WeightsCalSF {Frames2}

g-weights for the SF calibration estimator

Description

Computes the g-weights for the SF calibration estimator.

Usage

WeightsCalSF(ysA, ysB, pi_A, pi_B, pik_ab_B, pik_ba_A, 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_A.

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.

pik_ab_B

A numeric vector of size n_A containing first order inclusion probabilities according to sampling desing in frame B for units belonging to overlap domain that have been selected in s_A.

pik_ba_A

A numeric vector of size n_B containing first order inclusion probabilities according to sampling desing in frame A for units belonging to overlap domain that have been selected in s_B.

domains_A

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

domains_B

A character vector of size 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".

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

Value

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

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 SF calibration estimator for variable Clothing,
#without considering any auxiliary information
WeightsCalSF(DatA$Clo, DatB$Clo, PiklA, PiklB, DatA$ProbB, DatB$ProbA, 
DatA$Domain, DatB$Domain)

#Now, let calculate g-weights for the SF calibration estimator for variable Leisure
#when the frame sizes and the overlap domain size are known
WeightsCalSF(DatA$Lei, DatB$Lei, PiklA, PiklB, DatA$ProbB, DatB$ProbA, 
DatA$Domain, DatB$Domain, N_A = 1735, N_B = 1191, N_ab = 601)

#Finally, let calculate g-weights for the SF calibration estimator
#for variable Feeding, considering Income and Metres2 as auxiliary 
#variables and with frame sizes and overlap domain size known.
WeightsCalSF(DatA$Feed, DatB$Feed, PiklA, PiklB, DatA$ProbB, DatB$ProbA, 
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)