
RCB experiment of rice, 12 varieties with leafhopper survival
gomez.nonnormal3.RdRCB experiment of rice, 12 varieties with leafhopper survival
Usage
data("gomez.nonnormal3")Format
A data frame with 36 observations on the following 3 variables.
- gen
- genotype/variety of rice 
- rep
- replicate 
- hoppers
- percentage of surviving leafhoppers 
Details
For each rice variety, 75 leafhoppers were caged and the percentage of surviving insects was determined.
Gomez suggest replacing 0 values by 1/(4*75) and replacing 100 by 1-1/(4*75) where 75 is the number of insects.
In effect, this means, for example, that (1/4)th of an insect survived.
Because the data are percents, Gomez suggested using the arcsin transformation.
Used with permission of Kwanchai Gomez.
Source
Gomez, K.A. and Gomez, A.A.. 1984, Statistical Procedures for Agricultural Research. Wiley-Interscience. Page 307.
Examples
library(agridat)
data(gomez.nonnormal3)
dat <- gomez.nonnormal3
# First, replace 0, 100 values
dat$thoppers <- dat$hoppers
dat <- transform(dat, thoppers=ifelse(thoppers==0, 1/(4*75), thoppers))
dat <- transform(dat, thoppers=ifelse(thoppers==100, 100-1/(4*75), thoppers))
# Arcsin transformation of percentage p converted to degrees
# is arcsin(sqrt(p))/(pi/2)*90
dat <- transform(dat, thoppers=asin(sqrt(thoppers/100))/(pi/2)*90)
# QQ plots for raw/transformed data
libs(reshape2, lattice)
qqmath( ~ value|variable, data=melt(dat),
       main="gomez.nonnormal3 - raw/transformed QQ plot",
       scales=list(relation="free"))
#> Using gen, rep as id variables
 m1 <- lm(thoppers ~ gen, data=dat)
anova(m1) # Match Gomez table 7.25
#> Analysis of Variance Table
#> 
#> Response: thoppers
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> gen       11 16838.7 1530.79  16.502 1.316e-08 ***
#> Residuals 24  2226.4   92.77                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Response: thoppers
##           Df  Sum Sq Mean Sq F value    Pr(>F)
## gen       11 16838.7 1530.79  16.502 1.316e-08 ***
## Residuals 24  2226.4   92.77
m1 <- lm(thoppers ~ gen, data=dat)
anova(m1) # Match Gomez table 7.25
#> Analysis of Variance Table
#> 
#> Response: thoppers
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> gen       11 16838.7 1530.79  16.502 1.316e-08 ***
#> Residuals 24  2226.4   92.77                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Response: thoppers
##           Df  Sum Sq Mean Sq F value    Pr(>F)
## gen       11 16838.7 1530.79  16.502 1.316e-08 ***
## Residuals 24  2226.4   92.77