vonjd
• Member for 11 years, 6 months
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It means "Independent and identically distributed". A good example is a succession of throws of a fair coin: The coin has no memory, so all the throws are "independent". And every throw is 50:50 (...

I found the following explanation helpful: Confidence intervals tell you about how well you have determined the mean. Assume that the data really are randomly sampled from a Gaussian distribution....

To find the best fitting functional form (so called free-form or symbolic regression) for the data try this tool - to all of my knowledge this is the best one available (at least I am very excited ...

All of the given answers so far provide important insights but it should not be forgotten that you can transform the parameters of one into the other: Regression: $y = mx + b$ Connection between ...

I think there are excellent anwers already but just to add some intuition concerning the geometric interpretation: "The lasso performs $L1$ shrinkage, so that there are "corners" in the constraint, ...

You can use the cosine function from the lsa package: http://cran.r-project.org/web/packages/lsa

Another possibility are Historical Consistent Neural Networks (HCNN). This architecture might be more appropriate for the above mentioned setup because they eliminate the often arbitrary distinction ...

The best way to calculate it manually is: t.value = (mean(data) - 10) / (sd(data) / sqrt(length(data))) p.value = 2*pt(-abs(t.value), df=length(data)-1) You need the abs() function because ...

Just adding to @retsreg's answer this can also be demonstrated quite easily via numerical simulation in R: N <- 1e7 # number of instances and sample size bootstrap <- sample(c(1:N), N, replace =...

When I understand your question correctly you are asking which class is the positive one and which is the negative one. The answer is that this is to a certain extent arbitrary, so you have to decide ...

Please have a look at the following article from science - it addresses your point exactly: Detecting Novel Associations in Large Data Sets by David N. Reshef et al. From the abstract: ...

I wrote a whole blog post on the matter: https://blog.ephorie.de/zeror-the-simplest-possible-classifier-or-why-high-accuracy-can-be-misleading ZeroR, the simplest possible classifier, just takes the ...

With the OneR package (which basically builds a one level tree with the best predictor) you can have any number of levels in all input variables and in the output variable: https://cran.r-project.org/...

This is the most intuitive article that I have seen so far: The Cramér-Rao Lower Bound on Variance: Adam and Eve’s “Uncertainty Principle” by Michael R. Powers, Journal of Risk Finance, Vol. 7, No. ...

The $p$-value is the probability of the respective event under the condition that $H_0$ is true. The simplest possible toy example are two coin tosses. The 2-sided $H_0$ would be that you consider the ...

A great resource for this is: Bayesian Model Averaging with BMS by Stefan Zeugner (2012) It is using the R-package BMS, more info can be found here: http://bms.zeugner.eu/ Two hands-on tutorials for ...

You can use the zoo package: http://cran.r-project.org/web/packages/zoo/index.html You are very, very flexible to do all kinds of things, including aggregation and rolling functions: http://cran.r-...

PCA basically is a projection of a higher-dimensional space into a lower dimensional space while preserving as much information as possible. I wrote a blog post where I explain PCA via the ...

First you construct the matrix with the transition probabilities, then you calculate the long run staying proportions via the normalized eigenvector of the biggest eigenvalue and last you weight this ...

There is a new package out: rnn (on CRAN, on github), which implements a recurrent neural network in native R code. A nice example can be found here: http://firsttimeprogrammer.blogspot.de/2016/08/...

The basic ideas are not that difficult: First model: You just multiply the respective coefficients with the new data points and see whether the sum is bigger than the negative intercept (then am is 1)...

You can use the content method in the bin function in the OneR package for that. It works on vectors and dataframes. library(OneR) set.seed(2) df <- data.frame(a = rnorm(900), b = rnorm(900)) ...

You can use the MXNetR package for that. The command is mx.symbol.Convolution() For the package documentation see: http://mxnet-bing.readthedocs.io/en/latest/R-package/index.html For a complete ...

You can build a workaround by using nearPD from the Matrix package like so: nearPD(D)\$mat. nearPD computes the nearest positive definite matrix.

Although you gave some data it is still hard to tell what would be the best method to use for your challenge. Still I give it a shot. In general it is always a good idea to do the steps you did ...

Concerning your question: "Does higher value of correlation between two values indicate it is good predictor?" In general I would be very cautious because one of the most important facts in ...

The RHmm-package has been deprecated. We use the depmixS4-package for our research and it is way better (although the learning curve is a little bit steeper due to the many options it has). You find ...

This is a very broad and very basic question so I will recommend a very broad and basic book - but one that addresses your question thoroughly: Predictive Analytics For Dummies by Anasse Bari, ...