Seed germination with different temperatures/concentrations

Format

A data frame with 64 observations on the following 5 variables.

temp: temperature regimen
rep: replication factor (not blocking)
conc: chemical concentration
germ: number of seeds germinating
seeds: number of seeds tested = 50

Details

The rep factor is NOT a blocking factor.

Source

Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods in Agriculture and Experimental Biology, 3rd ed. Chapman and Hall. Page 350-351.

Used with permission of Roger Mead, Robert Curnow, and Anne Hasted.

References

Schabenberger, O. and Pierce, F.J., 2002. Contemporary statistical models for the plant and soil sciences, CRC.

Examples


library(agridat)

data(mead.germination)
dat <- mead.germination
dat <- transform(dat, concf=factor(conc))
libs(lattice)
xyplot(germ~log(conc+.01)|temp, dat, layout=c(4,1),
       main="mead.germination", ylab="number of seeds germinating")

m1 <- glm(cbind(germ, seeds-germ) ~ 1, dat, family=binomial)
m2 <- glm(cbind(germ, seeds-germ) ~ temp, dat, family=binomial)
m3 <- glm(cbind(germ, seeds-germ) ~ concf, dat, family=binomial)
m4 <- glm(cbind(germ, seeds-germ) ~ temp + concf, dat, family=binomial)
m5 <- glm(cbind(germ, seeds-germ) ~ temp * concf, dat, family=binomial)
anova(m1,m2,m3,m4,m5)
#> Analysis of Deviance Table
#> 
#> Model 1: cbind(germ, seeds - germ) ~ 1
#> Model 2: cbind(germ, seeds - germ) ~ temp
#> Model 3: cbind(germ, seeds - germ) ~ concf
#> Model 4: cbind(germ, seeds - germ) ~ temp + concf
#> Model 5: cbind(germ, seeds - germ) ~ temp * concf
#>   Resid. Df Resid. Dev Df Deviance
#> 1        63    1193.80            
#> 2        60     430.11  3   763.69
#> 3        60     980.10  0  -549.98
#> 4        57     148.11  3   831.99
#> 5        48      55.64  9    92.46
##   Resid. Df Resid. Dev Df Deviance
## 1        63    1193.80
## 2        60     430.11  3   763.69
## 3        60     980.10  0  -549.98
## 4        57     148.11  3   831.99
## 5        48      55.64  9    92.46

# Show logit and fitted values.  T2 has highest germination
subset(cbind(dat, predict(m5), fitted(m5)), rep=="R1")
#>    temp rep conc germ seeds concf predict(m5) fitted(m5)
#> 1    T1  R1  0.0    9    50     0 -1.81528997      0.140
#> 2    T1  R1  0.1   13    50   0.1 -0.99462258      0.270
#> 3    T1  R1  1.0   21    50     1 -0.10008346      0.475
#> 4    T1  R1 10.0   40    50    10  1.07212067      0.745
#> 5    T2  R1  0.0   19    50     0 -0.02000067      0.495
#> 6    T2  R1  0.1   33    50   0.1  0.42634269      0.605
#> 7    T2  R1  1.0   43    50     1  1.41784272      0.805
#> 8    T2  R1 10.0   48    50    10  3.31678004      0.965
#> 9    T3  R1  0.0    7    50     0 -2.14286337      0.105
#> 10   T3  R1  0.1    1    50   0.1 -2.84385174      0.055
#> 11   T3  R1  1.0    8    50     1 -1.69591151      0.155
#> 12   T3  R1 10.0    3    50    10 -2.19722458      0.100
#> 13   T4  R1  0.0    4    50     0 -2.04065552      0.115
#> 14   T4  R1  0.1   13    50   0.1 -1.35533214      0.205
#> 15   T4  R1  1.0   16    50     1 -0.70818506      0.330
#> 16   T4  R1 10.0   13    50    10 -0.89538405      0.290