I am trying to sample from a multivariate distribution given by a (quite complex, but continuous) density function in R. For the univariate case I used AbscontDistribution from the distr package, but I cannot make it work for the multivariate case.
I tried finding an appropriate package for this problem online, but cannot find one.
Any ideas?
Thanks! :)
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I am working on quantile forecasting with time-series data. The model I am using is ARIMA(1,1,2)-ARCH(2) and I am trying to get quantile regression estimates of my data.
So far, I have found "quantreg" package to perform quantile regression, but I have no idea how to put ARIMA-ARCH models as the model formula in function rq.
rq function seems to work for regressions with dependent and independent variables but not for time-series.
Is there some other package that I can put time-series models and do quantile regression in R? Any advice is welcome. Thanks.
I just put an answer on the Data Science forum.
It basically says that most of the ready made packages are using so called exact test based on assumption on the distribution (independent identical normal-Gauss distribution, or wider).
You also have a family of resampling methods in which you simulate a sample with a similar distribution of your observed sample, perform your ARIMA(1,1,2)-ARCH(2) and repeat the process a great number of times. Then you analyze this great number of forecast and measure (as opposed to compute) your confidence intervals.
The resampling methods differs in the way to generate the simulated samples. The most used are:
The Jackknife: in which you "forget" one point, that is you simulate a n samples of size n-1 (if n is the size of the observed sample).
The Bootstrap: in which you simulate a sample by taking n values of the original sample with replacements: some will be taken once, some twice or more, some never,...
It is a (not easy) theorem that the expectation of the confidence intervals, as most of the usual statistical estimators, are the same on the simulated sample than on the original sample. With the difference that you can measure them with a great number of simulations.
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I can try to address your question, although this is hard since you don't provide any code/data. Also, I guess by "put ARIMA-ARCH models" you actually mean that you want to make an integrated series stationary using an ARIMA(1,1,2) plus an ARCH(2) filters.
For an overview of the R time-series capabilities you can refer to the CRAN task list.
You can easily apply these filters in R with an appropriate function.
For instance, you could use the Arima() function from the forecast package, then compute the residuals with residuals() from the stats package. Next, you can use this filtered series as input for the garch() function from the tseries package. Other possibilities are of course possible. Finally, you can apply quantile regression on this filtered series. For instance, you can check out the dynrq() function from the quantreg package, which allows time-series objects in the data argument.
I've developed a successful function that outputs piecewise regression results into a dataframe using breakpoints() in the strucchange package. I then use this function with ddply across a large dataset to get piecewise regression data for each linear model (lm) in my dataset.
After doing this, I went back to my data to see how the piecewise regression results looked. It worked pretty well, but many times the piecewise regression results were "incorrect" because of the influence of outliers. So, I'd like to be able to do the piecewise regression WITH an IRLS method (instead of ordinary least squares) to reduce the influence of outliers. This maybe isn't so hard with a few lm's, but I'd like to do it hundreds-to-thousands of times.
Do any of you have any ideas about how to approach this problem? I've looked through the strucchange package file and I don't see any options for embedding IRLS into the breakpoints() function.
Any help is greatly appreciated!
I'm experimenting with Bayesian networks in R and have built some networks using the bnlearn package. I can use them to make predictions for new observations with predict(), however I would also like to have the posterior distribution over the possible classes. Is there a way of retrieving this information?
It seems like there is a prob-parameter that does this for the naive bayes implementation in the bnlearn package, but not for networks fitted with bn.fit.
Thankful for any help with this.
See the documentation of bnlearn.
predict function implements prob only for naive.bayes and TAN.
In short, because all other methods do not necessarily compute posterior probabilities.
[bnlearn] :: predict returns the predicted values for node given the data specified by data. Depending on the
value of method, the predicted values are computed as follows:
a)parents b)bayes-lw
When using bayes-lw , likelihood weighting simulations are performed for making predictions.
Hope this helps. :)
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.
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