Description Usage Arguments Details Value Author(s) References See Also Examples
Method for summarizing the results from a call to the functions
emNorm
or mcmcNorm.
1 2 3 4 5 6 7 |
object |
an object of class |
show.variables |
if |
show.patterns |
if |
show.params |
if |
x |
a result from |
... |
values to be passed to the methods. |
The result from a call to emNorm
or mcmcNorm
is an object of
class "norm"
, which is a list containing results from the EM
or MCMC run. The function summary.norm
, which is
invoked through the generic method summary
, summarizes the
information contained in this object.
The result from summary.norm
is an object of
class "summary.norm"
which can be displayed or printed via
the generic method print
.
A list that includes all
the original components of obj
plus some additional summaries
that are printed via a call to the generic method print
.
These include:
x.table |
a summary of all variables appearing in the model as predictors or covariates. |
y.table |
a summary of all variables appearing in the model as responses or outcomes. |
em.summary |
a summary of the results from the EM run, including: the number of iterations; whether EM converged; and an empirical estimate of the rate of convergence which estimates the worst fraction of missing information. |
mcmc.summary |
a summary of the results from the MCMC run, including: the number of iterations; whether imputations were created and, if so, how many; and whether parameter series were saved. |
Joe Schafer Joseph.L.Schafer@census.gov
For more information about this function and other functions in
the norm2
package, see User's Guide for norm2
in the library subdirectory doc
.
1 2 3 4 5 6 7 8 | ## run EM for cholesterol data and summarize
data(cholesterol)
emResult <- emNorm(cholesterol)
summary(emResult)
## run MCMC starting from the ML estimates and summarize
mcmcResult <- mcmcNorm(emResult)
summary(mcmcResult)
|
Predictor (X) variables:
Mean SD Observed Missing Pct.Missing
CONST 1 0 28 0 0
Response (Y) variables:
Mean SD Observed Missing Pct.Missing
Y1 253.9286 47.71049 28 0 0.00000
Y2 230.6429 46.96745 28 0 0.00000
Y3 221.4737 43.18355 19 9 32.14286
Missingness patterns for response (Y) variables
(. denotes observed value, m denotes missing value)
(variable names are displayed vertically)
(rightmost column is the frequency):
YYY
123
... 19
..m 9
Method: EM
Prior: "uniform"
Convergence criterion: 1e-05
Iterations: 15
Converged: TRUE
Max. rel. difference: 8.5201e-06
-2 Loglikelihood: 615.9902
-2 Log-posterior density: 615.9902
Worst fraction missing information: 0.4617
Estimated coefficients (beta):
Y1 Y2 Y3
CONST 253.9286 230.6429 222.2371
Estimated covariance matrix (sigma):
Y1 Y2 Y3
Y1 2194.9949 1454.617 835.3973
Y2 1454.6173 2127.158 1515.4584
Y3 835.3973 1515.458 1952.2182
Method: MCMC
Prior: "uniform"
Iterations: 1000
Cycles per iteration: 1
Impute every k iterations, k = NULL
No. of imputations created: 0
series.worst present: TRUE
series.beta present: TRUE
series.sigma present: TRUE
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.