| Title: | Goodness-of-Fit Tests for Capture-Recapture Models |
| Version: | 1.0.3 |
| Description: | Performs goodness-of-fit tests for capture-recapture models as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>. Also contains several functions to process capture-recapture data. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/oliviergimenez/R2ucare |
| BugReports: | https://github.com/oliviergimenez/R2ucare/issues |
| Depends: | R (≥ 3.3.0) |
| Imports: | RMark, stats, stringr, utils |
| Suggests: | knitr, rmarkdown, testthat |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-11-23 15:37:38 UTC; oliviergimenez |
| Author: | Olivier Gimenez 
[aut, cre],
Jean-Dominique Lebreton [ctb],
Remi Choquet [ctb],
Roger Pradel [ctb] |
| Maintainer: | Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-23 22:20:02 UTC |
Estimation of multinomial mixture distributions parameters
Description
This function performs maximum likelihood inference for multinomial mixture distributions.
Usage
coef_mixtures(Mp, Np)
Arguments
Mp |
a matrix of mixtures (a row matrix if a vector) |
Np |
a matrix of bases (a row matrix if a vector) |
Value
This function returns a list of maximum likelihood estimates for the cells of a mixture distribution:
P matrix of cell probabilities estimates for mixtures
PI matrix of mixture probabilities
GAM matrix of cell probabilities estimates for bases
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
References
Yantis, S., Meyer, D. E., and Smith, J. E. K. (1991). Analyses of multinomial mixture distributions: New tests for stochastic models of cognition and action. Psychological Bulletin 110, 350–374.
Deviance of multinomial mixture distributions
Description
This function calculates the deviance of multinomial mixture distributions.
Usage
deviance_mixture(x, M, N, s, n, nbmel)
Arguments
x |
value to which the deviance is to be evaluated |
M |
a vector of mixtures (see coef_mixtures.R) |
N |
a vector of bases (see coef_mixtures.R) |
s |
number of bases |
n |
number of cell probabilities |
nbmel |
number of mixtures |
Value
This function returns the value of the deviance for mixture distributions.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
References
Yantis, S., Meyer, D. E., and Smith, J. E. K. (1991). Analyses of multinomial mixture distributions: New tests for stochastic models of cognition and action. Psychological Bulletin 110, 350–374.
Expected values in a contingency table
Description
This function calculates expected values for a rxc contingency table.
Usage
expval_table(M)
Arguments
M |
a matrix of observed probabilities |
Value
A matrix of expected values.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Goodness-of-fit test for contingency tables
Description
This function carries out goodness-of-fit tests for contingency tables from the power-divergence family.
Usage
gof_test(lambda, observes, theoriques)
Arguments
lambda |
parameter defining the statistic to be used: lambda = -0.5 is for the Freeman-Tuckey statistic, lambda = 0 for the G2 statistic, lambda = 2/3 for the Cressie-Read statistic and lambda = 1 for the classical Chi-square statistic |
observes |
vector of observed probabilities |
theoriques |
vector of theoretical/expected probabilities |
Value
This function returns the value of the goodness-of-fit statistic.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Group individual capture-recapture data in encounter histories
Description
This function pools together individuals with the same encounter capture-recapture history.
Usage
group_data(X, effX)
Arguments
X |
matrix of capture-recapture histories |
effX |
vector with numbers of individuals with that particular capture-recapture history |
Value
matrix with grouped capture-recapture histories and counts in the last column
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# Generate fake capture-recapture dataset
X = matrix(round(runif(300)),nrow=100)
freq=rep(1,100)
cbind(X,freq)
group_data(X,freq)
Group individual capture-recapture data in encounter histories along specific column(s)
Description
This function pools together individuals with the same encounter capture-recapture history along specified directions given by columns.
Usage
group_data_gen(X, effX, s)
Arguments
X |
matrix of capture-recapture histories |
effX |
vector with numbers of individuals with that particular capture-recapture history |
s |
scalar or vector of columns along which the grouping should be done |
Value
matrix with grouped capture-recapture histories and counts in the last column
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Test of independence for 2x2 contingency tables
Description
This function tests independence in 2x2 contingency tables
Usage
ind_test_22(M, threshold = 2, rounding = 3)
Arguments
M |
is a 2x2 contingency table |
threshold |
is a threshold for low expected numbers; default is 2 |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a vector with statistic of quadratic chi2 or inv chi2 corresponding to pvalue of Fisher test, p-value of quadratic chi2 test or Fisher test for low numbers, signed test and test performed (Chi-square, Fisher or None).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>,Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Test of independence for rxc contingency tables
Description
This function tests independence in rxc contingency tables
Usage
ind_test_rc(M, threshold = 2, rounding = 3)
Arguments
M |
is an r by c table of non-negative integers |
threshold |
is a threshold for low expected numbers; default is 2 |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a vector with statistic of quadratic chi2 or inv chi2 corresponding to pvalue of Fisher test, p-value of quadratic chi2 test or Fisher test for low numbers, degree of freedom and test performed (Chi-square, Fisher or None).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>,Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Inverse generalized logit link
Description
This function computes the inverse (or reciprocal) of the generalized logit link function.
Usage
inv_logit_gen(petitv)
Arguments
petitv |
vector of values to be transformed |
Value
ev vector of transformed values
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
m-array: table of first recaptures
Description
This function calculates the m-array, the number of released and never seen again individuals; deals with more than 1 group
Usage
marray(X, freq)
Arguments
X |
a matrix of encounter histories over K occasions |
freq |
is a vector with the number of individuals having the corresponding encounter history |
Value
This function returns a list with R the number of released individuals (K-1 x g matrix), m the m-array (K-1 x K-1 x g array) with upper triangle filled only and never the number of individuals never recaptured (K-1 x g matrix).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>,Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# get female data
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
# get number of released individuals (R),
# the m-array (m) and
# the number of individuals never seen again (never)
marray(dip.fem.hist,dip.fem.freq)
Multistate m-array
Description
This function calculates the m-array for multistate capture-recapture data, the number of released and never seen again individuals.
Usage
multimarray(X, freq)
Arguments
X |
a matrix of encounter histories over K occasions |
freq |
is a vector with the number of individuals having the corresponding encounter history |
Value
This function returns a matrix in which R the number of released individuals is in the first column, the number of individuals never recaptured (K-1) is in the last column and m the m-array (K-1 x K-1) with upper triangle filled only is in sandwich between these two vectors.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>,Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# build m-array
multimarray(geese.hist, geese.freq)
Overall goodness-of-fit test for the Cormack-Jolly-Seber model
Description
This function performs the overall goodness-of-fit test for the Cormack-Jolly-Seber model. It is obtained as the sum of the 4 components Test3.SR, Test3.SM, Test2.CT and Test2.CL.
Usage
overall_CJS(X, freq, rounding = 3)
Arguments
X |
is a matrix of encounter histories |
freq |
is a vector of the number of individuals with the corresponding encounter history |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a data.frame with the value of the test statistic, the degrees of freedom and the p-value of the test.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>,Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# split the dataset in males/females
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
mask = (dip.group == 'Male')
dip.mal.hist = dip.hist[mask,]
dip.mal.freq = dip.freq[mask]
# for females
overall_CJS(dip.fem.hist, dip.fem.freq)
Overall goodness-of-fit test for the Jolly-Move model
Description
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)
Usage
overall_JMV(X, freq, rounding = 3)
Arguments
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 |
Value
This function returns a data.frame with the value of the test statistic, the degrees of freedom and the p-value of the test.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# 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)
Pooling algorithm
Description
This function pools columns of a 2xK contingency table (if needed, ie if low numbers present)
Usage
pool2K(M, low = 2)
Arguments
M |
is a 2 by K contingency table (or a K by 2 table) |
low |
is a threshold for low expected numbers; default is 2 (if this argument is big enough, the table is pooled down to 2 x 2; if this argument is 0, the table is not pooled) |
Value
This function returns a matrix with the pooled contingency table.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Pooling algorithm (multisite goodness-of-fit tests)
Description
This function pools rows and columns of a rxc contingency table according to Pradel et al. (2003).
Usage
pooling_ct(table)
Arguments
table |
is a rxc contingency table |
Value
This function returns a matrix with the pooled contingency table.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
References
Pradel R., Wintrebert C.M.A. and Gimenez O. (2003). A proposal for a goodness-of-fit test to the Arnason-Schwarz multisite capture-recapture model. Biometrics 59: 43-53.
Pooling algorithm (multisite goodness-of-fit tests)
Description
This function pools rows and columns of a rxc bases and mixture table according to Pradel et al. (2003). It provides the components of TestM in the multisite goodness-of-fit tests.
Usage
pooling_mixtures(nk, nj, a, mixandbases)
Arguments
nk |
number of mixtures |
nj |
number of bases |
a |
number of sites/states |
mixandbases |
matrix with mixtures and bases |
Value
This function returns a matrix with the pooled table.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Rémi Choquet, Jean-Dominique Lebreton, Anne-Marie Reboulet, Roger Pradel
References
Pradel R., Wintrebert C.M.A. and Gimenez O. (2003). A proposal for a goodness-of-fit test to the Arnason-Schwarz multisite capture-recapture model. Biometrics 59: 43-53.
Read capture-recapture data with Headed format used by program E-SURGE
Description
This function reads in capture-recapture dataset with the Headed format. It ignores all forms of censorship for now, and drops continuous covariates because no goodness-of-fit test exists for such models
Usage
read_headed(file)
Arguments
file |
text file with Headed format |
Value
list with first component the matrix of encounter histories, second components the vector of number of individuals with corresponding histories and, if relevant, third component vector/matrix with group(s)
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>
Examples
# read in Dipper dataset
dipper = system.file("extdata", "ed.txt", package = "R2ucare")
read_headed(dipper)
# read in Geese dataset
geese = system.file("extdata", "geese.txt", package = "R2ucare")
read_headed(geese)
Read capture-recapture data with Input (.inp) format used by program MARK
Description
This function reads in capture-recapture dataset with the Input format. It is a wrapper for the function convert.inp from package RMark. It drops continuous covariates because no goodness-of-fit test exists for such models
Usage
read_inp(file, group.df = NULL)
Arguments
file |
text file with Input format (extension .inp) |
group.df |
dataframe with grouping variables; contains a row for each group defined in the input file row1=group1, row2=group2 etc. Names and number of columns in the dataframe is set by user to define grouping variables in RMark dataframe |
Value
list with first component the matrix of encounter histories, second components the vector of number of individuals with corresponding histories and, if relevant, third component vector/matrix with group(s)
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>
Examples
# read in Dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# read in Geese dataset
geese = system.file("extdata", "geese.inp", package = "R2ucare")
read_inp(geese)
Reformat outputs of multinomial mixture distributions parameters
Description
This function reformat the outputs of multinomial mixture distributions parameters.
Usage
reconstitution(x, s, n, nbmel)
Arguments
x |
vector with cell probabilities estimates for mixtures and bases, along with mixture probilities |
s |
number of bases |
n |
number of cell probabilities |
nbmel |
number of mixtures |
Value
This function returns a list of maximum likelihood estimates for the cells of a mixture distribution with:
P matrix of cell probabilities estimates for mixtures
PI matrix of mixture probabilities
GAM matrix of cell probabilities estimates for bases
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Replicate and tile a matrix
Description
This function creates a large matrix consisting of an m-by-n tiling of copies of X. The dimensions of the returned matrix are nrow(X)*m x ncol(X)*n. This is the equivalent of the repmat MATLAB function.
Usage
repmat(X, m, n)
Arguments
X |
matrix to be replicated |
m |
row dimension of replication |
n |
column dimension of replication |
Value
A replicated matrix of X with dimensions nrow(X)*m x ncol(X)*n.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>
Test2.CL
Description
This function performs Test2.CL
Usage
test2cl(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with 5 columns for components i (2:K-3) (in rows) of test2.cli following Pradel 1993 (in Lebreton and North, Birkhauser Verlag): component, degree of freedom, statistic of the test, p-value, test performed.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# split the dataset in males/females
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
mask = (dip.group == 'Male')
dip.mal.hist = dip.hist[mask,]
dip.mal.freq = dip.freq[mask]
# for males
X = dip.mal.hist
freq = dip.mal.freq
res.males = test2cl(X,freq)
res.males
Test2.CT
Description
This function performs Test2.CT
Usage
test2ct(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with 5 columns for components i (2:K-2) (in rows) of test2.Cti: component, degree of freedom, statistic of the test, p-value, signed test, test performed.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# split the dataset in males/females
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
mask = (dip.group == 'Male')
dip.mal.hist = dip.hist[mask,]
dip.mal.freq = dip.freq[mask]
# for females
X = dip.fem.hist
freq = dip.fem.freq
res.females = test2ct(X,freq)
res.females
Test3G.SM
Description
This function performs Test3G.SM
Usage
test3Gsm(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with occasion, site, the value of the test statistic, degree of freedom, p-value and test performed (chi-square, Fisher or none).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# perform Test.3.GSm
test3Gsm(geese.hist,geese.freq)
Test3G.SR
Description
This function performs Test3G.SR
Usage
test3Gsr(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with occasion, site, the value of the test statistic, degree of freedom, p-value and test performed (chi-square, Fisher or none).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Rémi Choquet, Roger Pradel
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# perform Test3.GSR
test3Gsr(geese.hist,geese.freq)
Test3G.WBWA
Description
This function performs Test3G.WBWA
Usage
test3Gwbwa(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with occasion, site, the value of the test statistic, degree of freedom, p-value and test performed (chi-square, Fisher or none).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# perform Test.3GWBWA
test3Gwbwa(geese.hist,geese.freq)
Test3.SM
Description
This function performs Test3.SM
Usage
test3sm(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with 5 columns for components i (2:K-1) (in rows) of test3.smi: component, degree of freedom, statistic of the test, p-value, test performed.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# split the dataset in males/females
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
mask = (dip.group == 'Male')
dip.mal.hist = dip.hist[mask,]
dip.mal.freq = dip.freq[mask]
# for females
res.females = test3sm(dip.fem.hist, dip.fem.freq)
res.females
Test3.SR
Description
This function performs Test3.SR
Usage
test3sr(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with 4 columns for components i (2:K-1) (in rows) of test3.sri: component, statistic of the test, p-value, signed test, test performed.
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Jean-Dominique Lebreton, Rémi Choquet, Roger Pradel
Examples
# read in the classical dipper dataset
dipper = system.file("extdata", "ed.inp", package = "R2ucare")
dipper = read_inp(dipper,group.df=data.frame(sex=c('Male','Female')))
# Get encounter histories, counts and groups:
dip.hist = dipper$encounter_histories
dip.freq = dipper$sample_size
dip.group = dipper$groups
# split the dataset in males/females
mask = (dip.group == 'Female')
dip.fem.hist = dip.hist[mask,]
dip.fem.freq = dip.freq[mask]
mask = (dip.group == 'Male')
dip.mal.hist = dip.hist[mask,]
dip.mal.freq = dip.freq[mask]
# Test3SR for males
res.males = test3sr(dip.mal.hist, dip.mal.freq)
res.males
TestM.ITEC
Description
This function performs TestM.ITEC
Usage
testMitec(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with occasion, the value of the test statistic, degree of freedom, p-value and test performed (chi-square, Fisher or none).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Rémi Choquet, Roger Pradel
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# perform TestM.ITEC
testMitec(geese.hist,geese.freq)
TestM.LTEC
Description
This function performs TestM.LTEC
Usage
testMltec(X, freq, verbose = TRUE, rounding = 3)
Arguments
X |
is a matrix of encounter histories with K occasions |
freq |
is a vector of the number of individuals with the corresponding encounter history |
verbose |
controls the level of the details in the outputs; default is TRUE for all details |
rounding |
is the level of rounding for outputs; default is 3 |
Value
This function returns a list with first component the overall test and second component a data.frame with occasion, the value of the test statistic, degree of freedom, p-value and test performed (chi-square, Fisher or none).
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# Read in Geese dataset:
geese = system.file("extdata", "geese.inp", package = "R2ucare")
geese = read_inp(geese)
# Get encounter histories and number of individuals with corresponding histories
geese.hist = geese$encounter_histories
geese.freq = geese$sample_size
# perform TestM.LTEC
testMltec(geese.hist, geese.freq)
Ungroup encounter capture-recapture data in individual histories
Description
This function splits encounter histories in as many individual histories as required.
Usage
ungroup_data(X, effX)
Arguments
X |
matrix of encounter capture-recapture histories |
effX |
vector with numbers of individuals with that particular encounter history |
Value
matrix with ungrouped capture-recapture histories and counts in the last column (should be 1s)
Author(s)
Olivier Gimenez <olivier.gimenez@cefe.cnrs.fr>, Roger Pradel, Rémi Choquet
Examples
# Generate fake capture-recapture dataset
X = matrix(round(runif(9)),nrow=3)
freq=c(4,3,-8)
cbind(X,freq)
ungroup_data(X,freq)