plot.ECFOCF | R Documentation |
This function plots the result of fitCF().
The result data
plots the observed ECF-OCF table.
The result dataOCF
plots the observed OCF table.
The result dataECF
plots the observed ECF table.
The result CF
plots the true clutch frequency.
The result OCF
plots the observed clutch frequency.
The result ECF
plots the estimated clutch frequency.
The result ECFOCF
plots the bivariate observed vs. estimated clutch frequency.
The result ECFOCF0
plots the bivariate observed vs. estimated clutch frequency without the 0 OCF.
The result prob
plots the probabilities of capture.
The result period
plots the probabilities of nesting according to period.
If category is left to NA, the compound value for all the population is plotted.
When result="data" is used, this is a parser for plot.TableECFOCF().
See this function for the parameters.
The parameter y.axis is the shift of the x legends for result="prob".
When a resultMCMC
is indicated, if replicates is "all", all values are used;
if a value lower than number of iterations is indicated, a regular thinning is used and
if a value larger then number if iteration is indicated, a sampling with replacement is used.
## S3 method for class 'ECFOCF'
plot(
x,
...,
result = "CF",
category = NA,
period = 1,
resultMCMC = NULL,
chain = 1,
replicates = "all"
)
x |
A result for fitCF(). |
... |
Graphic parameters, see plot.TableECFOCF() or par. |
result |
What result will be plotted: data, dataOCF, dataECF, ECF, OCF, ECFOCF, ECFOCF0, CF, Prob, period |
category |
What category will be plotted, numeric or NA for all. |
period |
The period that will be plotted. |
resultMCMC |
A result from fitRMU_MHmcmc. |
chain |
Which chain to be used in resultMCMC. |
replicates |
How many replicates fron resultMCMC. |
plot.ECFOCF plots a result of clutch frequency fit.
Nothing
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f()
,
ECFOCF_full()
,
TableECFOCF()
,
fitCF()
,
fitCF_MHmcmc()
,
fitCF_MHmcmc_p()
,
generateCF()
,
lnLCF()
,
logLik.ECFOCF()
,
plot.TableECFOCF()
## Not run:
library(phenology)
# Example
data(MarineTurtles_2002)
ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002)
o_mu1p2_NB <- fitCF(x = c(mu = 4.6426989650675701,
sd = 75.828239144717074,
p1 = 0.62036295627161053,
p2 = -2.3923021862881511,
OTN = 0.33107456308054345),
data=ECFOCF_2002)
par(mar=c(4, 4, 1, 1)+0.4)
plot(o_mu1p2_NB, result="data", category=NA,
bty="n", las=1, cex.points=3, cex.axis = 0.8)
plot(o_mu1p2_NB,result="data", category=NA,
bty="n", las=1, cex.points=3, pch=NA,
col.labels = "red", show.labels=TRUE, cex.0=0.2,
show.0 = TRUE, col.0="blue", pch.0=4)
plot(o_mu1p2_NB, result="dataOCF", category=NA,
bty="n", las=1)
plot(o_mu1p2_NB, result="dataECF", category=NA,
bty="n", las=1)
plot(o_mu1p2_NB, result="CF", bty="n", las=1)
plot(o_mu1p2_NB, result="OCF", category=1, bty="n", las=1)
plot(o_mu1p2_NB, result="OCF", category=2, bty="n", las=1)
plot(o_mu1p2_NB, result="ECFOCF", bty="n", las=1)
plot(o_mu1p2_NB, result="ECFOCF0", bty="n", las=1)
plot(o_mu1p2_NB, result="ECFOCF0", category=1, bty="n", las=1)
plot(o_mu1p2_NB, result="ECFOCF0", category=2, bty="n", las=1)
plot(o_mu1p2_NB, result="Prob", category=c(1, 2), bty="n", las=1)
plot(o_mu1p2_NB, result="Prob", category=c(2, 1), bty="n", las=1)
## End(Not run)
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