summary.jmdem {jmdem}

Summarising Joint Mean and Dispersion Effects Model Fits

Description

These functions are all methods for class jmdem or summary.jmdem objects.

Usage

## S3 method for class 'jmdem'
summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)
              
## S3 method for class 'summary.jmdem'
print(x, digits = max(3L, getOption("digits") - 3L), 
      scientific = FALSE, symbolic.cor = x$symbolic.cor, 
      signif.stars = getOption("show.signif.stars"), ...)

Arguments

object

an object of class "jmdem", usually, a result of a call to jmdem.

x

an object of class "summary.jmdem", usually, a result of a call to summary.jmdem.

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

digits

the number of significant digits to use when printing.

scientific

logical; if TRUE, scientific notation is used when printing.

symbolic.cor

logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.

signif.stars

logical. If TRUE, 'significance stars' are printed for each coefficient.

...

further arguments passed to or from other methods.

Details

print.summary.jmdem tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives 'significance stars' if signif.stars is TRUE. The coefficients, mean.coefficients and dispersion.coefficients components of the result give the estimated coefficients and their estimated standard errors, together with their ratio. This third column is labelled t-ratio and a fourth column gives the two-tailed p-value corresponding to the t-ratio based on a Student t distribution.

Aliased coefficients are omitted in the returned object but restored by the print method.

Correlations are printed to the same decimal places specified in digits (or symbolically): to see the actual correlations print summary(object)$correlation directly.

For more details, see summary.glm.

Value

call

the component from object.

mean.family

the component from object.

dispersion.family

the component from object.

deviance

the component from object.

mean.terms

the component from object.

dispersion.terms

the component from object.

aic

the component from object.

mean.contrasts

the component from object.

dispersion.contrasts

the component from object.

df.residual

the component from object.

null.deviance

the component from object.

df.null

the component from object.

information.type

the component from object.

iter

the component from object.

mean.na.action

the component from object.

dispersion.na.action

the component from object.

deviance.resid

the deviance residuals.

pearson.resid

the pearson residuals.

resid

the working residuals depends on the setting of deviance.type.

coefficients

the matrix of coefficients, standard errors, z-values and p-values. Aliased coefficients are omitted.

mean.coefficients

the matrix of coefficients, standard errors, z-values and p-values of the mean submodel.

dispersion.coefficients

the matrix of coefficients, standard errors, z-values and p-values of the dispersion submodel.

deviance.type

the type of redidual deviance specified, it is either "deviance" or "pearson".

aliased

named logical vector showing if the original coefficients are aliased.

df

a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of coefficients (including aliased ones).

covariance

the estimated covariance matrix of the estimated coefficients.

digits

the number of significant digits to use when printing.

scientific

logical value of using scientific notation when printing.

covmat.method

named method used to invert the covariance matrix.

correlation

(only if correlation is true.) The estimated correlations of the estimated coefficients.

symbolic.cor

(only if correlation is true.) The value of the argument symbolic.cor.

Author(s)

Karl Wu Ka Yui ([e-mail])

See Also

jmdem, summary

Examples

## Example in jmdem(...)
MyData <- simdata.jmdem.sim(mformula = y ~ x, dformula = ~ z, 
                            mfamily = poisson(), 
                            dfamily = Gamma(link = "log"), 
                            beta.true = c(0.5, 4), 
                            lambda.true = c(2.5, 3), n = 100)
                            
fit <- jmdem(mformula = y ~ x, dformula = ~ z, data = MyData, 
             mfamily = poisson, dfamily = Gamma(link = "log"), 
             dev.type = "deviance", method = "CG")
              
## Summarise fit with correlation matrix
summary(fit, correlation = TRUE, digits = 4)