| Inferences Weibull Mixture | R Documentation | 
Estimates parameters of a univariate Weibull mixture with k-means clustering and EM-algorithm.
eweibull_mix(data, g, lim.em = 100, criteria = "dif.psi", epsilon = 1e-05, 
             plot.it = TRUE, empirical = FALSE, col.estimated = "orange", 
             col.empirical = "navy", ...)
data | 
 vector containing the sample, or list obtained with rweibull_mix.  | 
g | 
 number of components in the mixture.  | 
lim.em | 
 limit of EM Iterations.  | 
criteria | 
 the stop criteria to be used, could be "dif.psi" to calculate differences on parameters matrix or "dif.lh" to calculate differences on Likelihood function.  | 
epsilon | 
 minimal difference value to algorithm stops.  | 
plot.it | 
 logical, TRUE to plot the histogram with estimated distribution curve.  | 
empirical | 
 logical, TRUE to add the empirical curve ("Kernel Density Estimation") in the plot.  | 
col.estimated | 
 a colour to be used in the curve of estimated density.  | 
col.empirical | 
 a colour to be used in the curve of empirical density.  | 
... | 
 further arguments and graphical parameters passed to hist.  | 
CASTRO, M. O.; MONTALVO, G. S. A.
## Generate a sample.
data = rweibull_mix(n = 1000, pi = c(0.6, 0.4), shape = c(2, 9),
                    scale = c(2, 5))
## And now, estimate the parameters, using the 'data' list.
eweibull_mix(data, g = 2)
## Or using the sample vector.
eweibull_mix(data$sample, g = 2)
## Using the diference in the log-likelihood as stop criteria.
eweibull_mix(data, g = 2, criteria = "dif.lh")
## Not plotting the graphic.
eweibull_mix(data, g = 2, plot.it = FALSE)
## Adding the empirical curve to the graphic.
eweibull_mix(data, g = 2, empirical = TRUE)
## Changing the color of the curves.
eweibull_mix(data, g = 2, empirical = TRUE, col.estimated = "pink", col.empirical = "red3")
## Using "..."
eweibull_mix(data, g = 2, empirical = TRUE, col.estimated = "pink", col.empirical = "red3",
          breaks = 300)
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