eppSimDat: Type I error rate simulations

Description

A helper function to perform Type I error rate simulations.

Usage

eppSimDat(N = 10, meanClutch = 10, eppRate = 0.1, eppMax = 12,
  eppMales = 0.35, nLags = 3)

Arguments

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

maxlag parameter to pass to DirichletPolygons

Value

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.

Details

All default values match the values found in one of our study populations ('Westerholz').

Examples

Run this code

# NOT RUN {
d = eppSimDat()
plot(d)


# }
# NOT RUN {
# 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)


# }
# NOT RUN {

# }

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