fitsad-class {sads} | R Documentation |

`"fitsad"`

for maximum likelihood fitting of
species abundance distributionsThis class extends `mle2-class`

to encapsulate models of species
abundance distributions (SADs) fitted by maximum likelihood.

Objects created by a call to function `fitsad`

, which fits a
probability distribution to an abundance vector.

`sad`

:Object of class

`"character"`

; root name of the species abundance distribution fitted. See man page of`fitsad`

for available models.`distr`

:Deprecated since sads 0.2.4. See

`distr`

function`trunc`

:Object of class

`"numeric"`

; truncation value used in the fitted model. 'NA' for a non-truncated distribution.`call`

:Object of class

`"language"`

; The call to`mle2`

.`call.orig`

:Object of class

`"language"`

The call to`mle2`

, saved in its original form (i.e. without data arguments evaluated).`coef`

:Object of class

`"numeric"`

; Vector of estimated parameters.`fullcoef`

:Object of class

`"numeric"`

; Fixed and estimated parameters.`vcov`

:Object of class

`"matrix"`

; Approximate variance-covariance matrix, based on the second derivative matrix at the MLE.`min`

:Object of class

`"numeric"`

; Minimum value of objective function = minimum negative log-likelihood.`details`

:Object of class

`"list"`

; Return value from`optim`

.`minuslogl`

:Object of class

`"function"`

; The negative log-likelihood function.`method`

:Object of class

`"character"`

; The optimization method used.`data`

:Object of class

`"data.frame"`

; Data with which to evaluate the negative log-likelihood function.`formula`

:Object of class

`"character"`

; If a formula was specified, a character vector giving the formula and parameter specifications.`optimizer`

:Object of class

`"character"`

; The optimizing function used.

Class `"mle2"`

, directly.

- octavpred
`signature(object = "fitsad", sad = "missing", rad = "missing", coef = "missing", trunc = "missing", oct = "ANY", S = "missing", N = "missing")`

: expected number of species per abundance octave, see`octav`

and`octavpred`

.- plot
`signature(x = "fitsad", y = "ANY")`

: diagnostic plots of the fitted model.- show
`signature(object = "fitsad")`

: Displays object.- ppsad
`signature(x = "fitsad", sad = "missing", coef = "missing", trunc = "missing")`

: plot of observed vs predicted percentiles of the abundance distribution, details in`ppsad`

.- qqsad
`signature(x = "fitsad", sad = "missing", coef = "missing", trunc = "missing", distr = "missing")`

: plot of observed vs predicted quantiles of the abundance distribution, details in`qqsad.`

- radpred
`signature(object = "fitsad", sad = "missing", rad = "missing", coef = "missing", trunc = "missing", distr = "missing", S = "missing", N = "missing")`

: expected abundances of the 1st to n-th most abundant species, see`rad`

and`radpred`

.

Class `fitsad`

only adds three slots to class
`mle2`

. The descriptions of slots inherited from `mle2-class`

replicate those in `mle2-class`

.

Paulo I Prado prado@ib.usp.br and Murilo Dantas Miranda, after Ben Bolker and R Core Team.

this class builds on `mle2-class`

of bbmle package (Bolker
2012), which in turn builds on `mle-class`

.

Bolker, B. and R Development Core Team 2012. bbmle: Tools for general maximum likelihood estimation. R package version 1.0.5.2. http://CRAN.R-project.org/package=bbmle

`mle2-class`

for all methods available from which
`fitsad-class`

inherits; `fitsad`

for details on
fitting SADs models; `octavpred`

and
`radpred`

to get rank-abundance and
frequencies of species in octaves predicted
from fitted models.

moths.ls <- fitsad(moths, "ls") ## The class has a plot method to show diagnostic plots par(mfrow=c(2,2)) plot(moths.ls) # the same plot, but with relative abundances plot(moths.ls, prop = TRUE) par(mfrow=c(1,1)) ## Some useful methods inherited from mle2-class coef(moths.ls) confint(moths.ls) logLik(moths.ls) ## Model selection moths.ln <- fitsad(moths, "lnorm", trunc=0.5) AICctab(moths.ls, moths.ln, nobs=length(moths), base=TRUE)

[Package *sads* version 0.4.2 Index]