Description Usage Arguments Details Author(s) References Examples
ei
is the main command in the package EI
. It gives observationlevel estimates (and various related statistics) of β_i^b and β_i^w given variables T_i and X_i (i=1,...,n) in this accounting identity: T_i=β_i^b*X_i + β_i^w*(1X_i). Results are stored in an ei
object, that can be read with summary()
or eiread()
and graphed in plot()
.
1 2 3 4 5 6  ei(formula, total = NULL, Zb = 1, Zw = 1, id = NA, data =NA, erho = 0.5,
esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA,
simulate = TRUE, covariate = NULL, lambda1 = 4, lambda2 = 2,
covariate.prior.list = NULL, tune.list = NULL, start.list = NULL,
sample = 1000, thin = 1, burnin = 1000, verbose = 0, ret.beta = "r",
ret.mcmc = TRUE, usrfun = NULL)

formula 
A formula of the form t ~x in the 2x2 case and cbind(col1,col2,...) ~ cbind(row1,row2,...) in the RxC case. 
total 
‘total’ is the name of the variable in the dataset that contains the number of individuals in each unit 
Zb 
p x k^b matrix of covariates or the name of covariates in the dataset 
Zw 
p x k^w matrix of covariates or the name of covariates in the dataset 
id 
‘id’ is the nae of the variable in the dataset that identifies the precinct. Used for ‘movie’ and ‘movieD’ plot functions. 
data 
data frame that contains the variables that
correspond to formula. If using covariates and data is specified, data should also contain 
erho 
The standard deviation of the normal prior on φ_5 for the correlation. Default =0.5. 
esigma 
The standard deviation of an underlying normal distribution, from which a half normal is constructed as a prior for both \breve{σ}_b and \breve{σ}_w. Default = 0.5 
ebeta 
Standard deviation of the "flat normal" prior on \breve{B}^b and \breve{B}^w. The flat normal prior is uniform within the unit square and dropping outside the square according to the normal distribution. Set to zero for no prior. Setting to positive values probabilistically keeps the estimated mode within the unit square. Default=0.5 
ealphab 
cols(Zb) x 2 matrix of means (in the first column) and standard deviations (in the second) of an independent normal prior distribution on elements of α^b. If you specify Zb, you should probably specify a prior, at least with mean zero and some variance (default is no prior). (See Equation 9.2, page 170, to interpret α^b). 
ealphaw 
cols(Zw) x 2 matrix of means (in the first column) and standard deviations (in the second) of an independent normal prior distribution on elements of α^w. If you specify Zw, you should probably specify a prior, at least with mean zero and some variance (default is no prior). (See Equation 9.2, page 170, to interpret α^w). 
truth 
A length(t) x 2 matrix of the true values of the quantities of interest. 
simulate 
default = TRUE:see documentation in 
covariate 
see documentation in 
lambda1 
default = 4:see documentation in 
lambda2 
default = 2:see documentation in 
covariate.prior.list 
see documentation in 
tune.list 
see documentation in 
start.list 
see documentation in 
sample 
default = 1000 
thin 
default = 1 
burnin 
default = 1000 
verbose 
default = 0:see documentation in 
ret.beta 
default = "r": see documentation in 
ret.mcmc 
default = TRUE: see documentation in 
usrfun 
see documentation in 
The EI
algorithm is run using the ei
command. A summary of the results can be seen graphically using plot(ei.object)
or numerically using summary(ei.object)
. Quantities of interest can be calculated using eiread(ei.object)
.
Gary King <<email: king@harvard.edu>> and Molly Roberts <<email: molly.e.roberts@gmail.com>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
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