ject.org] On Behalf Of Samor Gandhi
> Sent: Wednesday, February 24, 2010 5:23 AM
> To: r-help@r-project.org
> Subject: [R] Bimodal distribution
>
> Hello,
>
> Is there any test for bimodality in R that
>
> x <- c(rnorm(1000,0,1),rnorm(1000,3,1))
> hist(x,nclas
Hi Ingmar,
Thank you for your reply! How to fit a mixture distribution to the data, do you
mean by using mixed model?
Regards,
Samor
--- On Wed, 24/2/10, Ingmar Visser wrote:
From: Ingmar Visser
Subject: Re: [R] Bimodal distribution
To: "Samor Gandhi"
Cc: r-help@r-projec
Samor,
A somewhat indirect answer: you could fit a mixture distribution to your
data and test
how many components are needed to best describe your data.
hth, Ingmar
On Wed, Feb 24, 2010 at 1:22 PM, Samor Gandhi wrote:
> Hello,
>
> Is there any test for bimodality in R that
>
> x <- c(rnorm(1
Hello,
Is there any test for bimodality in R that
x <- c(rnorm(1000,0,1),rnorm(1000,3,1))
hist(x,nclass=100)
Thank you in advance for any help.
Regards,
Samor
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Hi Mike,
if you can decompose the bimodal distribution into (eg two) known
forms, then you could try a stepwise approach, eg:
If uniform < 0.5 then double it and use it to draw from the inverse
cdf of A,
else, double (uniform - 0.5) and use it to draw from the inverse cdf of B.
You can change
Hello R Users,
I am doing a Latin Hypercube type simulation. I have found the
improvedLHS function and have used it to generate a bunch of properly
distributed uniform probabilities. Now I am using functions like qlnorm
to transform that into the appropriately lognormal or triangularly
distri
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