Germination of alfalfa seeds at various salt concentrations
Germination of alfalfa seeds at various salt concentrations
data("carlson.germination")A data frame with 120 observations on the following 3 variables.
gengenotype factor, 15 levels
germgermination percent, 0-100
naclsalt concentration percent, 0-2
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.
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
## Not run:
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
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.