## Not run: ------------------------------------
#
# ## house mouse dataset, morning trap clearances
# ## 81 female, 78 male, 1 unknown
# morning <- subset(housemouse, occ = c(1,3,5,7,9))
# summary(covariates(morning))
#
# ## speedy model fitting with coarse mask
# mmask <- make.mask(traps(morning), buffer = 20, nx = 32)
#
# ## assuming equal detection of males and females
# ## fitted sex ratio p(female) = 0.509434 = 81 / (81 + 78)
# fit.0 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE)
# predict(fit.0)
#
# ## allowing sex-specific detection parameters
# ## this leads to new estimate of sex ratio
# fit.h2 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE,
# model = list(g0 ~ h2, sigma ~ h2))
# predict(fit.h2)
#
# ## specifying newdata for h2 - equivalent to predict(fit.h2)
# predict(fit.h2, newdata = data.frame(h2 = factor(c('f','m'))))
#
# ## conditional likelihood fit of preceding model
# ## estimate of sex ratio does not change
# fit.CL.h2 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE,
# CL = TRUE, model = list(g0 ~ h2, sigma ~ h2))
# predict(fit.CL.h2)
#
# ## did sexes differ in detection parameters?
# fit.CL.0 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE,
# CL = TRUE, model = list(g0 ~ 1, sigma ~ 1))
# LR.test(fit.CL.h2, fit.CL.0)
#
# ## did sex ratio deviate from 1:1?
# fit.CL.h2.50 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE,
# CL = TRUE, model = list(g0 ~ h2, sigma ~ h2), fixed = list(pmix = 0.5))
# LR.test(fit.CL.h2, fit.CL.h2.50)
#
# ## did sexes show extra-compensatory variation in lambda0?
# ## (Efford and Mowat 2014)
# fit.CL.a0 <- secr.fit(morning, hcov = "sex", mask = mmask, trace = FALSE,
# CL = TRUE, model = list(a0 ~ 1, sigma ~ h2))
# LR.test(fit.CL.h2, fit.CL.a0)
#
# ## trend in ovenbird sex ratio, assuming sex-specific detection
# omask <- make.mask(traps(ovenCH), buffer = 300, nx = 32)
# fit.sextrend <- secr.fit(ovenCH, model = list(g0~h2, sigma~h2, pmix~Session),
# hcov = "Sex", CL = TRUE, mask = omask, trace = FALSE)
# predict(fit.sextrend)[1:5]
#
## ---------------------------------------------
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