PML {Frames2} | R Documentation |
Pseudo Maximum Likelihood estimator
Description
Produces estimates for population totals and means using PML estimator from survey data obtained from a dual frame sampling design. Confidence intervals are also computed, if required.
Usage
PML(ysA, ysB, pi_A, pi_B, domains_A, domains_B, N_A, N_B, conf_level = NULL)
Arguments
ysA |
A numeric vector of length |
ysB |
A numeric vector of length |
pi_A |
A numeric vector of length |
pi_B |
A numeric vector of length |
domains_A |
A character vector of size |
domains_B |
A character vector of size |
N_A |
A numeric value indicating the size of frame A |
N_B |
A numeric value indicating the size of frame B |
conf_level |
(Optional) A numeric value indicating the confidence level for the confidence intervals, if desired. |
Details
Pseudo Maximum Likelihood estimator of population total is given by
\hat{Y}_{PML}(\hat{\theta}) = \frac{N_A - \hat{N}_{ab,PML}}{\hat{N}_a}\hat{Y}_a^A + \frac{N_B - \hat{N}_{ab,PML}}{\hat{N}_b}\hat{Y}_b^B + \frac{\hat{N}_{ab,PML}}{\hat{\theta}\hat{N}_{ab}^A + (1 - \hat{\theta})\hat{N}_{ab}^B}[\hat{\theta}\hat{Y}_{ab}^A + (1 - \hat{\theta})\hat{Y}_{ab}^B]
where \hat{\theta} \in [0, 1]
and \hat{N}_{ab,PML}
is the smaller of the roots of the quadratic equation
[\hat{\theta}/N_B + (1 - \hat{\theta})/N_A]x^2 - [1 + \hat{\theta}\hat{N}_{ab}^A/N_B + (1 - \hat{\theta})\hat{N}_{ab}^B/N_A]x + \hat{\theta}\hat{N}_{ab}^A + (1 - \hat{\theta})\hat{N}_{ab}^B=0.
Optimal value for \hat{\theta}
is \frac{\hat{N}_aN_B\hat{V}(\hat{N}_{ab}^B)}{\hat{N}_aN_B\hat{V}(\hat{N}_{ab}^B) + \hat{N}_bN_A\hat{V}(\hat{N}_{ab}^A)}
.
Variance is estimated according to following expression
\hat{V}(\hat{Y}_{PML}(\hat{\theta})) = \hat{V}(\sum_{i \in s_A}\tilde{z}_i^A) + \hat{V}(\sum_{i \in s_B}\tilde{z}_i^B)
where, \tilde{z}_i^A = y_i - \frac{\hat{Y}_a}{\hat{N}_a}
if i \in a
and \tilde{z}_i^A = \hat{\gamma}_{opt}(y_i - \frac{\hat{Y}_a}{\hat{N}_a}) + \hat{\lambda} \hat{\phi}
if i \in ab
with
\hat{\gamma}_{opt} = \frac{\hat{N}_a N_B \hat{V}(\hat{N}_{ab}^B)}{\hat{N}_a N_B \hat{V}(\hat{N}_{ab}^B) + \hat{N}_b + N_A + \hat{V}(\hat{N}_{ab}^A)}
\hat{\lambda} = \frac{n_A/N_A \hat{Y}_{ab}^A + n_B/N_B \hat{Y}_{ab}^B}{n_A/N_A \hat{N}_{ab}^A + n_B/N_B \hat{N}_{ab}^B} - \frac{\hat{Y}_a}{\hat{N}_a} - \frac{\hat{Y}_b}{\hat{N}_b}
\hat{\phi} = \frac{n_A \hat{N}_b}{n_A \hat{N}_b + n_B\hat{N}_a}
Similarly, we define \tilde{z}_i^B = y_i - \frac{\hat{Y}_b}{\hat{N}_b}
if i \in b
and \tilde{z}_i^B = (1 - \hat{\gamma}_{opt})(y_i - \frac{\hat{Y}_{ba}}{\hat{N}_{ab}}) + \hat{\lambda}(1 - \hat{\phi})
if i \in ba
Value
PML
returns an object of class "EstimatorDF" which is a list with, at least, the following components:
Call |
the matched call. |
Est |
total and mean estimation for main variable(s). |
VarEst |
variance estimation for main variable(s). |
If parameter conf_level
is different from NULL
, object includes component
ConfInt |
total and mean estimation and confidence intervals for main variables(s). |
In addition, components TotDomEst
and MeanDomEst
are available when estimator is based on estimators of the domains. Component Param
shows value of parameters involded in calculation of the estimator (if any).
By default, only Est
component (or ConfInt
component, if parameter conf_level
is different from NULL
) is shown. It is possible to access to all the components of the objects by using function summary
.
References
Skinner, C. J. and Rao, J. N. K. (1996) Estimation in Dual Frame Surveys with Complex Designs. Journal of the American Statistical Association, Vol. 91, 433, 349 - 356.
Examples
data(DatA)
data(DatB)
data(PiklA)
data(PiklB)
#Let calculate Pseudo Maximum Likelihood estimator for population total for variable Clothing
PML(DatA$Clo, DatB$Clo, PiklA, PiklB, DatA$Domain, DatB$Domain,
N_A = 1735, N_B = 1191)
#Now, let calculate Pseudo Maximum Likelihood estimator for population total for variable
#Feeding, using first order inclusion probabilities
PML(DatA$Feed, DatB$Feed, DatA$ProbA, DatB$ProbB, DatA$Domain, DatB$Domain,
N_A = 1735, N_B = 1191)
#Finally, let calculate Pseudo Maximum Likelihood estimator and a 90% confidence interval for
#population total for variable Leisure
PML(DatA$Lei, DatB$Lei, PiklA, PiklB, DatA$Domain, DatB$Domain,
N_A = 1735, N_B = 1191, 0.90)