MLfullGB2 | R Documentation |

Performs maximum pseudo-likelihood estimation through the general-purpose optimisation function `optim`

from package `stats`

. Two methods of optimization are considered: BFGS and L-BFGS-B
(see `optim`

documentation for more details). Initial values of the parameters to be optimized over (*a*, *b*,
*p* and *q*) are given from the Fisk distribution and *p=q=1*. The function to be maximized by `optim`

is the negative of the full log-likelihood and the gradient is equal to the
negative of the scores, respectively for the case of a sample of persons and a sample of households.

ml.gb2(z, w=rep(1, length(z)), method=1, hess=FALSE) mlh.gb2(z, w=rep(1, length(z)), hs=rep(1, length(z)), method=1, hess = FALSE)

`z` |
numeric; vector of data values. |

`w` |
numeric; vector of weights. Must have the same length as |

`hs` |
numeric; vector of household sizes. Must have the same length as |

`method` |
numeric; the method to be used by |

`hess` |
logical; By default, |

Function `ml.gb2`

performs maximum likelihood estimation through the general-purpose optimization function `optim`

from package `stats`

,
based on the full log-likelihood calculated in a sample of persons. Function `mlh.gb2`

performs maximum likelihood estimation through the general-purpose optimization function `optim`

from package `stats`

, based on the full log-likelihood calculated in a sample of households.

`ml.gb2`

and `mlh.gb2`

return a list with 1 argument: `opt1`

for the output of the BFGS fit or `opt2`

for the output of the L-BFGS fit. Further values are given by the values of `optim`

.

Monique Graf

Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011)
AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and
Indicators of Poverty and Social Exclusion.
*Technical report*, AMELI-Project.

`optim`

for the general-purpose optimization and `fisk`

for the Fisk distribution.

## Not run: library(laeken) data(eusilc) # Income inc <- as.vector(eusilc$eqIncome) # Weights w <- eusilc$rb050 # Data set d <- data.frame(inc, w) d <- d[!is.na(d$inc),] # Truncate at 0 inc <- d$inc[d$inc > 0] w <- d$w[d$inc > 0] # Fit using the full log-likelihood fitf <- ml.gb2(inc, w) # Fitted GB2 parameters af <- fitf$par[1] bf <- fitf$par[2] pf <- fitf$par[3] qf <- fitf$par[4] # Likelihood flik <- fitf$value # If we want to compare the indicators # GB2 indicators indicf <- round(main.gb2(0.6,af,bf,pf,qf), digits=3) # Empirical indicators indice <- round(main.emp(inc,w), digits=3) # Plots plotsML.gb2(inc,af,bf,pf,qf,w) ## End(Not run)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.