This function performs the overall goodness-of-fit test for the Jolly-Move model. It is obtained as the sum of the 5 components Test3G.SR, Test3G.SM, Test3G.WBWA, TestM.ITEC, TestM.LTEC. To perform the goodness-of-fit test for the Arnason-Schwarz model, both the Arnason-Schwarz (AS) and the Jolly-Move models need to be fitted to the data (to our knowledge, only E-SURGE can fit the JMV model). Assuming the overall goodness-of-fit test for the JMV model has produced the value stat_jmv for the test statistic, get the deviance (say dev_as and dev_jmv) and number of estimated parameters (say dof_as and dof_jmv) for both the AS and JMV models. Then, finally, the p-value of the goodness-of-fit test for the AS model is obtained as 1 - pchisq(stat_as,dof_as) where stat_as = stat_jmv + (dev_as - dev_jmv) and dof_as = dof_jmv + (dof_jmv - dof_as)
overall_JMV(X, freq, rounding = 3)
| X | is a matrix of encounter histories |
|---|---|
| freq | is a vector of the number of individuals with the corresponding encounter history |
| rounding | is the level of rounding for outputs; default is 3 |
This function returns a data.frame with the value of the test statistic, the degrees of freedom and the p-value of the test.
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
if (FALSE) { # read in Geese dataset library(RMark) geese = system.file("extdata", "geese.inp", package = "R2ucare") geese = convert.inp(geese) geese.hist = matrix(as.numeric(unlist(strsplit(geese$ch, ''))),nrow=nrow(geese),byrow=TRUE) geese.freq = geese$freq # encounter histories and number of individuals with corresponding histories X = geese.hist freq = geese.freq # load R2ucare package library(R2ucare) # perform overall gof test overall_JMV(X, freq) }