Analysis of Variety Effect in Wheat Yield using RBD, Study notes of Data Analysis & Statistical Methods

Download Analysis of Variety Effect in Wheat Yield using RBD and more Study notes Data Analysis & Statistical Methods in PDF only on Docsity! > # This example shows the analyses for the RBD > # using the wheat data example we looked at in class > > # Entering the data and defining the variables: > > ########## > ## > # Reading the data into R: > > my.datafile <- tempfile() > cat(file=my.datafile, " + A 1 31.0 + A 2 39.5 + A 3 30.5 + A 4 35.5 + A 5 37.0 + B 1 28.0 + B 2 34.0 + B 3 24.5 + B 4 31.5 + B 5 31.5 + C 1 25.5 + C 2 31.0 + C 3 25.0 + C 4 33.0 + C 5 29.5 + ", sep=" ") > > options(scipen=999) # suppressing scientific notation > > wheat <- read.table(my.datafile, header=FALSE, col.names=c("variety", "block", "yield")) > > # Note we could also save the data columns into a file and use a command such as: > # wheat <- read.table(file = "z:/stat_516/filename.txt", header=FALSE, col.names = c("variety", "block", "yield")) > > attach(wheat) > > # The data frame called wheat is now created, > # with three variables, variety, block, and yield. > ## > ######### > > ################################################################** > # What if we ignored the blocks and just did a one-way CRD ANOVA? > > # We specify that variety is a (qualitative) factor with the factor() function: > > # Making "variety" a factor: > > variety <- factor(variety) > > # The lm statement specifies that yield is the response > # and variety is the factor > # The ANOVA table is produced by the anova() function > > wheat.fit <- lm(yield ~ variety); > anova(wheat.fit) Analysis of Variance Table Response: yield Df Sum Sq Mean Sq F value Pr(>F) variety 2 98.433 49.217 3.6167 0.05899 . Residuals 12 163.300 13.608 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > # The effect of variety is not significant at the 5% level (p-value = .0590). > # This is NOT the best way to analyze these data!!!! > > ############################################################################### > # Here we treat the experiment as a Randomized Block Design (RBD). > # The lm() and anova() functions will do a standard ANOVA for a RBD. > > # We specify that block and variety are factors with the factor() function: > > variety <- factor(variety) > block <- factor(block) > > wheat.RBD.fit <- lm(yield ~ variety + block); > anova(wheat.RBD.fit)