Once one starts writing more R code the need for consistency increases, as it facilitates managing larger projects and their maintenance. There are several style guides or suggestions for R; for example, Andrew Gelman’s, Hadley Wickham’s, Bioconductor’s and this one. I tend to write closer to Google’s R style guide, which contains some helpful suggestions. I use something similar but:
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Author: Luis (Page 67 of 71)
In a previous post I explained how to create all possible combinations of the levels of two factors using expand.grid(). Another use for this function is to create a regular grid for two variables to create a levelplot or a contour plot.
For example, let’s say that we have fitted a multiple linear regression to predict wood stiffness (stiff, the response) using basic density (bd) and a measure of microfibril angle (t) as explanatory variables. The regression equation could be something like stiffness = 3.439 + 0.009 bd - 0.052 t. In our dataset bd had a range of 300 to 700 kg m-3, while t had a range from 50 to 70.
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A short while ago there was a discussion on linkedin about the use of SAS versus R for the enterprise. I have thought a bit about the issue but, as I do not use Linkedin, I did not make any comments there.
Disclaimer: I did use SAS a lot between 1992 and 1997, mostly for genetic evaluation, heavily relying on BASE, STAT, IML and GRAPH. From that point on, I was a light SAS user (mostly STAT and IML) until 2009. The main reason I left SAS was that I started using ASReml in 1997 and, around two years ago asreml-R, the R package version of ASReml. Through my job I can access any statistical software; if the university does not have a license, I can buy an academic one without any issues.
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Linear regression with correlated data
I started following the debate on differential minimum wage for youth (15-19 year old) and adults in New Zealand. Eric Crampton has written a nice series of blog posts, making the data from Statistics New Zealand available. I will use the nzunemployment.csv data file (with quarterly data from March 1986 to June 2011) and show an example of multiple linear regression with autocorrelated residuals in R.
A first approach could be to ignore autocorrelation and fit a linear model that attempts to predict youth unemployment with two explanatory variables: adult unemployment (continuous) and minimum wage rules (categorical: equal or different). This can be done using:
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A common problem when running a simple (or not so simple) analysis is forgetting that the levels of a factor has been coded using integers. R doesn’t know that this variable is supposed to be a factor and when fitting, for example, something as simple as a one-way anova (using lm()) the variable will be used as a covariate rather than as a factor.
There is a series of steps that I follow to make sure that I am using the right variables (and types) when running a series of analyses. I always define the working directory (using setwd()), so I know where the files that I am reading from and writing to are.
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