282 search results for "anova"

Two-way analysis of variance: two-way ANOVA in R

August 7, 2009
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The one-way analysis of variance is a useful technique to verify if the means of more groups are equals. But this analysis may not be very useful for more complex problems. For example, it may be necessary to take into account two factors of variability to determine if the averages between the groups depend on the group classification...

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Analysis of variance: ANOVA, for multiple comparisons

July 30, 2009
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Analysis of variance: ANOVA, for multiple comparisonsThe ANOVA model can be used to compare the mean of several groups with each other, using a parametric method (assuming that the groups follow a Gaussian distribution).Proceed with the following example:The manager of a supermarket chain wants to see if the consumption in kilowatts of 4 stores between them are equal. He...

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Analysis of variance: ANOVA, for multiple comparisons

July 30, 2009
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Analysis of variance: ANOVA, for multiple comparisonsThe ANOVA model can be used to compare the mean of several groups with each other, using a parametric method (assuming that the groups follow a Gaussian distribution).Proceed with the following example:The manager of a supermarket chain wants to see if the consumption in kilowatts of 4 stores between them are equal. He...

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Repeated Measures ANOVA using R

March 9, 2009
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Repeated Measures ANOVA using R

While so-called “between-subjects” ANOVA is absolutely straightforward in R, performing repeated measures (within-subjects) ANOVA is not so obvious. I have come across at least three different ways of performing repeated measures ANOVA in R. Which method you use depends on … Continue reading →

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Analysis of Variance (ANOVA) using R

February 5, 2009
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Analysis of Variance (ANOVA) using R

I found some useful websites showing examples of how to use R for various sorts of ANOVA (between, within, mixed designs, etc): Using R for Psychological Research Quick-R for SAS/SPSS/Stata users enjoy

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Interpreting regression coefficient in R

November 23, 2014
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Interpreting regression coefficient in R

Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for

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A look at the igraph package

November 13, 2014
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A look at the igraph package

by Joseph Rickert The igraph package has become a fundamental tool for the study of graphs and their properties, the manipulation and visualization of graphs and the statistical analysis of networks. To get an idea of just how firmly igraph has become embedded into the R package ecosystem consider that currently igraph lists 72 reverse depends, 59 reverse imports...

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Let’s Do Some MORE Hierarchical Bayes Choice Modeling in R!

November 3, 2014
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Let’s Do Some MORE Hierarchical Bayes Choice Modeling in R!

R enables us to "test drive" statistical models by running simulations and analyzing actual data. We can look at the output and get some sense of how the whole thing works. The goal is to provide a realistic application that is not too difficult to fol...

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Analysing a randomised complete block design with vegan

November 3, 2014
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Analysing a randomised complete block design with vegan

It has been a long time coming. Vegan now has in-built, native ability to use restricted permutation designs when testing effects in constrained ordinations and in range of other methods. This new-found functionality comes courtesy of Jari (mainly) and my efforts to have vegan permutation routines use the permute package. Jari also cooked up a standard interface...

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Type III tests and R

October 28, 2014
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by Terry M. Therneau Ph.D. Faculty, Mayo Clinic About a year ago there was a query about how to do "type 3" tests for a Cox model on the R help list, which someone wanted because SAS does it. The SAS addition looked suspicious to me, but as the author of the survival package I thought I should understand...

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