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.

Source

Carlson, JR and Ditterline, RL and Martin, JM and Sands, DC and Lund, RE. (1983). Alfalfa Seed Germination in Antibiotic Agar Containing NaCl. Crop science, 23, 882-885. https://doi.org/10.2135/cropsci1983.0011183X002300050016x

Examples

if (FALSE) { # \dontrun{

library(agridat)
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
libs(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.
## libs(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

} # }