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Suppose I have a large data set(~10 GB) and I want to run a support vector machine or a linear model. Typically when I run these functions, I get an error message: 'Error: Cannot allocate vector of size 308.4 MB'. What is the best way to deal with this? Would creating random subsets and running the models on the individual subsets be a better approach?
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Download an R software and R studio, write a hardcopy that has the 4probability sampling techniques that does: loads data; simple random sampling with and without replacement,stratified sampling, systematic and cluster sampling. Write a well commented code (R markdown)
I'm new to R so I haven't tried
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I have 22 companies response about 22 questions/parameters in a 22x22 matrix. I applied clustering technique which gives me different groups with similarities.
Now I would like to find correlations between parameters and companies preferences. Which technique is more suitable in R?
Normally we build Bayesian network to find a graphical relationship between different parameters from data. As this data is very limited, how i can build Bayesian Network for it?
Any suggestion to analyze this data.
Try looking at Feature selection and Feature Importance in R, it's simple,
this could lead you: http://machinelearningmastery.com/feature-selection-with-the-caret-r-package/
Some packages are good: https://cran.r-project.org/web/packages/FSelector/FSelector.pdf
, https://cran.r-project.org/web/packages/varSelRF/varSelRF.pdf
this is good SE question with good answers: https://stats.stackexchange.com/questions/56092/feature-selection-packages-in-r-which-do-both-regression-and-classification
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For one of my projects I would like to create several random matrices, which have full rank. Does anybody know a quick way to do this in R or has an idea how to proceed?
You are overwhelmingly likely to get a full-rank matrix if you generate a matrix with iid elements, with no additional constraints:
library(Matrix)
set.seed(101)
r <- replicate(1000,rankMatrix(matrix(rnorm(10000),100)))
table(r) ## all values are equal to 100
(Someone who spent more time on the math might be able to prove that the set of reduced-rank matrices within this space of matrices actually has measure 0 ...)
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I am working on a data set that has a bunch of raw text that I am vectorizing and using in my matrix for a random forest regression. My question is, should I be treating each word as a .factor or a .numeric if it is a sparse matrix? Which one speed up the computation time?
My understanding is that R matrices coerce factors to characters, so you're better off using numeric.
I'm not terribly familiar with RandomForest -- I have a general idea of what it does, but I'm not sure about the guts of its R implementation. If you need to give it a design matrix (for instance, how ANOVAs or GLMs work when you implement them by hand), you can try using the model.matrix function.
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i want to fit a tweedie compound Poisson Gamma to my loss data using ptweedie.series R command. I am getting problems how to start with my fitting in R. Thanks in advance.
Performing such a fit is illustrated here:
library(tweedie)
example("tweedie-package")