doebioresearch (version 0.1.0)

rcbd: Analysis of Randomized Complete Block Design

Description

The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.

Usage

rcbd(data, treatmentvector, replicationvector, MultipleComparisonTest)

Arguments

data

dependent variables

treatmentvector

vector containing treatments

replicationvector

vector containing replications

MultipleComparisonTest

0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test

Value

ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result

Examples

# NOT RUN {
data<-data.frame(GFY=c(16,13,14,16,16,17,16,17,16,16,17,16,15,15,15,13,15,14,
16,14,15,14,15,17,18,15,15,15,14,14,14,14,15,15,13,15,14,14,13,13,13,12,15,12,15),
DMY=c(5,5,6,5,6,7,6,8,6,9,8,7,5,5,5,4,6,5,8,5,5,5,4,6,6,5,5,6,6,6,5,5,5,5,5,6,5,5,5,4,5,4,5,5,5),
Rep=rep(c("R1","R2","R3"),each=15),
Trt=rep(c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15"),3))
#' #RCBD analysis with duncan test for GFY only
rcbd(data[1],data$Trt,data$Rep,2)
#RCBD analysis with duncan test for both GFY and DMY
rcbd(data[1:2],data$Trt,data$Rep,2)
# }