This function performs some basic error checking on the input files for a BaSTA analysis. A number of checks are performed including; (1) individuals dying before the observation window starts; (2) individuals with no observations of any kind (i.e. phantom individuals); (3) individuals with birth date recorded as being after death date; (4) individuals with observations recorded after death; (5) individuals with observations before birth; (6) years of birth must appear as 0 in the observation matrix; (7) years of death must appear as 0 in the observation matrix.
DataCheck(object, studyStart, studyEnd, autofix = rep(0, 7),
silent = TRUE)
A logical indicator that indicates if the data are free of errors or not. i.e. TRUE = the data have no apparent errors, and FALSE = there is at leat one error.
A corrected data frame.
A vector of row numbers in the original data frame where there are deaths occurring before the study starts.
A vector of row numbers in the original data frame where there are no birth/death AND no obervations.
A vector of row numbers in the original data frame where there are births recorded after death.
A vector of row numbers in the original data frame where there are observations (i.e. recaptures) after death.
A vector of row numbers in the original data frame where there are observations (i.e. recaptures) before birth.
A vector of row numbers in the original data frame where the year of birth is not a zero in the recapture matrix.
A vector of row numbers in the original data frame where the year of death is not a zero in the recapture matrix.
A data.frame to be used as an input data file for BaSTA. The first column is the individual's ID, the second and third columns are birth and death years respectively. Columns 4 to nt+3 represent the observation window of nt years. This is followed (optionally) by columns for covariate.
The start year of the observation window.
The end year of the observation window.
A vector argument with a length of 7 indicating whether to automatically fix any errors (see details). This should be used with extreme caution. We recommend going back to the individual-based data and fixing each error "by hand".
A logical argument indicating whether to print a detailed report to the screen or not.
Owen R. Jones jones@biology.sdu.dk and Fernando Colchero colchero@imada.sdu.dk
Argument autofix allows the user to fix the potential errors by specifying a code for each fix. Below are the descriptions of the actions that are taken depending on the error type and the fix code:
Type 1: 0 = do nothing; 1 = remove from dataframe.
Type 2: 0 = do nothing; 1 = remove from dataframe.
Type 3: 0 = do nothing; 1 = replace death records with 0; 2 = replace birth records with 0; 3 = replace both birth and death records with 0.
Type 4: 0 = do nothing; 1 = remove spurious post-death observations.
Type 5: 0 = do nothing; 1 = remove observations that pre-date year of birth.
Type 6: 0 = do nothing; 1 = replace birth year element of observation matrix with 0.
Type 7: 0 = do nothing; 1 = replace death year element of observation matrix with 0.
basta