" perfectly works as well. But
using "norm", my R code does not work anymore.
Is it possible to solve that problem and to make my R code work ?
#
I am not sure Eric's solution is what is wanted:
Consider:
xr1 <- c(1,2,1)
xr2 <- c(4,5,4)
xs1 <- c(6,6)
xs2 <- c(7,7)
> z1 <- cbind(xr1, xr2)
> z2 <- cbind(xs1,xs2)
> z1
xr1 xr2
[1,] 1 4
[2,] 2 5
[3,] 1 4
> z2
xs1 xs2
[1,] 6 7
[2,] 6 7
If what is wanted is to find
> a <- cbind(c(xr1,xs1),c(xr2,xs2))
> a[duplicated(a)]
[1] 4 4
On Thu, Dec 16, 2021 at 10:18 PM Erin Hodgess wrote:
>
> Hello!
>
> I have the following:
>
> cbind(xr1,xr2)
>
> xr1 xr2
>
> [1,] 8 0
>
> [2,] 7 1
>
> [3,] 6 2
>
> [4,] 5 3
>
> [5,] 4 4
>
> [6,] 3
Hello!
I have the following:
cbind(xr1,xr2)
xr1 xr2
[1,] 8 0
[2,] 7 1
[3,] 6 2
[4,] 5 3
[5,] 4 4
[6,] 3 5
[7,] 2 6
[8,] 1 7
[9,] 0 8
> cbind(xs1,xs2)
xs1 xs2
[1,] 9 4
[2,] 8 4
[3,] 7 4
[4,] 6 4
[5,] 5 4
Hi all,
I have the following results from a study. I ran this model for binary
distributed outcome log(event/non_event). I need to write out a complete
GAM fitted model equation, using parameter estimates given below. I have
difficulty putting the parameter estimates of Spline part in the fitted
m
On Wed, 15 Dec 2021, Avi Gross wrote:
I still do not see what you want to do, sorry.
Avi,
Backing up to my original post on this thread I've realized that no one
addressed my main question: do variable measurement intervals affect
analyses of the data. And, if so, how and how to compensate fo
On Thu, 16 Dec 2021, Chris Evans wrote:
What you said earlier was:
For me the next step, in tidyverse pseudocode, might be something like:
tibData %>%
arrange(nbr, datetime) %>% # just in case things are not ordered nicely
group_by(site_nbr) %>% # as you want to get changes within site I
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