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    66 | \item \code{Y} {if requested (the default), the \code{y} vector used.}
       |                ^
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    67 | \item \code{W} {the weights supplied, a vector of \code{1}s if none were.}
       |                ^
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    68 | \item \code{covars} {names of the covariates in the model.}
       |                     ^
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    69 | \item \code{nobs} {number of observations.}
       |                   ^
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    70 | \item \code{covoffset} {a logical value specifying if an offset is present.}
       |                        ^
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    71 | \item \code{loglik} {the maximized log-likelihood.}
       |                     ^
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    72 | \item \code{aic} {a version of Akaike's \emph{An Information Criterion}, minus twice the maximized log-likelihood plus twice the number of parameters.}
       |                  ^
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    73 | \item \code{bic} {Bayesian Information Criterion, minus twice the maximized log-likelihood plus the number of parameters multiplied by the logarithm of the number of observations.}
       |                  ^
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    74 | \item \code{df.residual} {the residual degrees of freedom.}
       |                          ^
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    75 | \item \code{residuals} {the residuals in the final iteration of the fit.}
       |                        ^
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    76 | \item \code{coefficients} {a named vector of coefficients.}
       |                           ^
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    77 | \item \code{betaIIpars} {parameters estimates of the BetaII distribution.}
       |                         ^
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    78 | \item \code{betascoefs} {a vector of coefficients.}
       |                         ^
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    79 | \item \code{fitted.values} {the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.}
       |                            ^
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    80 | \item \code{hessian} {a symmetric matrix giving an estimate of the Hessian at the solution found in the optimization of the log-likelihood function.}
       |                      ^
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    81 | \item \code{cov} {an estimate of the covariance matrix of the model coefficients.}
       |                  ^
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    82 | \item \code{se} {a vector of the standard errors estimates of the estimated coefficients.}
       |                 ^
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    83 | \item \code{corr} {an estimate of the correlation matrix of the model coefficients.}
       |                   ^
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    84 | \item \code{code} {a code that indicates successful convergence of the fitter function used (see \code{nlm} and \code{optim} helps).}
       |                   ^
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    85 | \item \code{converged} {logical value that indicates if the optimization algorithms succesfull.}
       |                        ^
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    86 | \item \code{method} {the name of the fitter function used.}
       |                     ^
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    87 | \item \code{k} {if requested, the \code{k} value used.}
       |                ^
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    88 | \item \code{kBool} {a logical value specifying whether there is a \code{k} value or it is estimated.}
       |                    ^
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    89 | \item \code{call} {the matched call.}
       |                   ^
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    90 | \item \code{formula} {the formula supplied.}
       |                      ^
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    91 | \item \code{terms} {the \code{terms} object used.}
       |                    ^
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    92 | \item \code{data} {the \code{data} argument.}
       |                   ^
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    93 | \item \code{offset} {the offset vector used.}
       |                     ^
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    94 | \item \code{control} {the value of the \code{control} argument used.}
       |                      ^
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    95 | \item \code{method} {the name of the fitter function used.}
       |                     ^
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    96 | \item \code{contrasts} {(where relevant) the contrasts used.}
       |                        ^
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    97 | \item \code{xlevels} {(where relevant) a record of the levels of the factors used in fitting.}
       |                      ^