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Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.

1
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Suppose you have a n-inputs 1-output useful predictor that predicts $P(Y=1|X)$. Logistic regression or random forest for example. One possibility to implement it for a p-output $Y=(Y_1,Y_2,...Y_p)$ i …
answered Oct 13 '17 by Benoit Sanchez
1
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It is possible to use only the dissimilarity matrix for clustering (without the original points), in a way that close to Kmeans. It's called "kernel Kmeans" and is very similar to "spectral clustering …
answered Sep 13 '17 by Benoit Sanchez
4
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The general idea to compute p-values on finite possibilities is to run over all possibilities and count how many time the statistics is greater than your observed statistics. Example for a binomial: …
answered Dec 21 '17 by Benoit Sanchez
9
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variables. You may also provide your own preferred weights. Note that R function kNN() does it for you : https://www.rdocumentation.org/packages/DMwR/versions/0.4.1/topics/kNN As a first attempt, just use …
answered Mar 31 '17 by Benoit Sanchez
2
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I'm not sure I'm going to answer your specific problem exactly, but most often the issue with normality testing is the following. The Shapiro-Wilk test does not test if your distribution is approxima …
answered Aug 16 '17 by Benoit Sanchez
2
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Yes, you can use dummy coding. Imagine the factor (categorical) variable $X$ has $N$ possible values: 1,2,3,, N. Then transform it into a vector of $N$ variables where all of them are 0 except the c …
answered Dec 12 '17 by Benoit Sanchez
3
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Everything is correct. Except that the phrase "the probability for a ball to be placed in the bin A is 30% higher than in the bin B" is ambiguous. It could mean either $P(A)-P(B)=0.3$ or $P(A)=1.3P( …
answered Feb 11 '18 by Benoit Sanchez
1
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Causation is not in the data and cannot be. Data only contains correlation. Most simply, if a variable $Y$ is correlated to $X$, $X$ can be seen as a cause of $Y$ if $X$ is controlled freely by the e …
answered Dec 20 '17 by Benoit Sanchez
1
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A) how I can see (plot) the bold-faced statement above (e.g., in R)? Draw values of $\sigma$ on a logarithmic scale: ---- 0.01 ------------ 0.1 ----------- 1 ------------ 10 ---------- 100 …
answered Feb 4 '18 by Benoit Sanchez
1
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If the drift changes over time, then you can use double exponential smoothing known as the Holt-Winters procedure. see: https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing R has an implementation. …
answered Oct 9 '17 by Benoit Sanchez
5
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They are very similar. In both models, the probability that $Y=1$ given $X$ can be seen as the probability that a random hidden variable $S$ (with a certain fixed distribution) is below a certain thr …
answered Jun 10 '17 by Benoit Sanchez