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2
votes
2answers
834 views

Get quantile function of dynamic mixture model

I have a dynamic mixture distribution fitted to my risk data (i.e., I have all parameters) of Weibull and Generalized Pareto, with a Cauchy CDF mixing function, that can be written as: \begin{align} ...
16
votes
1answer
31k views

Generating random samples from a custom distribution

I am trying to generate random samples from a custom pdf using R. My pdf is: $$f_{X}(x) = \frac{3}{2} (1-x^2), 0 \le x \le 1$$ I generated uniform samples and then tried to transform it to my custom ...
14
votes
1answer
7k views

How to draw random samples from a non-parametric estimated distribution?

I have a sample of 100 points which are continuous and one-dimensional. I estimated its non-parametric density using kernel methods. How can I draw random samples from this estimated distribution?
13
votes
3answers
590 views

Estimate the size of a population being sampled by the number of repeat observations

Say I have a population of 50 million unique things, and I take 10 million samples (with replacement)... The first graph is I've attached shows how many times I sample the same "thing", which is ...
7
votes
0answers
1k views

How to generate 2 correlated Beta random variables

I was wondering if it might be possible to generate 2 correlated $Beta$ random variables? In other words, I want to generate two Beta random variables which can be said to have come from two Beta ...
7
votes
2answers
690 views

How to simulate effectiveness of treatment in R?

Let's say I want to write a simulation for the table below to decide if Xylitol treatment and ear infections are independent. How would I go about doing this?
1
vote
1answer
600 views

Sampling from copula given a particular value of marginal

I am working in the bivariate case with copulas as follows: I have two marginal Gamma distributions $f_1=Ga(\alpha_1, 1)$ and $f_2=Ga(\alpha_2, 1)$ that are bound by a copula Frank copula $C$, with ...
0
votes
1answer
1k views

How to exact prediction from over sampled data(Undoing oversampling)?

We are oversampling the data to use in logistic regression. Aim is to predict CTR(click probability) which is rare event scenario. I have predicted the probabilities of click but CTR results are ...
8
votes
3answers
3k views

Using Uniform Distribution to Generate Correlated Random Samples in R

[On recent questions I was looking into generating random vectors in R, and I wanted to share that "research" as an independent Q&A on a specific point.] Generating random data with correlation ...
11
votes
3answers
12k views

How to resample in R without repeating permutations?

In R, if I set.seed(), and then use the sample function to randomize a list, can I guarantee I won't generate the same permutation? ie... ...
4
votes
2answers
4k views

Sample from distribution given by histogram

Given a histogram obtained using given data points, how do I randomly sample from the distribution predicted by the histogram? Any conceptual comment / R code would be welcome.
3
votes
3answers
260 views

How to sample from a distribution so that mean of samples equals expected value?

Given a random variable $X$, how do I have obtain $N$ random variates of $X$ so that the mean value of my samples equals the expected value of $X$? E.g. let $X$ have uniform distribution on the ...
7
votes
2answers
7k views

How do I sample without replacement using a sampling-with-replacement function?

I vaguely recall from grad school that the following is a valid approach to do a weighted sampling without replacement: Start with an initially empty "sampled set". Draw a (single) weighted sample ...
2
votes
1answer
5k views

Leave-one-subject-out cross validation in Caret

Hi Dear Colleagues, I wonder how to correctly setup a leave-one-subject-out cross validation (LOSO) for train() function in caret. Here is my example code: ...
3
votes
1answer
72 views

Is there a more efficient method for creating a probability distribution?

I have written a short function in R to estimate the expected number of mutations that will be observed in a set of DNA sequences. The parameters are the mutation rate (x), the length of the DNA ...
4
votes
1answer
686 views

Absent categorical data levels in Bootstrap samples

I have a huge dataset ($n$ around five million, $p$ around three thousand) for a classification problem, where my interest is predictive class probabilities for test data, not the target. I shall be ...
2
votes
1answer
184 views

Why do two equivalent approaches to selecting numbers that sum to 100 produce different results

This question is inspired by two seemingly equivalent attempts to solve a question asked yesterday: In R, how to sample from the output of combn(a,b) if the "a choose b" is too large? ...
4
votes
1answer
297 views

Sampling from under/over-dispersed count data in R

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population. For each type, I build ...
3
votes
3answers
2k views

In R, how to sample from the output of combn(a,b) if the “a choose b” is too large?

I would like to sample 1 million vectors from the set of vectors $$\mathbf{v} = \{v_1,v_2,...,v_n \} $$ subject to $\sum_{i=1}^n v_i = 1$ $\forall i: v_i \ge 0$ each $v_i$ has at most 2 decimal ...
1
vote
0answers
671 views

R: estimate joint density and sample from conditional densities

I'm trying to implement an algorithm that estimates time-homogeneous Markov chain with continuous state space. The probability of transition from state $i$ to state $i+1$ has continuous conditional ...