goodfit  R Documentation 
Fits a discrete (count data) distribution for goodnessoffit tests.
goodfit(x, type = c("poisson", "binomial", "nbinomial"), method = c("ML", "MinChisq"), par = NULL) ## S3 method for class 'goodfit' predict(object, newcount = NULL, type = c("response", "prob"), ...) ## S3 method for class 'goodfit' residuals(object, type = c("pearson", "deviance", "raw"), ...) ## S3 method for class 'goodfit' print(x, residuals_type = c("pearson", "deviance", "raw"), ...)
x 
either a vector of counts, a 1way table of frequencies of counts or a data frame or matrix with frequencies in the first column and the corresponding counts in the second column. 
type 
character string indicating: for 
residuals_type 
character string indicating the type of
residuals: either 
method 
a character string indicating whether the distribution should be fit via ML (Maximum Likelihood) or Minimum Chisquared. 
par 
a named list giving the distribution parameters (named as
in the corresponding density function), if set to 
object 
an object of class 
newcount 
a vector of counts. By default the counts stored in

... 
currently not used. 
goodfit
essentially computes the fitted values of a discrete
distribution (either Poisson, binomial or negative binomial) to the
count data given in x
. If the parameters are not specified
they are estimated either by ML or Minimum Chisquared.
To fix parameters,
par
should be a named list specifying the parameters lambda
for "poisson"
and prob
and size
for
"binomial"
or "nbinomial"
, respectively.
If for "binomial"
, size
is not specified it is not
estimated but taken as the maximum count.
The corresponding Pearson Chisquared or likelihood ratio statistic,
respectively, is computed and given with their p values by the
summary
method. The summary
method always prints this
information and returns a matrix with the printed information
invisibly. The plot
method produces a
rootogram
of the observed and fitted values.
In case of count distribtions (Poisson and negative binomial), the
minimum Chisquared approach is somewhat ad hoc. Strictly speaking,
the Chisquared asymptotics would only hold if the number of cells
were fixed or did not increase too quickly with the sample size. However,
in goodfit
the number of cells is datadriven: Each count is
a cell of its own. All counts larger than the maximal count are merged
into the cell with the last count for computing the test statistic.
A list of class "goodfit"
with elements:
observed 
observed frequencies. 
count 
corresponding counts. 
fitted 
expected frequencies (fitted by ML). 
type 
a character string indicating the distribution fitted. 
method 
a character string indicating the fitting method (can
be either 
df 
degrees of freedom. 
par 
a named list of the (estimated) distribution parameters. 
Achim Zeileis Achim.Zeileis@Rproject.org
M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.
rootogram
## Simulated data examples: dummy < rnbinom(200, size = 1.5, prob = 0.8) gf < goodfit(dummy, type = "nbinomial", method = "MinChisq") summary(gf) plot(gf) dummy < rbinom(100, size = 6, prob = 0.5) gf1 < goodfit(dummy, type = "binomial", par = list(size = 6)) gf2 < goodfit(dummy, type = "binomial", par = list(prob = 0.6, size = 6)) summary(gf1) plot(gf1) summary(gf2) plot(gf2) ## Real data examples: data("HorseKicks") HK.fit < goodfit(HorseKicks) summary(HK.fit) plot(HK.fit) data("Federalist") ## try geometric and full negative binomial distribution F.fit < goodfit(Federalist, type = "nbinomial", par = list(size = 1)) F.fit2 < goodfit(Federalist, type = "nbinomial") summary(F.fit) summary(F.fit2) plot(F.fit) plot(F.fit2)
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