summary.jmdem {jmdem} | R Documentation |
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$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)
Arguments
object |
an object of class " |
x |
an object of class " |
correlation |
logical; if |
digits |
the number of significant digits to use when printing. |
scientific |
logical; if |
symbolic.cor |
logical. If |
signif.stars |
logical. If |
... |
further arguments passed to or from other methods. |
Details
print.summary.jmdem
tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives 'significance stars' if signif.stars
is TRUE
. The coefficients
, mean.coefficients
and dispersion.coefficients
components of the result give the estimated coefficients and their estimated standard errors, together with their ratio. This third column is labelled t-ratio
and a fourth column gives the two-tailed p-value corresponding to the t-ratio
based on a Student t distribution.
Aliased coefficients are omitted in the returned object but restored by the print
method.
Correlations are printed to the same decimal places specified in digits
(or symbolically): to see the actual correlations print summary(object)$correlation
directly.
For more details, see summary.glm
.
Value
call |
the component from |
mean.family |
the component from |
dispersion.family |
the component from |
deviance |
the component from |
mean.terms |
the component from |
dispersion.terms |
the component from |
aic |
the component from |
mean.contrasts |
the component from |
dispersion.contrasts |
the component from |
df.residual |
the component from |
null.deviance |
the component from |
df.null |
the component from |
information.type |
the component from |
iter |
the component from |
mean.na.action |
the component from |
dispersion.na.action |
the component from |
deviance.resid |
the deviance residuals. |
pearson.resid |
the pearson residuals. |
resid |
the working residuals depends on the setting of |
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 " |
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 (karlwuky@suss.edu.sg)
See Also
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)