Huggins89.t1 {VGAM} | R Documentation |
Simulated capture data set for the linear logistic model depending on an occasion covariate and an individual covariate for 10 trapping occasions and 20 individuals.
data(Huggins89table1) data(Huggins89.t1)
The format is a data frame.
Table 1 of Huggins (1989) gives this toy data set.
Note that variables t1
,...,t10
are
occasion-specific variables. They correspond to the
response variables y1
,...,y10
which
have values 1 for capture and 0 for not captured.
Both Huggins89table1
and Huggins89.t1
are identical.
The latter used variables beginning with z
,
not t
, and may be withdrawn very soon.
Huggins, R. M. (1989). On the statistical analysis of capture experiments. Biometrika, 76, 133–140.
Huggins89table1 <- transform(Huggins89table1, x3.tij = t01, T02 = t02, T03 = t03, T04 = t04, T05 = t05, T06 = t06, T07 = t07, T08 = t08, T09 = t09, T10 = t10) small.table1 <- subset(Huggins89table1, y01 + y02 + y03 + y04 + y05 + y06 + y07 + y08 + y09 + y10 > 0) # fit.tbh is the bottom equation on p.133. # It is a M_tbh model. fit.tbh <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10) ~ x2 + x3.tij, xij = list(x3.tij ~ t01 + t02 + t03 + t04 + t05 + t06 + t07 + t08 + t09 + t10 + T02 + T03 + T04 + T05 + T06 + T07 + T08 + T09 + T10 - 1), posbernoulli.tb(parallel.t = TRUE ~ x2 + x3.tij), data = small.table1, trace = TRUE, form2 = ~ x2 + x3.tij + t01 + t02 + t03 + t04 + t05 + t06 + t07 + t08 + t09 + t10 + T02 + T03 + T04 + T05 + T06 + T07 + T08 + T09 + T10) # These results differ a bit from Huggins (1989), probably because # two animals had to be removed here (they were never caught): coef(fit.tbh) # First element is the behavioural effect sqrt(diag(vcov(fit.tbh))) # SEs constraints(fit.tbh, matrix = TRUE) summary(fit.tbh, presid = FALSE) fit.tbh@extra$N.hat # Estimate of the population site N; cf. 20.86 fit.tbh@extra$SE.N.hat # Its standard error; cf. 1.87 or 4.51 fit.th <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10) ~ x2, posbernoulli.t, data = small.table1, trace = TRUE) coef(fit.th) constraints(fit.th) coef(fit.th, matrix = TRUE) # M_th model summary(fit.th, presid = FALSE) fit.th@extra$N.hat # Estimate of the population size N fit.th@extra$SE.N.hat # Its standard error fit.bh <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10) ~ x2, posbernoulli.b(I2 = FALSE), data = small.table1, trace = TRUE) coef(fit.bh) constraints(fit.bh) coef(fit.bh, matrix = TRUE) # M_bh model summary(fit.bh, presid = FALSE) fit.bh@extra$N.hat fit.bh@extra$SE.N.hat fit.h <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10) ~ x2, posbernoulli.b, data = small.table1, trace = TRUE) coef(fit.h, matrix = TRUE) # M_h model (version 1) coef(fit.h) summary(fit.h, presid = FALSE) fit.h@extra$N.hat fit.h@extra$SE.N.hat Fit.h <- vglm(cbind(y01, y02, y03, y04, y05, y06, y07, y08, y09, y10) ~ x2, posbernoulli.t(parallel.t = TRUE ~ x2), data = small.table1, trace = TRUE) coef(Fit.h) coef(Fit.h, matrix = TRUE) # M_h model (version 2) summary(Fit.h, presid = FALSE) Fit.h@extra$N.hat Fit.h@extra$SE.N.hat