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()))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.
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.
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.
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.
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.
a (non-empty) object containing column name for the ID column.
a (non-empty) character naming a gamlss family.
a (non-empty) character object with the name of the distribution.
a (non-empty) vector of link characters naming functions with the following items: muLink, sigmaLink, nuLink, and tauLink.
a (non-empty)gamlss function for calculating quantiles, this should match the distribution in distribution.
a (non-empty) numeric vectors of the desired quantiles, these are used in calculating metrics.
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.
an optional lists modifying gamlss.control.
an optional logical value indicating whether to save the quantiles.
an optional numerical value for the starting value of mu.
an optional numerical value for the starting value of sigma.
an optional numerical value for the starting value of nu.
an optional numerical value for the starting value of tau.
an logical value indicating whether mu is fixed or is free to vary during the fitting process.
an logical value indicating whether sigma is fixed or is free to vary during the fitting process.
an logical value indicating whether nu is fixed or is free to vary during the fitting process.
an logical value indicating whether tau is fixed or is free to vary during the fitting process.
a vector of logical values indicating whether each of the four parameters is free == TRUE or fixed == FALSE.
a logical indicating whether a smart starting place should be chosen, this applies only when fitting the GB2 distribution.
a time stamp.
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)
Fits a GAMLSS and estimates a number of metrics, see value.
FIXME - references
# NOT RUN {
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')
# }
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