Description Usage Arguments Details Value Examples
A helper function to perform Type I error rate simulations.
1 2 3 4 5 6 7 8 | eppSimDat(
N = 10,
meanClutch = 10,
eppRate = 0.1,
eppMax = 12,
eppMales = 0.35,
nLags = 3
)
|
N |
Number of breeding pairs; default value is 10 |
meanClutch |
Mean clutch size (integer); clutch size it is assumed to be Poisson distributed; default is 10 |
eppRate |
Proportion of extra-pair young in population; default is 0.10 |
eppMax |
Maximum number of extra-pair young by male; default is 12 |
eppMales |
Proportion of extra-pair males in population; default is 0.35 |
nLags |
|
All default values match the values found in one of our study populations ('Westerholz').
An object of class
epp The data.frame
of the
EPP
slot contains two variable (trait_MALE
trait_FEMALE
) simulated independent from the epp
variable.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | d = eppSimDat()
plot(d)
# Type I error rate simulation
require(lme4)
pval_glmer = vector(mode = "numeric", length = 0)
pval_glm = vector(mode = "numeric", length = 0)
# For meaningful results increase i to e.g. 500 and N in eppSimDat to e.g. 120
for(i in 1:5) {
x = as.data.frame(eppSimDat(N = 25, meanClutch = 10, eppRate = 0.10, eppMax = 12,
eppMales = 0.35, nLags = 3))
fm1glmer = glmer(epp ~ rank + trait_MALE + trait_FEMALE + (1 | male) + (1 | female) ,
data = x, family = binomial, nAGQ = 0)
fm0glmer = update(fm1glmer, epp ~ 1 + (1 | male) + (1 | female) )
pval_glmer[i] = anova(fm0glmer, fm1glmer)$"Pr(>Chisq)"[2]
fm1glm = glm(epp ~ rank + trait_MALE + trait_FEMALE , data = x, family = binomial)
fm0glm = update(fm1glm, epp ~ 1 )
pval_glm[i] = anova(fm0glm, fm1glm, test = "Chisq")$"Pr(>Chi)"[2]
print(i)
}
# Type I error rate of glmer models
table(pval_glmer<0.05)[2]/length(pval_glmer)
# Type I error rate of the equivalent glm models
table(pval_glm<0.05)[2]/length(pval_glm)
|
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