fitFunc {binequality} | R Documentation |

This function fits a parametric distribution binned data. The data are subdivided using ID.

fitFunc(ID, hb, bin_min, bin_max, obs_mean, ID_name, distribution = "LOGNO", distName = "LNO", links = c(muLink = "identity", sigmaLink = "log", nuLink = NULL, tauLink = NULL), qFunc = qLOGNO, quantiles = seq(0.006, 0.996, length.out = 1000), linksq = c(identity, exp, NULL, NULL), con = gamlss.control(c.crit=0.1,n.cyc=200, trace=FALSE), saveQuants = FALSE, muStart = NULL, sigmaStart = NULL, nuStart = NULL, tauStart = NULL, muFix = FALSE, sigmaFix = FALSE, nuFix = FALSE, tauFix = FALSE, freeParams = c(TRUE, TRUE, FALSE, FALSE), smartStart = FALSE, tstamp = as.numeric(Sys.time()))

`ID` |
a (non-empty) object containing the group ID for each row. Importantly, ID, bh, bin_min, bin_max, and obs_mean MUST be the same length and be in the SAME order. |

`hb` |
a (non-empty) object containing the number of observations in each bin. Importantly, ID, bh, bin_min, bin_max, and obs_mean MUST be the same length and be in the SAME order. |

`bin_min` |
a (non-empty) object containing the lower bound of each bin. Currently, this package cannot handle data with open lower bounds. Importantly, ID, bh, bin_min, bin_max, and obs_mean MUST be the same length and be in the SAME order. |

`bin_max` |
a (non-empty) object the upper bound of each bin. Currently, this package can only handle the upper-most bin being open ended. Importantly, ID, bh, bin_min, bin_max, and obs_mean MUST be the same length and be in the SAME order. |

`obs_mean` |
a (non-empty) object containing the mean for each group. Importantly, ID, bh, bin_min, bin_max, and obs_mean MUST be the same length and be in the SAME order. |

`ID_name` |
a (non-empty) object containing column name for the ID column. |

`distribution` |
a (non-empty) character naming a gamlss family. |

`distName` |
a (non-empty) character object with the name of the distribution. |

`links` |
a (non-empty) vector of link characters naming functions with the following items: muLink, sigmaLink, nuLink, and tauLink. |

`qFunc` |
a (non-empty)gamlss function for calculating quantiles, this should match the distribution in distribution. |

`quantiles` |
a (non-empty) numeric vectors of the desired quantiles, these are used in calculating metrics. |

`linksq` |
a (non-empty) vector of functions, which undue the link functions. For example, if muLink = log, then the first entry in linksq should be exp. If you are using an indentity link function in links, then the corresponding entry in linksq should be indentity. |

`con` |
an optional lists modifying gamlss.control. |

`saveQuants` |
an optional logical value indicating whether to save the quantiles. |

`muStart` |
an optional numerical value for the starting value of mu. |

`sigmaStart` |
an optional numerical value for the starting value of sigma. |

`nuStart` |
an optional numerical value for the starting value of nu. |

`tauStart` |
an optional numerical value for the starting value of tau. |

`muFix` |
an logical value indicating whether mu is fixed or is free to vary during the fitting process. |

`sigmaFix` |
an logical value indicating whether sigma is fixed or is free to vary during the fitting process. |

`nuFix` |
an logical value indicating whether nu is fixed or is free to vary during the fitting process. |

`tauFix` |
an logical value indicating whether tau is fixed or is free to vary during the fitting process. |

`freeParams` |
a vector of logical values indicating whether each of the four parameters is free == TRUE or fixed == FALSE. |

`smartStart` |
a logical indicating whether a smart starting place should be chosen, this applies only when fitting the GB2 distribution. |

`tstamp` |
a time stamp. |

Fits a GAMLSS and estimates a number of metrics, see value.

returns a list with 'datOut' a data.frame with the IDs, observer mean, distribution, estimated mean, variance, coefficient of variation, cv squared, gini, theil, MLD, aic, bic, the results of a convergence test, log likelihood, number of parameters, median, and std. deviation; 'timeStamp' a time stamp; 'parameters' the estiamted parameter; and 'quantiles' the quantile estimates if saveQuants == TRUE)

FIXME - references

data(state_bins) use_states <- which(state_bins[,'State'] == 'Texas' | state_bins[,'State'] == 'California') ID <- state_bins[use_states,'State'] hb <- state_bins[use_states,'hb'] bmin <- state_bins[use_states,'bin_min'] bmax <- state_bins[use_states,'bin_max'] omu <- rep(NA, length(use_states)) fitFunc(ID = ID, hb = hb, bin_min = bmin, bin_max = bmax, obs_mean = omu, ID_name = 'State')

[Package *binequality* version 1.0.4 Index]