Description Usage Arguments Details References See Also Examples
View source: R/zi_zipfpssFit.R
For a given sample of strictly positive integer numbers, usually of the type of ranking data or frequencies of frequencies data, estimates the parameters of the zero inflated Zipf-PSS distribution by means of the maximum likelihood method. The input data should be provided as a frequency matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | zi_zipfpssFit(data, init_alpha = 1.5, init_lambda = 1.5,
init_w = 0.1, level = 0.95, ...)
## S3 method for class 'zi_zipfpssR'
residuals(object, ...)
## S3 method for class 'zi_zipfpssR'
fitted(object, ...)
## S3 method for class 'zi_zipfpssR'
coef(object, ...)
## S3 method for class 'zi_zipfpssR'
plot(x, ...)
## S3 method for class 'zi_zipfpssR'
print(x, ...)
## S3 method for class 'zi_zipfpssR'
summary(object, ...)
## S3 method for class 'zi_zipfpssR'
logLik(object, ...)
## S3 method for class 'zi_zipfpssR'
AIC(object, ...)
## S3 method for class 'zi_zipfpssR'
BIC(object, ...)
|
data |
Matrix of count data in form of table of frequencies. |
init_alpha |
Initial value of α parameter (α > 1). |
init_lambda |
Initial value of λ parameter (λ > 0). |
init_w |
Initial value of w parameter (0 < w < 1). |
level |
Confidence level used to calculate the confidence intervals (default 0.95). |
... |
Further arguments to the generic functions. The extra arguments are passing to the optim function. |
object |
An object from class "zpssR" (output of zipfpssFit function). |
x |
An object from class "zpssR" (output of zipfpssFit function). |
The argument data
is a two column matrix with the first column containing the observations and
the second column containing their frequencies.
Panjer, H. H. (1981). Recursive evaluation of a family of compound distributions. ASTIN Bulletin: The Journal of the IAA, 12(1), 22-26.
Sundt, B., & Jewell, W. S. (1981). Further results on recursive evaluation of compound distributions. ASTIN Bulletin: The Journal of the IAA, 12(1), 27-39.
1 2 3 4 5 | data <- rzipfpss(100, 2.5, 1.3)
data <- as.data.frame(table(data))
data[,1] <- as.numeric(as.character(data[,1]))
data[,2] <- as.numeric(as.character(data[,2]))
obj <- zipfpssFit(data, init_alpha = 1.5, init_lambda = 1.5)
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