secr (version 4.6.10)

RMarkInput: Convert Data to RMark Input Format

Description

A single-session capthist object is formed by RMarkInput into a dataframe that may be passed directly to RMark.

Usage

RMarkInput(object, grouped = FALSE, covariates = TRUE)
unRMarkInput(df, covariates = TRUE)

Value

For RMarkInput --

Dataframe with fields ch and freq. `ch' is a character string of 0's and 1's. If grouped = FALSE the rownames are retained and the value of `freq' is 1 or -1. Negative values of `freq' indicate removal.

The dataframe also includes individual covariates specified with

covariates.

The attribute `intervals' is copied from `object', if present; otherwise it is set to a vector of zeros (indicating a closed-population sample).

For unRMarkInput --

A single-session capthist object with no traps attribute and hence no detector type (i.e. non-spatial capture histories). Covariates are copied as requested.

From secr 4.6.6, missing values (.) in input capture histories are converted to NA in the output, with a warning. The resulting capthist is unusable until the NAs are removed.

Arguments

object

secr capthist object

grouped

logical for whether to replace each group of identical capture histories with a single line

covariates

logical or character vector; see Details

df

dataframe with fields `ch' and `freq'

Details

To convert a multi-session object first collapse the sessions with join.

If covariates is TRUE then all columns of individual covariates in the input are appended as columns in the output. If covariates is a character-valued vector then only the specified covariates will be appended.

If both grouped and covariates are specified in RMarkInput, grouped will be ignored, with a warning.

References

Laake, J. and Rexstad E. (2008) Appendix C. RMark - an alternative approach to building linear models in MARK. In: Cooch, E. and White, G. (eds) Program MARK: A Gentle Introduction. 6th edition. Most recent edition available at www.phidot.org/software/mark/docs/book/.

See Also

join

Examples

## ovenCH is a 5-year mist-netting dataset
ovenRD <- RMarkInput (join(ovenCH))
head(ovenRD)

unRMarkInput(ovenRD)

RMarkInput(deermouse.ESG, covariates = FALSE, grouped = TRUE)
RMarkInput(deermouse.ESG, covariates = TRUE)

if (FALSE) {
## fit robust-design model in RMark (MARK must be  installed)
library(RMark)
MarkPath <- 'c:/MARK'    ## adjust for your installation
ovenRD.data <- process.data(ovenRD, model = "Robust",
    time.interval = attr(ovenRD, "intervals"))
ovenRD.model <- mark(data = ovenRD.data, model = "Robust",
    model.parameters = list(p = list(formula = ~1, share = TRUE),
    GammaDoublePrime = list(formula = ~1),
    GammaPrime = list(formula = ~1),
    f0 = list(formula = ~1)))   
cleanup(ask = FALSE)
}