print.secr | R Documentation |
Print results from fitting a spatially explicit capture–recapture model or generate a list of summary values.
## S3 method for class 'secr'
print(x, newdata = NULL, alpha = 0.05, deriv = FALSE, call = TRUE, ...)
## S3 method for class 'secr'
summary(object, newdata = NULL, alpha = 0.05, deriv = FALSE, ...)
x |
|
object |
|
newdata |
optional dataframe of values at which to evaluate model |
alpha |
alpha level |
deriv |
logical for calculation of derived D and esa |
call |
logical; if TRUE the call is printed |
... |
other arguments optionally passed to derived.secr |
Results from print.secr
are potentially complex and depend upon the analysis (see
below). Optional newdata
should be a dataframe with a column for
each of the variables in the model. If newdata
is missing then a
dataframe is constructed automatically. Default newdata
are for
a naive animal on the first occasion; numeric covariates are set to zero
and factor covariates to their base (first) level. Confidence intervals
are 100 (1 – alpha) % intervals.
call | the function call (optional) |
version,time | secr version, date and time fitting started, and elapsed time |
Detector type | `single', `multi', `proximity' etc. |
Detector number | number of detectors |
Average spacing | |
x-range | |
y-range | |
New detector type | as fitted when details$newdetector specified |
N animals | number of distinct animals detected |
N detections | number of detections |
N occasions | number of sampling occasions |
Mask area | |
Model | model formula for each `real' parameter |
Fixed (real) | fixed real parameters |
Detection fn | detection function type (halfnormal or hazard-rate) |
N parameters | number of parameters estimated |
Log likelihood | log likelihood |
AIC | Akaike's information criterion |
AICc | AIC with small sample adjustment (Burnham and Anderson 2002) |
Beta parameters | coef of the fitted model, SE and confidence intervals |
vcov | variance-covariance matrix of beta parameters |
Real parameters | fitted (real) parameters evaluated at base levels of covariates |
Derived parameters | derived estimates of density and mean effective sampling area (optional) |
Derived parameters (see derived
) are computed only if
deriv = TRUE
.
The summary
method constructs a list of outputs similar to those printed by the print
method, but somewhat more concise and re-usable:
versiontime | secr version, and date and time fitting started |
traps | detector summary |
capthist | capthist summary |
mask | mask summary |
modeldetails | miscellaneous model characteristics (CL etc.) |
AICtable | single-line output of AIC.secr |
coef | table of fitted coefficients with CI |
predicted | predicted values (`real' parameter estimates) |
derived | output of derived.secr (optional) |
Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Second edition. New York: Springer-Verlag.
AIC.secr
, secr.fit
## load & print previously fitted null (constant parameter) model
print(secrdemo.0)
summary(secrdemo.0)
## combine AIC tables from list of summaries
do.call(AIC, lapply(list(secrdemo.b, secrdemo.0), summary))
## Not run:
print(secrdemo.CL, deriv = TRUE)
## End(Not run)
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