summary.jmdem.sim {jmdem} | R Documentation |
Summarising JMDEM Simulations
Description
These functions are all methods
for class jmdem.sim
or summary.jmdem.sim
objects.
Usage
## S3 method for class 'jmdem.sim'
summary(object, digits = max(3L, getOption("digits") - 3L),
scientific = FALSE, pvalue = 0.05,
minv.method = c("solve", "chol2inv", "ginv"),
other.call = FALSE, details = FALSE, ...)
## S3 method for class 'summary.jmdem.sim'
print(x, digits = max(3L, getOption("digits") - 3L), scientific = FALSE,
pvalue = 0.05, signif.stars = getOption("show.signif.stars"),
other.call = FALSE, details = FALSE, ...)
Arguments
object |
an object of class " |
x |
an object of class " |
digits |
the number of significant digits to use when printing. |
scientific |
logical; if |
pvalue |
a value between 0 and 1. It is used to compute the coverage proportion of the true parameter values by the simulated fits. |
minv.method |
the method used to invert matrices during the estimation process. " |
signif.stars |
logical. If |
other.call |
logical. If |
details |
logical. If |
... |
further arguments passed to or from other methods. |
Details
The arithmetric mean of the coefficients, standard errors and coverage by the confidence intervals estimated in all simulations will be listed in a table. A detail listing of each simulation's results can be provided if required by details = TRUE
. The summary also includes the averages of the Rao's score and Wald test statistics of all simulation fits.
print.summary.jmdem.sim
tries to be smart about formatting the coefficients, standard errors, etc according the number of significant digits
(default of user-specified) or the usage of scientific
notation or not.
Value
digits |
the number of significant digits to use when printing. |
scientific |
logical value of using scientific notation when printing. |
details |
logical value of printing details of each simulation. |
other.call |
logical value of printing other parameters of the simulation call. |
pvalue |
numeric value between 0 and 1 used for the computation of the true parameter coverage. |
beta.true |
user-defined vector containing the true parameter values of the mean submodel. |
lambda.true |
user-defined vector containing the true parameter values of the dispersion submodel. |
simcall |
the component from |
mformula |
the component from |
dformula |
the component from |
mfamily |
the component from |
dfamily |
the component from |
coefficients |
mean and dispersion submodel parameter coefficients fitted in each simulation saved in a |
stderr |
standard erros of all mean and dispersion submodel parameter coefficients estimated in each simulation saved in a |
iterations |
a vectror containing the running numbers of each simulation. |
confint |
confidence intervals of all mean and dispersion submodel parameter coefficients estimated in each simulation saved in a |
coverage |
the coverage of all true submodel parameters by the confidence intervals estimated in each simulation saved in a |
asymp.test |
Rao's score and Wald test statistics of each simulation saved in a |
average.summary |
Arithmetric means of the coefficients, standard errors, confidence interval converage estimated in all simulations saved in a |
average.asymp.test |
(Arithmetric means of the Rao's score and Wald test statistics estimated in all simulations saved in a |
Author(s)
Karl Wu Ka Yui (karlwuky@suss.edu.sg)
See Also
Examples
## Example in jmdem.sim(...)
sim <- jmdem.sim(mformula = y ~ x, dformula = ~ z, beta.first = TRUE,
mfamily = gaussian, dfamily = Gamma(link = "log"),
x.str = list(type = "numeric", random.func = "rnorm",
param = list(mean = 0, sd = 2)),
z.str = list(type = "factor", random.func = "runif",
param = list(min = 0, max = 2)),
beta.true = c(1.5, 4), lambda.true = c(2.5, 3, -0.2),
grad.func = TRUE, method = "BFGS", n = 50,
simnum = 10)
## Summarise simulation
summary(sim, details = FALSE, other.call = TRUE)