Description Usage Arguments Details Value See Also Examples
indAggEi
(Individual and Aggregate Ecological
Inference) Calculating ecological Inference including
Individual data using the two functions
convertEiData
and
runMBayes
1 2 3 4 |
form |
|
aggr |
|
indi |
|
IDCols |
vector of length 2 (or 1) giving the
column-names or numbers of ID column. First element for
|
whichPriori |
character string specifying the chosen
hyperpriori. Options are |
prioriPars |
vector or matrix of parameters for the specified hyperpriori in whichPriori |
startValsAlpha |
matrix with dimension
|
startValsBeta |
array with dimension
|
sample |
the sample size to be saved in output.
Total length of chain will be burnin |
burnin |
number of draws to be cut away from the
beginning of the Markov-Chain. |
thinning |
number specifying the thinning interval.
|
verbose |
an integer specifying whether the progress of the sampler is printed to the screen (defaults to 0). If verbose is greater than 0, the iteration number is printed to the screen every verboseth iteration |
betaVars |
array-object with dimensions |
alphaVars |
matrix of dimensions |
retBeta |
logical |
seed |
Default is |
indi
is a districts x [(r*c)+1]
data.frame
containing one district per line. One
column gives the ID of the districts which will be
connection to the ID column in the aggr
-data.frame.
For example a 2x3 ecological Inference problem with
formula
cbind(col1,col2,col3) ~
cbind(row1,row2)
will have the row format : [ID,
row1.col1, row1.col2, row1.col3, row2.col1, row2.col2,
row2.col3]
It is important that the formula
names correspond to
the exact column number in the indi-data.frame.
The aggr data.frame can have more columns than the names given in formula as long as the colnames exist
The whichPriori
-parameter has the options
"gamma"
or "expo"
and corresponding
prioriPars
-parameters in a "list"
:
"expo"
and numeric
list-element called
"lam"
corresponding to: α_{rc} \sim
Exp(λ)
"expo"
and matrix
list-element called "lam"
corresponding to:
α_{rc} \sim Exp(λ_{rc})
"gamma"
and two numeric
list-element called
"shape"
and "rate"
corresponding to:
α_{rc} \sim Gamma(λ_1, λ_2)
"gamma"
and two matrix
list-element called
"shape"
and "rate"
corresponding to:
α_{rc} \sim Gamma(λ_1^{rc},
λ_2^{rc})
The "seed"
attribute is generated by the
.Random.seed
-function.
object of class "eiwild"
which is a nested
list
with elements:
draws
alphaDraws
mcmc
-object
cellCounts
mcmc
-object
betaDraws
mcmc
-object
betaAcc
"numeric"
with Acceptance ratios
alphaAcc
"numeric"
with Acceptance ratios
alphaVars
matrix
with variances for
proposal density
betaVars
array
with
variances for proposal density
rowdf
original aggregate data
coldf
original
aggregate data
An Attribute called "seed"
is also
saved to reproduce the eiwild-object
convertEiData
,
runMBayes
, mcmc
tuneVars
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
# loading some fake election data
data(topleveldat)
form <- cbind(CSU_2, SPD_2, LINK_2, GRUN_2) ~ cbind(CSU_1, SPD_1, Link_1)
set.seed(1234)
res <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
sample=1000, thinning=2, burnin=100,verbose=100)
res
summary(res)
# with individual alpha-hyperpriori-parameters
hypMat <- list(shape = matrix(c(30,4,4,4,
4,30,4,4,
4,4,30,4), nrow=3, ncol=4, byrow=TRUE),
rate = matrix(c(1,2,2,2,
2,1,2,2,
2,2,1,2), nrow=3, ncol=4, byrow=TRUE))
set.seed(12345)
res2 <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
sample=1000, thinning=2, burnin=100, verbose=100,
prioriPars=hypMat, whichPriori="gamma")
## End(Not run)
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