RMark (version 3.0.0)

convert.inp: Convert MARK input file to RMark dataframe

Description

Converts encounter history inp files used to create a MARK project into a dataframe for use with RMark. Group structure in frequencies is converted to factor variables that can be used to create groups in RMark. Covariates are copied straight across. Only works with encounter history format for input files and not specialized ones for known-fate or Brownie models.

Usage

convert.inp(
  inp.filename,
  group.df = NULL,
  covariates = NULL,
  use.comments = FALSE
)

Value

Dataframe with fields ch(character encounter history), freq (frequency of encounter history), followed by grouping variables (if any) and then covariates (if any)

Arguments

inp.filename

name of input file; inp extension is assumed and does not need to be specified

group.df

dataframe with grouping variables that 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

covariates

names to be assigned to the covariates defined in the inp file

use.comments

if TRUE values within /* and */ on data lines are used as row.names for the RMark dataframe. Only use this option if they are unique values.

Author

Jeff Laake

Details

The encounter history format for MARK is structured as follows: capture (encounter) history, followed by a frequency field for each group, followed by any covariates and then a semi-colon at the end of the line. Comments are allowed within /* and */. The RMark format is a dataframe with a different structure. Each record(row) in the dataframe is for one or more animals within a single group and if there is group structure then the dataframe contains factor variables that can be used to create groups. For example, the following is a little snippet of the same data with 2 groups Males/Females and a covariate weight in the two different formats:

 MARK encounter history file (in make believe test.inp): 1001
1 0 10; 1101 0 2 5; 0101 3 1 6;
RMark dataframe: ch freq sex weight 1001 1 M 10 1101 2 F 5 0101 3 M 6 0101 1
F 6 

To convert from the MARK format to the RMark format it is necessary to define the variables used to define the groups (if any) and to define the covariate field names (if any). For the example above, if test.inp is in the same directory as the current working directory, the call would be:

test = convert.inp("test",group.df=data.frame(sex=c("M","F")),
covariates="weight")

Comments spanning lines in the .inp file are ignored and deleted as are blank lines. If each line has a unique identifier in the comments then by setting use.comments=TRUE, the text of the comment (e.g.,tag number) will be assigned as the row name in the RMark dataframe. This will only work if each line only represents a single animal or a set of animals in a single group. If file was structured as follows:

 MARK encounter history file (in make believe test.inp): 1001
1 0 10 /*1*/; 1101 0 2 5 /*2*/; 0101 3 1 6 /*3*/; 

an error would occur

 Error in convert.inp("test", group.df = data.frame(sex =
c("M", "F")),: Row names not unique. Set use.comments to default value FALSE

because it would try to use "3" as the row name for the 3 males and the 1 female represented by the last row.

The extension .inp is optional for files with that extension. If the file has a different extension the entire filename must be specified.

Note that there are limitations to this function. You cannot have extra blank lines in the file, the number of columns (tab, space or comma delimited) must be the same in each line unless the line is just a comment line /* */. In the latter case, the /* must begin the line and the */ must end the line with no extra characters (blanks included) in before or after.

See Also

process.data

Examples

# \donttest{
# This example is excluded from testing to reduce package check time
# MARK example input file
pathtodata=paste(path.package("RMark"),"extdata",sep="/")
dipper=convert.inp(paste(pathtodata,"dipper",sep="/"),
                    group.df=data.frame(sex=c("M","F")))
# Example input files that accompany the MARK electronic book 
#  \url{http://www.phidot.org/software/mark/docs/book/}
bd=convert.inp(paste(pathtodata,"blckduck",sep="/"),
         covariates=c("age","weight","winglen","ci"),use.comments=TRUE)
aa=convert.inp(paste(pathtodata,"aa",sep="/"),
      group.df=data.frame(sex=c("Poor","Good")))
adult=convert.inp(paste(pathtodata,"adult",sep="/"))
age=convert.inp(paste(pathtodata,"age",sep="/"))
age_ya=convert.inp(paste(pathtodata,"age_ya",sep="/"),
      group.df=data.frame(age=c("Young","Adult")))
capsid=convert.inp(paste(pathtodata,"capsid",sep="/"))
clogit_demo=convert.inp(paste(pathtodata,"clogit_demo",sep="/"))
deer=convert.inp(paste(pathtodata,"deer",sep="/"))
ed_males=convert.inp(paste(pathtodata,"ed_males",sep="/"))
F_age=convert.inp(paste(pathtodata,"f_age",sep="/"))
indcov1=convert.inp(paste(pathtodata,"indcov1",sep="/"),
         covariates=c("cov1","cov2"))
indcov2=convert.inp(paste(pathtodata,"indcov2",sep="/"),
          covariates=c("cov1","cov2"))
island=convert.inp(paste(pathtodata,"island",sep="/"))
linear=convert.inp(paste(pathtodata,"linear",sep="/"))
young=convert.inp(paste(pathtodata,"young",sep="/"))
transient=convert.inp(paste(pathtodata,"transient",sep="/"))
ms_gof=convert.inp(paste(pathtodata,"ms_gof",sep="/"))
m_age=convert.inp(paste(pathtodata,"m_age",sep="/"))
ms_cjs=convert.inp(paste(pathtodata,"ms_cjs",sep="/"))
ms_directional=convert.inp(paste(pathtodata,"ms_directional",sep="/"))
ed=convert.inp(paste(pathtodata,"ed",sep="/"),
            group.df=data.frame(sex=c("Male","Female")))
multigroup=convert.inp(paste(pathtodata,"multi_group",sep="/"),
            group.df=data.frame(sex=c(rep("Female",2),rep("Male",2)),
            Colony=rep(c("Good","Poor"),2)))
LD1=convert.inp(paste(pathtodata,"ld1",sep="/"),
           group.df=data.frame(age=c("Young","Adult")))
yngadt=convert.inp(paste(pathtodata,"yngadt",sep="/"),
            group.df=data.frame(age=c("Young","Adult")))
effect_size=convert.inp(paste(pathtodata,"effect_size",sep="/"),
             group.df=data.frame(colony=c("Poor","Good")))
effect_size3=convert.inp(paste(pathtodata,"effect_size3",sep="/"),
             group.df=data.frame(colony=c("1","2","3")))
# }