Hello Ablaye,
I didn't find any function xi ,yi.
This is an english mailing list. You will maximize your chance of
getting help by sticking to english.
I would also recommend that you comment your code in English, it will be
easier to share. But on this one it is your call.
PLEASE do read the p
Hello Maria Cristina,
On Friday, 9 Oct 2020 at 19:39, Maria Cristina Maurizio wrote:
> Hi! I'm trying to perform propensity score matching on survey data and so
> for each individual observation I have a statistical weight attached. My
> question is: is there a way within the package to consider
Hello.
Here is my R code. I used the functional data . Now I need to use the
functional data by applying the kernels instead of the xi, yi functions.
Bonjour.
Voici mon code en R . J'ai utiliser les données fonctionnelles . Maintenant
j'ai besoin d'utiliser les données fonctionnelles en appliquan
I’m wondering if you do any searching of the Web or use the help facilities
before asking questions? When I posed the question to Google’s search
facilities I immediately was directed to, unsurprisingly, the help page text in
a webpage format:
https://ggplot2.tidyverse.org/reference/geom_densit
> SNP$density <- get_density(SNP$mean, SNP$var)
> > summary(SNP$density)
>Min. 1st Qu. MedianMean 3rd Qu.Max.
> 0 383 696 73811701789
This doesn't look accurate.
The density values shouldn't all be integers.
And I wouldn't expect the smallest density to be ze
Hello,
My programming is as follows.
library(highfrequency)
library(data.table)
library(xts)
tm<-seq.POSIXt(from = as.POSIXct("2020-08-20 09:30:00"),to =
as.POSIXct("2020-08-20 15:59:00"),by='min')
data<-xts(x=data$PRICE,order.by=tm)
data <- as.data.table(data)
setnames(data,c('index'),c('DT'))
Hi Abby,
Thanks for getting back to me, yes I believe I did that by doing this:
SNP$density <- get_density(SNP$mean, SNP$var)
> summary(SNP$density)
Min. 1st Qu. MedianMean 3rd Qu.Max.
0 383 696 73811701789
where get_density() is function from here:
https://
You could assign a density value to each point.
Maybe you've done that already...?
Then trim the lowest n (number of) data points
Or trim the lowest p (proportion of) data points.
e.g.
Remove the data points with the 20 lowest density values.
Or remove the data points with the lowest 5% of densit
Hi! I'm trying to perform propensity score matching on survey data and so
for each individual observation I have a statistical weight attached. My
question is: is there a way within the package to consider these weights in
the matching procedure?
Thank you very much.
--
Maria Cristina Maurizio
Good Morning,
I am using the neuralnet package in R, and am able to produce some basic neural
nets, and use the output.
I would like to exclude some of the weights and biases from the iteration
process and fix their values.
However I do not seem to be able to correctly define the exclude and
Hello Giovanni,
I don't know if my workflow would suit you but I tend to want the
opposite when I launch a parallel process. I tend to want to keep the
processes alive as long as they can. If the computation time is long I
would not want to lose everything.
lapply..8 <- function(X,FUN,...){
m
See the "details" section, which clearly states:
"*theta = (mu_x - mu_y - mu) / sigma "*
where mu is an optional shift parameter. For simplicity, let mu = 0 ,and
the text in the details section
shows that eps is on the theta scale, i.e. the "standardized" difference.
Bert Gunter
"The trouble wi
I am trying to understand the meaning of the eps parameter of the equiv.test
parameter of the equiv.test function (package equivUMP)
The help file for equiv.test states that the parameter eps is "a single
strictly positive number giving the equivalence limits"
What is the scale of measurement of
Hi Bert,
Another confrontational response from you...
You might have noticed that I use the word "outlier" carefully in this
post and only in relation to the plotted ellipses. I do not know the
underlying algorithm of geom_density_2d() and therefore I am having an
issue of how to interpret the pl
I recommend that you consult with a local statistical expert. Much of what
you say (outliers?!?) seems to make little sense, and your statistical
knowledge seems minimal. Perhaps more to the point, none of your questions
can be properly answered without subject matter context, which this list is
no
Hi Abby,
thank you for getting back to me and for this useful information.
I'm trying to detect the outliers in my distribution based of mean and
variance. Can I see that from the plot I provided? Would outliers be
outside of ellipses? If so how do I extract those from my data frame,
based on whi
> Steven Yen
> on Fri, 9 Oct 2020 05:39:48 +0800 writes:
> Oh Hi Arne, You may recall we visited with this before. I
> do not believe the problem is algorithm specific. The
> algorithms I use the most often are BFGS and BHHH (or
> maxBFGS and maxBHHH). For simple econo
Hey folks,
Is there any way to exit an mclapply early on error?
For example, in the following mclapply loop, I have to wait for all the
processes to finish before the error is returned.
```
mclapply(X = 1:12, FUN = function(x) {Sys.sleep(0.1); if(x == 4) stop()},
mc.cores = 4, mc.preschedule
> My understanding is that this represents bivariate normal
> approximation of the data which uses the kernel density function to
> test for inclusion within a level set. (please correct me)
You can fit a bivariate normal distribution by computing five parameters.
Two means, two standard deviation
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