Strip-plot experiment of rice — gomez.stripplot • agridat

A strip-plot experiment with three reps, variety as the horizontal strip and nitrogen fertilizer as the vertical strip.

Format

yield: Grain yield in kg/ha
rep: Rep
nitro: Nitrogen fertilizer in kg/ha
gen: Rice variety
col: column
row: row

Details

Note, this is a subset of the the 'gomez.stripsplitplot' data.

Used with permission of Kwanchai Gomez.

Source

Gomez, K.A. and Gomez, A.A.. 1984, Statistical Procedures for Agricultural Research. Wiley-Interscience. Page 110.

References

Jan Gertheiss (2014). ANOVA for Factors With Ordered Levels. J Agric Biological Environmental Stat, 19, 258-277.

Examples


library(agridat)
data(gomez.stripplot)
dat <- gomez.stripplot

# Gomez figure 3.7
libs(desplot)
desplot(dat, gen ~ col*row,
        # aspect unknown
        out1=rep, out2=nitro, num=nitro, cex=1,
        main="gomez.stripplot")



# Gertheiss figure 1
# library(lattice)
# dotplot(factor(nitro) ~ yield|gen, data=dat)

# Gomez table 3.12
# tapply(dat$yield, dat$rep, sum)
# tapply(dat$yield, dat$gen, sum)
# tapply(dat$yield, dat$nitro, sum)

# Gomez table 3.15.  Anova table for strip-plot
dat <- transform(dat, nf=factor(nitro))
m1 <- aov(yield ~ gen * nf + Error(rep + rep:gen + rep:nf), data=dat)
summary(m1)
#> 
#> Error: rep
#>           Df  Sum Sq Mean Sq F value Pr(>F)
#> Residuals  2 9220962 4610481               
#> 
#> Error: rep:gen
#>           Df   Sum Sq  Mean Sq F value  Pr(>F)   
#> gen        5 57100201 11420040   7.653 0.00337 **
#> Residuals 10 14922619  1492262                   
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Error: rep:nf
#>           Df   Sum Sq  Mean Sq F value  Pr(>F)   
#> nf         2 50676061 25338031   34.07 0.00307 **
#> Residuals  4  2974908   743727                   
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Error: Within
#>           Df   Sum Sq Mean Sq F value   Pr(>F)    
#> gen:nf    10 23877979 2387798   5.801 0.000427 ***
#> Residuals 20  8232917  411646                     
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Error: rep
##           Df  Sum Sq Mean Sq F value Pr(>F)
## Residuals  2 9220962 4610481

## Error: rep:gen
##           Df   Sum Sq  Mean Sq F value  Pr(>F)
## gen        5 57100201 11420040   7.653 0.00337 **
## Residuals 10 14922619  1492262
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Error: rep:nf
##           Df   Sum Sq  Mean Sq F value  Pr(>F)
## nf         2 50676061 25338031   34.07 0.00307 **
## Residuals  4  2974908   743727
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Error: Within
##           Df   Sum Sq Mean Sq F value   Pr(>F)
## gen:nf    10 23877979 2387798   5.801 0.000427 ***
## Residuals 20  8232917  411646


# More compact view
## libs(agricolae)
## with(dat, strip.plot(rep, nf, gen, yield))

## Analysis of Variance Table
## Response: yield
##        Df   Sum Sq  Mean Sq F value    Pr(>F)
## rep     2  9220962  4610481 11.2001 0.0005453 ***
## nf      2 50676061 25338031 34.0690 0.0030746 **
## Ea      4  2974908   743727  1.8067 0.1671590
## gen     5 57100201 11420040  7.6528 0.0033722 **
## Eb     10 14922619  1492262  3.6251 0.0068604 **
## gen:nf 10 23877979  2387798  5.8006 0.0004271 ***
## Ec     20  8232917   411646


# Mixed-model version
## libs(lme4)
## m3 <- lmer(yield ~ gen * nf + (1|rep) + (1|rep:nf) + (1|rep:gen), data=dat)
## anova(m3)

## Analysis of Variance Table
##        Df   Sum Sq  Mean Sq F value
## gen     5 15751300  3150260  7.6528
## nf      2 28048730 14024365 34.0690
## gen:nf 10 23877979  2387798  5.8006