Eumenedies
• Member for 4 years, 9 months
• Last seen more than a month ago
• Swindon, United Kingdom

| means 'group by' and I'm not sure but I don't think it is used in lm at all. In a mixed effects model, it is used to define the random effect. That is, a different random effect is fitted for each ...

If you read: Validation and updating of predictive logistic regression models: a study on sample size and shrinkage (pdf) The same author explains in a little bit of detail. The recalibration ...

K-means clustering works by creating a Voronoi diagram that has linear decision boundaries between clusters, as you know. This means that you can draw lines/planes/hyperplanes to classify your data. ...

I would like to know if that value is significantly greater than the other values From your question, I am interpreting that the order of your values is not important to you. As such, you have a ...

The simple answer is, if you do not have any bootstrap datasets that do not include an observation, then that observation cannot be used to calculate the OOB error rate because you don't have any out ...

Ordinary Least Squares regression ("normal" linear regression) makes certain assumptions about the data. Here, the most salient assumptions are: Observed values can take any real number The errors ...

I feel like this question would better belong on StackOverflow but I'll give it a go anyway: paraphrasing the code for knnImputation (https://gist.github.com/tengpeng/fb6809717361319d8bde), you get ...

So you can use a test for normality as you are currently doing where the skewness should ideally be between -0.8 and 0.8 but you should probably also test for kurtosis if you are going this way. ...

I will assume that $x_i$ are a set of observations with known values of $Y_i$ to which you are fitting a model. Once the model has been fitted, you are left with known values of $\beta_0$ and $\beta_1$...

I would start by reading the question, How would PCA help with a k-means clustering analysis?, which suggests doing the sort of PCA in your question.