logLikplot | R Documentation |

`llplot`

plots the (log)likelihood around the estimation for distributions fitted
by maximum likelihood.

llplot(mlefit, loglik = TRUE, expansion = 1, lseq = 50, back.col = TRUE, nlev = 10, pal.col = terrain.colors(100), fit.show = FALSE, fit.pch = 4, ...)

`mlefit` |
An object of class |

`loglik` |
a logical to plot log-likelihood or likelihood function. |

`expansion` |
a expansion factor to enlarge the default range of values explored for each parameter. |

`lseq` |
length of sequences of parameters. |

`back.col` |
logical (for llsurface only). Contours are plotted with a background gradient of colors if TRUE. |

`nlev` |
number of contour levels to plot. |

`pal.col` |
Palette of colors. Colors to be used as back (for llsurface only). |

`fit.show` |
a logical to plot the mle estimate. |

`fit.pch` |
the type of point used to plot the mle estimate. |

`...` |
Further graphical arguments passed to graphical functions. |

`llplot`

plots the (log)likelihood surface(s) (or curve if there there is only one
estimated parameter) around the maximum likelihood estimation.
It internally calls function `llsurface`

and `llcurve`

. When there is more than two estimated parameters, the
(log)likehood surface is plotted for each combination of two parameters, fixing
the other ones to their estimated value.
For each (log)likelihood surface, when `back.col`

`image`

(2D-plot) is used and when `nlev > 0`

`contour`

(2D-plot) is used to add
`nlev`

contours. By default the range of values explored for each estimated
parameter is of 2 standard error around the mle estimate but this range can be expanded
(or contracted) using the argument `expansion`

.

Marie-Laure Delignette-Muller and Christophe Dutang.

Delignette-Muller ML and Dutang C (2015),
*fitdistrplus: An R Package for Fitting Distributions*.
Journal of Statistical Software, 64(4), 1-34.

See `llsurface`

and `llcurve`

for manual (log)likelihood plots (surface ou curve)
and `plot`

, `contour`

,
`image`

for classic plotting functions.

# (1) a distribution with one parameter # x <- rexp(50) fite <- fitdist(x, "exp") llplot(fite) llplot(fite, col = "red", fit.show = TRUE) llplot(fite, col = "red", fit.show = TRUE, loglik = FALSE) # (2) a distribution with two parameters # data(groundbeef) serving <- groundbeef$serving fitg <- fitdist(serving, "gamma") llplot(fitg) llplot(fitg, expansion = 2) llplot(fitg, pal.col = heat.colors(100), fit.show = TRUE) llplot(fitg, back.col = FALSE, nlev = 25, fit.show = TRUE) # (3) a distribution with two parameters with one fixed # fitg2 <- fitdist(serving, "gamma", fix.arg = list(rate = 0.5)) llplot(fitg2, fit.show = TRUE) # (4) a distribution with three parameters # data(endosulfan) ATV <-endosulfan$ATV library("actuar") fBurr <- fitdist(ATV, "burr", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) llplot(fBurr) llplot(fBurr, back.col = FALSE, fit.show = TRUE, fit.pch = 16) llplot(fBurr, nlev = 0, pal.col = rainbow(100), lseq = 100) # (5) a distribution with two parameters fitted on censored data # data(salinity) fsal <- fitdistcens(salinity, "lnorm") llplot(fsal, fit.show = TRUE) llplot(fsal, fit.show = TRUE, loglik = FALSE)

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