Description Usage Arguments Details Value See Also Examples
Uses optim
to obtain a MLE for the hazard function and animal
perpendicular distance distribution. Functional forms for the hazard and perpendicular distance
distribution must be specified.
1 2 3 |
y |
forward distance observations |
x |
perpendicular distance observations |
b |
two to four element vector of hazard rate parameters, some of which may be logged |
hr |
hazard rate function |
ystart |
max forward distance at which could possibly detect animal (see details). |
pi.x |
perpendicular distance density distribution |
logphi |
parameters for pi.x (some maybe logged) |
w |
perpendicular truncation distance. |
method |
optimisation method to be used by |
lower, |
upper Bounds for parameters for use with methods such as |
control |
see |
itnmax |
maximum number of iterations for the |
hessian |
return hessian. See also |
... |
arguments to be passed into |
corrFlag=0.7 |
Absolute parameter correlation value above which a warning is issued. |
Must to ensure the hazard function has decayed to (very close to) zero by
ystart
.
optim
fit object and
$hr
= hazard rate function used.
$pi.x
= perpendicular distance function used.
$ystart
= ystart max forward distance detection used.
$w
= perpendicular truncation distance used.
$b
= estimated hazard parameters
$dat
= data frame with data ($x
and $y
)
$logphi
AIC
AIC value
And if hessian=TRUE
:
vcov
variance covariance matrix. Will warn if there is a problem inverting
the hessian.
CVpar
Coefficient of variation for each paramter estimate.
error
Boolean, TRUE
if convergence!=0 or problem inverting the hessian,
or parameter correlation is exceeded.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ## Not run:
#Data preparation:
w=0.03;ystart=0.05
library(xlsx)
dat=read.xlsx("~/Dropbox/packages/LT2D/data/Jantho Primate Line.xlsx",1)
x=dat$PP.Distance
y=dat$Forward.Distance
nas=which(is.na(y))
x=x[-nas]
y=y[-nas]
gtw=which(x>w)
x=x[-gtw]
y=y[-gtw]
#Example model fits:
b=c(-7.3287948, 0.9945317)
logphi=c(.01646734, -4.67131261)
fit.n.optx=NULL
fit.n.optx=fityxOptimx(y,x,b=b,
hr=h1,ystart=ystart,
control=list(trace=5),hessian=TRUE,
pi.x=pi.norm,logphi=logphi,w=w)
b=c(-7.3329141,0.9948721)
logphi=c(-0.05,-4.7)
fit.chn.optx=NULL
fit.chn.optx=fityxOptimx(y,x,b=b,hr=h1,ystart=ystart,
pi.x=pi.chnorm,logphi=logphi,w=w,itnmax=5000,
hessian=TRUE,control=list(trace=5))
b=c(5.2919208, -0.2205593, 8.4701307)
logphi=c(0.01784102, -4.42209067)
fit.n.ip1.optx=NULL
fit.n.ip1.optx=fityxOptimx(y,x,b=b,hr=ip1,ystart=ystart,
pi.x=pi.norm,logphi=logphi,w=w,
hessian=TRUE,control=list(trace=6))
## End(Not run)
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