rankBySlidingWindow {cNORM} | R Documentation

## Determine the norm scores of the participants by sliding window (experimental)

### Description

The function retrieves all individuals in the predefined age range (x +/- width/2) around each case and ranks that individual based on this individually drawn sample. This function can be directly used with a continuous age variable in order to avoid grouping. When collecting data on the basis of a continuous age variable, cases located far from the mean age of the group receive distorted percentiles when building discrete groups and generating percentiles with the traditional approach. The distortion increases with distance from the group mean and this effect can be avoided by the sliding window. Nonetheless, please ensure, that the optional grouping variable in fact represents the correct mean age of the respective age groups, as this variable is later on used for displaying the manifest data in the percentile plots.

### Usage

    rankBySlidingWindow(
      data = NULL,
      age = "age",
      raw = "raw",
      weights = NULL,
      width,
      method = 4,
      scale = "T",
      descend = FALSE,
      descriptives = TRUE,
      nGroup = 0,
      group = NA,
      covariate = NULL,
      na.rm = TRUE
    )

### Arguments

`data` | data.frame with norm sample data
`age` | the continuous age variable. Setting 'age' to FALSE inhibits computation of powers of age and the interactions
`raw` | name of the raw value variable (default 'raw')
`weights` | Vector or variable name in the dataset with weights for each individual case. It can be used to compensate for moderate imbalances due to insufficient norm data stratification. Weights should be numerical and positive. Please note, that this feature is currently EXPERIMENTAL and subject to ongoing work! Precision of weighting increases with sample size. On the other hand, in large samples, it is easy to stratificate and then weighting is not needed anymore.
`width` | the width of the sliding window
`method` | Ranking method in case of bindings, please provide an index, choosing from the following methods: 1 = Blom (1958), 2 = Tukey (1949), 3 = Van der Warden (1952), 4 = Rankit (default), 5 = Levenbach (1953), 6 = Filliben (1975), 7 = Yu & Huang (2001)
`scale` | type of norm scale, either T (default), IQ, z or percentile (= no transformation); a double vector with the mean and standard deviation can as well, be provided f. e. c(10, 3) for Wechsler scale index points
`descend` | ranking order (default descent = FALSE): inverses the ranking order with higher raw scores getting lower norm scores; relevant for example when norming error scores, where lower scores mean higher performance
`descriptives` | If set to TRUE (default), information in n, mean, median and standard deviation per group is added to each observation
`nGroup` | If set to a positive value, a grouping variable is created with the desired number of equi distant groups, named by the group mean age of each group. It creates the column 'group' in the data.frame and in case, there is already one with that name, overwrites it.
`group` | Optional parameter for providing the name of the grouping variable (if present; overwritten if ngroups is used)
`covariate` | Include a binary covariate into the preparation and subsequently modeling, either by specifying the variable name or including the variable itself. BEWARE! Not all subsequent functions are already prepared for it. It is an experimental feature.
`na.rm` | remove values, where the percentiles could not be estimated, most likely happens in the context of weighting

### Details

In case of bindings, the function uses the medium rank and applies the algorithms already described in the `rankByGroup` function. At the upper and lower end of the data sample, the sliding stops and the sample is drawn from the interval min + width and max - width, respectively.

### Value

the dataset with the individual percentiles and norm scores

### Remarks on using covariates

So far the inclusion of a binary covariate is experimental and far from optimized. The according variable name has to be specified in the ranking procedure and the modeling includes this in the further process. At the moment, during ranking the data are split into the according degrees of the covariate and the ranking is done separately. This may lead to small sample sizes. Please take care to have enough cases in each combination. Additionally, covariates can lead to unstable modeling solutions. The question, if it is really reasonable to include covariates when norming a test is a decision beyond the pure data modeling. Please use with care or alternatively split the dataset into the two groups beforehand and model them separately.

### See Also

rankByGroup, computePowers

Other prepare: `computePowers()`, `prepareData()`, `rankByGroup()`

### Examples

    ## Not run:
    # Transformation using a sliding window
    data.elfe2 <- rankBySlidingWindow(relfe, raw = "raw", age = "group", width = 0.5)

    # Comparing this to the traditional approach should give us exactly the same
    # values, since the sample dataset only has a grouping variable for age
    data.elfe <- rankByGroup(elfe, group = "group")
    mean(data.elfe$normValue - data.elfe2$normValue)

    ## End(Not run)

[Package cNORM version 2.0.3 Index]