ImputeRegression2: Imputation of a sigle variable (y) by a regression model...

In statisticsnorway/Kostra: Functions for Kostra

Imputation of a sigle variable (y) by a regression model using a primary explanatory variable (x1) and a secondary explanatory variable (x2) for cases where the primary is missing.

Description

Imputation of a sigle variable (y) by a regression model using a primary explanatory variable (x1) and a secondary explanatory variable (x2) for cases where the primary is missing.

Usage

ImputeRegression2(
  data,
  idName = names(data)[1],
  strataName = NULL,
  x1Name = names(data)[3],
  x2Name = names(data)[4],
  yName = names(data)[5],
  method1 = "ordinary",
  method2 = "ordinary",
  limitModel = 2.5,
  limitIterate = 4.5,
  limitImpute = 50,
  returnSameType = TRUE
)

ImputeRegression2NewNames(
  ...,
  oldNames = c("yImputed", "Ntotal", "nImputedTotal", "AnImputedTotal", "BnImputedTotal",
    "estimateTotal", "AyHat", "ByHat", "AestimateOrig", "cvTotal"),
  newNames = c("estimate", "N", "nImputed", "AnImputed", "BnImputed", "estimate",
    "AestimateYHat", "BestimateYHat", "y", "cv")
)

ImputeRegression2Tall(..., iD = TalliD())

ImputeRegression2TallSmall(
  ...,
  iD = TalliD(),
  keep = c("ID", "estimate", "cv", "nImputed")
)

ImputeRegression2Wide(
  ...,
  addName = WideAddName(),
  sep = WideSep(),
  idNames = c("", "strata", ""),
  addLast = FALSE
)

ImputeRegression2WideSmall(
  ...,
  keep = c("id", "strata", "estimate", "cv", "nImputed"),
  addName = WideAddName(),
  sep = WideSep(),
  idNames = c("", "strata", ""),
  addLast = FALSE
)

Arguments

data	Input data set of class data.frame
idName	Name of id-variable(s)
strataName	Name of starta-variable. Single strata when NULL (default)
x1Name	Name of x1-variable
x2Name	Name of x2-variable
yName	Name of y-variable
method1	The method (model and weight) coded as a string: "ordinary" (default), "ratio", "noconstant", "mean" or "ratioconstant". I addition "ratio2" and "ratioconstant2" are alternatives where the weights are based on the other x-variable (x1<->x2).
method2	Similar to method2 above.
limitModel	Studentized residuals limit. Above limit -> group 2.
limitIterate	Studentized residuals limit for iterative calculation of studentized residuals.
limitImpute	Studentized residuals limit. Above limit -> group 3.
returnSameType	When TRUE (default) and when the type of input y variable(s) is integer, the output type of yImputed/estimate/estimateTotal is also integer. Estimates/sums are then calculated from rounded imputed values.

Details

Imputations are initially performed by running method1 using x1 within each strata. Division into three groups are based on studentized residuals. Calculations of studentized residuals are performed by iterativily throwing out observations from the model fitting. Missing imputed values caused by missing x1-values are thereafter imputed by running method2 using x2 within each strata. Combined estimates of seRobust,seEStimate and cv are calculated.

Value

Output of the alternative variants of the function are constructed similar to the variants of ImputeRegression.

Output of ImputeRegression2 and ImputeRegression2NewNames (using the names after or below) is a list of three data sets. micro has as many rows as input, aggregates has one row for each strata and total has a single row. Variables from the two imputations are named using "A" and "B". The individual variables (dropping "A" and "B") are:

micro consists of the following elements:

id	id from input
x1	The input x1 variable
x2	The input x2 variable
strata	The input strata variable (can be NULL)
category123	The three imputation groups: representative (1), correct but not representative (2), wrong (3).
yHat \emph{or estimateYHat}	Fitted values
yImputed \emph{or estimate}	Imputed y-data
rStud	The final studentized residuals
dffits	The final DFFITS statistic
hii	The final leverages (diagonal elements of hat matrix)
leaveOutResid	The final outside-model residual

aggregates consists of the following elements:

N	Number of observations in each strata
nImputed	Number of imputed observations in each strata
estimate	Total estimates from imputed data
cv	Coefficient of variation = seEstimate/estimate
estimateYhat	Totale estimate based on model fits
estimateOrig \emph{or y}	Estimate based on original data with missing set to zero
coef	The final first model coefficient
coefB	The final second model coefficient or zeros when only one coefficient in model.
n	The final number of observations in model.
sigmaHat	The final square root of the estimated variance parameter
seEstimate	The final standard error estimate of the total estimate from imputed data
seRobust	Robust variant of seEstimate (experimental)

total consists of the following elements:

Ntotal \emph{or N}	Number of observations
nImputedTotal \emph{or nImputed}	Total number of imputed observations
estimateTotal \emph{or estimate}	Total estimate for all strata
cvTotal or \emph{cv}	Total cv for all strata

Examples

rateData <- KostraData("rateData")               # Real Kostra data set
w <- rateData$data[, c(17,19,3,16,5)]              # Data with id, strata, x1, x2 and y
w <- w[is.finite(w[,"Ny.kostragruppe"]), ]       # Remove Longyearbyen
ImputeRegression2(w, strataName = names(w)[2])    # Works without combining strata
w[w[,"Ny.kostragruppe"]>13,"Ny.kostragruppe"]=13 # Combine small strata
ImputeRegression2(w, strataName = names(w)[2]) # Ordinary regressions
ImputeRegression2(w, strataName = names(w)[2],x1Name = names(w)[4], method1="ratio") # x1=x2 and no imputation in round 2
ImputeRegression2(w, strataName = names(w)[2],method1="ratio2",method2="ratio") # ratio2 needed since x1=0
ImputeRegression2(w, strataName = names(w)[2],method1="ratioconstant2",method2="ratioconstant")
ImputeRegression2Tall(w, strataName = names(w)[2])
ImputeRegression2TallSmall(w, strataName = names(w)[2])
ImputeRegression2Wide(w, strataName = names(w)[2])
ImputeRegression2WideSmall(w, strataName = names(w)[2])

statisticsnorway/Kostra documentation built on Nov. 2, 2024, 6:40 p.m.