Hi all,
Recently I sent an email and I was asked to provide reproducible code of a
simple example of my situation. Instead of providing the code, I decided to
describe what I need in my code.
I've written a function V, which is a function of (r,s); so I have a
function V(r,s) in fact. The output of
Hi,
Consider the line below:
for(r in a)for (s in a) x=rbind(x,apply(replicate(1000,V(r,s)),1,mean))
V is a vector of (n-1) variables calculated by some rule and is a functions
of (r,s). So the line above produces 1000 replicates of V for each (r,s),
puts them in a matrix, calculates the m
I'm trying to draw the density function of a mixed normal distribution
in the form of:
.6*N(.4,.1)+ .4*N(.8,.1)
At first I generate a random sample with size 200 by the below code:
means = c(.4,.8)
sds = sqrt(c(.1,.1))
ind = sample(1:2, n, replace=TRUE, prob=c(.6,.4))
x=rnorm(n,mean=means[ind],sd=s
How can I understand if a distribution is bimodal? The quantiles of
desired probablities and also cumulative probablities in desired data
points are provided.
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How I can generate a random sample from a mixed norml distribution?
(i.e. a mixed normal distribution in the form f(x)=.6*N(1,5)+.4*N(2,3)
in which N(A,B) is a normal distribution with mean A and variance B.)
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How I can create some sample data from a density in the form:
f(x)=sum(a(j)M(j)),
in which M(j) is the B-spline basis function. In fact my density
function is a linear combination of B-splines. (Is there any other way
than using the well-known and general uniform distribution method?)
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