A single-session capthist object is formed by RMarkInput into a dataframe that may be passed directly to RMark.
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secr capthist object
logical for whether to replace each group of identical capture histories with a single line
logical or character vector; see Details
dataframe with fields ‘ch’ and ‘freq’
To convert a multi-session object first collapse the sessions with
covariates is TRUE the 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.
covariates are specified in
grouped will be ignored, with a warning.
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
The dataframe also includes individual covariates specified with
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.
In versions before 2.4.0, a spurious occasion was added by
RMarkInput when grouped = FALSE. Thanks to Jeff Stetz for
The default value for
grouped changed to FALSE in secr 2.4.0
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. Available at http://www.phidot.org/software/mark/docs/book/.
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## 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) ## Not run: ## fit robust-design model in RMark (MARK must be installed) ## may need to set MarkPath, e.g., MarkPath <- 'c:/MARK' library(RMark) 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) ## End(Not run)
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