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histogram

November Member Training: Preparing to Use (and Interpret) a Linear Regression Model

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?

[Read more…] about November Member Training: Preparing to Use (and Interpret) a Linear Regression Model

Tagged With: Bivariate Statistics, histogram, interpreting regression coefficients, linear regression, Multiple Regression, scatterplot, Univariate statistics

Related Posts

  • Member Training: Using Transformations to Improve Your Linear Regression Model
  • Member Training: Segmented Regression
  • The General Linear Model, Analysis of Covariance, and How ANOVA and Linear Regression Really are the Same Model Wearing Different Clothes
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction

What R Commander Can do in R Without Coding–More Than You Would Think

by Karen Grace-Martin 4 Comments

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.

Data Sets and Variables

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.

Data Analysis

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.

Graphs

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.

Tagged With: box plot, cluster analysis, generalized linear models, histogram, linear regression, logistic regression, principal component analysis, R, Rcommander, scatterplot

Related Posts

  • Generalized Linear Models in R, Part 5: Graphs for Logistic Regression
  • Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation
  • R Is Not So Hard! A Tutorial, Part 13: Box Plots
  • R is Not So Hard! A Tutorial, Part 12: Creating Histograms & Setting Bin Widths

R is Not So Hard! A Tutorial, Part 12: Creating Histograms & Setting Bin Widths

by guest 2 Comments

by David Lillis, Ph.D.

This is Part 12 in my R Tutorial Series: R is Not so Hard.  Go back to Part 11 or start with Part 1.

I’m sure you’ve heard that R creates beautiful graphics.

It’s true, and it doesn’t have to be hard to do so.  Let’s start with a simple histogram using the hist() command, which is easy to use, but actually quite sophisticated.

First, we set up a vector of numbers and then we create a histogram.

B <- c(2, 4, 5, 7, 12, 14, 16)
hist(B)

image001

That was easy, but you need more from your histogram. [Read more…] about R is Not So Hard! A Tutorial, Part 12: Creating Histograms & Setting Bin Widths

Tagged With: graphics, histogram, R, statistics

Related Posts

  • R Is Not So Hard! A Tutorial, Part 13: Box Plots
  • R is Not So Hard! A Tutorial, Part 22: Creating and Customizing Scatter Plots
  • R is Not So Hard! A Tutorial, Part 21: Pearson and Spearman Correlation
  • What R Commander Can do in R Without Coding–More Than You Would Think

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