Tag Archives: Statistics

Julia-R Cheatsheet – Creating Vectors

Creating Vectors

How to create vectors in Julia and R? The following table translates the most common Julia commands into R language.

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Seasonal Adjustment in R

The package ‘Seasonal’ facilitates seasonal adjustment in R. The R package provides an easy-to-handle wrapper around the X-13ARIMA-SEATS Fortran libraries provided by the US Census Bureau. X-13ARIMA-SEATS is the state-of-the-art seasonal adjustment software produced, distributed, and maintained by the Census Bureau. Continue reading Seasonal Adjustment in R

R – Pros and Cons

Learning a new programming language is costly. Usually it takes a considerable amount of time to get acquainted with a new language. Especially the first phase can be painful and frustrating. The good thing is that with enough time and effort most of us will learn how to master a programming language eventually. However, note that, once we are comfortable with one language, we hardly want to change again. It turns out that the cost of abandoning on programming language and switch to another are even higher than at the beginning. Knowing this, we really want to make sure not to invest in the wrong language. There might be nothing worse than after finally mastering a programming language, recognizing that there is no use for this language anymore. While in a former post I highlighted reason why to use R, I concentrate on the Pros and Cons of R in this post.

Advantages of R

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Clustered Standard Errors in R

The easiest way to compute clustered standard errors in R is the modified summary(). I added an additional parameter, called cluster, to the conventional  summary() function. This parameter allows to specify a variable that defines the group / cluster in your data. The summary output will return clustered standard errors. Here is the syntax:

summary(lm.object, cluster=c("variable")) Continue reading Clustered Standard Errors in R

Example data – Clustered Standard Errors

The following R script creates an example dataset to illustrate the application of clustered standard errors. You can download the dataset here.

The script creates a dataset with a specific number of student test results. Individual students are identified via the variable student_id . The variable id_score comprises a student’s test score. In the test, students can score from 1 to 10 with 10 being the highest score possible. Continue reading Example data – Clustered Standard Errors