There are some packages in R which produce numbers coming from a loglogistic distribution.
One example is the package FAdist. In particular I am trying to use the function rllog to obtain numbers coming from a loglogistic distribution, but it's not clear to my how are the parameters defined.
What is the full version of the pdf of a number produced when using the rllog function for shape parameter a and scale parameter b?
Related
I'm wondering if there is an R package which can help me to get the correct parameters for a distribution of my choice and for intervals of my choice.
For Instance, here Betancourt is looking at inverse gamma and he wants to learn which set of parameters will give >1% below 2 and >1% above 20 (like the graph below). Stan's solver returns the parameters for inv-gamma which results the intervals of interest. Is there any solution applied directly on R?
Or in other words,
I have the distribution
I have the intervals
Can I learn the correct parameters?
Thanks
I am trying to fit my data using the fitdist() function from the fitdistrplus package in R. I succeeded using a normal and a lognormal distribution via key words 'norm' and 'lnorm' which I found via searching online.
I was not able to get other distributions working; the help of fitdist() says:
distr: A character string "name" naming a distribution for which the corresponding density function dname, the corresponding distribution function pname and the corresponding quantile function qname must be defined, or directly the density function.
I checked and when entering ?norm or ?norm into the R command line, neither function norm() nor lnorm() is found. This confuses me totally.
When I try for example fitdist(data, 'poisson'), I get the following error message:
Error in fitdist(data$time, "poisson") :
The dpoisson function must be defined
I am somewhat a noob in R, can anybody give a hint?
norm() in R is a different function to compute norms of a matrix, so not directly related to the normal distribution.
?Normal brings up the documentation related to the normal distribution, and you'll see the 4 functions dnorm, pnorm, qnorm and rnorm belonging to this family.
If you look at ?Lognormal you'll see the same convention with the typical 4 functions.
More generally, you can look-up ?Distributions, which links all of them. There you can see that the keyword for the poisson distribution should actually be pois.
I am interested in frequency distributions that are not normally distributed.
If I have a frequency distributions table which is not normally distributed.
Is there a function or package that will identify the type of distribution for me?
You can use the fitdistr function (library MASS i think) and check for yourself if you find a 'fitting' distribution. However i suggest that you plot the function first and see how it looks like. This approach is generally not recommended as you always can use different parameters to fit a distribution and thus confuse one distribution with another. If you have found a suited distribution you should test it against data.
Edit: For instance a normal distribution may look like a poisson distribution. Fitting is in my oppinion only useful if you have enough random variables. Otherwise just draw variables from your data if you need to
You can always try to test whether a distribution is adequate for your data with QQ plot. If you have data that is dynamic, I would suggest that you use ECDF (Empirical Cumulative Distribution Function) which will give you more precise distributions as your data grows. You can use ECDF in R with the ecdf() function.
How to define a customized probability distribution in a "R-standard way"? I know my cumulative distribution function(cdf) and random sampling function(rsf), say pnewdist and rnewdist. It is needed to define a probability distribution in R like it is done in the standard R case, viz, normal distribution where rnorm or pnorm are the rsf and cdf respectively. So, how to define my distribution newdist and link its cdf and rsf with pnewdist and rnewdist?
Ideally for using the rnewdist or pnewdist I dont need to do so but I want to use the R package HiddenMarkov with customized/user defined probability distribution for the observables instead of the available distributions in R(like normal or poisson etc.) so that in the command dthmm, I can use distn=newdist. Thanks.
ecdf uses a user-defined distribution to give you a function based on that distribution.
I am actually a novice to R and stats.. Could something like this be done in R
Determining the density estimates of two samples ( 2 Vectors )..??
I have done this Using R and obtained 2 density curves for the 2 samples using kernel density estimation ..
Is there anyway to quantitatively compare how similar/Dissimilar the density estimates of 2 samples are..?
I am trying to find out which data sample exhibits has a similar distribution to a particular distribution..
I am using R Language... Can somebody please help..??
You can use Kolmogorov-Smirnov test (ks.test) to compare two distributions. Cramer-von-Mises test is another one. There is this PDF Fitting Distributions with R where they also list other tests that are available (although the nortest package that he uses only tests for normality).
Apprentice Queue is right about using the Kolmogorov-Smirnoff test, but I wanted to add a warning: don't use it on its own. You should visually compare the distributions as well, either with two kernel density plots or histograms, or with a qqplot. Human brains are very good at playing spot-the-difference.
You can try calculating the Earth mover's distance