Description Usage Arguments Details Value Author(s) References See Also Examples
Adjusts the weights of survey repondents so that the marginal distributions of certain variables fit more closely to those from a more precise source (e.g. Census Bureau's data).
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ori |
a matrix containing the factor levels. The levels should start from 1 and count upwards as in 1,2,…. |
mar |
a vector giving the marginal distributions for each of the
factors that are listed in the same order as in |
raw |
a vector of the raw counts of survey respondents
corresponding to each line in |
wgt |
a vector of the original weights corresponding to each
line in |
unique |
whether the factor level combination in |
bound |
a vector of two elements giving the lower and upper bounds for the final weight ratios. The extreme weight ratio is reset to either the lower or upper bound. Default is c(0,100). In application a more practical bound might be c(0.5,2). |
trace |
if |
tolerance |
the percentage of the smallest eigenvalue that is to be used as the lower start point of the golden selection searhch. Default is 0.1. |
penalty |
measures the strength of a penalty term (it puts penalty
if the number of zero weighting ratios is large) in the GCV
function. It is a multiplicative factor of the form (1+q)^p,
where p is the |
x |
an object returned by calling |
object |
an object returned by calling |
... |
parameters to be passed to the generic fucntion. |
ori
, raw
, wgt
typically come from survey
data with categorical responses. The intent is to adjust the wgt
so that the survey sample is more representative of the universe from
where it comes. It is accomplished by fitting the marginal distributions
of the sample to those of the universe, or those from a more precise
source (e.g. census data). The method is based on the Tikhonov
regularization.
The print
method prints out the weight ratios, along with their
corresponding factor level combinations. This data can then be matched
back to the original sample data to adjust the original weights (by
multipling each original weight with the weight ratio).
The summary
method prints out various running statistics.
The plot
method makes a panel of four diagnostic plots.
None.
Feiming Chen
Feiming Chen (2006) A Heuristic Method for Weighting Survey Respondents. JSM 2006 Proceedings.
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