Frames2 (version 0.2.1)

WeightsCalDF: g-weights for the dual frame calibration estimator

Description

Computes the g-weights for the dual frame calibration estimator.

Usage

WeightsCalDF(ysA, ysB, pi_A, pi_B, domains_A, domains_B, N_A = NULL, N_B = NULL, 
N_ab = NULL, xsAFrameA = NULL, xsBFrameA = NULL, xsAFrameB = NULL, xsBFrameB = NULL, 
xsT = NULL, XA = NULL, XB = NULL, X = NULL, met = "linear")

Arguments

ysA

A numeric vector of length $n_A$ or a numeric matrix or data frame of dimensions $n_A$ x $c$ containing information about variable(s) of interest from $s_A$.

ysB

A numeric vector of length $n_B$ or a numeric matrix or data frame of dimensions $n_B$ x $c$ containing information about variable(s) of interest from $s_B$.

pi_A

A numeric vector of length $n_A$ or a square numeric matrix of dimension $n_A$ containing first order or first and second order inclusion probabilities for units included in $s_A$.

pi_B

A numeric vector of length $n_B$ or a square numeric matrix of dimension $n_B$ containing first order or first and second order inclusion probabilities for units included in $s_B$.

domains_A

A character vector of length $n_A$ indicating the domain each unit from $s_A$ belongs to. Possible values are "a" and "ab".

domains_B

A character vector of length $n_B$ indicating the domain each unit from $s_B$ belongs to. Possible values are "b" and "ba".

N_A

(Optional) A numeric value indicating the size of frame A.

N_B

(Optional) A numeric value indicating the size of frame B.

N_ab

(Optional) A numeric value indicating the size of the overlap domain.

xsAFrameA

(Optional) A numeric vector of length $n_A$ or a numeric matrix or data frame of dimensions $n_A$ x $m_A$, with $m_A$ the number of auxiliary variables in frame A, containing auxiliary information in frame A for units included in $s_A$.

xsBFrameA

(Optional) A numeric vector of length $n_B$ or a numeric matrix or data frame of dimensions $n_B$ x $m_A$, with $m_A$ the number of auxiliary variables in frame A, containing auxiliary information in frame A for units included in $s_B$. For units in domain $b$, these values are 0.

xsAFrameB

(Optional) A numeric vector of length $n_A$ or a numeric matrix or data frame of dimensions $n_A$ x $m_B$, with $m_B$ the number of auxiliary variables in frame B, containing auxiliary information in frame B for units included in $s_A$. For units in domain $a$, these values are 0.

xsBFrameB

(Optional) A numeric vector of length $n_B$ or a numeric matrix or data frame of dimensions $n_B$ x $m_B$, with $m_B$ the number of auxiliary variables in frame B, containing auxiliary information in frame B for units included in $s_B$.

xsT

(Optional) A numeric vector of length $n$ or a numeric matrix or data frame of dimensions $n$ x $m_T$, with $m_T$ the number of auxiliary variables in both frames, containing auxiliary information for all units in the entire sample $s = s_A \cup s_B$.

XA

(Optional) A numeric value or vector of length $m_A$, with $m_A$ the number of auxiliary variables in frame A, indicating the population totals for the auxiliary variables considered in frame A.

XB

(Optional) A numeric value or vector of length $m_B$, with $m_B$ the number of auxiliary variables in frame B, indicating the population totals for the auxiliary variables considered in frame B.

X

(Optional) A numeric value or vector of length $m_T$, with $m_T$ the number of auxiliary variables in both frames, indicating the population totals for the auxiliary variables considered in both frames.

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".

Value

A numeric vector containing the g-weights for the dual frame calibration estimator.

Details

Function provides g-weights in following scenarios:

• There is not any additional auxiliary variable
  • $N_A, N_B$ and $N_{ab}$ unknown
  • $N_{ab}$ known and $N_A$ and $N_B$ unknown
  • $N_A$ and $N_B$ known and $N_{ab}$ unknown
  • $N_A, N_B$ and $N_{ab}$ known

• At least, one additional auxiliary variable is available
  • $N_{ab}$ known and $N_A$ and $N_B$ unknown
  • $N_A$ and $N_B$ known and $N_{ab}$ unknown
  • $N_A, N_B$ and $N_{ab}$ known

References

Ranalli, M. G., Arcos, A., Rueda, M. and Teodoro, A. (2013) Calibration estimationn in dual frame surveys. arXiv:1312.0761 [stat.ME]

Deville, J. C., S\"arndal, C. E. (1992) Calibration estimators in survey sampling. Journal of the American Statistical Association, 87, 376 - 382

Examples

data(DatA)
data(DatB)
data(PiklA)
data(PiklB)

#Let calculate g-weights for the dual frame calibration estimator for variable Feeding, 
#without considering any auxiliary information
WeightsCalDF(DatA$Feed, DatB$Feed, PiklA, PiklB, DatA$Domain, DatB$Domain)

#Now, let calculate g-weights for the dual frame calibration estimator for variable Clothing 
#when the frame sizes and the overlap domain size are known
WeightsCalDF(DatA$Clo, DatB$Clo, PiklA, PiklB, DatA$Domain, DatB$Domain, 
N_A = 1735, N_B = 1191, N_ab = 601)

#Finally, let calculate g-weights for the dual frame calibration estimator
#for variable Feeding, considering Income as auxiliary variable in frame A
#and Metres2 as auxiliary variable in frame B and with frame sizes and overlap 
#domain size known.
WeightsCalDF(DatA$Feed, DatB$Feed, PiklA, PiklB, DatA$Domain, DatB$Domain, 
N_A = 1735, N_B =  1191, N_ab = 601, xsAFrameA = DatA$Inc, xsBFrameA = DatB$Inc, 
xsAFrameB = DatA$M2, xsBFrameB = DatB$M2, XA = 4300260, XB = 176553)