agridat (version 1.16)

carlson.germination: Germination of alfalfa seeds at various salt concentrations

Description

Germination of alfalfa seeds at various salt concentrations

Usage

data("carlson.germination")

Format

A data frame with 120 observations on the following 3 variables.

gen

genotype factor, 15 levels

germ

germination percent, 0-100

nacl

salt concentration percent, 0-2

Details

Data are means averaged over 5, 10, 15, and 20 day counts. Germination is expressed as a percent of the no-salt control to account for differences in germination among the cultivars.

Examples

# NOT RUN {
data(carlson.germination)
dat <- carlson.germination
dat$germ <- dat$germ/100 # Convert to percent

# Separate response curve for each genotype.
# Really, we should use a glmm with random int/slope for each genotype
m1 <- glm(germ~ 0 + gen*nacl, data=dat, family=quasibinomial)

# Plot data and fitted model
if(require(latticeExtra)){
  newd <- data.frame(expand.grid(gen=levels(dat$gen), nacl=seq(0,2,length=100)))
  newd$pred <- predict(m1, newd, type="response")
  xyplot(germ~nacl|gen, dat, as.table=TRUE, main="carlson.germination",
         xlab="Percent NaCl", ylab="Fraction germinated") +
  xyplot(pred~nacl|gen, newd, type='l', grid=list(h=1,v=0))
}

# Calculate LD50 values.  Note, Carlson et al used quadratics, not glm.
# MASS::dose.p cannot handle multiple slopes, so do a separate fit for
# each genotype.  Results are vaguely similar to Carlson table 5.
if(require(MASS)){
  for(ii in unique(dat$gen)){
    cat("\n", ii, "\n")
    mm <- glm(germ ~ 1 + nacl, data=dat, subset=gen==ii, family=quasibinomial(link="probit"))
    print(dose.p(mm))
  }
  ##              Dose         SE
  ## Anchor    1.445728  0.05750418
  ## Apollo    1.305804  0.04951644
  ## Baker     1.444153  0.07653989
  ## Drylander 1.351201  0.03111795
  ## Grimm     1.395735  0.04206377
}

# }

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