significance

I would like to visualize my data and ANOVA statistics. It is common to do this using a barplot with added lines indicating significant differences and interactions. How do you make plot like this using R? This is what I would like: Significant differences: Significant interactions: Background I am currently using barplot2{ggplots} to plot bars and confidence intervals, but I am willing to use any package/procedure to get the job done. To get the statistics I am currently using TukeyHSD{stats} or pairwise.t.test{stats} for differences and one of the anova functions ( aov , ezANOVA{ez} , gls

I wonder how one can add another layer of important and needed complexity to a matrix correlation heatmap like for example the p value after the manner of the significance level stars in addition to the R2 value (-1 to 1)? It was NOT INTENDED in this question to put significance level stars OR the p values as text on each square of the matrix BUT rather to show this in a graphical out-of-the-box representation of significance level on each square of the matrix. I think only those who enjoy the blessing of INNOVATIVE thinking can win the applause to unravel this kind of solution in order to

So I've decided to try to solve my physics homework by writing some python scripts to solve problems for me. One problem that I'm running into is that significant figures don't always seem to come out properly. For example this handles significant figures properly: from decimal import Decimal >>> Decimal('1.0') + Decimal('2.0') Decimal("3.0") But this doesn't: >>> Decimal('1.00') / Decimal('3.00') Decimal("0.3333333333333333333333333333") So two questions: Am I right that this isn't the expected amount of significant digits, or do I need to brush up on significant digit math? Is there any way

Some time ago I asked a question about drawing boxplot Link1 . I have got some data with 3 different groups (or labels) Please down load here . I can use the following R codes to get the boxplot library(reshape2) library(ggplot2) morphData <- read.table(".\\TestData3.csv", sep=",", header=TRUE); morphData.reshaped <- melt(morphData, id.var = "Label") ggplot(data = morphData.reshaped, aes(x=variable, y=value)) + + geom_boxplot(aes(fill=Label)) Here I just wondering how to put the significant level above the boxplot. To make myself clear I put a screenshot cut from a paper here: I don't quite