anova

I am trying to understand how to work with ANOVAs and post-hoc tests in R. So far, I have used aov() and TukeyHSD() to analyse my data. Example: uni2.anova <- aov(Sum_Uni ~ Micro, data= uni2) uni2.anova Call: aov(formula = Sum_Uni ~ Micro, data = uni2) Terms: Micro Residuals Sum of Squares 0.04917262 0.00602925 Deg. of Freedom 15 48 Residual standard error: 0.01120756 Estimated effects may be unbalanced My problem is, now I have a huge list of pairwise comparisons but cannot do anything with it: TukeyHSD(uni2.anova) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov

We're teaching a stats class for biology students and trying to use R as the computing and data visualization platform. As much as possible, we'd like to avoid using extra packages and doing anything terribly "fancy" in R; the focus of the course is on the statistics, not the programming. Nevertheless, we haven't found a very good way of generating an errorbar plot in R for a two factor ANOVA design. We're using the ggplot2 package to make the plot, and while it does have a built-in stat_summary method of generating 95% CI errorbars, the way these are calculated may not always be the right way

I am looking for a short and efficient method to run individual ANOVA analysis on each level of a factor. What I currently have, I think, is redundant and clutters the work space. Let's say I have the following: Letter Number Question A 1 1 A 2 1 A 3 1 B 1 1 B 2 1 B 3 1 C 1 1 C 2 1 C 3 1 I could run the following code to split the data frame into subsets A, B, and C: > list2env(split(data, data$Letter), globalenv()) > ANOVA.A <- aov(Question~Number, data=A) > ANOVA.B <- aov(Question~Number, data=B) > ANOVA.C <- aov(Question~Number, data=C) While this provides me with the required results, it

Custom contrasts are very widely used in analyses, e.g.: "Do DV values at level 1 and level 3 of this three-level factor differ significantly?" Intuitively, this contrast is expressed in terms of cell means as: c(1,0,-1) One or more of these contrasts, bound as columns, form a contrast coefficient matrix, e.g. mat = matrix(ncol = 2, byrow = TRUE, data = c( 1, 0, 0, 1, -1, -1) ) [,1] [,2] [1,] 1 0 [2,] 0 1 [3,] -1 -1 However, when it comes to running these contrasts specified by the coefficient matrix, there is a lot of (apparently contradictory) information on the web and in books. My question

Trying to learn R. A question from an old stats text want's to know if there is a difference in break times at different construction sites. Trouble is, the text decided that each site employs a different number of workers. So, I am stuck and looking for help using ANOVA with unequal sample sizes. site1 <- c(34,25,27,31,26,34,21) site2 <- c(33,35,31,31,42,33) site3 <- c(17,30,30,26,32,28,26,29) site4 <- c(28,33,31,27,32,33,40) Try this one site1 <- c(34,25,27,31,26,34,21) site2 <- c(33,35,31,31,42,33) site3 <- c(17,30,30,26,32,28,26,29) site4 <- c(28,33,31,27,32,33,40) Data <- data.frame( Y=c