Hi,
Thank you for your answers, I'm not completely sure if it's to bino.test I need
or the uniroot. Perhaps I should explain more the idea behind the code and the
actual task I'm trying to do. The idea is to calculate a confidence interval as
to the age of two DNA sequences which have diverged, where I know the number of
mutations that happened in them, and I know the mutation rate.
The binomial probability can be used since, mutations have a probability of
occurring or being observed so many times in a sequence. This is dependent on
the length of the DNA stretch (which equates to the number of trials since each
base is a possibility of observing a mutation), the probability of a single
mutation occurring which is p = t * u, since more time means a higher
probability a mutation may have occurred.
So my code, using pbinom, is supposed to calculate the probability that my DNA
stretches contain the number of mutations observed P(X = k), given their size
(trials) and the probability of a single mutation (p = t * u). However I'm
interested in finding t: t is what is unknown, so the loop repeatedly evaluates
the calculation, increasing t each time and checking P(X=k), when it is 0.05,
0.50 and 0.95, we record t.
Ideally I'd like to rearrange this so I can get the probability of a single
success (mutation) p, and then divide by the mutation rate to get my t. My
supervisor gave my the loopy code but I imagine there is a way to plug in
P(X=k) as 0.05 and 0.95 and get my upper and lower t estimates.
According to the R built in docs:
binom.test
Description:
Performs an exact test of a simple null hypothesis about the
probability of success in a Bernoulli experiment.
Perhaps this is the one I need rather than uniroot?
Best,
Ben.
________________________________________
From: Stefan Evert [stefa...@collocations.de]
Sent: 10 October 2013 09:37
To: R-help Mailing List
Cc: Benjamin Ward (ENV)
Subject: Re: [R] Small p from binomial probability function.
Sounds like you want a 95% binomial confidence interval:
binom.test(N, P)
will compute this for you, and you can get the bounds directly with
binom.test(N, P)$conf.int
Actually, binom.test computes a two-sided confidence interval, which
corresponds roughly to 2.5 and 97.5 percentages in your approach. It doesn't
give you the 50% point either, but I don't think that's a meaningful quantity
with a two-sided test.
Hope this helps,
Stefan
On 9 Oct 2013, at 15:53, Benjamin Ward (ENV) <b.w...@uea.ac.uk> wrote:
> I got given some code that uses the R function pbionom:
>
> p <- mut * t
> sumprobs <- pbinom( N, B, p ) * 1000
>
> Which gives the output of a probability as a percentage like 5, 50, 95.
>
> What the code currently does is find me the values of t I need, by using the
> above two code lines in a loop, each iteration it increaces t by one and runs
> the two lines. When sumprobs equals 5, it records the value t, then again
> when sumprobs is equal to 50, and again when sumprobs is equal to 95 - giving
> me three t values. This is not an efficient way of doing this if t is large.
> Is it possible to rearrange pbinom so it gives me the small p (made of mut*t)
> as the result of plugging in the sumprobs instead, and is there an R function
> that already does this?
>
> Since pbinom is the binomial probability equation I suppose the question is -
> in more mathematical terminology - can I change this code so that instead of
> calculating the Probability of N successes given the number of trials and the
> probability of a single success, can I instead calculate the probability of a
> single success using the probability of N successes and number of trials, and
> the number of successes? Can R do this for me. So instead I plug in 5, 50,
> and 95, and then get the small p out?
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