agridat (version 1.25)

crowder.seeds: Germination of Orobanche seeds for two genotypes and two treatments.

Description

Number of Orobanche seeds tested/germinated for two genotypes and two treatments.

Format

plate

Factor for replication

gen

Factor for genotype with levels O73, O75

extract

Factor for extract from bean, cucumber

germ

Number of seeds that germinated

n

Total number of seeds tested

Details

Egyptian broomrape, orobanche aegyptiaca is a parasitic plant family. The plants have no chlorophyll and grow on the roots of other plants. The seeds remain dormant in soil until certain compounds from living plants stimulate germination.

Two genotypes were studied in the experiment, O. aegyptiaca 73 and O. aegyptiaca 75. The seeds were brushed with one of two extracts prepared from either a bean plant or cucmber plant.

The experimental design was a 2x2 factorial, each with 5 or 6 reps of plates.

References

N. E. Breslow and D. G. Clayton. 1993. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88:9-25. https://doi.org/10.2307/2290687

Y. Lee and J. A. Nelder. 1996. Hierarchical generalized linear models with discussion. J. R. Statist. Soc. B, 58:619-678.

Examples

if (FALSE) {

  library(agridat)
  data(crowder.seeds)
  dat <- crowder.seeds
  m1.glm <- m1.glmm <- m1.glmmtmb <- m1.hglm <- NA


  # ----- Graphic
  libs(lattice)
  dotplot(germ/n~gen|extract, dat, main="crowder.seeds")


  # --- GLMM.  Assumes Gaussian random effects
  libs(MASS)
  m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate,
                     family=binomial(), data=dat)
  summary(m1.glmm)
  ## round(summary(m1.glmm)$tTable,2)
  ##                        Value Std.Error DF t-value p-value
  ## (Intercept)            -0.44      0.25 17   -1.80    0.09
  ## genO75                 -0.10      0.31 17   -0.34    0.74
  ## extractcucumber         0.52      0.34 17    1.56    0.14
  ## genO75:extractcucumber  0.80      0.42 17    1.88    0.08


  # ----- glmmTMB
  libs(glmmTMB)
  m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate),
                        data=dat,
                        family=binomial)
  summary(m1.glmmtmb)
  ## round(summary(m1.glmmtmb)$coefficients$cond , 2)
  ##                        Estimate Std. Error z value Pr(>|z|)
  ## (Intercept)               -0.45       0.22   -2.03     0.04
  ## genO75                    -0.10       0.28   -0.35     0.73
  ## extractcucumber            0.53       0.30    1.74     0.08
  ## genO75:extractcucumber     0.81       0.38    2.11     0.04
 
}