Package 'StratSel' reference manual

Title:	Strategic Selection Estimator
Description:	Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
Authors:	Lucas Leemann
Maintainer:	Lucas Leemann <[email protected]>
License:	GPL (>= 2)
Version:	1.4
Built:	2025-03-05 05:02:26 UTC
Source:	https://github.com/lleemann/stratsel

This package allows to estimate strategic selection models.

Description

This package provides functionality to estimate, summarize, plot, predict, and export strategic selection estimates. It allows researchers to incorporate the strategic nature of the DGP while not constraining the errors to be orthogonal. By relaxing the assumptions, this estimator becomes a blend of an agent error model and a Heckman selection model.

Author(s)

Lucas Leemann [email protected]

References

Lucas Leemann. 2014. "Strategy and Sample Selection - A Strategic Selection Estimator", Political Analysis 22: 374-397.

Examples

# replicate the example from Leemann (2014):
library(memisc)
data(war1800)
## Not run: out1 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc + 
 dem2 + mixed2, data=war1800, corr=TRUE)
## End(Not run)
out2 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc + 
 dem2 + mixed2, data=war1800, corr=FALSE)
setStratSelDefault()
## Not run: z <- mtable(out1,out2)
# toLatex(z) for a LaTeX output or just regular table:
# replicate the example from Leemann (2014):
library(memisc)
data(war1800)
## Not run: out1 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc + 
 dem2 + mixed2, data=war1800, corr=TRUE)
## End(Not run)
out2 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc + 
 dem2 + mixed2, data=war1800, corr=FALSE)
setStratSelDefault()
## Not run: z <- mtable(out1,out2)
# toLatex(z) for a LaTeX output or just regular table:

Fake Data for Illustration

Description

This data is just for illustration. The code to generate it is:

set.seed(124)
n <- 1000
x24 <- cbind(rnorm(n), rnorm(n))
error <- rmnorm(n,c(0,0),matrix(c(1,0.6,0.6,1),2,2))
e24 <- error[,2]
y24.latent <- x24%*%c(1,1) + e24
y2 <- rep(NA,n)
y2[y24.latent>0] <- 1
y2[y24.latent<0] <- 0
mod2 <- glm(y2 ~ x24, family=binomial(link=probit))
p24 <- pnorm(predict(mod2))
x11 <- cbind(rnorm(n, sd=0.2), rnorm(n, sd=0.2))
x14 <- cbind(x24[,2],rnorm(n))
e14 <- error[,1]
y14.latent <- x14%*%c(2,1) * p24 - x11%*%c(1,1) + e14
y1 <- rep(NA,n)
y1[y14.latent>0] <- 1
y1[y14.latent<0] <- 0
Y <- rep(NA,n)
Y[y1==0] <- 1
Y[y1==1&y2==0] <- 3
Y[y1==1&y2==1] <- 4
colnames(x11) <- c("var A", "var B")
colnames(x14) <- c("var C", "var D")
colnames(x24) <- c("var E", "var C")
data.fake <- data.frame(Y,x11,x14,x24)

Usage

data(data.fake)data(data.fake)

Format

A data frame with 1000 observations on the following 7 variables.

Y: A numeric vector with values 1,3, and 4 depending on which outcome occurred.
var.A: A numeric vector mimicking an explanatory variable as part of $X11$ .
var.B: A numeric vector mimicking an explanatory variable as part of $X11$ .
var.C: A numeric vector mimicking an explanatory variable as part of $X14$ and of $X24$ .
var.D: A numeric vector mimicking an explanatory variable as part of $X14$ .
var.E: A numeric vector mimicking an explanatory variable as part of $X24$ .
var.C.1: A numeric vector mimicking an explanatory variable as part of $X14$ and of $X24$ . Identical to var.C.

Source

Can be independently re-created by anybody.

Examples

data(data.fake)
summary(data.fake)
## Not run: out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | 
	var.E + var.C, data=data.fake, corr=TRUE)
## End(Not run)
## Not run: summary(out1)
# True parameters are 1 or 2 except the three constant terms (which are 0). 
# The correlation parameter was set to +0.6. 
data(data.fake)
summary(data.fake)
## Not run: out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | 
	var.E + var.C, data=data.fake, corr=TRUE)
## End(Not run)
## Not run: summary(out1)
# True parameters are 1 or 2 except the three constant terms (which are 0). 
# The correlation parameter was set to +0.6.

Function to transform f( $\rho$ ) back to $\rho$

Description

The model has a correlation parameter which is estimated and theoretically bound between -1 and +1. To ensure that the estimated parameters are within the theoretical bounds a transformation is necessary. The chosen transformation is:

$f(\rho): \rho = \frac{2}{(1-exp(-\theta))}- 1$

Whereas $\rho$ is the actual correlation coefficient and $\theta$ is the parameter we estimate in the model. This parametrization has been worked into the likelihood function and ensures that $\rho$ will be between $-1$ and $+1$ .

Usage

fetch.rho.b(b)
fetch.rho.b(b)

Arguments

`b`	The vector of estimated coefficients ( $\beta$ ) whereas the last element is the transformed correlation coefficient (here: $\theta$ ).

Details

This function is for internal use but documented as a regular function to enable any user to assess the estimator and its functionality.

Value

The function returns the correct estimate for $\rho$ .

Note

We want to estimate $\rho$ but because it is theoretically bound, we estimate $\theta$ which is not bound can range from $-\infty$ to $+\infty$ .

Author(s)

Lucas Leemann [email protected]

Examples

test <- c(1,1,-2.35)
fetch.rho.b(test)
test <- c(1,1,-2.35)
fetch.rho.b(test)

Function to transform $var(\theta)$ back to $var(\rho)$

Description

$f(\rho): \rho = \frac{2}{(1-exp(-\theta))}- 1$

The variance covariance matrix thus contains entries based on $\theta$ but not $\rho$ . Hence, this function takes the variance of the transformed correlation parameter ( $\theta$ ) and produces the value correct for $\rho$ .

To create the correct measure of $var(\rho)$ this function simulates 1,000 $\theta$ 's and then transforms them to $\rho$ 's. The variance of these $\rho$ 's is then reported. Note, this means that the variance-covariance returned by StratSel is only correct for all diagonals and off-diagonals for the parameters ( $\beta$ ) but for the correlation coefficient only the variance is correct. Given that there is no reason to use the full variance-covariance for post-estimation commands this is not a problem.

Usage

fetch.rho.v(v, b)
fetch.rho.v(v, b)

Arguments

`v`	Variance-covariance matrix based on the regular parameters ( $\beta$ ) and $\theta$ .
`b`	Coefficient vector, first $n$ elements are for the regular parameter estimates and the last element is $\theta$ .

Details

This function is for internal use but documented as a regular function to enable any user to assess the estimator and its functionality.

Value

Returns the correct variance estimate for the estimate of the correlation coefficient $\rho$ .

Author(s)

Lucas Leemann [email protected]

Examples

fetch.rho.v(matrix(c(1,0,0,1),2,2),c(0,0))
fetch.rho.v(matrix(c(1,0,0,2),2,2),c(0,0))
fetch.rho.v(matrix(c(1,0,0,1),2,2),c(0,0))
fetch.rho.v(matrix(c(1,0,0,2),2,2),c(0,0))

Generates good starting values for a strategic selection model

Description

The function creates good starting values based on the supplied data and model which are to be estimated. To do so, the function runs two probit models, whereas the first one is just on the lower node of the game tree (see StratSel). It then creates predicted probabilities ( $p24$ ) to estimate a second probit at the first node whereas the variables which are part of $X14$ are weighted by $p24$ .

Usage

gen.Startval(Startval, user.supplied.startval, corr, ys, xs11, xs14, xs24,
		dim.x11, dim.x14, dim.x24)
gen.Startval(Startval, user.supplied.startval, corr, ys, xs11, xs14, xs24,
		dim.x11, dim.x14, dim.x24)

Arguments

`Startval`	Optional. A vector of user supplied starting values.
`user.supplied.startval`	Logical. If TRUE this function just returns the vector `Startval`. If the estimated model does not restrict the correlation (so `corr=TRUE`) then a 0 is attached to the vector `Startval`.
`corr`	Logical. Indicates whether the estimated agent error model assumes orthogonal errors (`=FALSE`) or whether the correlation is estimated (`=TRUE`).
`ys`	Vector. The outcome variable which is supplied by the user to StratSel.
`xs11`	Matrix. Explanatory variables for player 1 and measuring utility from outcome 1.
`xs14`	Matrix. Explanatory variables for player 1 and measuring utility from outcome 4.
`xs24`	Matrix. Explanatory variables for player 2 and measuring utility from outcome 4.
`dim.x11`	Vector. Has two elements for the dimension of X11.
`dim.x14`	Vector. Has two elements for the dimension of X14.
`dim.x24`	Vector. Has two elements for the dimension of X24.

Details

This function is for internal use but documented as a regular function to enable any user to assess the estimator and its functionality.

Value

Vector. Has length of the number of parameters to be estimated.

Author(s)

Lucas Leemann [email protected]

getSummary Method for extending mtable()

Description

This function extends the mtable() to report strategic selection models (StratSel). Together with setStratSelDefault and the mtable command from the memisc package users can create multi-model tables and export them to LaTeX.

Usage

## S3 method for class 'StratSel'
getSummary(obj, alpha = 0.05, ...)## S3 method for class 'StratSel'
getSummary(obj, alpha = 0.05, ...)

Arguments

`obj`	An object of class `StratSel`.
`alpha`	Significance level.
`...`	additional arguments

Value

Returns a list of objects to be fed to mtable. Do not use this command directly. The command mtable will automatically call this function for an object of the StratSel class.

Author(s)

Lucas Leemann [email protected]

References

Elff, Martin. (2013). memisc: Tools for Management of Survey Data, Graphics, Programming, Statistics, and Simulation R package version 0.96-7.

Examples

data(data.fake)
out1 <- StratSel(Y ~ var.A | var.D | var.E , data=data.fake, corr=FALSE)
out2 <- StratSel(Y ~ var.A | var.C | var.E, data=data.fake, corr=FALSE)
mtable(out1,out2)
data(data.fake)
out1 <- StratSel(Y ~ var.A | var.D | var.E , data=data.fake, corr=FALSE)
out2 <- StratSel(Y ~ var.A | var.C | var.E, data=data.fake, corr=FALSE)
mtable(out1,out2)

Function to Extract Log-Likelihood from Objects of Class `StratSel`

Description

Generic logLik function for objects of class StratSel.

Usage

## S3 method for class 'StratSel'
logLik(object, ...)
## S3 method for class 'StratSel'
logLik(object, ...)

Arguments

`object`	An object of class `StratSel`
`...`	additional arguments.

Author(s)

Lucas Leemann [email protected]

Log-Likelihood Function of an Agent Error Model

Description

This function calculates the log-likelihood value for an agent error model (belongs to the general class of quantal response models). The underlying formal structure is

and shows a game where there are two players which move sequentially. Player 1 decides to move left or right and if she does move right player 2 gets to move. The final outcome in this case depends on the move of player 2.

Usage

logLikStrat(x11, x14, x24, y, beta)
logLikStrat(x11, x14, x24, y, beta)

Arguments

`x11`	A vector or a matrix containing the explanatory variables used to parametrize `u11`.
`x14`	A vector or a matrix containing the explanatory variables used to parametrize `u14`.
`x24`	A vector or a matrix containing the explanatory variables used to parametrize `u24`.
`y`	Vector. Outcome variable which can take values 1, 3, and 4 depending on which outcome occurred.
`beta`	Vector. Coefficients of the model.

Details

This function provides the likelihood of an agent error model (Signorino, 2003). Note, that to derive it one assumes that the two errors are independent. Further, as with probit and logit models, one needs to assume an error variance to achieve identification. Signorino uses $\sqrt 2$ while logLikStrat uses 1. Hence, the numeric results will differ, but all relevant statistics (predicted probabilities, z-values, ...) will be identical. Finally, u13 and u23 are set to 0 to achieve identification.

Value

Returns a numeric value for the log-likelihood function evaluated for $\beta$ .

Note

The log-likelihood function:

$\ell\ell = \sum_{i=1}^n \left(\log(p_{i1})\cdot I(Y_{i}=1) + \log((1-p_{i1})(1-p_{i4}))\cdot I(Y_{i}=3) + \log((1-p_{i1})(p_{i4}))\cdot I(Y_{i}=4) \right)$

whereas

$p_{i24} = \Phi(x_{24}\cdot\beta_{24})$

and

$p_{i1} = \Phi(x_{11}\cdot\beta_{11}-p_{24}(x_{14}\cdot\beta_{14}))$

Author(s)

Lucas Leemann [email protected]

References

Curtis S. Signorino. 2003. "Structure and Uncertainty in Discrete Choice Models." Political Analysis 11:316–344.

Log-Likelihood Function of an Agent Error Model with Correlated Errors (strategic selection model)

Description

This function calculates the log-likelihood value for an agent error model (belongs to the general class of quantal response models) with correlated errors. The underlying formal structure is

Usage

logLikStratSel(x11, x14, x24, y, beta)
logLikStratSel(x11, x14, x24, y, beta)

Arguments

`x11`	A vector or a matrix containing the explanatory variables used to parametrize `u11`.
`x14`	A vector or a matrix containing the explanatory variables used to parametrize `u14`.
`x24`	A vector or a matrix containing the explanatory variables used to parametrize `u24`.
`y`	Vector. Outcome variable which can take values 1, 3, and 4 depending on which outcome occurred.
`beta`	Vector. Coefficients of the model whereas the last element is the correlation coefficient $\rho$ . Note, that this parameter has been re-paramterized (see details).

Details

This function provides the likelihood of an agent error model (Signorino, 2003) but in addition allows the random components to be correlated and hence can take selection into account. The correlation parameter is re-paramaterized (see Note). Further, as with probit and logit models, one needs to assume an error variance to achieve identification, here 1 is chosen as with a regular probit model. Finally, u13 and u23 are set to 0 to achieve identification.

Value

Returns a numeric value for the log-likelihood function evaluated for $\beta$ .

Note

The notation $\boldsymbol{\Phi_2}(a;b;c)$ indicates a bivariate standard normal cumulative distribution evaluated at the values a,b whereas the two random variables have a correlation of c.

$\ell\ell = \sum_{i=1}^n \log\left(\boldsymbol{\Phi_2}(p_{i4}(\mathbf{x}_{i14} \boldsymbol{\beta}_{14})-\mathbf{x}_{i11}\boldsymbol{\beta}_{11}; \mathbf{x}_{i24} \boldsymbol{\beta}_{24}; -\rho)^{(1-I(y_{i}=1))(1-I(y_{i}=4))} \right)$

$+ \sum_{i=1}^n \log\left(\boldsymbol{\Phi_2}(p_{i4}(\mathbf{x}_{i14} \boldsymbol{\beta}_{14})-\mathbf{x}_{i11}\boldsymbol{\beta}_{11}; \mathbf{x}_{i24} \boldsymbol{\beta}_{24}; \rho)^{(1-I(y_{i}=1))I(y_{i}=4)} \right)$

$+ \sum_{i=1}^n \log\left(1-\Phi(p_{i4} \mathbf{x}_{i14} \boldsymbol{\beta}_{14} -\mathbf{x}_{i1} \boldsymbol{\beta}_{11})\right)$

whereas

$p_{i24} = \Phi(x_{24}\cdot\beta_{24})$

and

$p_{i1} = \Phi(x_{11}\cdot\beta_{11}-p_{24}(x_{14}\cdot\beta_{14}))$

The re-parametrization is as follows:

$\rho = \frac{2}{1-exp(-\theta)}- 1$

Author(s)

Lucas Leemann [email protected]

References

Lucas Leemann. 2014. "Strategy and Sample Selection - A Strategic Selection Estimator", Political Analysis 22: 374-397.

A Function To Create Dyadic Data Sets

Description

This function allows the user to create dyadic data sets which can be directed or undirected.

Usage

makeDyadic(x, directed = FALSE, show.progress = 5)
makeDyadic(x, directed = FALSE, show.progress = 5)

Arguments

`x`	The data matrix whereas the first variable is the country code and the second column has to be the time variable.
`directed`	Logical. If `directed=TRUE` the dyadic data set will be directed and have variables with amended names (creating sen_varname for sender and rec_varname for receiver).
`show.progress`	Logical. The process may take some time depending on the size of the supplied data matrix. This option allows users to receive feedback of how far along the process is at periodical steps. Default is set to 5.

Details

This function was first written for Simon Collrad-Wexler and then later amended for Fabio Wasserfallen.

Value

Returns a data frame with the dyadic data set.

Author(s)

Lucas Leemann [email protected]

Examples

dataOrig <- matrix(c( rep(c(1:4),3), rep(1,4), rep(2,4), rep(3,4),
 rnorm(4,1.5,0.1), rnorm(4,2.5,0.1), rnorm(4,3.5,0.1), rnorm(4,4.5,0.1),
 rnorm(4,5.5,0.1), rnorm(4,6.5,0.1)),12,4)
colnames(dataOrig) <- c("countryCODE", "Year", "Variable 1", "Variable 2")
dataNew <- makeDyadic(dataOrig, directed=TRUE)
dataOrig <- matrix(c( rep(c(1:4),3), rep(1,4), rep(2,4), rep(3,4),
 rnorm(4,1.5,0.1), rnorm(4,2.5,0.1), rnorm(4,3.5,0.1), rnorm(4,4.5,0.1),
 rnorm(4,5.5,0.1), rnorm(4,6.5,0.1)),12,4)
colnames(dataOrig) <- c("countryCODE", "Year", "Variable 1", "Variable 2")
dataNew <- makeDyadic(dataOrig, directed=TRUE)

Plots a StratSel Object

Description

Plots predicted probabilities for all three possible outcomes based on an object of class StratSel.

Usage

	## S3 method for class 'StratSel'
plot(x, profile, x.move, x.range, uncertainty = FALSE, 
 n.sim = 100, ci = 0.95, ylim, xlab, ylab1, ylab2, ylab3, plot.nr, ...)
## S3 method for class 'StratSel'
plot(x, profile, x.move, x.range, uncertainty = FALSE, 
 n.sim = 100, ci = 0.95, ylim, xlab, ylab1, ylab2, ylab3, plot.nr, ...)

Arguments

`x`	An object of class `StratSel`
`profile`	Vector. The values of all independent variables including the three constants.
`x.move`	Scalar. Indicates which variable is changing (and displayed on the x-axis).
`x.range`	Vector. A vector with two elements. The `x` which is moving will start at `x.range[1]` and end at `x.range[2]`.
`uncertainty`	Logical. Indicates whether confidence bands should be displayed or not.
`n.sim`	Scalar. If `uncertainty=TRUE` the prediction uncertainty is generated with a simulation based on `n.sim` simulations. Default is 100. It is recommended to use 1000 simulations.
`ci`	Scalar. Indicates which confidence interval should be plotted, the default is 0.95.
`ylim`	Vector. A vector with two elements defining the range of the plotted y (predicted probability).
`xlab`	String. A label to be used for the x-axis. Will be recycled in all three plots.
`ylab1`	String. Label for the y-axis of the first plot (predicted probability of outcome 1).
`ylab2`	String. Label for the y-axis of the second plot (predicted probability of not outcome 1).
`ylab3`	String. Label for the y-axis of the third plot (predicted probability of outcome 4).
`plot.nr`	Vector. If one does not want to plot all three outcomes, one can use this vector to indicate which plot(s) should be shown.
`...`	Further arguments to be supplied to `plot`.

Author(s)

Lucas Leemann [email protected]

Examples

data(data.fake)
# Running just an agent error model (note: corr=FALSE) with \code{var.C} being 
#part of both actors' utilities
out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | var.E + var.C, data=data.fake, corr=FALSE)
par(mfrow=c(3,1))
plot(out1, profile=c(1,0.2,-0.2,1,0.2,-0.2,1,0.1,-0.3), 
 x.move=c(5,9),x.range=c(-15,15), ci=0.7, uncertainty=TRUE)
data(data.fake)
# Running just an agent error model (note: corr=FALSE) with \code{var.C} being 
#part of both actors' utilities
out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | var.E + var.C, data=data.fake, corr=FALSE)
par(mfrow=c(3,1))
plot(out1, profile=c(1,0.2,-0.2,1,0.2,-0.2,1,0.1,-0.3), 
 x.move=c(5,9),x.range=c(-15,15), ci=0.7, uncertainty=TRUE)

Prediction Function for Objects of the `StratSel` Class

Description

Prediction function for objects of the StratSel class. Provides either predictions for all observations in a model or for a specified profile. In addition, the function will either predict an outcome or three probabilities (indicating the probability for each outcome).

Usage

	## S3 method for class 'StratSel'
predict(object, prob = FALSE, profile, ...)
## S3 method for class 'StratSel'
predict(object, prob = FALSE, profile, ...)

Arguments

`object`	An object of class `StratSel`.
`prob`	Logical. If `prob=TRUE` the function will compute the predicted probabilities for each of the three possible outcomes. Default is `prob=FALSE`.
`profile`	Vector. A vector defining a specific profile for which the prediction is made.
`...`	...

Value

Either a matrix with dimension n * m, where there are n observations in the original model and m is three (for the three possible outcomes) or it will be a vector with n elements indicating for each observation which the most likely outcome would be.

Author(s)

Lucas Leemann [email protected]

Examples

data(data.fake)
out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | var.E + var.C, data=data.fake, corr=FALSE)
predict(out1)
predict(out1, prob=TRUE)
predict(out1, profile=c(1,0.2,0.2,1,0.2,0.2,1,0.2,0.2))
data(data.fake)
out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | var.E + var.C, data=data.fake, corr=FALSE)
predict(out1)
predict(out1, prob=TRUE)
predict(out1, profile=c(1,0.2,0.2,1,0.2,0.2,1,0.2,0.2))

Print Function for Objects of Class `StratSel`

Description

Generic print function for objects of class StratSel.

Usage

## S3 method for class 'StratSel'
print(x,...)
## S3 method for class 'StratSel'
print(x,...)

Arguments

`x`	An object of class `StratSel`
`...`	additional arguments.

Author(s)

Lucas Leemann [email protected]

Function to Print the Summary Output of an Object of Class `StratSel`

Description

Function to print the summary output of an object of class StratSel

Usage

	## S3 method for class 'StratSel'
print.summary(x, ...)
## S3 method for class 'StratSel'
print.summary(x, ...)

Arguments

`x`	An object of class `StratSel`
`...`	additional arguments.

Author(s)

Lucas Leemann [email protected]

Function Changes Default Settings to Use `mtable` Command

Description

Function changes default settings to use mtable command.

Usage

setStratSelDefault()
setStratSelDefault()

Author(s)

Lucas Leemann [email protected]

References

Elff, Martin. (2013). memisc: Tools for Management of Survey Data, Graphics, Programming, Statistics, and Simulation R package version 0.96-7.

Fitting Strategic Selection Models

Description

This function estimates a strategic selection estimator. This function fits a strategic selection estimator which is based on an agent error model (belongs to the general class of quantal response models). The underlying formal structure is

Usage

StratSel(formula, corr = TRUE, Startval, optim.method = "BFGS", data, ...)
StratSel(formula, corr = TRUE, Startval, optim.method = "BFGS", data, ...)

Arguments

`formula`	The formula has the following form `Y ~ X11 \| X14 \| X24` whereas `Y` is the outcome variable taking values 1, 3, or 4 depending on which outcome resulted. The explanatory variables are supplied via `X11`, `X14`, and `X24` depending on which part of the utility function they should explain.
`corr`	Logical. If `corr=TRUE` a strategic selection estimator is estimated. If `corr=FALSE` an ordinary agent error model is estimated as the command `egame12` in the `games` package does. Note, that the variance of the error terms is fixed differently and hence the coefficients will be different, but all resulting statistics (z-values, predicted probabilities, ...) are identical as the fixed variances are identifying assumptions. See `logLikStratSel` for details.
`Startval`	Vector. Allows the user to specify starting values. If there is no user-supplied vector the function will generate starting values itself. It is strongly recommended to to let the function determine the optimal starting values.
`optim.method`	Optimization method to be used by `optim`; can be: "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", or "Brent". See `optim` for details. The default is "BFGS".
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `StratSel` is called.
`...`	additional arguments.

Value

StratSel returns an object of class StratSel for which appropriate plot, print, summary, and predict functions exist.

Author(s)

Lucas Leemann [email protected]

References

Lucas Leemann. 2014. "Strategy and Sample Selection - A Strategic Selection Estimator", Political Analysis 22: 374-397.

Curtis S. Signorino. 2003. "Structure and Uncertainty in Discrete Choice Models." Political Analysis 11:316–344.

Examples

# replicate the example from Leemann (2014):
data(war1800)
## Not run: out1 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc
 + dem2 + mixed2, data=war1800, corr=TRUE)
## End(Not run)
out2 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc
 + dem2 + mixed2, data=war1800, corr=FALSE)
# replicate the example from Leemann (2014):
data(war1800)
## Not run: out1 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc
 + dem2 + mixed2, data=war1800, corr=TRUE)
## End(Not run)
out2 <- StratSel(Y ~ s_wt_re1 + revis1 | dem1 + mixed1 | balanc
 + dem2 + mixed2, data=war1800, corr=FALSE)

Summary Function for `StratSel` Objects

Description

Summary function for StratSel objects which displays a table of estimated coefficients and their standard errors.

Usage

	## S3 method for class 'StratSel'
summary(object, ...)
## S3 method for class 'StratSel'
summary(object, ...)

Arguments

`object`	An object of class `StratSel`
`...`	...

Note

See StratSel help-file for an example.

Author(s)

Lucas Leemann [email protected]

Functionto Extract Variance-Covariance from Objects of Class `StratSel`

Description

Generic vcov function for objects of class StratSel.

Usage

## S3 method for class 'StratSel'
vcov(object,...)
## S3 method for class 'StratSel'
vcov(object,...)

Arguments

`object`	An object of class `StratSel`
`...`	additional arguments.

Author(s)

Lucas Leemann [email protected]

A Data Set for Illustrative Purposes

Description

This is a subset (only some variables included) of the data set which is also included in the package games. The data set can also be used to replicate the example that is provided in Leemann (2014) illustrating the strategic selection estimator. It is a data set of militarized international disputes between 1816 and 1899.

Usage

data(war1800)data(war1800)

Format

A data frame with 313 observations on the following 10 variables.

esc: a numeric vector
war: a numeric vector
dem1: a numeric vector
mixed1: a numeric vector
dem2: a numeric vector
mixed2: a numeric vector
s_wt_re1: a numeric vector
revis1: a numeric vector
balanc: a numeric vector
Y: a numeric vector

Source

This data set is taken from the package games.

References

Daniel M. Jones, Stuart A. Bremer and J. David Singer. 1996. "Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns." Conflict Management and Peace Science 15(2): 163–213.

Lucas Leemann. 2014. "Strategy and Sample Selection - A Strategic Selection Estimator", Political Analysis 22: 374-397.

Examples

data(war1800)
summary(war1800)
data(war1800)
summary(war1800)

Package 'StratSel'

Help Index

This package allows to estimate strategic selection models.

Description

Author(s)

References

See Also

Examples

Fake Data for Illustration

Description

Usage

Format

Source

Examples

Function to transform f(ρ\rhoρ) back to ρ\rhoρ

Description

Usage

Arguments

Details

Value

Note

Author(s)

See Also

Examples

Function to transform var(θ)var(\theta)var(θ) back to var(ρ)var(\rho)var(ρ)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Generates good starting values for a strategic selection model

Description

Usage

Arguments

Details

Value

Author(s)

See Also

getSummary Method for extending mtable()

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Function to Extract Log-Likelihood from Objects of Class StratSel

Description

Usage

Arguments

Author(s)

Log-Likelihood Function of an Agent Error Model

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Log-Likelihood Function of an Agent Error Model with Correlated Errors (strategic selection model)

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

A Function To Create Dyadic Data Sets

Description

Usage

Arguments

Details

Function to transform f( $\rho$ ) back to $\rho$

Function to transform $var(\theta)$ back to $var(\rho)$

Function to Extract Log-Likelihood from Objects of Class `StratSel`

Prediction Function for Objects of the `StratSel` Class

Print Function for Objects of Class `StratSel`

Function to Print the Summary Output of an Object of Class `StratSel`

Function Changes Default Settings to Use `mtable` Command

Summary Function for `StratSel` Objects

Functionto Extract Variance-Covariance from Objects of Class `StratSel`