Title: | Split-Population Duration (Cure) Regression |
Version: | 0.17.2 |
Description: | An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989). |
Encoding: | UTF-8 |
Depends: | R (≥ 3.1.2) |
License: | GPL-3 |
LazyData: | true |
Imports: | corpcor, graphics, forecast, MASS, stats, Rcpp (≥ 0.11.0), separationplot, xtable |
Suggests: | covr, devtools, testthat, knitr, rmarkdown, tibble |
LinkingTo: | Rcpp, RcppArmadillo |
RoxygenNote: | 7.3.1 |
VignetteBuilder: | knitr |
URL: | https://github.com/andybega/spduration, https://www.andybeger.com/spduration/ |
BugReports: | https://github.com/andybega/spduration/issues |
NeedsCompilation: | yes |
Packaged: | 2024-02-08 09:42:36 UTC; andybega |
Author: | Andreas Beger |
Maintainer: | Andreas Beger <adbeger@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2024-02-08 10:10:02 UTC |
Split-Population Duration (Cure) Regression Models
Description
The spduration package provides functions to estimate split-population duration regression models in which only a subset of the population is at risk for failure, while the remainder is immune, or cured, from the possibility of experiencing a failure event. In practice, this class of models also may produce better performance in sparse data with few actual failure events.
Details
The main function spdur
is used to estimate the model
objects with class spdur
.
Postestimation tools include predict.spdur
, for calculating
fitted values with arbitrary data and for several probabilities that
might be of interest, as well as plot.spdur
for visual
display of model fit.
Author(s)
Maintainer: Andreas Beger adbeger@gmail.com (ORCID)
Authors:
Daina Chiba dchiba@essex.ac.uk (ORCID)
Daniel W. Hill, Jr. dwhill@uga.edu
Nils W. Metternich n.metternich@ucl.ac.uk (ORCID)
Shahryar Minhas hermes829@gmail.com
Michael D. Ward michael.don.ward@gmail.com (ORCID) [copyright holder]
References
Boag, J.W. 1949. “Maximum Likelihood Estimates of the Proportion of Patients Cured by Cancer Therapy.” <https://www.jstor.org/stable/2983694>
Berkson, J. and Gage, R.P. “Survival Curve for Cancer Patients Following Treatment.” <https://www.jstor.org/stable/2281318>
Leisch, Friedrich. 2009. “Creating R Packages: A Tutorial.”
Schmidt, Peter and Witte, Ann Dryden. 1989. “Predicting Criminal Recidivism Using "Split Population" Survival Time Models.” <doi:10.1016/0304-4076(89)90034-1>
Svolik, Milan. 2008. “Authoritarian Reversals and Democratic Consolidation.” American Political Science Review.
See Also
Useful links:
Report bugs at https://github.com/andybega/spduration/issues
AIC method for spdur
Description
Computes the Akaike Information Criterion for an spdur
class object.
Usage
## S3 method for class 'spdur'
AIC(object, ..., k = 2)
Arguments
object |
An object of class |
... |
Optional arguments. |
k |
The penalty parameter, by default 2. For |
See Also
link{AIC}
, link{BIC.spdur}
Examples
data(model.coups)
AIC(model.coups)
BIC method for spdur
Description
Computes the Bayesian Information Criterion for an spdur
class object.
Usage
## S3 method for class 'spdur'
BIC(object, ...)
Arguments
object |
An object of class |
... |
Optional arguments. |
Details
Computed as AIC(object, k = log(nobs(object)))
.
See Also
Examples
data(model.coups)
BIC(model.coups)
Accessor methods for spdur Objects
Description
Several standard accessor methods for a spdur
class object.
Usage
## S3 method for class 'spdur'
logLik(object, ...)
## S3 method for class 'spdur'
nobs(object, ...)
## S3 method for class 'spdur'
coef(object, model = c("full", "duration", "risk", "distr"), ...)
## S3 method for class 'spdur'
vcov(object, model = c("full", "duration", "risk", "distr"), ...)
## S3 method for class 'spdur'
model.matrix(object, model = c("duration", "risk"), ...)
## S3 method for class 'spdur'
terms(x, model = c("duration", "risk"), ...)
Arguments
object |
an object inheriting from class |
... |
not used |
model |
return full model, or only duration or risk equations, or distribution parameters. |
x |
|
See Also
Examples
data(model.coups)
logLik(model.coups)
nobs(model.coups)
coef(model.coups)
vcov(model.coups)
head(model.matrix(model.coups))
terms(model.coups)
Add duration variables to panel data
Description
Builds a duration version of a data frame representing panel data.
Usage
add_duration(
data,
y,
unitID,
tID,
freq = "month",
sort = FALSE,
ongoing = TRUE,
slice.last = FALSE
)
Arguments
data |
Data frame representing panel data. |
y |
A binary indicator of the incidence of some event, e.g. a coup. |
unitID |
Name of the variable in the data frame identifying the
cross-sectional units, e.g. |
tID |
Name of the variable in the data frame identifying the time unit,
preferably as class |
freq |
Frequency at which units are measured in |
sort |
Sort data by unit and time? Default is |
ongoing |
If |
slice.last |
Set to |
Details
This function processes a panel data frame by creating a failure
variable from y
and corresponding duration counter, as well as
risk/immunity indicators. Supported time resolutions are year, month, and
day, and input data should be (dis-)aggregated to one of these levels.
The returned data frame should have the same number of rows at the original.
If y
is an indicator of the incidence of some event, rather than an
onset indicator, then ongoing spells of failure beyond the initial event are
coded as NA (e.g. 000111 becomes a spell of 0001 NA NA). This is to preserve
compatibility with the base dataset. Note that the order of rows may be
different though.
There cannot be missing values ("NA
") in any of the key variables
y
, unitID
, or tID
; they will stop the function.
Furthermore, series that start with an event, e.g. (100), are treated as
experiencing failure in the first time period. If those events are in fact
ongoing, e.g. the last year of a war that started before the start time of
the dataset, they should be dropped manually before using
buildDuration()
.
t.0
is the starting time of the period of observation at tID
.
It is by default set as duration - 1
and currently only serves as a
placeholder to allow future expansion for varying observation times.
Value
Returns the original data frame with 8 duration-specific additional variables:
failure |
Binary indicator of an event. |
ongoing |
Binary indicator for ongoing events, not counting the initial failure time. |
end.spell |
Binary indicator for the last observation in a spell, either due to censoring or failure. |
cured |
Binary indicator for spells that are coded as cured, or immune
from failure. Equal to 1 - |
atrisk |
Binary indicator for spells that are coded as at risk for
failure. Equal to 1 - |
censor |
Binary indicator for right-censored spells. |
duration |
|
t.0 |
Starting time for period observed during |
See Also
panel_lag
for lagging variables in a panel data frame
before building duration data.
Examples
# Yearly data
data <- data.frame(y=c(0,0,0,1,0),
unitID=c(1,1,1,1,1),
tID=c(2000, 2001, 2002, 2003, 2004))
dur.data <- add_duration(data, "y", "unitID", "tID", freq="year")
dur.data
Convert spdur results to summary data frame
Description
table
-like function for class “spdur
”.
Usage
## S3 method for class 'spdur'
as.data.frame(x, row.names = TRUE, optional = FALSE, ...)
Arguments
x |
An object with class |
row.names |
Indicates whether parameter names should be added as row names to the data frame returned, or as a separate column with blank row row names. |
optional |
Not used |
... |
Not used. |
Details
This will create a data frame containing the estimated coefficients and standard errors for the risk and duration equations of a split-population duration model. It's intended purpose is to help create larger tables combining several model results.
Value
An data frame with model coefficients and p-values.
See Also
xtable.spdur
for formatting a single model to
Latex output.
Examples
data(model.coups)
data.frame(model.coups)
Attempt to convert to R date format
Description
For internal use only
Usage
attempt_date(date, by)
Arguments
date |
character or numeric |
by |
character, "year", "month", or "day" |
B&S 2003 coup data
Description
Replication data from Belkin and Schofer's 2003 paper on coups.
Usage
bscoup
Format
A data frame with 5463 observations of 14 variables:
countryid
Gleditsch and Ward country codes.
year
Year
couprisk
Structural coup risk index, see paper for details.
recentcoups
Alternative coup risk measure, running count of coups in past 10 years.
rwar
Country participated in war in past 10 years.
milreg
1=Military regime, 0=other
wealth
log of GDP per capita
instab
Domestic instability and violence.
coup
Indicator for successful coup.
africa
Indicator for countries in Africa.
eurnam
Indicator for countries in Europe and N. America.
samerica
Indicator for countries in South America.
camerica
Indicator for countries in Central America.
regconf
Regional conflict.
Source
Belkin, Aaron and Evan Schofer. 2003. “Toward a structural understanding of coup risk.” Journal of Conflict Resolution Vol. 47 No. 5.
Examples
data(bscoup)
table(bscoup$coup)
range(bscoup$year)
Global coups, 1979 to 2010
Description
Data on global coups from 1979 to 2010 from Powell & Thyne
Usage
coups
Format
A data frame with 5828 observations of 9 variables:
gwcode
Gleditsch and Ward country codes.
year
Year, in date format.
coup1
succ.coup
Successful coup, 0/1.
democ
Polity democracy score (0-10).
autoc
Polity autocracy score (0-10).
polity
Polity score (democ-autoc).
polity2
Polity score with correction for regime transitions.
regtrans
Regime transitions.
Source
Powell, Jonathan M. and Clayton L. Thyne. “Global instances of coups from 1950 to 2010: A new dataset.” Journal of Peace Research Vol. 48 No. 2.
Gleditsch, Kristian S. and Michael D. Ward. 1999. “Interstate System Membership: A Revised List of the Independent States since 1816." International Interactions 25.
Examples
data(coups)
table(coups$succ.coup)
Expand call to full names.
Description
Return a call in which all of the arguments which were supplied or have presets are specified by their full names and supplied or default values.
Usage
expand.call(
definition = NULL,
call = sys.call(sys.parent(1)),
expand.dots = TRUE,
eval = FALSE
)
Arguments
definition |
a function. See |
call |
an unevaluated call to the function specified by definition.
See |
expand.dots |
logical. Should arguments matching ... in the call be
included or left as a ... argument? See |
eval |
logical, defaults to TRUE. Should function arguments be evaluated in the returned call or not? |
Value
An object of class call.
Author(s)
fabians
See Also
Forecast from a spdur model
Description
forecast
method for spdur
class objects.
Usage
## S3 method for class 'spdur'
forecast(
object,
...,
pred.data = NULL,
stat = "conditional hazard",
n.ahead = 6
)
Arguments
object |
A |
... |
Optional arguments, not used. |
pred.data |
Data on which to base forecasts, i.e. slice of last time unit's observations for all cross-sectional units. |
stat |
Which statistic to forecast, see |
n.ahead |
How many time periods to predict ahead. Default is 6. |
Details
This function will create out-of-sample predictions of “stat”
using model estimates and the prediction data provided. It is assumed that
prediction data consist of a slice of the last time period observed for
the data used to estimate the model in object
. For each row,
forecast.spdur
will estimate the model predictions for that time point
and then extrapolate the resulting probability to n.ahead
time
periods using appropriate probability theory.
For situations in which the covariate values are known for future time
periods, e.g. in a test sample use predict.spdur
instead.
Examples
library(forecast)
data(coups)
data(model.coups)
coups.dur <- add_duration(coups, "succ.coup", "gwcode", "year", freq="year")
pred.data <- coups.dur[coups.dur$year==max(coups.dur$year), ]
pred.data <- pred.data[complete.cases(pred.data), ]
fcast <- forecast(model.coups, pred.data=pred.data)
Calculate hazard function values
Description
Calculate hazard function values
Usage
hazard(ti, lambda, cure, alpha, out, dist)
Arguments
ti |
Vector of duration values over which to evaluate the hazard function. |
lambda |
density function parameter |
cure |
density function parameter |
alpha |
density function parameter |
out |
not used |
dist |
"weibull" or "loglog" |
Regular Log-logistic regression
Description
Regular Log-logistic regression
Usage
loglog(Y, X, inits = NULL, max.iter, silent = TRUE)
Arguments
Y |
Dependent variables. |
X |
Duration equation covariates. |
inits |
Vector of starting values. |
max.iter |
Maximum iterations to try. |
silent |
Suppress |
Regular Log-logistic likelihood
Description
Regular Log-logistic likelihood
Usage
loglog_lnl(theta, y, X)
Arguments
theta |
Current parameter values. |
y |
Dependent variables. |
X |
Duration equation covariates. |
Model of global coups from 1979 to 2010
Description
This is a model object for a split-duration model of the Powell & Thyne coups. It is used in several example code sections to speed up package testing by eliminating the need to re-estimate a model each time.
Usage
model.coups
Format
An object of class spdur
.
Source
For information on the data used in this model, see the data documentation,
coups
.
Examples
data(model.coups)
str(model.coups)
Lag panel data
Description
A function that correctly lags panel data where units are identified by
id
and time periods are identified with t
. Results are in same
order as data
and are padded with NA
as needed.
Usage
panel_lag(x, id, t, lag = 1, data = NULL)
Arguments
x |
String identifying the vectors to be lagged in |
id |
String identifying the unit (e.g. country) identifier in
|
t |
String identifying the time identifier in |
lag |
Lag order, i.e. by how many time periods should |
data |
A data frame. If not provided, a new one will be constructed with the vectors supplied for the other parameters. |
Value
A vector of same length as x
representing lagged values with
leading NA
's.
Examples
data(coups)
# No need to order before using panelLag, just do it here so we can compare results below.
coups <- coups[order(coups$gwcode, coups$year), ]
test <- panel_lag("polity2", "gwcode", "year", data=coups)
# Compare output
head(coups$polity2)
head(test)
Plot split-duration model results.
Description
Plot results from a spduration model. Two types are currently implemented: a separation plot for evaluating model predictions ("sepplot"), and a plot of the conditional hazard rate ("hazard"), with or without simulation-based confidence intervals.
Usage
## S3 method for class 'spdur'
plot(x, type = "sepplot", ci = TRUE, ...)
Arguments
x |
An object of class " |
type |
What kind of plot? "sepplot" or "hazard". |
ci |
For plots of the hazard rate, should a confidence interval be included? |
... |
Optional parameters passed to |
See Also
Examples
# get model estimates
data(model.coups)
# plot
plot(model.coups, type = "hazard")
plot(model.coups)
Plot hazard function
Description
plot_hazard
plots the shape of estimated hazard function in respect
to duration, given a set of values for the duration and risk equations
covariates. Confidence intervals are provided through simulation.
Usage
plot_hazard(x, t = NULL, ci = TRUE, n = 1000, xvals = NULL, zvals = NULL, ...)
Arguments
x |
An object of class |
t |
Time values at which to evaluate hazard function, e.g. |
ci |
Compute simulation-based confidence interval? |
n |
Number of simulations to use for CI, defaults to 1,000. |
xvals |
A vector of values for the duration equation variables, in the
same order as the duration equation in |
zvals |
A vector of values for the risk equation variables, in the same
order as the risk equation in |
... |
Additional parameters passed to |
See Also
Examples
# Get model estimates
data(model.coups)
# Plot
plot_hazard(model.coups, ci = FALSE)
plot_hazard(model.coups, ci = TRUE)
Plot conditional hazard rate
Description
Plot hazard function without simulated confidence intervals. See
plot_hazard
instead.
Usage
plot_hazard1(x, ...)
Arguments
x |
class "spdur" object |
... |
passed to |
Value
NULL, plots.
Simulate and plot hazard function
Description
Plot hazard function with simulated confidence intervals. See
plot_hazard
instead.
Usage
plot_hazard2(x, ...)
Arguments
x |
class "spdur" object |
... |
passed to |
Value
NULL, plots.
Predict methods for spdur Objects
Description
predict
and related methods for class “spdur
”.
Usage
## S3 method for class 'spdur'
predict(
object,
newdata = NULL,
type = "response",
truncate = TRUE,
na.action = na.exclude,
...
)
## S3 method for class 'spdur'
fitted(object, ...)
## S3 method for class 'spdur'
residuals(object, type = c("response"), ...)
Arguments
object |
Object of class “ |
newdata |
Optional data for which to calculate fitted values, defaults to training data. |
type |
Quantity of interest to calculate. Default conditional hazard,
i.e. conditioned on observed survival up to time |
truncate |
For conditional hazard, truncate values greater than 1. |
na.action |
Function determining what should be done with missing values
in newdata. The default is to predict NA ( |
... |
not used, for compatibility with generic function. |
Details
Calculates various types of probabilities, where “conditional” is used in
reference to conditioning on the observed survival time of a spell up to
time t
, in addition to conditioning on any variables included in the
model (which is always done). Valid values for the type
option
include:
“conditional risk”:
Pr(Cure=0|Z\gamma, T>t)
“conditional cure”:
Pr(Cure=1|Z\gamma, T>t)
“hazard”:
Pr(T=t|T>t, C=0, X\beta) * Pr(Cure=0|Z\gamma)
“failure”:
Pr(T=t|T>t-1, C=0, X\beta) * Pr(Cure=0|Z\gamma)
“unconditional risk”:
Pr(Cure=0|Z\gamma)
“unconditional cure”:
Pr(Cure=1|Z\gamma)
“conditional hazard” or “response”:
Pr(T=t|T>t, C=0, X\beta) * Pr(Cure=0|Z\gamma, T>t)
“conditional failure”:
Pr(T=t|T>t-1, C=0, X\beta) * Pr(Cure=0|Z\gamma, T>t)
The vector Z\gamma
indicates the cure/at risk equation
covariate vector, while X\beta
indicates the duration equation
covariate vector.
Value
Returns a data frame with 1 column corresponding to type
, in the same
order as the data frame used to estimate object
.
Note
See forecast.spdur
for producing forecasts when future
covariate values are unknown.
Examples
# get model estimates
data(model.coups)
ch <- predict(model.coups)
head(fitted(model.coups))
head(residuals(model.coups))
Print a split-population duration model results summary
Description
print
method for class “summary.spdur
”.
Usage
## S3 method for class 'summary.spdur'
print(x, ...)
Arguments
x |
An object with class |
... |
Further arguments passed to or from other methods. |
Details
Formats spdur
summaries for printing.
See Also
The model fitting function is spdur
, and see
summary.spdur
for associated summary method.
Examples
data(model.coups)
s <- summary(model.coups)
class(s)
print(s)
Generate a Separation Plot
Description
A separationplot
wrapper for class
“spdur
”.
Usage
sepplot(
x,
pred_type = "conditional hazard",
obs = NULL,
endSpellOnly = FALSE,
lwd1 = 5,
lwd2 = 2,
shuffle = TRUE,
heading = "",
show.expected = TRUE,
newplot = FALSE,
type = "line",
...
)
Arguments
x |
An object of class " |
pred_type |
Which statistic to plot, i.e. "conditional hazard" or "conditional risk". |
obs |
Variable that captures observed outcomes. If |
endSpellOnly |
Should only the last observation in each spell be kept?
|
lwd1 |
See |
lwd2 |
See |
shuffle |
See |
heading |
See |
show.expected |
See |
newplot |
See |
type |
See |
... |
Optional parameters passed to |
Details
Creates a separation plot of fitted values from
split-duration model results using predict.spdur
.
See Also
Examples
# get model estimates
library(separationplot)
data(model.coups)
# plot
p <- plot(model.coups)
p
Split-population duration (cure) regression
Description
This function estimates a split-population duration model and returns a
object of class spdur
.
Usage
spdur(
duration,
atrisk,
data = NULL,
last = "end.spell",
t.0 = "t.0",
fail = "failure",
distr = c("weibull", "loglog"),
max.iter = 300,
na.action,
silent = FALSE,
...
)
Arguments
duration |
A formula of the form Y ~ X1 + X2 ..., where Y is duration until failure or censoring. |
atrisk |
A formula of the form C ~ Z1 + Z2 ..., where C is a binary indicator of risk (1 - cure). |
data |
A data frame containing the variables in formula and formula2. |
last |
A string identifying the vector in |
t.0 |
The starting point for time-varying covariate intervals, by
default |
fail |
Name of the variable indicating that a spell ended in failure. |
distr |
The type of distribution to use in the hazard rate. Valid options are “weibull” or “loglog”; defaults to “weibull”. |
max.iter |
Maximum number of iterations to use in the likelihood maximization. |
na.action |
a function which indicates what should happen when the data
contain NAs. The default is set by the |
silent |
Suppress optimization output, |
... |
Optional arguments, see details. |
Details
See summary.spdur
, predict.spdur
,
and plot.spdur
for post-estimation options.
Optional arguments:
- base.inits
Initial values for the base duration model that is estimated to get initial values for the full split-population model. This needs to be a vector with starting values for the constant, coefficients in the duration equation, and an additional value for the shape parameter of the density used, e.g. Weibull. By default they are 0 for all coefficients and 0 or 1 for the Weibull and LogLog shape parameters respectively.
Value
Returns an object of class spdur
, with attributes:
coefficients |
A named vector of coefficient point estimates. |
vcv |
Estimated covariance matrix. |
se |
Standard error estimates. |
zstat |
Z-statistic values. |
pval |
P-values. |
mf.dur |
Model frame for the duration equation. |
mf.risk |
Model frame for the risk equation. |
Y |
Matrix of duration variables: risk, duration, end of spell, and t.0. |
na.action |
What action was taken for missing values in |
call |
The original, unevaluated |
distr |
Distribution used for the hazard rate. |
Examples
# Prepare data
data(coups)
dur.coups <- add_duration(coups, "succ.coup", unitID="gwcode", tID="year",
freq="year")
# Estimate model
model.coups <- spdur(duration ~ polity2, atrisk ~ polity2, data=dur.coups)
model.coups <- spdur(duration ~ polity2, atrisk ~ polity2, data=dur.coups,
distr="loglog")
Split-population Log-logistic regression
Description
Split-population Log-logistic regression
Usage
sploglog(Y, X, Z, max.iter, silent = FALSE)
Arguments
Y |
Dependent variables. |
X |
Duration equation covariates. |
Z |
Risk equation covariates. |
max.iter |
Maximum iterations to try. |
silent |
Suppress |
Split-population Log-logistic log likelihood
Description
Split-population Log-logistic log likelihood
Usage
sploglog_lnl(theta, y, X, Z)
Arguments
theta |
Current parameter values. |
y |
Dependent variables. |
X |
Duration equation covariates. |
Z |
Risk equation covariates. |
Split-population Weibull log likelihood
Description
Split-population Weibull log likelihood
Usage
spweib_lnl(theta, y, X, Z)
Arguments
theta |
Current parameter values. |
y |
Dependent variables. |
X |
Duration equation covariates. |
Z |
Risk equation covariates. |
Split-population Weibull regression
Description
Split-population Weibull regression
Usage
spweibull(Y, X, Z, max.iter, silent = FALSE)
Arguments
Y |
Dependent variables. |
X |
Duration equation covariates. |
Z |
Risk equation covariates. |
max.iter |
Maximum iterations to try. |
silent |
Suppress |
Summarize split-population duration results
Description
summary
method for class “spdur
”.
Usage
## S3 method for class 'spdur'
summary(object, ...)
Arguments
object |
An object with class |
... |
Further arguments passed to or from other methods. |
Details
This will list the estimated coefficients and standard errors for the risk and duration equations of a split-population duration model.
Value
An object with class summary.spdur
.
See Also
The model fitting function is spdur
, and see
summary
for the generic function.
For print formatting, see print.summary.spdur
.
Examples
data(model.coups)
s <- summary(model.coups)
class(s)
print(s)
Regular weibull log likelihood
Description
Regular weibull log likelihood
Usage
weib_lnl(theta, y, X)
Arguments
theta |
Current parameter values. |
y |
Dependent variables. |
X |
Duration equation covariates. |
Regular Weibull regression
Description
Regular Weibull regression
Usage
weibull(Y, X, inits = NULL, max.iter, silent = TRUE)
Arguments
Y |
Dependent variables. |
X |
Duration equation covariates. |
inits |
Vector of starting values. |
max.iter |
Maximum iterations to try. |
silent |
Suppress |
Create export table for a split-duration model
Description
xtable
-like function for class “spdur
”.
Usage
## S3 method for class 'spdur'
xtable(x, ...)
Arguments
x |
An object with class |
... |
Further arguments passed to |
Details
Format a split-duration model for export to Latex or html.
Value
An object with class xtable
.
See Also
xtable
, or as.data.frame.spdur
for a
simpler alternative that will convert a spdur
object to a data frame
containing model parameter estimates.
For print formatting, see print.xtable
.
Examples
library(xtable)
data(model.coups)
xtable(model.coups)
print(xtable(model.coups), include.rownames=FALSE)