*Built using Zelig version 5.1.4.90000*

Generalized Estimating Equation for Probit Regression with `probit.gee`

.

The GEE probit estimates the same model as the standard probit regression (appropriate when you have a dichotomous dependent variable and a set of explanatory variables). Unlike in probit regression, GEE probit allows for dependence within clusters, such as in longitudinal data, although its use is not limited to just panel data. The user must first specify a “working” correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The “working” correlation matrix is a \(T \times T\) matrix of correlations, where \(T\) is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of the standard errors can be obtained using a robust “sandwich” estimator even if the “working” correlation matrix is incorrectly specified. If the “working” correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects.

```
z.out <- zelig(Y ~ X1 + X2, model = "probit.gee",
id = "X3", weights = w, data = mydata)
x.out <- setx(z.out)
s.out <- sim(z.out, x = x.out)
```

where `id`

is a variable which identifies the clusters. The data should be sorted by `id`

and should be ordered within each cluster when appropriate.

Use the following arguments to specify the structure of the “working” correlations within clusters:

`corstr`

: character string specifying the correlation structure: “independence”, “exchangeable”, “ar1”, “unstructured” and “userdefined”See

`geeglm`

in package`geepack`

for other function arguments.

Attaching the sample turnout dataset:

`data(turnout)`

Variable identifying clusters

```
turnout$cluster <- rep(c(1:200), 10)
sorted.turnout <- turnout[order(turnout$cluster),]
```

Estimating parameter values:

```
z.out1 <- zelig(vote ~ race + educate, model = "probit.gee",
id = "cluster", data = sorted.turnout)
```

```
## How to cite this model in Zelig:
## Patrick Lam. 2011.
## probit-gee: General Estimating Equation for Probit Regression
## in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
## "Zelig: Everyone's Statistical Software," http://zeligproject.org/
```

Summarize estimated paramters:

`summary(z.out1)`

```
## Model:
##
## Call:
## z5$zelig(formula = vote ~ race + educate, id = "cluster", data = sorted.turnout)
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) -0.725949 0.129687 31.33 2.17e-08
## racewhite 0.299076 0.084725 12.46 0.000416
## educate 0.097119 0.009436 105.94 < 2e-16
##
## Estimated Scale Parameters:
## Estimate Std.err
## (Intercept) 0.9868 0.04964
##
## Correlation: Structure = independenceNumber of clusters: 200 Maximum cluster size: 10
## Next step: Use 'setx' method
```

Setting values for the explanatory variables to their default values:

`x.out1 <- setx(z.out1)`

Simulating quantities of interest:

`s.out1 <- sim(z.out1, x = x.out1)`

`summary(s.out1)`

```
##
## sim x :
## -----
## ev
## mean sd 50% 2.5% 97.5%
## [1,] 0.7718 0.01118 0.7718 0.7495 0.7941
## pv
## 0 1
## [1,] 0.229 0.771
```

`plot(s.out1)`

Estimating the risk difference (and risk ratio) between low education (25th percentile) and high education (75th percentile) while all the other variables held at their default values.

```
x.high <- setx(z.out1, educate = quantile(turnout$educate,
prob = 0.75))
x.low <- setx(z.out1, educate = quantile(turnout$educate,
prob = 0.25))
```

`s.out2 <- sim(z.out1, x = x.high, x1 = x.low)`

`summary(s.out2)`

```
##
## sim x :
## -----
## ev
## mean sd 50% 2.5% 97.5%
## [1,] 0.8248 0.01054 0.8253 0.8032 0.8458
## pv
## 0 1
## [1,] 0.181 0.819
##
## sim x1 :
## -----
## ev
## mean sd 50% 2.5% 97.5%
## [1,] 0.7076 0.01426 0.7076 0.679 0.7354
## pv
## 0 1
## [1,] 0.277 0.723
## fd
## mean sd 50% 2.5% 97.5%
## [1,] -0.1172 0.01164 -0.1169 -0.1404 -0.09583
```

`plot(s.out2)`

User-defined correlation structure

```
library(geepack)
corr.mat <- matrix(rep(0.5, 100), nrow = 10, ncol = 10)
diag(corr.mat) <- 1
corr.mat <- fixed2Zcor(corr.mat, id = sorted.turnout$cluster,
waves = sorted.turnout$race)
```

Generating empirical estimates:

```
z.out2 <- zelig(vote ~ race + educate, model = "probit.gee",
id = "cluster", data = sorted.turnout,
corstr = "fixed", zcor = corr.mat)
```

```
## How to cite this model in Zelig:
## Patrick Lam. 2011.
## probit-gee: General Estimating Equation for Probit Regression
## in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
## "Zelig: Everyone's Statistical Software," http://zeligproject.org/
```

Viewing the regression output:

`summary(z.out2)`

Suppose we have a panel dataset, with \(Y_{it}\) denoting the binary dependent variable for unit \(i\) at time \(t\). \(Y_{i}\) is a vector or cluster of correlated data where \(y_{it}\) is correlated with \(y_{it^\prime}\) for some or all \(t, t^\prime\). Note that the model assumes correlations within \(i\) but independence across \(i\).

- The
*stochastic component*is given by the joint and marginal distributions

\[ \begin{aligned} Y_{i} &\sim& f(y_{i} \mid \pi_{i})\\ Y_{it} &\sim& g(y_{it} \mid \pi_{it}) \end{aligned} \]

where \(f\) and \(g\) are unspecified distributions with means \(\pi_{i}\) and \(\pi_{it}\). GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.

- The
*systematic component*is the*mean function*, given by:

\[ \pi_{it} = \Phi(x_{it} \beta) \]

where \(\Phi(\mu)\) is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, \(x_{it}\) is the vector of \(k\) explanatory variables for unit \(i\) at time \(t\) and \(\beta\) is the vector of coefficients.

- The
*variance function*is given by:

\[ V_{it} = \pi_{it} (1-\pi_{it}) \]

- The
*correlation structure*is defined by a \(T \times T\) “working” correlation matrix, where \(T\) is the size of the largest cluster. Users must specify the structure of the “working” correlation matrix*a priori*. The “working” correlation matrix then enters the variance term for each \(i\), given by:

\[ V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}} \]

where \(A_{i}\) is a \(T \times T\) diagonal matrix with the variance function \(V_{it} = \pi_{it} (1-\pi_{it})\) as the \(t\) th diagonal element, \(R_{i}(\alpha)\) is the “working” correlation matrix, and \(\phi\) is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.

In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust “sandwich” estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently and the standard errors can be estimated consistently with the sandwich estimator. If all three are specified correctly, then the estimates of the parameters are more efficient.

The robust “sandwich” estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units \(i\) is relatively large and the number of repeated periods \(t\) is relatively small. Otherwise, one should use the “naïve” model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See for more details.

All quantities of interest are for marginal means rather than joint means.

The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.

The expected values (qi$ev) for the GEE probit model are simulations of the predicted probability of a success:

\[ E(Y) = \pi_{c}= \Phi(x_{c} \beta), \]

given draws of \(\beta\) from its sampling distribution, where \(x_{c}\) is a vector of values, one for each independent variable, chosen by the user.

- The first difference (qi$fd) for the GEE probit model is defined as

\[ \textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x). \]

- The risk ratio (qi$rr) is defined as

\[ \textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x). \]

- In conditional prediction models, the average expected treatment effect (att.ev) for the treatment group is

\[ \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) - E[Y_{it}(tr_{it}=0)] \right\}, \]

where \(tr_{it}\) is a binary explanatory variable defining the treatment (\(tr_{it}=1\)) and control (\(tr_{it}=0\)) groups. Variation in the simulations are due to uncertainty in simulating \(E[Y_{it}(tr_{it}=0)]\), the counterfactual expected value of \(Y_{it}\) for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to \(tr_{it}=0\).

The output of each Zelig command contains useful information which you may view. For examle, if you run `z.out <- zelig(y ~ x, model = probit.gee, id, data)`

, then you may examine the available information in `z.out`

by using `names(z.out)`

, see the coefficients by using z.out$coefficients, and a default summary of information through `summary(z.out)`

. Other elements available through the $ operator are listed below.

From the zelig() output object z.out, you may extract:

coefficients: parameter estimates for the explanatory variables.

residuals: the working residuals in the final iteration of the fit.

fitted.values: the vector of fitted values for the systemic component, \(\pi_{it}\).

linear.predictors: the vector of \(x_{it}\beta\)

max.id: the size of the largest cluster.

From summary(z.out), you may extract:

coefficients: the parameter estimates with their associated standard errors, \(p\)-values, and \(z\)-statistics.

working.correlation: the “working” correlation matrix

From the sim() output object s.out, you may extract quantities of interest arranged as matrices indexed by simulation \(\times\) x-observation (for more than one x-observation). Available quantities are:

qi$ev: the simulated expected probabilities for the specified values of x.

qi$fd: the simulated first difference in the expected probabilities for the values specified in x and x1.

qi$rr: the simulated risk ratio for the expected probabilities simulated from x and x1.

qi$att.ev: the simulated average expected treatment effect for the treated from conditional prediction models.

The geeglm function is part of the geepack package by Søren Højsgaard, Ulrich Halekoh and Jun Yan. Advanced users may wish to refer to `help(geepack)`

and `help(family)`

.

HÃ¸jsgaard S, Halekoh U and Yan J (2006). “The R Package geepack for Generalized Estimating Equations.” *Journal of Statistical Software*, **15/2**, pp. 1-11.

Yan J and Fine JP (2004). “Estimating Equations for Association Structures.” *Statistics in Medicine*, **23**, pp. 859-880.

Yan J (2002). “geepack: Yet Another Package for Generalized Estimating Equations.” *R-News*, **2/3**, pp. 12-14.