cglm {cglm} | R Documentation |
cglm
estimates the ratio of the regression coefficients and the
dispersion parameter in conditional generalized linear models. This
is of particular interest in the so-called case-time-control design.
cglm(method, formula, data, id, link, ...)
method |
a string specifying the desired estimation method; either |
formula |
a symbolic description of the model to be fitted. |
data |
a data frame containing the variables in the model. |
id |
a string containing the name of the cluster identification variable. |
link |
a string specifying the desired link function. This argument is not used
when |
... |
optional arguments passed on to the |
Let y_{ij}
and x_{ij}
be the outcome and covariate(s) for subject
j
in cluster i
, respectively. Consider the conditional generalized
linear model
p(y_{ij}|i,x_{ij})=\textrm{exp}\left[\frac{\theta_{ij}y_{ij}-A(\theta_{ij})}{\phi}+k(y_{ij},\phi)\right]
where
\theta_{ij}=\eta\{E(y_{ij}|x_{ij})\}=b_i+\beta x_{ij}.
cglm
estimates the ratio
\beta / \phi.
This ratio is of particular interest in so-called case-time-control designs; see Sjolander (2016) and Sjolander and Ning (2018) for details. Two estimation methods are allowed; the two-step method proposed by Sjolander (2016) and the conditional maximum likelihood method proposed by Sjolander and Ning (2018).
An object of class "cglm"
is a list containing
call |
the matched call. |
coefficients |
the ratio of the estimated coefficients and the estimated dispersion parameter. |
var |
the variance-covariance matrix. |
convergence |
was a solution found to the estimating equations? |
Missing data are not allowed.
Arvid Sjolander.
Sjolander A. (2017). The case-time-control method for non-binary exposures. Sociological Methodology 47(1), 182-211.
Sjolander A., Ning Y. (2018). A general and robust estimation method for the case-time-control design. Sociological Methodology 49(1), 349-365.
data(teenpov)
fit.ide <- cglm(method="ts", formula=hours~nonpov+inschool+spouse+age+mother,
data=teenpov, id="ID", link="identity")
summary(fit.ide)
fit.log <- cglm(method="ts", formula=hours~nonpov+inschool+spouse+age+mother,
data=teenpov, id="ID", link="log")
summary(fit.log)
fit.cglm <- cglm(method="cml", formula=hours~nonpov+inschool+spouse+age+mother,
data=teenpov, id="ID")
summary(fit.cglm)