MLCDW {Frames2} | R Documentation |
Multinomial logistic calibration estimator under dual frame approach with auxiliary information from the whole population
Description
Produces estimates for class totals and proportions using multinomial logistic regression from survey data obtained from a dual frame sampling design using a model calibrated dual frame approach with auxiliary information from the whole population. Confidence intervals are also computed, if required.
Usage
MLCDW (ysA, ysB, pik_A, pik_B, domains_A, domains_B, xsA, xsB, x, ind_sam, N_A,
N_B, N_ab = NULL, met = "linear", conf_level = NULL)
Arguments
ysA |
A data frame containing information about one or more factors, each one of dimension |
ysB |
A data frame containing information about one or more factors, each one of dimension |
pik_A |
A numeric vector of length |
pik_B |
A numeric vector of length |
domains_A |
A character vector of size |
domains_B |
A character vector of size |
xsA |
A numeric vector of length |
xsB |
A numeric vector of length |
x |
A numeric vector or length |
ind_sam |
A numeric vector of length |
N_A |
A numeric value indicating the size of frame A |
N_B |
A numeric value indicating the size of frame B |
N_ab |
(Optional) A numeric value indicating the size of the overlap domain |
met |
(Optional) A character vector indicating the distance that must be used in calibration process. Possible values are "linear", "raking" and "logit". Default is "linear". |
conf_level |
(Optional) A numeric value indicating the confidence level for the confidence intervals, if desired. |
Details
Multinomial logistic calibration estimator in dual frame using auxiliary information from the whole population for a proportion is given by
with the number of categories of the response variable,
the indicator variable for the i-th category of the response variable,
and
calibration weights which are calculated having into account a different set of constraints, depending on the case. For instance, if
and
are known, calibration constraints are
and
with and
being the maximum likelihood parameters of the multinomial logistic model considering weights
.
Value
MLCDW
returns an object of class "MultEstimatorDF" which is a list with, at least, the following components:
Call |
the matched call. |
Est |
class frequencies and proportions estimations for main variable(s). |
References
Molina, D., Rueda, M., Arcos, A. and Ranalli, M. G. (2015) Multinomial logistic estimation in dual frame surveys Statistics and Operations Research Transactions (SORT). To be printed.
See Also
Examples
data(DatMA)
data(DatMB)
data(DatPopM)
IndSample <- c(DatMA$Id_Pop, DatMB$Id_Pop)
N_FrameA <- nrow(DatPopM[DatPopM$Domain == "a" | DatPopM$Domain == "ab",])
N_FrameB <- nrow(DatPopM[DatPopM$Domain == "b" | DatPopM$Domain == "ab",])
N_Domainab <- nrow(DatPopM[DatPopM$Domain == "ab",])
#Let calculate proportions of categories of variable Prog using MLCDW estimator
#using Read as auxiliary variable
MLCDW(DatMA$Prog, DatMB$Prog, DatMA$ProbA, DatMB$ProbB, DatMA$Domain, DatMB$Domain,
DatMA$Read, DatMB$Read, DatPopM$Read, IndSample, N_FrameA, N_FrameB)
#Now, let suppose that the overlap domian size is known
MLCDW(DatMA$Prog, DatMB$Prog, DatMA$ProbA, DatMB$ProbB, DatMA$Domain, DatMB$Domain,
DatMA$Read, DatMB$Read, DatPopM$Read, IndSample, N_FrameA, N_FrameB, N_Domainab)
#Let obtain 95% confidence intervals together with the estimations
MLCDW(DatMA$Prog, DatMB$Prog, DatMA$ProbA, DatMB$ProbB, DatMA$Domain, DatMB$Domain,
DatMA$Read, DatMB$Read, DatPopM$Read, IndSample, N_FrameA, N_FrameB, N_Domainab,
conf_level = 0.95)