Hallo Bernard
I did not follow all emails in this thread but it seems to me that your request
is similar to Bioconductor packages dealing with Flow Cytometry data.
Especially flowViz package is designed to visualise such data.
Cheers
Petr
> -Original Message-
> From: R-help On Behalf
Hi Cyrus,
Try this:
pcr<-data.frame(Ct=runif(66,10,20),Gene=rep(LETTERS[1:22],3),
Type=rep(c("Std","Unkn"),33),Rep=rep(1:3,each=22))
testagg<-aggregate(pcr$Ct,c(pcr["Gene"],pcr["Type"],pcr["Rep"]),
FUN=function(x){c(mean(x), sd(x), sd(x)/sqrt(sd(x)))})
nxcol<-dim(testagg$x)[2]
newxs<-paste("x",1
Dear users,
i am trying to summarize data using "aggregate" with the following command:
aggregate(pcr$Ct,c(pcr["Gene"],pcr["Type"],pcr["Rep"]),FUN=function(x){c(mean(x),
sd(x), sd(x)/sqrt(sd(x)))})
and the structure of the resulting data frame is
'data.frame':66 obs. of 4 variables:
$ Gen
https://cran.r-project.org/web/views/Multivariate.html
https://cran.r-project.org/web/views/
On March 27, 2019 4:05:31 PM PDT, Bernard Comcast
wrote:
>No - how do I access that?
>
>Bernard
>Sent from my iPhone so please excuse the spelling!"
>
>> On Mar 27, 2019, at 6:57 PM, Jeff Newmiller
> wr
You might be wishing for a contour plot of the density, labeled by the
probability mass outside of each contour, but there is no general simple
connection between density contours and the mass inside of them. You can work
it out (I think) for elliptically contoured distributions, but I suspect t
No - how do I access that?
Bernard
Sent from my iPhone so please excuse the spelling!"
> On Mar 27, 2019, at 6:57 PM, Jeff Newmiller wrote:
>
> I don't know. Have you looked at the Multivariate Task View?
>
>> On March 27, 2019 3:43:52 PM PDT, Bernard Comcast
>> wrote:
>> To follow on Jeff,
I don't know. Have you looked at the Multivariate Task View?
On March 27, 2019 3:43:52 PM PDT, Bernard Comcast
wrote:
>To follow on Jeff, is there a function to do 2-D (double) numerical
>integration in R?
>
>Bernard
>Sent from my iPhone so please excuse the spelling!"
>
>> On Mar 27, 2019, at 6
To follow on Jeff, is there a function to do 2-D (double) numerical integration
in R?
Bernard
Sent from my iPhone so please excuse the spelling!"
> On Mar 27, 2019, at 6:38 PM, Jeff Newmiller wrote:
>
> Regardless of how many dimensions you have for independent variables, the
> density is one
Regardless of how many dimensions you have for independent variables, the
density is one-dimensional, and if you assume the density function has been
determined (e.g. by kernel estimation or by a Gaussian copula) then if you
integrate the density function along that dimension there will be uniqu
That thought had crossed my mind so thanks for that clarification Bert. i think
you are correct and so the plot I am looking at must be doing something
different than I was thinking.
Thanks
Bernard
Sent from my iPhone so please excuse the spelling!"
> On Mar 27, 2019, at 5:18 PM, Bert Gunter
You are missing a crucial point. The reals are well ordered; higher
dimensions are not. Therefore 2d quantile contours are not unique.
Of course assuming I understand your query correctly.
Bert
On Wed, Mar 27, 2019, 13:55 Bernard McGarvey
wrote:
> If I understand correctly the ContourLines fu
If I understand correctly the ContourLines function gives you the contour lines
when you put in the data. But before this I need to data to put into that
function. I think this is something like a 2D CDF of the data that then leads
to the 2D quantiles but I am not 100% sure. What I am basically
Are you looking for the contourLines() function ?
Paul
On 28/03/19 8:37 AM, Bernard McGarvey wrote:
John, I have attached a pdf of the plot. Hopefully you can read this.
If I understand correctly, this plot is basically the 2-D version of the 1-D
quantile plot.
Thanks
Bernard McGarvey
D
John, I have attached a pdf of the plot. Hopefully you can read this.
If I understand correctly, this plot is basically the 2-D version of the 1-D
quantile plot.
Thanks
Bernard McGarvey
Director, Fort Myers Beach Lions Foundation, Inc.
Retired (Lilly Engineering Fellow).
> On March 27, 20
Thank you, Dr. Wood, that is very helpful.
Best,
Susan
On Wed, Mar 27, 2019 at 12:10 PM Simon Wood wrote:
> These are a bit of a throwback really, and are not very useful - they
> are reference degrees of freedom used in computing test statistic and
> the p-values, but since the null distribu
These are a bit of a throwback really, and are not very useful - they
are reference degrees of freedom used in computing test statistic and
the p-values, but since the null distributions are non-standard the
reference DoF is not very interpretable.
best,
Simon
On 27/03/2019 13:58, Susan Elia
Hello Group.
Regarding degrees of freedom in GAM models fit by mgcv, I do understand the
estimated df, but what does "ref.df" in the summary output mean?
I didn't see an explanation in Wood (2017) or find it in the function
code. The answer may be on page 222 of
Wood SN. On p-values for smooth
The figure did not get through. Perhaps try a pdf?
On Tue, 26 Mar 2019 at 13:41, Bernard McGarvey
wrote:
>
> I want to see if I can reproduce the plot below in R. If I understand it
> correctly, i takes my bivariate data and creates quantile density contours.
> My interpretation of these conto
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