FISH 397C Winter 2009 Evan Girvetz Basic Statistical Analyses and Contributed Packages in R © R Foundation, from

Basic Statistical Analysis in R correlation – cor.test() linear modeling – lm() t-test – t.test() ANOVA – aov() Chi squared – chisq.test()

Linear Regession: lm() > lm(taill ~ totlngth, data = possum) > taill.lm <- lm(taill ~ totlngth) > summary(taill.lm) Call: lm(formula = taill ~ totlngth) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) ** totlngth e-05 *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 41 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 41 DF, p-value: 2.883e-05

ANOVA > sexTaill.aov <- aov(taill~sex, data = possum) > summary(sexTaill.aov)

ANOVA: interactions > sexPopTaill.aov <- aov(taill~sex + Pop + sex*Pop, data = possum) > summary(sexPopTaill.aov)

Contributed Packages Go to –Click on CRAN –Select a CRAN mirror close (e.g. USA-WA) Click on “packages” to look at descriptions and more information about contributed packages Go look at the vegan package –Open up the reference manual link

Contributed Packages First you must install a package on your machine (and must re-install when R is updated) –This can be done from the pull down menu in the R GUI (this is the easiest) –Or can be done using the command install.packages() –Or the packages can be downloaded and installed manually as.zip files

Contributed Package Once a package is installed on your computer, you must load it into an R session each time you open the R session. –This can be done from the GUI pull down menu (under packages) –Or can be done using the command line > library(vegan)

Hands-on Exercise Install the following packages on your machine: vegan Hmisc Now load these packages into your R session (and add the code to your script for the class)

Cluster Analysis Example Select only possums greater than age 5 > possum5 5) | (is.na(possum$age)),] Calculate Jaccard distance matrix: > possum5.jac <- vegdist(possum5[,6:14], method = "jaccard") Run cluster analysis on distance matrix: > possum5.jac.hclust <- hclust(possum5.jac, method = "ward")

Plotting Cluster Analysis > plot(possum5.jac.hclust, xlab = "Possum Individuals", sub = "") This adds rectangles to create k = 4 groups: > par(lwd = 3, lty = 2) > rect.hclust(possum5.jac.hclust, k = 4 # add rectangles to show groups > par(lwd = 1, lty = 1)

Writing to graphic files Remember that this plot can be written to a graphics file using the command: > png(“dendrogram.png”, 1500, 1000, pointsize = 30) Put code for graphics here > dev.off()

Adding Error Bars to Graphics There are many ways to do this. –Hmisc has capability for this > library(Hmisc) > ?errbar

Hands On Exercise Create a new data table called hdlngthBySite, with three columns: –The site number –The mean hdlngth for each site –The standard deviation of hdlngth for each site –(Remember you can use aggregate to do this) Then plot hdlnth vs site (scatter plot is fine)

Adding Error Bars to Graphics > ?errbar > yplus <- hdlngthBySite$hdlngth.mean + hdlngthBySite$hdlngth.sd > yminus <- hdlngthBySite$hdlngth.mean + hdlngthBySite$hdlngth.sd

Adding Error Bars to Graphics > plot(hdlngth.mean ~ site, data = hdlngthBySite, ylim = c(80,105)) > errbar(x= hdlngthBySite$site, y= hdlngthBySite$hdlngth.mean, yplus = yplus, yminus = yminus, add=T, ylim = c(80,105))