This should be posted on the r-sig-mixed-models list rather than here. The
interest and expertise you seek is more likely to be found there.
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloo
Dear Bert Gunter and Mathew Guilfoyle,
Thanks for the reply.
I also agree with you. But that is the actual slope of the straight line
connecting from the first estimated value to last estimated value ( like
dy/dx slope). I tried in that way also, but I couldn't replicate the
results as given in t
Following Ben Bolker's methodology (described here
https://rpubs.com/bbolker/3423) I incorporated non-trivial fixed effects in
NLMER for a four-parameter logistic. I placed a reproducible example here:
https://rpubs.com/ramirob/648103
To summarize the question, if we have a dataset with indi
I think that the current documentation is correct, but that does not
mean that it cannot be improved.
The key phrase for me is "from the current position" which says to me
that the match needs to happen right there, not just somewhere in the
rest of the string.
If you used the expression " +t" t
One option is to use the my.symbols and ms.image functions from the
TeachingDemos package. There is an example under ?ms.image.
On Mon, Aug 10, 2020 at 7:43 AM Pedro páramo wrote:
>
> Hi,
>
> There is a way to add a photo like a free text but images on a plot, (hist,
> chart trough ggplot) to a
??
Change per year = (estimated end value - estimated begin value)/## years
Am I missing something subtle here?
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
O
Hi,
I have tried to develop a simple method of correcting some artefacts in an
image with R. Before proceeding further with image analysis.
An example of my attempt:
library(imager)
im <- load.example('coins')
imr <- as.raster(im)
plot(imr)
sel <- locator(n=1)
sel
x1 <- floor(sel$x)
x2 <-
Thank you for your reply.
Yes, we can get the curve fit values on the line (as the length of input
data frame) from predict.gam() function. But I wish to get the trend value
(in Trends in °C per decade or °C per year) as given in the Box 2.2, Table
1. But I couldn't find any option in GAM method
I have a tokenized txt document with 'div' tags and 'id' to it :
library(quanteda)
library(htmltools)
library(tidyverse)
text <- But how do you do?
I see I have frightened you—sit... ”
It was in July, 1805, and the speaker..
With
Caveat: Did not look at any of your links.
However, the usual answer for this sort of question is ?predict.gam (in
general, predict.whatevermethod)
Have you consulted the man page? If this is not what you want, you may need
to explain more carefully.
Bert Gunter
"The trouble with having an open
Thanks to all who responded. Will take me some time to digest it all.
-Roy
> On Aug 11, 2020, at 6:24 AM, J C Nash wrote:
>
> Thanks to Peter for noting that the numerical derivative part of code doesn't
> check bounds in optim().
> I tried to put some checks into Rvmmin and Rcgmin in optimx
Thanks to Peter for noting that the numerical derivative part of code doesn't
check bounds in optim().
I tried to put some checks into Rvmmin and Rcgmin in optimx package (they were
separate packages before, and
still on CRAN), but I'm far from capturing all the places where numerical
derivative
Dear All,
I am trying to estimate the non -linear trend value from smooth spline
trend fit (using the generalized additive model (GAM)).
I want to estimate the trend value from a temperature dataset (spatial
averaged annual meantime from 1906 to 2005) as given in the Box 2.2, Table
1 in the atta
Hello Raj,
Please include a reproducible example. If you only give a generic error
message, the best solution we can offer is to reboot your server and try it
again.
Anyway, from the information you gave, it seems like you should ask this
question in the Rstudio community(I assume you are running
I am an expert user for Docker. Unfortunately this is not a use case that will
work with Docker.
The goal is to provide a self-contained artifact as a solution so that no
effort needs to be put into the environment configuration.
If Docker was used, the users would need to download docker and f
This stuff is of course dependent on exactly which optimization problem you
have, but optimx::optimr is often a very good drop-in replacement for optim,
especially when bounds are involved (e.g., optim has an awkward habit of
attempting evaluations outside the domain when numerical derivatives a
16 matches
Mail list logo
