repQuantile {eatRep}

Replication methods (JK1, JK2 and BRR) for quantiles and trend estimation.

Description

Compute quantiles with standard errors for complex cluster designs with multiple imputed variables (e.g. plausible values) based on Jackknife (JK1, JK2) or balanced repeated replicates (BRR) procedure. Conceptually, the function combines replication methods and methods for multiple imputed data. Technically, this is a wrapper for the svyquantile() function of the survey package.

Usage

repQuantile(datL, ID, wgt = NULL, type = c("none", "JK2", "JK1", "BRR", "Fay"),
            PSU = NULL, repInd = NULL, repWgt = NULL, nest=NULL, imp=NULL,
            groups = NULL, group.splits = length(groups), cross.differences = FALSE,
            group.delimiter = "_", trend = NULL, linkErr = NULL, dependent,
            probs = c(0.25, 0.50, 0.75),  na.rm = FALSE, nBoot = NULL,
            bootMethod = c("wSampling","wQuantiles") , doCheck = TRUE,
            scale = 1, rscales = 1, mse=TRUE,
            rho=NULL, verbose = TRUE, progress = TRUE)

Arguments

datL

Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis.

ID

Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values.

wgt

Optional: Variable name or column number of weighting variable. If no weighting variable is specified, all cases will be equally weighted.

type

Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt).

PSU

Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample.

repInd

Variable name or column number of variable indicating replicate ID. In a jackknife procedure, this is the jackknife replicate variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample.

repWgt

Normally, replicate weights are created by repQuantile directly from PSU and repInd variables. Alternatively, if replicate weights are included in the data.frame, specify the variable names or column number in the repWgt argument.

nest

Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets.

imp

Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets.

groups

Optional: vector of names or column numbers of one or more grouping variables.

group.splits

Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also in the whole group or overlying groups. See examples for more details.

cross.differences

Either a list of vectors, specifying the pairs of levels for which cross-level differences should be computed. Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level differences are computed. (see examples 2a, 3, and 4 in the help file of the repMean function)

group.delimiter

Character string which separates the group names in the output frame.

trend

Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both 'sub populations' partitioned by the trend variable.

linkErr

Optional: name or column number of the linking error variable. If 'NULL', a linking error of 0 will be assumed in trend estimation. Alternatively, the linking error may be given as a single scalar value (i.e. 'linkErr = 1.225').

dependent

Variable name or column number of the dependent variable.

probs

Numeric vector with probabilities for which to compute quantiles.

na.rm

Logical: Should cases with missing values be dropped?

nBoot

Optional: Without replicates, standard error cannot be computed in a weighted sample. Alternatively, standard errors may be computed using the boot package. nBoot therefore specifies the number of bootstrap samples. If not specified, no standard errors will be given. In analyses containing replicates or samples without specifying person weights, nBoot will be ignored.

bootMethod

Optional: If standard error are computed in a bootstrap, two possible methods may be applied. wSampling requests the function to draw nBoot weighted bootstrap samples for which unweighted quantiles are computed. wQuantiles requests the function to draw nBoot unweighted bootstrap samples for which weighted quantiles are computed.

doCheck

Logical: Check the data for consistency before analysis? If TRUE groups with insufficient data are excluded from analysis to prevent subsequent functions from crashing.

scale

scaling constant for variance, for details, see help page of svrepdesign from the survey package

rscales

scaling constant for variance, for details, see help page of svrepdesign from the survey package

mse

Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates. See help page of svrepdesign from the survey package for further details.

rho

Shrinkage factor for weights in Fay's method. See help page of svrepdesign from the survey package for further details.

verbose

Logical: Show analysis information on console?

progress

Logical: Show progress bar on console?

Details

Function first creates replicate weights based on PSU and repInd variables according to JK2 or BRR procedure implemented in WesVar. According to multiple imputed data sets, a workbook with several analyses is created. The function afterwards serves as a wrapper for svyquantile called by svyby implemented in the survey package. The results of the several analyses are then pooled according to Rubins rule, which is adapted for nested imputations if the dependent argument implies a nested structure.

Value

A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data frames with at least six columns each. For each subpopulation denoted by the groups statement, each dependent variable, each parameter (i.e., the values of the corresponding categories of the dependent variable) and each coefficient (i.e., the estimate and the corresponding standard error) the corresponding value is given.

group

Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘wholeGroup’.

depVar

Denotes the name of the dependent variable in the analysis.

modus

Denotes the mode of the analysis. For example, if a JK2 analysis without sampling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’.

parameter

Denotes the parameter of the regression model for which the corresponding value is given further. For frequency tables, this is the value of the category of the dependent variable which relative frequency is given further.

coefficient

Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate).

value

The value of the parameter, i.e. the relative frequency or its standard error.

If groups were specified, further columns which are denoted by the group names are added to the data frame.

Examples

data(lsa)
### Example 1: only means, SD and variances for each country
### We only consider domain 'reading'
rd     <- lsa[which(lsa[,"domain"] == "reading"),]

### We only consider the first "nest".
rdN1   <- rd[which(rd[,"nest"] == 1),]

### First, we only consider year 2010
rdN1y10<- rdN1[which(rdN1[,"year"] == 2010),]

### First example: Computes percentile in a nested data structure for reading 
### scores conditionally on country and for the whole group 
perzent   <- repQuantile(datL = rd, ID = "idstud", wgt = "wgt", type = "JK2",
             PSU = "jkzone", repInd = "jkrep", imp = "imp", nest="nest",
             groups = "country", group.splits = c(0:1), dependent = "score", 
             probs = seq(0.1,0.9,0.2) )
res       <- report(perzent, add = list(domain = "reading"))

### Second example: Computes percentile for reading scores conditionally on country,
### use 100 bootstrap samples, assume no nested structure 
perzent   <- repQuantile(datL = rdN1y10, ID = "idstud", wgt = "wgt",
             imp = "imp", groups = "country", dependent = "score",
             probs = seq(0.1,0.9,0.2), nBoot = 100 )
res       <- report(perzent, add = list(domain = "reading"))