# eiread: Quantities of Interest from Ecological Inference Estimation In ei: Ecological Inference

## Description

eiread is the command that pulls quantities of interest from the ei object. The command returns a list of quantities of interest requested by the user.

## Usage

 1 eiread(ei.object, ...) 

## Arguments

 ei.object An ei object from the function ei. ... A list of quantities of interest for eiread() to return. See values below.

## Value

 betab p x 1 point estimate of β_i^b based on its mean posterior. See section 8.2 betaw p x 1 point estimate of β_i^w based on its mean posterior. See section 8.2 sbetab p x 1 standard error for the estimate of β_i^b, based on the standard deviation of its posterior. See section 8.2 sbetaw p x 1 standard error for the estimate of β_i^w, based on the standard deviation of its posterior. See section 8.2 phi Maximum posterior estimates of the CML psisims Matrix of random simulations of ψ. See section 8.2 bounds p x 4: bounds on β_i^b and β_i^w, lowerB ~ upperB ~ lowerW ~ upperW. See Chapter 5. abounds 2 x 2: aggregate bounds rows:lower, upper; columns: betab, betaw. See Chapter 5. aggs Simulations of district-level quantities of interest \hat{B^b} and \hat{B^w}. See Section 8.3. maggs Point estimate of 2 district-level parameters, \hat{B^b} and \hat{B^w} based on the mean of aggs. See Section 8.3. VCaggs Variance matrix of 2 district-level parameters, \hat{B^b} and \hat{B^w}. See Section 8.3. CI80b p x 2: lower~upper 80\% confidence intervals for β_i^b. See section 8.2. CI80w p x 2: lower~upper 80\% confidence intervals for β_i^w. See section 8.2. eaggbias Regressions of estimated β_i^b and β_i^w on a constant term and X_i. goodman Goodman's Regression. See Section 3.1

## Author(s)

Gary King <<email: king@harvard.edu>> and Molly Roberts <<email: molly.e.roberts@gmail.com>>

## References

Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.

## Examples

 1 2 3 4 5 data(sample) formula = t ~ x dbuf <- ei(formula=formula, total="n",data=sample) eiread(dbuf, "phi") eiread(dbuf, "betab", "betaw") 

### Example output

Loading required package: eiPack
[1] "Running 2x2 ei"
Maximizing likelihood
Importance Sampling..
[1] -1.1534  0.8214 -2.2861 -1.8817  0.4128  0.0000  0.0000
$betab [1] 0.1975 0.1430 0.1633 0.2879 0.1288 0.1255 0.1029 0.1041 0.2217 0.2516 [11] 0.1681 0.2518 0.1683 0.2063 0.2117 0.1960 0.2840 0.1718 0.1504 0.1052 [21] 0.3079 0.2296 0.2401 0.2063 0.3984 0.1618 0.1637 0.3994 0.2131 0.1927 [31] 0.2224 0.1957 0.1760 0.2432 0.2272 0.1545 0.1953 0.3710 0.2263 0.2665 [41] 0.1738 0.1975 0.2016 0.1104 0.1071 0.1158 0.2118 0.2154 0.1344 0.1492 [51] 0.2474 0.1616 0.2176 0.2104 0.2421 0.1805 0.2401 0.2516 0.2949 0.2084 [61] 0.2120 0.1395 0.2316 0.1650 0.1144 0.1513 0.1457 0.3668 0.2242 0.0412 [71] 0.2741 0.1577 0.1826 0.1535 0.3122$betaw
[1] 0.6911 0.6538 0.6276 0.8798 0.5667 0.3789 0.5237 0.5532 0.7846 0.9005
[11] 0.5835 0.9260 0.5976 0.7350 0.7636 0.7109 0.7473 0.6957 0.6889 0.6404
[21] 0.8928 0.8742 0.7937 0.7177 0.8453 0.6309 0.5679 0.9542 0.7416 0.7651
[31] 0.7685 0.6859 0.7085 0.9281 0.7693 0.5951 0.6778 0.8368 0.7468 0.8175
[41] 0.6560 0.7024 0.7338 0.6402 0.5556 0.5014 0.7503 0.7649 0.6600 0.6191
[51] 0.8778 0.6779 0.7629 0.7461 0.8838 0.6762 0.8537 0.7648 0.8211 0.7109
[61] 0.7301 0.6722 0.7574 0.5926 0.6273 0.6831 0.5477 0.8896 0.7395 0.4890
[71] 0.7666 0.6840 0.6808 0.4687 0.9728


ei documentation built on May 2, 2019, 11:02 a.m.