I am currently translating some MATLAB scripts to R for Multivariate Data Analysis. Currently I am trying to generate the same data as the Coeffs.Linear and Coeffs.Const part of the fitdiscr function in MATLAB.
The code being used is:
fitcdiscr(data, groups, 'DiscrimType', 'linear');
The data consists of 3 groups.
Unfortunately the R function seems to do the LDA only for two LDs and MATLAB seems to always compare all groups in all constellations. Does anybody have an idea how I could obtain that data?
I suspect you mean information on the implementation of various MATLAB function, which would be doc <functionname> (doc fitcdiscr would yield this documentation page on fitcdscr) to get the documentation, and edit <functionname> to get the implementation, if it is not obscured by The MathWorks. If those two do not give you enough information, I'm afraid you're out of luck, since not all TMW codes are available non-obscured.
fitcdiscr is non-obscured, although very brief; it's just a wrapper for some other functions. Keep doing edit <functionname> and doc <functionname> and see how deep the rabbit hole takes you.
NB: there's no built-in function called fitdiscr, but the syntax you describe is that of fitcdiscr (note the c), so I used that as examples. If the actual function being called is named fitdiscr, it's custom-made and you'll have to spit through its file by edit fitdiscr and hope for the best.
Related
I am working with the 'indicspecies' package - multipatt function and am unable to extract summary values of the package. Unfortunately I can't print all the summary and am left with impartial information for my model. The reason is the huge amount of data that needs to be printed from the summary (300.000 different species, 3 groups, 6 comparable combinations).
This is what happens with summary being saved (pre-code incl.):
x <- multipatt(data, ...)
sumx <-summary(x)
sumx
NULL
str(sumx)
NULL
So, the summary does not work exactly like a generic summary. It seems that the function is based around the older indval function from the 'labdsv' package (which is mentioned in the documentation). I found an archived thread where a similar problem is discussed: http://r.789695.n4.nabble.com/extract-values-from-summary-of-function-indval-of-the-package-labdsv-td4637466.html
but it seems not resolved (and is not exactly about the same function, rather the base function indval).
I was wondering if anyone has experience with the indicspecies package and knows a way to either extract the info from the summary.
It is possible to extract significance and other information from the other saved data from the model, but it might be nice to just get a quick complete overview from the data.
ps. I tried
options(max.print=1000000)
but this didn't solve it for me.
I use to capture the summary output for a multipatt object, but don't any more because the p-values reported are not corrected for multiple testing. To answer the OP's question you can capture the summary output using capture.output
ex.
dat.multipatt.summary<-capture.output(summary(dat.multipatt, indvalcomp=TRUE))
Again, I do not recommend this. It is very important to correct the p-values for multiple testing, so the summary output actually isn't helpful. To be clear ?multipatt states:
"sign Data table with results of the best matching pattern, the association value and the degree of statistical significance of the association (i.e. p-values from permutation test). Note that p-values are not corrected for multiple testing."
I just posted an answer for how to correct the p-values here https://stats.stackexchange.com/questions/370724/indiscpecies-multipatt-and-overcoming-multi-comparrisons/401277#401277
I don't have any experience with this package and since you haven't provided the data, it's difficult to reproduce. But since summary is returning NULL, are you sure your x is computed properly? Check the object.size or class or something else of x to see if it indeed has any content.
Also instead of accessing all the contents of summary(x) together, you can use # to access slots of it (similar to $ in dataframe).
If you need further assistance, it'd be better t provide atleast a small subset or some other sample data so that the community can work with it.
In GBM model, following parameters are used -
col_sample_rate
col_sample_rate_per_tree
col_sample_rate_change_per_level
I understand how the sampling works and how many variables get considered for splitting at each level for every tree. I am trying to understand how many times each feature gets considered for making a decision. Is there a way to easily extract all sample of features used for making a splitting decision from the model object?
Referring to the explanation provided by H2O, http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/col_sample_rate.html, is there a way to know 60 randomly chosen features for each split?
Thank you for your help!
If you want to see which features were used at a given split in a give tree you can navigate the H2OTree object.
For R see documentation here and here
For Python see documentation here
You can also take a look at this Blog (if this link ever dies just do a google search for H2OTree class)
I don’t know if I would call this easy, but the MOJO tree visualizer spits out a graphviz dot data file which is turned into a visualization. This has the information you are interested in.
http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/overview-summary.html#viewing-a-mojo
I want to follow the tutorial found on this site, but despite being thorough in all other aspects, the author has not included information on what packages need to be used for the code to function.
As far as I understand one of them will be the PerformanceAnalytics package, yet my inexperienced eye is not sure about what else I will need to include.
The fapply function used in the code is one example that I cannot find.
fapply()
Error: could not find function "fapply"
library(sos)
findFn("fapply", sortby = "Function")
The findFN(...) function is great. It should open an internet browser window with the search results by itself at least it does for me.
The tutorial on Backtesting a Trading Strategy uses time series data as seen its part 1 and part 2. fapply is also used in part 2
As the data being collected and processed is time-series data, fapply() function belongs to far package which is used for Modelization for Functional AutoRegressive Processes.
I hope this helps.
I have used biglm in R and found it very useful. Now I need the same type of functionality in python. Any ideas? I have seen that patsy/statsmodels has an incremental mode, but have not been able to find any samples to copy/adapt. Any pointers would be much appreciated.
from a related answer of Nathaniel Smith on the statsmodels mailing list
My incremental LS code might be useful here, it's basically the same
problem:
https://github.com/njsmith/pyrerp/blob/master/pyrerp/incremental_ls.py#L330
The new X'X is the sum of the old X'Xs, then you have to re-do the
scaling and inversion to get the new vcov matrix for the estimates.
Should be doable so long as you know how many data points are in each
and the various sums-of-squares. (The code I linked has some extra
complexity because of handling a particular sort of heteroskedasticity
via FGLS, but it can pretty much be ignored.)
statsmodels doesn't have anything in this area yet.
There is an incremental OLS function in statsmodels, however that was written as helper function for cusum tests (in memory) and hasn't been used or checked for any other purpose:
http://statsmodels.sourceforge.net/devel/generated/statsmodels.stats.diagnostic.recursive_olsresiduals.html
What type of p-values do R calculate in a binomial logistic regression, and where is this documented?
When i read the documentation for ?glm() I find no reference to the calculation of the p-values.
The p-values are calculated by the function summary.glm. See ?summary.glm for a (very brief) bit about how those are calculated.
For more information, look at the source code by typing
summary.glm
at the R command prompt. There you will find the lines of code where an object pvalue is created. Follow the code back to see how the components of the p-value calculation are (conditionally) calculated.
The authors of R wrote the help system with several principles in mind: compactness (don't write more than is needed, it's not a textbook), accuracy, and a curious and well-educated audience. It really was written for other statisticians. The "curious" part of that opening sentence was included to raise the question why you did not also follow the various links in the ?glm page: to summary.glm where you would have found one answer to your ambiguous question or to anova.glm where you would have found another possible answer. The help-authors do expect that you will follow those links and read the whole page and execute the examples. You will notice that even after you get to summary.glm that there is no mention of "binary logistic regression" since they pretty much assume that you are well-grounded in statistics and have copy of McCullagh and Nelder handy, or if not that you will go read the references.
The other principle: sometimes it is the code itself (given the open-source nature of R) that performs the documentation. Technically glm doesn't print anything and print.glm doesn't print p-values. It would be print.summary.glm or print.anova.glm that would be doing any printing. Part of learning R is learning that the results printed to the console will have gone through a eval-print loop and that output can be tailored with object-class-specific functions.
These assumptions are just part of what many people see as a "steep learning curve for R" (although I would have called it a shallow curve if plotted with time/effort on x-axis.)