Hello,
I have fitted two curves to the data. How can I tell which one is more
fitted? By eye (see plot underneath) I would say that the function
Gompertz is better than the function Holling type III; how can I give
a number to this hunch?
This is an example:
```
# functions
holling = function(a, b,
On 2020-07-06 17:41 -0700, Philip wrote:
> Thanks for getting back to me. It is good
> to know that I am on the right track.
Oh, it's always such a pleasure to be of help
to someone in need :-)
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R-help@r-pr
Thanks for getting back to me. It is good to know that I am on the right
track.
-Original Message-
From: Rasmus Liland
Sent: Monday, July 6, 2020 10:42 AM
To: r-help
Subject: Re: [R] National Weather Service Data
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R-help@r-project.org mai
fisher.test() computes exact confidence intervals for the odds ratio.
> On 6 Jul 2020, at 15:01, Luigi Marongiu wrote:
>
> Is there a simple function from some package that can also add a
> p-value to this test? Or how can I calculate the p-value on my own?
_
And since this is about RNA expression data, you would do better posting on
the Bioconductor Help site rather than here. You are more likely to find
the expertise and interest you seek there.
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things in
Dear Sarah,
thank you very much for pointing to the list of available packages and
algorithms.
On Mon, Jul 6, 2020 at 10:20 AM Sarah Goslee wrote:
> Hi,
>
> Unsupervised classification (clustering) is a huge field. There's an
> entire task view devoted to it, where you can see many of the large
Hi Philip:
Results look correct to me. This might help you:
https://www.cpc.ncep.noaa.gov/products/wesley/wgrib2/default_inv.html
-Roy
> On Jul 6, 2020, at 9:29 AM, Philip wrote:
>
> I am trying to access National Weather Service forecasting data through the
> rNOMADS package. I’m not sur
On 2020-07-06 09:29 -0700, Philip wrote:
> $inventory
> [1] "1:0:d=2020070606:TMP:800 mb:6 hour fcst:"
> "2:148450:d=2020070606:RH:800 mb:6 hour fcst:"
> [3] "3:414132:d=2020070606:TMP:2 m above ground:6 hour fcst:"
> "4:571266:d=2020070606:RH:2 m above ground:6 hour fcs
Hi,
Unsupervised classification (clustering) is a huge field. There's an
entire task view devoted to it, where you can see many of the large
array of R packages that perform some sort of clustering.
https://cran.r-project.org/web/views/Cluster.html
Since that is an overwhelming list, you may be
Dear all,
please may I ask for a suggestion regarding the algorithms to cluster the
expression data in single cells (scRNA-seq) at multiple time points :
we do have expression data for 30 000 genes in 10 datasets that have been
collected at multiple time points,
and i was wondering if you could
I am trying to access National Weather Service forecasting data through the
rNOMADS package. I’m not sure if the Weather Service software – grib2 – loaded
correctly. Second, some of the examples in the rNOMADS documentation seem to
run correctly but I’m not sure what the output means. Any anv
On 2020-07-06 12:03 +0300, Eric Berger wrote:
> On Mon, Jul 6, 2020 at 2:07 AM Richard M. Heiberger wrote:
> > On Sun, Jul 5, 2020 at 2:51 PM Christopher W. Ryan
> > wrote:
> > >
> > > I've been conducting relatively simple
> > > COVID-19 surveillance for our
> > > jurisdiction.
> >
> > Have
Dear Luigi
You could try the epitools package which gives a large number of ways of
doing this. I would have thought that using Wald intervals for the log
odds ration was not optimal with small frequencies.
Michael
On 06/07/2020 14:01, Luigi Marongiu wrote:
Hello,
Is it possible to calculat
Luigi,
Odds ratios can be produced using a logistic regression, which can be performed
using the glm function. The following has a detailed description of how
logistic regression can be performed using R:
https://stats.idre.ucla.edu/r/dae/logit-regression/
John
John David Sorkin M.D., Ph.D.
P
Hello,
Is it possible to calculate with a single function the odd ratios?
Now I can use this implement:
```
or <- (De/He)/(Dn/Hn) # Disease exposed, Healthy non-exposed
logo <- log(or)
x <- sqrt(((1/De) + (1/He) + (1/Dn) + (1/Hn)))
lower_ci = exp(logo - 1.96*x)
upper_ci = exp(logo + 1.96*x)
cat("OR
Hi Christopher,
This seems pretty standard and straightforward, unless I am missing
something. You can do the "full join" without changing variable names.
Here's a small code example with two tibbles, a and b, where the
column 'x' in a corresponds to the column 'u' in b.
a <- tibble(x=1:15,y=21:35
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