Title: | Ecological Inference |
---|---|
Description: | Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407. |
Authors: | Gary King [aut] |
Maintainer: | James Honaker <[email protected]> |
License: | GPL (>= 2) |
Version: | 2.0.0 |
Built: | 2024-08-27 04:22:56 UTC |
Source: | https://github.com/iqss-research/ei |
ei_tbl
objectsConvert to ei_tbl
objects
as_ei_tbl(x) ei_as_ei_tbl(ei.object)
as_ei_tbl(x) ei_as_ei_tbl(ei.object)
x |
an object to be coerced |
ei.object |
list-based ei object to convert to tibble-based object |
ei_tbl object
data(sample_ei) form <- t ~ x dbuf <- ei(form, total = "n", data = sample_ei) dbuf <- ei_as_ei_tbl(dbuf)
data(sample_ei) form <- t ~ x dbuf <- ei(form, total = "n", data = sample_ei) dbuf <- ei_as_ei_tbl(dbuf)
A dataset of aggregate literacy rates (t) and fraction of the population that is black (x), from the 1910 US Census. Each observation represents one county.
A data frame containing 1030 observations.
Gary King, 1997, "Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data", http://hdl.handle.net/1902.1/LWMMKUTYXS UNF:3:DRWozWd89+vNLO7lY2AHbg== IQSS Dataverse Network Distributor V3 Version
Gary King. (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press. Section 13.2:241-5.
Robinson, William S. (1950). “Ecological Correlation and the Behavior of Individuals.” American Sociological Review 15:351-357.
ei
is the main command in the package EI
. It gives
observation-level estimates (and various related statistics) of
and
given variables
and
(
) in this accounting identity:
. Results are stored in an
ei
object, that can be
read with summary()
or eiread()
and graphed in plot()
.
ei( formula, total = NULL, Zb = 1, Zw = 1, id = NA, data, erho = c(0.5, 3, 5, 0.1, 10), esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA, simulate = TRUE, ndraws = 99, nsims = 100, 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 )
ei( formula, total = NULL, Zb = 1, Zw = 1, id = NA, data, erho = c(0.5, 3, 5, 0.1, 10), esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA, simulate = TRUE, ndraws = 99, nsims = 100, 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 |
total |
‘total’ is the name of the variable in the dataset that contains the number of individuals in each unit |
Zb |
|
Zw |
|
id |
|
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 |
esigma |
The standard deviation of an underlying normal distribution,
from which a half normal is constructed as a prior for both
|
ebeta |
Standard deviation of the "flat normal" prior on
|
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 |
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 |
truth |
A length(t) x 2 matrix of the true values of the quantities of interest. |
simulate |
default = TRUE:see documentation in |
ndraws |
integer. The number of draws. Default is 99. |
nsims |
integer. The number of simulations within each draw. Default is 100. |
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)
.
ei
object
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(sample_ei) form <- t ~ x dbuf <- ei(form, total = "n", data = sample_ei) summary(dbuf)
data(sample_ei) form <- t ~ x dbuf <- ei(form, total = "n", data = sample_ei) summary(dbuf)
Run (tidy) Ecological Inference Estimation and Simulation
ei_( data, x, t, n, Zb = NULL, Zw = NULL, id = NA, erho = c(0.5, 3, 5, 0.1, 10), esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA, simulate = TRUE, ndraws = 99, nsims = 100, 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 )
ei_( data, x, t, n, Zb = NULL, Zw = NULL, id = NA, erho = c(0.5, 3, 5, 0.1, 10), esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA, simulate = TRUE, ndraws = 99, nsims = 100, 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 )
data |
data where |
x |
< |
t |
< |
n |
< |
Zb |
< |
Zw |
< |
id |
< |
erho |
The standard deviation of the normal prior on |
esigma |
The standard deviation of an underlying normal distribution,
from which a half normal is constructed as a prior for both
|
ebeta |
Standard deviation of the "flat normal" prior on
|
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 |
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 |
truth |
A length(t) x 2 matrix of the true values of the quantities of interest. |
simulate |
default = TRUE:see documentation in |
ndraws |
integer. The number of draws. Default is 99. |
nsims |
integer. The number of simulations within each draw. Default is 100. |
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 |
an ei_tbl
data(sample_ei) dbuf <- ei_(sample_ei, x, t, n)
data(sample_ei) dbuf <- ei_(sample_ei, x, t, n)
Run (tidy) Ecological Inference Estimation
ei_est( data, t, x, n, id = seq_len(nrow(data)), Zb = NULL, Zw = NULL, erho = 0.5, esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA )
ei_est( data, t, x, n, id = seq_len(nrow(data)), Zb = NULL, Zw = NULL, erho = 0.5, esigma = 0.5, ebeta = 0.5, ealphab = NA, ealphaw = NA, truth = NA )
data |
data where |
t |
< |
x |
< |
n |
< |
id |
< |
Zb |
< |
Zw |
< |
erho |
The standard deviation of the normal prior on |
esigma |
The standard deviation of an underlying normal distribution,
from which a half normal is constructed as a prior for both
|
ebeta |
Standard deviation of the "flat normal" prior on
|
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 |
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 |
truth |
A length(t) x 2 matrix of the true values of the quantities of interest. |
ei_tbl
data(sample_ei) dbuf <- ei_est(sample_ei, x, t, n)
data(sample_ei) dbuf <- ei_est(sample_ei, x, t, n)
Run Ecological Inference Simulation
ei_sim(data, ndraws = 99, nsims = 100)
ei_sim(data, ndraws = 99, nsims = 100)
data |
an |
ndraws |
integer, default 99. The number of draws. |
nsims |
integer, default 10. The number of simulations with each draw. |
ei_tbl
data(sample_ei) dbuf <- ei_est(sample_ei, x, t, n) %>% ei_sim()
data(sample_ei) dbuf <- ei_est(sample_ei, x, t, n) %>% ei_sim()
Returns analytical bounds from accounting identity on unknown table relationships beta_b, beta_w, from known, observed, table marginals, x, t (and sample size n).
ei.bounds(x, t, n)
ei.bounds(x, t, n)
x |
vector of characteristics, e.g. percentage of blacks in each district |
t |
vector of characteristics, e.g. percentage of people that voted in each district |
n |
size of each observation, e.g. number of voters in each district |
a numeric matrix
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(census1910) output <- ei.bounds(x = census1910$x, t = census1910$t, n = census1910$n)
data(census1910) output <- ei.bounds(x = census1910$x, t = census1910$t, n = census1910$n)
Simulate EI Solution via Importance Sampling
ei.sim(ei.object, ndraws = 99, nsims = 100)
ei.sim(ei.object, ndraws = 99, nsims = 100)
ei.object |
|
ndraws |
integer. The number of draws. Default is 99. |
nsims |
integer. The number of simulations within each draw. Default is 100. |
ei.sim
object
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(sample_ei) form <- t ~ x ei_obj <- ei(form, total = "n", data = sample_ei, simulate = FALSE) sims <- ei.sim(ei_obj)
data(sample_ei) form <- t ~ x ei_obj <- ei(form, total = "n", data = sample_ei, simulate = FALSE) sims <- ei.sim(ei_obj)
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.
eiread(ei.object, ...)
eiread(ei.object, ...)
ei.object |
An |
... |
A list of quantities of interest for |
betab |
|
betaw |
|
sbetab |
|
sbetaw |
|
phi |
Maximum posterior estimates of the CML |
psisims |
Matrix of random simulations of |
bounds |
|
abounds |
|
aggs |
Simulations of
district-level quantities of interest |
maggs |
Point estimate of 2 district-level
parameters, |
VCaggs |
Variance matrix of 2 district-level
parameters, |
CI80b |
|
CI80w |
|
eaggbias |
Regressions of estimated |
goodman |
Goodman's Regression. See Section 3.1 |
numeric values
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) eiread(dbuf, "phi") eiread(dbuf, "betab", "betaw")
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) eiread(dbuf, "phi") eiread(dbuf, "betab", "betaw")
Sample EI data for RxC with estimates for White, Black, and Hispanic groups.
A data frame containing 93 observations.
Sample data
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
Aggregated data from 289 precincts in Fulton County, Georgia. The variable
t
represents the fraction voting in 1994 and x
the fraction in
1992. Beta_b is then the fraction who vote in both elections, and Beta_w
the fraction of nonvoters in 1992 who vote in the midterm election of 1994.
A data frame containing 289 observations.
Gary King, 1997, "Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data", http://hdl.handle.net/1902.1/LWMMKUTYXS UNF:3:DRWozWd89+vNLO7lY2AHbg== IQSS Dataverse Network Distributor V3 Version
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press. Section 13.1:235-41.
The fraction of blacks registered voters (x) and fraction of voter turnout (t) in each Louisiana precinct, along with the true fraction of black turnout (tb) and non-black turnout (tw).
A data frame containing 3262 observations.
Gary King, 1997, "Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data", http://hdl.handle.net/1902.1/LWMMKUTYXS UNF:3:DRWozWd89+vNLO7lY2AHbg== IQSS Dataverse Network Distributor V3 Version
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press. Section 1.4:22-4.
Aggregate voter registration and fraction black, in counties in Florida, Louisiana, North Carolina and South Carolina
A data frame containing 268 observations.
Gary King, 1997, "Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data", http://hdl.handle.net/1902.1/LWMMKUTYXS UNF:3:DRWozWd89+vNLO7lY2AHbg== IQSS Dataverse Network Distributor V3 Version
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press. Chapter 10.
New Jersey turnout and counts for White and non-white voters.
A data frame containing 493 observations.
Gary King, 1997, "Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data", http://hdl.handle.net/1902.1/LWMMKUTYXS UNF:3:DRWozWd89+vNLO7lY2AHbg== IQSS Dataverse Network Distributor V3 Version
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press. Section 1.4:24-5.
Visualizing EI (bound)
plot_bound(ei.object, options = list())
plot_bound(ei.object, options = list())
ei.object |
The output of |
options |
The list of options
|
a ggplot object
# 2x2 data(matproii) truth <- cbind(matproii$tb, matproii$tw) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", truth = truth, data = matproii) }) plot_bound(ei_res, options = list(parameter = "betab")) plot_bound(ei_res, options = list(parameter = "betaw")) # RxC data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) suppressMessages({ ei_resRxC <- ei(formula, data = RxCdata) }) plot_bound(ei_resRxC)
# 2x2 data(matproii) truth <- cbind(matproii$tb, matproii$tw) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", truth = truth, data = matproii) }) plot_bound(ei_res, options = list(parameter = "betab")) plot_bound(ei_res, options = list(parameter = "betaw")) # RxC data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) suppressMessages({ ei_resRxC <- ei(formula, data = RxCdata) }) plot_bound(ei_resRxC)
Get data used to create a plot
plot_data(x)
plot_data(x)
x |
a plot object. |
a data.frame
Visualizing EI (density)
plot_density(ei.object, options = list(parameter = "betab"))
plot_density(ei.object, options = list(parameter = "betab"))
ei.object |
The output of |
options |
The list of options
|
a ggplot object
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_density(ei_res, options = list(parameter = "betab")) plot_density(ei_res, options = list(parameter = "betaw"))
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_density(ei_res, options = list(parameter = "betab")) plot_density(ei_res, options = list(parameter = "betaw"))
Visualizing EI (Shiny visualization)
plot_movie(ei.object)
plot_movie(ei.object)
ei.object |
The output of |
Visualizing EI (simulation)
plot_sims(ei.object)
plot_sims(ei.object)
ei.object |
The output of |
a ggplot object
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_sims(ei_res)
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_sims(ei_res)
Tomography plot with various options.
plot_tomog( ei.object, options = list(color = TRUE, linecolor = "length", category = 0, breaks = "even", CI = NULL, points = FALSE, contour_ML = FALSE, contour_posterior = FALSE) )
plot_tomog( ei.object, options = list(color = TRUE, linecolor = "length", category = 0, breaks = "even", CI = NULL, points = FALSE, contour_ML = FALSE, contour_posterior = FALSE) )
ei.object |
The output of |
options |
The list of options
|
a ggplot object
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_tomog(ei_res) plot_tomog(ei_res, options = list(linecolor = "betab")) plot_tomog(ei_res, options = list(linecolor = "betaw", category = 5)) plot_tomog(ei_res, options = list(CI = 0.8)) plot_tomog(ei_res, options = list(points = TRUE, contour_ML = TRUE))
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) plot_tomog(ei_res) plot_tomog(ei_res, options = list(linecolor = "betab")) plot_tomog(ei_res, options = list(linecolor = "betaw", category = 5)) plot_tomog(ei_res, options = list(CI = 0.8)) plot_tomog(ei_res, options = list(points = TRUE, contour_ML = TRUE))
A tomography plot for an estimated Ecological Inference model in RxC data. This function supports the 2x3 case.
plot_tomogRxC(formula, data, total = NULL)
plot_tomogRxC(formula, data, total = NULL)
formula |
A formula of the form |
data |
data that contains the data that corresponds to the formula |
total |
‘total’ is the name of the variable in the dataset that contains the number of individuals in each unit |
a ggplot object
data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) plot_tomogRxC(formula, RxCdata)
data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) plot_tomogRxC(formula, RxCdata)
Compares truth to estimates at the district and precinct-level. Requires the truth
argument in the ei
object.
plot_truth(ei.object)
plot_truth(ei.object)
ei.object |
The output of |
a ggplot object
data(matproii) truth <- cbind(matproii$tb, matproii$tw) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", truth = truth, data = matproii) }) plot_truth(ei_res)
data(matproii) truth <- cbind(matproii$tb, matproii$tw) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", truth = truth, data = matproii) }) plot_truth(ei_res)
by
scatterplot with circles sized proportional to
.
plot_xt( ei.object, options = list(density = TRUE, fit = TRUE, CI = 0.8, goodman = FALSE) )
plot_xt( ei.object, options = list(density = TRUE, fit = TRUE, CI = 0.8, goodman = FALSE) )
ei.object |
The output of |
options |
The list of options
|
a ggplot object
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) # `plot_xt()` function plot_xt(ei_res) # `plot_xt()` with options plot_xt(ei_res, options = list(CI = 0.95, fit = FALSE, goodman = TRUE))
data(matproii) suppressMessages({ ei_res <- ei(formula = t ~ x, total = "n", data = matproii) }) # `plot_xt()` function plot_xt(ei_res) # `plot_xt()` with options plot_xt(ei_res, options = list(CI = 0.95, fit = FALSE, goodman = TRUE))
plot' method for the class
ei'.
## S3 method for class 'ei' plot(x, ...)
## S3 method for class 'ei' plot(x, ...)
x |
An |
... |
A list of options to return in graphs. See values below. |
Returns any of a set of possible graphical objects, mirroring those in the
examples in King (1997). Graphical option lci
is a logical value
specifying the use of the Law of Conservation of Ink, where the implicit
information in the data is represented through color gradients, i.e. the
color of the line is a function of the length of the tomography line. This
can be passed as an argument and is used for tomogD'' and
tomog” plots.
tomogD |
Tomography plot with the data only. See Figure 5.1, page 81. |
tomog |
Tomography plot with ML contours. See Figure 10.2, page 204. |
tomogCI |
Tomography plot with |
tomogCI95 |
Tomography plot with |
tomogE |
Tomography plot with estimated mean posterior |
tomogP |
Tomography plot with mean posterior contours. |
betab |
Density estimate (i.e., a smooth version
of a histogram) of point estimates of |
betaw |
Density estimate (i.e., a smooth version of a histogram) of
point estimates of |
xt |
Basic
|
xtc |
Basic |
xtfit |
|
xtfitg |
|
estsims |
All the simulated |
boundXb |
|
boundXw |
|
truth |
Compares truth to
estimates at the district and precinct-level. Requires |
movieD |
For each observation, one tomography plot appears with the line for the particular observation darkened. After the graph for each observation appears, the user can choose to view the next observation (hit return), jump to a specific observation number (type in the number and hit return), or stop (hit "s" and return). |
movie |
For each observation,
one page of graphics appears with the posterior distribution of
|
a base plot
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) plot(dbuf, "tomog") plot(dbuf, "tomog", "betab", "betaw", "xtfit")
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) plot(dbuf, "tomog") plot(dbuf, "tomog", "betab", "betaw", "xtfit")
Sample EI data for RxC with estimates for White, Black, and Hispanic groups.
A data frame containing 60 observations.
Sample data
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
A sample data.frame containing turnout, proportion Black, and total voters.
A data.frame containing 141 observations.
Sample data
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
summary' method for the class
ei'.
## S3 method for class 'ei' summary(object, ...)
## S3 method for class 'ei' summary(object, ...)
object |
An |
... |
A list of options to return in graphs. See values below. |
formatted summary object
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) summary(dbuf)
data(sample_ei) formula <- t ~ x dbuf <- ei(formula = formula, total = "n", data = sample_ei) summary(dbuf)
A tomography plot for an estimated Ecological Inference model in RxC data.
tomogRxC(formula, data, total = NULL, refine = 100)
tomogRxC(formula, data, total = NULL, refine = 100)
formula |
A formula of the form |
data |
data that contains the data that corresponds to the formula |
total |
‘total’ is the name of the variable in the dataset that contains the number of individuals in each unit |
refine |
specifies the amount of refinement for the image. Higher numbers mean better resolution. |
Gary King <<email: [email protected]>> and Molly Roberts <<email: [email protected]>>
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) tomogRxC(formula, data = RxCdata)
data(RxCdata) formula <- cbind(turnout, noturnout) ~ cbind(white, black, hisp) tomogRxC(formula, data = RxCdata)
A tomography plot in 3 dimensions for RxC Ecological Inference data and an estimated Ecological Inference model in RxC data.
tomogRxC3d( formula, data, total = NULL, lci = TRUE, estimates = FALSE, ci = FALSE, level = 0.95, seed = 1234, color = hcl(h = 30, c = 100, l = 60), transparency = 0.75, light = FALSE, rotate = TRUE )
tomogRxC3d( formula, data, total = NULL, lci = TRUE, estimates = FALSE, ci = FALSE, level = 0.95, seed = 1234, color = hcl(h = 30, c = 100, l = 60), transparency = 0.75, light = FALSE, rotate = TRUE )
formula |
A formula of the form |
data |
data that contains the data that corresponds to the formula |
total |
‘total’ is the name of the variable in the dataset that contains the number of individuals in each unit |
lci |
logical value specifying the use of the Law of Conservation of Ink, where the implicit information in the data is represented through color gradients, i.e. the color of the plane is a function of the area of the tomography plane. |
estimates |
logical value specifying whether the point estimates of
|
ci |
logical value specifying whether the estimated confidence ellipse is included on the tomography plot. |
level |
numeric value from 0 to 1 specifying the significance level of the confidence ellipse; eg. .95 refers to 95% confidence ellipse. |
seed |
seed value for model estimation. |
color |
color of tomography planes if lci=F. |
transparency |
numeric value from 0 to 1 specifying transparency of tomography planes; 0 is entirely transparent. |
light |
logical value specifying whether lights should be included in the rgl interface. The inclusion of lights will create shadows in the plot that may distort colors. |
rotate |
logical value specifying whether the plot will rotate for 20 seconds. |
Requires rgl package and rgl viewer.
a base plot
Gary King <<email: [email protected]>>; Molly Roberts <<email: [email protected]>>; Soledad Prillaman <<email: [email protected]..
Gary King (1997). A Solution to the Ecological Inference Problem. Princeton: Princeton University Press.
Returning an element in the ei object
values_ei(object, name)
values_ei(object, name)
object |
An |
name |
The name of the element to extract from the |