Description Usage Arguments Details Value Note Author(s) References Examples
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.
1 |
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}≤ft[\frac{θ_{ij}y_{ij}-A(θ_{ij})}{φ}+k(y_{ij},φ)\right]
where
θ_{ij}=η\{E(y_{ij}|x_{ij})\}=b_i+β x_{ij}.
cglm
estimates the ratio
β / φ.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 | 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)
|
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