Statistics

Statistical learning - topics

Basic topics of statistical learning.

tSNE

tSNE.

Models - reference

Linear regression Test linear moedel One-way ANOVA Linear regression lm(y ~ x1 + x2 + x3) # multiple linear regression lm(log(y) ~ x) # log transformed lm(sqrt(y) ~ x) # sqrt transformed lm( y ~ log(x)) # fields transformed llm(log(y) ~ log(x)) # everything is transformed lm(y ~ .) # use all fields for regression model lm(y ~ x + 0) # forced zero intercept lm(y ~ x*k) # interaction of two variables lm(y ~ x + k + x:k) # product of xkl but without interaction lm(y ~ (x + k + .

Linear Regression (Math)

Pure math for linear regression model.

Principal component analysis

Principal component analysis.

Determining the optimal number of clusters

Elbow method Average silhouette method Gap statistic method Using NbCLust Sources Elbow method The basic idea is to find minimal the total intra-cluster variation or total Within-cluster Sum ofSquares (WSS). Plot number of clusters ~ WSS show how WSS is reduced with increase of number of clusters. The optimal number of clusters is when adding another cluster doesn’t improve much better the total WSS. The optimal number of clusters can be defined as follow:

Differential expression analysis

Here you can find a beautiful example of differential expression analysis using standard R packages.

Exploratory analysis in R

Exploratory analysis in R.

Compare Models And Select The Best Using The Caret R Package

Compare Models And Select The Best Using The Caret R Package.

Gradient Boosting Machine

Gradient Boosting Machine