repQuantile {eatRep}  R Documentation 
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.
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 = seq(0, 1, 0.25), na.rm = FALSE, nBoot = NULL,
bootMethod = c("wSampling","wQuantiles") , doCheck = TRUE,
engine = c("survey", "BIFIEsurvey"), scale = 1, rscales = 1, mse=TRUE,
rho=NULL, verbose = TRUE, progress = TRUE)
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 
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 
repInd 
Variable name or column number of variable indicating replicate ID. In a jackknife procedure, this is the jackknife replicate
variable. If 
repWgt 
Normally, replicate weights are created by 
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, 
cross.differences 
Either a list of vectors, specifying the pairs of levels for which crosslevel differences should be computed.
Alternatively, if TRUE, crosslevel differences for all pairs of levels are computed. If FALSE, no crosslevel
differences are computed. (see examples 2a, 3, and 4 in the help file of the 
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 
bootMethod 
Optional: If standard error are computed in a bootstrap, two possible methods may be applied.

doCheck 
Logical: Check the data for consistency before analysis? If 
engine 
Which package should be used for estimation? 
scale 
scaling constant for variance, for details, see help page of 
rscales 
scaling constant for variance, for details, see help page of 
mse 
Logical: If 
rho 
Shrinkage factor for weights in Fay's method. See help page of 
verbose 
Logical: Show analysis information on console? 
progress 
Logical: Show progress bar on console? 
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.
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.
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"))