Logistic Regression In R

Steph Locke has a presentation on performing logistic regression using R:

Logistic regressions are a great tool for predicting outcomes that are categorical. They use a transformation function based on probability to perform a linear regression. This makes them easy to interpret and implement in other systems.

Logistic regressions can be used to perform a classification for things like determining whether someone needs to go for a biopsy. They can also be used for a more nuanced view by using the probabilities of an outcome for thinks like prioritising interventions based on likelihood to default on a loan.

It’s a good introduction to an important statistical method.

Related Posts

Loops Versus Apply: Speed Comparison

Mike Spencer compares lapply (single core and its multi-core version) versus a for loop in R: But how fast were they? Can we get faster? Thankfully R provides `system.time()` for timing code execution. In order to get faster, it makes sense to use all the processing power our machines have. The ‘parallel’ library has some […]

Read More

Quoted Concatenation In R

John Mount has a quick tip for R users: Here is an R tip. Need to quote a lot of names at once? Use qc(). This function is part of wrapr.

Read More

Categories

April 2017
MTWTFSS
« Mar May »
 12
3456789
10111213141516
17181920212223
24252627282930