Description Usage Arguments Details Value Author(s) References Examples
Compute frequency tables for categorical variables (e.g. factors: dichotomous or polytomous) in complex cluster designs. Estimation of standard errors optionally takes the clustered structure and multiple imputed variables into account. To date, Jackknife-1 (JK1), Jackknife-2 (JK2) and Balanced repeated replicate (BRR) methods are implemented to account for clustered designs. Procedures of Rubin (1987) and Rubin (2003) are implemented to account for multiple imputed data and nested imputed data, if necessary. Conceptually, the function combines replication and imputation methods. Technically, this is a wrapper for the svymean() function of the survey package.
1 2 3 4 5 | jk2.table(datL, ID, wgt = NULL, type = c("JK1", "JK2", "BRR"), PSU = NULL, repInd = NULL,
repWgt = NULL, nest=NULL, imp=NULL, groups = NULL, group.splits = length(groups),
group.differences.by = NULL, cross.differences = FALSE, chiSquare = FALSE, correct = TRUE, group.delimiter = "_",
trend = NULL, linkErr = NULL, dependent, separate.missing.indicator = FALSE, na.rm=FALSE,
expected.values = NULL, doCheck = TRUE, forceTable = FALSE )
|
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. Without cluster replicates (i.e., if |
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, |
group.differences.by |
Optional: Specifies one grouping variable for which a chi-square test should be applied.
The corresponding variable must be included in the |
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 jk2.mean) |
chiSquare |
Logical. Applies only if 'group.differences.by' was specified and does not contain 'wholePop'. Defines whether group differences should be represented in a chi square test or in (mean) differences of each group's relative frequency. |
correct |
Logical. Applies only if 'group.differences.by' is requested without cluster replicates. A logical indicating whether to apply continuity correction when computing the test statistic for 2 by 2 tables. See help page of 'chisq.test' for further details. |
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 trend 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. |
separate.missing.indicator |
Logical. Should frequencies of missings in dependent variable be integrated? Note: That is only useful if missing occur as |
na.rm |
Logical: Should cases with missing values be dropped? |
expected.values |
Optional. A vector auf values expected in dependent variable. Recommend to left this argument empty. |
doCheck |
Logical: Check the data for consistency before analysis? If |
forceTable |
Logical: Function decides internally whether the table or the mean function of |
Function first creates replicate weights based on PSU and repInd variables according to JK2 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 svymean()
called by svyby()
implemented in the 'survey'
package.
Relative frequencies of the categories of the dependent variable are computed by the means of the dichotomous indicators
(e.g. dummy variables) of each category. The results of the several analyses are then pooled according to Rubin's 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.
Sebastian Weirich
Rubin, D.B. (2003): Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica 57, 1, 3–18.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | 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 frequencies of polytomous competence levels (1, 2, 3, 4, 5)
### conditionally on country, using a chi-square test to decide whether the distribution
### varies between countries (it's an overall test, i.e. with three groups, df1=8).
freq.tab1 <- jk2.table(datL = rdN1y10, ID = "idstud", wgt = "wgt", imp="imp",
type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country",
group.differences.by = "country", dependent = "comp", chiSquare = TRUE)
res1 <- report(freq.tab1, add = list ( domain = "reading" ))
### Second example: Computes frequencies of polytomous competence levels (1, 2, 3, 4, 5)
### conditionally on country. Now we test whether the frequency of each single category
### differs between pairs of countries (it's not an overall test ... jk2.table now
### calls jk2.mean internally, using dummy variables
freq.tab2 <- jk2.table(datL = rdN1y10, ID = "idstud", wgt = "wgt", imp="imp",
type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country",
group.differences.by = "country", dependent = "comp", chiSquare = FALSE)
res2 <- report(freq.tab2, add = list ( domain = "reading" ))
### Third example: trend estimation and nested imputation and 'by' loop
freq.tab3 <- by ( data = lsa, INDICES = lsa[,"domain"], FUN = function (subdat) {
jk2.table(datL = subdat, ID = "idstud", wgt = "wgt", imp="imp", nest = "nest",
type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country",
group.differences.by = "country", group.splits = 0:1, cross.differences = TRUE,
dependent = "comp", chiSquare = FALSE, trend = "year", linkErr = "leComp") })
res3 <- do.call("rbind", lapply(names(freq.tab3), FUN = function (domain) {
report(freq.tab3[[domain]], trendDiffs = TRUE, add = list ( domain = domain ))}))
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