You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

by TAF Support

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

by guest

The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.

[Read more…] about Member Training: Practical Suggestions for Improving Your Scatterplots

I mentioned in my last post that R Commander can do a LOT of data manipulation, data analyses, and graphs in R without you ever having to program anything.

Here I want to give you some examples, so you can see how truly useful this is.

Let’s start with a simple scatter plot between Time and the number of Jobs (in thousands) in 67 counties. Time is measured in decades since 1960.

The green line is the best fit linear regression line.

This wasn’t the default in R Commander (I actually had to remove a few things to get to this), but it’s a useful way to start out.

A few ways we can easily customize this graph:

We see here a common issue in scatter plots–because the X values are discrete, the points are all on top of each other.

It’s difficult to tell just how many points there are at the bottom of the graph–it’s just a mass of black.

One great way to solve this is by jittering the points.

All this means is that instead of putting identical points right on top of each other, we move it slightly, randomly, in either one or both directions. In this example, I jittered only horizontally:

So while the points aren’t graphed exactly where they are, we can see the trends and we can now see how many points there are in each decade.

How hard is this to do in R Commander? One click:

Another useful change to a scatter plot is to add a separate regression line to the graph based on some sort of factor in the data set.

In this example, the observations are measured for counties and each county is classified as being either Rural or Metropolitan.

If we’d like to see if the growth in jobs over time is different in Rural and Metropolitan counties, we need a separate line for each group.

In R Commander we can do this quite easily. Not only do we get two regression lines, but each point is clearly designated as being from either a Rural or Metropolitan county through its color and shape.

It’s quite clear that not only was there more growth in the number of jobs in Metro counties, there was almost no change at all in the Rural counties.

And once again, how difficult is this? This time, *two *clicks.

There are quite a few modifications you can make just using the buttons, but of course, R Commander doesn’t do everything.

For example, I could not figure out how to change those red triangles to green rectangles through the menus.

But that’s the best part about R Commander. It works very much like the Paste button in SPSS.

Meaning, it creates the code for you. So I can take the code it created, then edit it to get my graph looking the way I want.

I don’t have to memorize which command creates a scatter plot.

I don’t have to memorize how to pull my SPSS data into R or tell R that Rural is a factor. I can do all that through R Commander, then just look up the option to change the color and shape of the red triangles.

I received a question recently about R Commander, a free R package. R Commander is the powerhouse of our upcoming workshop R for SPSS Users.

R Commander overlays a menu-based interface to R, so just like SPSS or JMP, you can run analyses using menus. Nice, huh?

The question was whether R Commander does everything R does, or just a small subset.

Unfortunately, R Commander can’t do everything R does. Not even close.

But it does *a lot*. More than just the basics.

So I thought I would show you some of the things R Commander can do entirely through menus–no programming required, just so you can see just how unbelievably useful it is.

**Import data sets from other software:**

- SPSS
- Stata
- Excel
- Minitab
- Text
- SAS Xport

**Define Numerical Variables as categorical and label the values**

**Open the data sets that come with R packages**

**Merge Data Sets**

**Edit and show the data in a data spreadsheet**

Personally, I think that if this was all R Commander did, it would be incredibly useful. These are the types of things I just cannot remember all the commands for, since I just don’t use R often enough.

Yes, R Commander does many of the simple statistical tests you’d expect:

- Chi-square tests
- Paired and Independent Samples t-tests
- Tests of Proportions
- Common nonparametrics, like Friedman, Wilcoxon, and Kruskal-Wallis tests
- One-way ANOVA and simple linear regression

What is surprising though, is how many higher-level statistics and models it runs:

- Hierarchical and K-Means Cluster analysis (with 7 linkage methods and 4 options of distance measures)
- Principal Components and Factor Analysis
- Linear Regression (with model selection, influence statistics, and multicollinearity diagnostic options, among others)
- Logistic regression for binary, ordinal, and multinomial responses
- Generalized linear models, including Gamma and Poisson models

In other words–you can use R Commander to run in R most of the analyses that most researchers need.

A sample of the types of graphs R Commander creates in R without you having to write any code:

- QQ Plots
- Scatter plots
- Histograms
- Box Plots
- Bar Charts

The nice part is that it does not only do simple versions of these plots. You can, for example, add regression lines to a scatter plot or run histograms by a grouping factor.

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