Infestation of carrots by fly larvae — jansen.carrot • agridat

Infestation of 16 carrot genotypes by fly larvae, comparing 2 treatments in 16 blocks.

data("jansen.carrot")

Format

A data frame with 96 observations on the following 5 variables.

trt: treatment
gen: genotype
block: block
n: number of carrots sampled per plot
y: number of carrots infested per plot

Details

This experiment was designed to compare different genotypes of carrots with respect to their resistance to infestation by larvae of the carrotfly.

There were 16 genotypes, 2 levels of pest-control treatments, conducted in 3 randomized complete blocks. About 50 carrots were sampled from each plot and evaluated. The data show the number of carrots and the number infested by fly larvae.

Used with permission of J. Jansen. Electronic version supplied by Miroslav Zoric.

Source

J. Jansen & J.A. Hoekstra (1993). The analysis of proportions in agricultural experiments by a generalized linear mixed model. Statistica Neerlandica, 47(3), 161-174. https://doi.org/10.1111/j.1467-9574.1993.tb01414.x

References

None.

Examples


library(agridat)
data(jansen.carrot)
dat <- jansen.carrot

libs(lattice)
dotplot(gen ~ y/n, data=dat, group=trt, auto.key=TRUE,
        main="jansen.carrot",
        xlab="Proportion of carrots infected per block", ylab="Genotype")

# \dontrun{
  # Not run because CRAN wants < 5 seconds per example.  This is close.
  libs(lme4)
  # Tentative model.  Needs improvement.
  m1 <- glmer(cbind(y,n-y) ~ gen*trt + (1|block),
              data=dat, family=binomial)
  summary(m1)
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: cbind(y, n - y) ~ gen * trt + (1 | block)
#>    Data: dat
#> 
#>      AIC      BIC   logLik deviance df.resid 
#>    608.9    693.5   -271.4    542.9       63 
#> 
#> Scaled residuals: 
#>    Min     1Q Median     3Q    Max 
#> -3.973 -1.016 -0.273  1.115  2.908 
#> 
#> Random effects:
#>  Groups Name        Variance Std.Dev.
#>  block  (Intercept) 0.02472  0.1572  
#> Number of obs: 96, groups:  block, 3
#> 
#> Fixed effects:
#>              Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)   1.07446    0.20722   5.185 2.16e-07 ***
#> genG02       -0.06075    0.26611  -0.228  0.81941    
#> genG03       -2.07415    0.26296  -7.888 3.08e-15 ***
#> genG04       -2.79945    0.30159  -9.282  < 2e-16 ***
#> genG05       -2.09256    0.26415  -7.922 2.34e-15 ***
#> genG06       -2.58980    0.28306  -9.149  < 2e-16 ***
#> genG07       -2.11827    0.26541  -7.981 1.45e-15 ***
#> genG08       -2.52386    0.28125  -8.974  < 2e-16 ***
#> genG09       -2.82317    0.29709  -9.503  < 2e-16 ***
#> genG10       -2.63026    0.29208  -9.005  < 2e-16 ***
#> genG11       -2.81654    0.30587  -9.208  < 2e-16 ***
#> genG12       -4.42573    0.49179  -8.999  < 2e-16 ***
#> genG13       -2.01259    0.27119  -7.421 1.16e-13 ***
#> genG14       -3.09043    0.30759 -10.047  < 2e-16 ***
#> genG15       -2.88159    0.29641  -9.722  < 2e-16 ***
#> genG16       -3.79170    0.39154  -9.684  < 2e-16 ***
#> trtT2        -1.88405    0.25129  -7.498 6.50e-14 ***
#> genG02:trtT2  0.18457    0.36089   0.511  0.60905    
#> genG03:trtT2  1.15294    0.38887   2.965  0.00303 ** 
#> genG04:trtT2  2.20759    0.39970   5.523 3.33e-08 ***
#> genG05:trtT2  1.05088    0.39904   2.634  0.00845 ** 
#> genG06:trtT2  0.91731    0.44606   2.056  0.03973 *  
#> genG07:trtT2  0.43275    0.43498   0.995  0.31980    
#> genG08:trtT2  1.22717    0.42157   2.911  0.00360 ** 
#> genG09:trtT2  1.29446    0.44172   2.931  0.00338 ** 
#> genG10:trtT2  1.06684    0.44495   2.398  0.01650 *  
#> genG11:trtT2  1.27707    0.46195   2.765  0.00570 ** 
#> genG12:trtT2  1.89552    0.69089   2.744  0.00608 ** 
#> genG13:trtT2  0.89074    0.40707   2.188  0.02866 *  
#> genG14:trtT2  2.14280    0.42006   5.101 3.38e-07 ***
#> genG15:trtT2  1.81864    0.41727   4.358 1.31e-05 ***
#> genG16:trtT2  1.79019    0.56060   3.193  0.00141 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Correlation matrix not shown by default, as p = 32 > 12.
#> Use print(x, correlation=TRUE)  or
#>     vcov(x)        if you need it
  # Todo: Why are these results different from Jansen?
  # Maybe he used ungrouped bernoulli data?  Too slow with 4700 obs  
# }