goodfit {vcd} | R Documentation |
Fits a discrete (count data) distribution for goodness-of-fit 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"), ...)
x |
either a vector of counts, a 1-way 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 |
a character string indicating which distribution should be
fit (for goodfit ) or indicating the type of prediction
(fitted response or probabilities in predict ) respectively. |
method |
a character string indicating whether the distribution should be fit via ML (Maximum Likelihood) or Minimum Chi-squared. |
par |
a named list giving the distribution parameters (named as
in the corresponding density function), if set to NULL , the
default, the parameters are estimated. If the parameter size
is not specified if type is "binomial" it is taken to
be the maximum count. If type is "nbinomial" , then
parameter size can be specified to fix it so that only the
parameter prob will be estimated (see the examples below). |
object |
an object of class "goodfit" . |
newcount |
a vector of counts. By default the counts stored in
object are used, i.e., the fitted values are computed. These
can also be extracted by fitted(object) . |
... |
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 Chi-squared.
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 Chi-squared or likelihood ratio statistic
respectively is computed and given with their p values by the
summary
method. The plot
method produces a
rootogram
of the observed and fitted values.
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 "ML" , "MinChisq" or "fixed" if the
parameters were specified). |
df |
degrees of freedom. |
par |
a named list of the (estimated) distribution parameters. |
Achim Zeileis Achim.Zeileis@R-project.org
M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.
## 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)