Contents
What do you need to know about fitting distributions with R?
Fitting distribution with R is something I have to do once in a while. A good starting point to learn more about distribution fitting with R is Vito Ricci’s tutorial on CRAN. I also find the vignettes of the actuar and fitdistrplus package a good read.
How to determine which distribution fits my data better?
So in case the p-value of my sample data is > 0.05 for a normal distribution as well as a weibull distribution, how can I know which distribution fits my data better?
Which is the Best Plot method for fitdistrplus?
However, since I discovered the fitdistrplus package I have become very fond of the function, as it comes with a wonderful plot method. It plots an empirical histogram with a theoretical density curve, a QQ and PP-plot and the empirical cumulative distribution with the theoretical distribution.
How to choose a probability distribution for R?
A more comprehensive overview of the various R packages is given by the CRAN Task View: Probability Distributions, maintained by Christophe Dutang. How do you decide which distribution might be a good starting point? I came across the paper Probabilistic approaches to risk by Aswath Damodaran.
How to fit a distribution to sample data?
We draw 50 random numbers from a log-normal distribution, fit the distribution to the sample data and repeat the exercise 50 times and plot the results using the plot function of the fitdistrplus package. You will notice quite a big variance in the results.
Can you fit a gamma distribution to a normal distribution?
I’ve gotten some success in a normal distribution to the sample data, but as soon as I try to fit something more complex (a gamma distribution, as seen in the code), I get all kinds of errors. What am I doing wrong?
How to fitting mixture distributions with are package mixtools?
If, however, we specify the correct standard deviations (i.e., specify “sigma = c (1,1,1)” when we invoke normalmixEM) and we increase the maximum number of iterations to 3000 (i.e., specify “maxit = 3000”), the procedure does converge after 2417 iterations for the seed value 101, yielding the following parameter estimates:
Which is the first step in fitting a distribution?
The first step in fitting distributions consists in choosing the mathematical model or function to represent data in the better way.
Which is an example of a fitdistrplus method?
Detailed examples are given in food risk assessment, ecotoxicology and insurance contexts. Keywords: probability distribution \\ftting, bootstrap, censored data, maximum likelihood, moment matching, quantile matching, maximum goodness-of-\\ft, distributions, R.