# GLDFitting: Fitting FMKL GLD In bda: Density Estimation for Grouped Data

## Description

To fit a FMKL GLD to raw/binned data.

## Usage

 ```1 2``` ``` fit.GLD.FMKL(x, lbound, ubound, percentile='exact', mle=FALSE) fit.GLD(x, lbound, ubound, method='chisquare') ```

## Arguments

 `x` A vector of raw data, or a histogram or binned data. `percentile` Use the exact percentiles (`exact`) or approxiated values (`approximate`). `mle` Logical. To find the MLE or not. `lbound,ubound` lower and upper bound for the support of the density. The bounds could be finite values, or positive or negative infinity. `method` Method for goodness-of-fit test.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ``` data(hhi) hmob <- binning(counts=hhi\$mob, breaks=hhi\$breaks) lmd5 <- fit.GLD.FMKL(hmob) lmd6 <- fit.GLD.FMKL(hmob, mle=TRUE) plot(lmd5) lines(lmd6, col=4) ## GOP example (handbook) -- Hahn & Sapiro (1967) ## KS-GLD based on original data: (0.0345, 0.00009604, 0.87, 4.92) ## Table 3.6-1 breaks <- c(-Inf, seq(0.015, length=10, by=0.005), Inf) counts <- c(1,9,30,44,58,45,29,17,9,4,4) rho.mid <- c(0.0325, 0.0250, 0.667, 0.600) rho.unif <- c(0.03352, 0.02531, 0.7786, 0.5009) ## histogram for chi-square test ## KS = 0.0225, p-value = 0.999. Chi=0.5176, p-value=0.7720 breaks <- c(-Inf, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05, Inf) counts <- c(40,44,58,45, 29,17,17) ```

bda documentation built on Jan. 5, 2018, 9:04 a.m.