Description Usage Arguments Details Value Author(s) References See Also Examples
Function that computes the twostep estimator proposed in Craiu et al.
(2011) and its print
method.
1 2 3 4 5 
formula 
A formula object, with the response on the left of a 
data 
A data frame (or object coercible by as.data.frame to a data frame) containing the variables in the model. 
random 
A formula object, with a blank on the left of a 
all.m.1 

D 
The form of the betweencluster variancecovariance matrix of the regression
coefficients (matrix D) : either 
itermax 
maximal number of EM iterations (default = 2000) 
tole 
maximal distance between successive EM iterations tolerated before declaring convergence (default = 0.000001) 
x 
An object, produced by the 
... 
Further arguments to be passed to 
Calls coxph
from the package survival.
beta 
A vector: the regression coefficients. 
se 
A vector: the regression coefficients' standard errors. 
vcov 
A matrix: the variancecovariance matrix of the regression coefficients. 
D 
A matrix: estimate of the betweencluster variancecovariance matrix of the regression coefficients (matrix D). 
r.effect 
The random effect estimates. 
coxph.warn 
A list of character string vectors. If the 
Call 
The function call. 
Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie Baillargeon
Craiu, R.V., Duchesne, T., Fortin, D. and Baillargeon, S. (2011), Conditional Logistic Regression with Longitudinal Followup and IndividualLevel Random Coefficients: A Stable and Efficient TwoStep Estimation Method, Journal of Computational and Graphical Statistics. 20(3), 767784.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  # Two ways for specifying the same model
# Data: bison
# Model: covariates forest, biomass and pmeadow
# Random effects in front of forest and biomass
# Main diagonal covariance structure for D (the default)
way1 < Ts.estim(formula = Y ~ forest + biomass + pmeadow +
strata(Strata) + cluster(Cluster), data = bison,
random = ~ forest + biomass)
way1
way2 < Ts.estim(formula = bison[,3] ~ as.matrix(bison[,c(6,8:9)]) +
strata(bison[,2]) + cluster(bison[,1]), data = bison,
random = ~ as.matrix(bison[,c(6,8)]))
way2
# Unstructured covariance for D
Fit < Ts.estim(formula = Y ~ forest + biomass + pmeadow +
strata(Strata) + cluster(Cluster), data = bison,
random = ~ forest + biomass, D="UN")
Fit

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