rawTable {cNORM} | R Documentation |
This function is comparable to 'normTable', despite it reverses the assignment: A table with raw scores and the according norm scores for a specific age based on the regression model is generated. This way, the inverse function of the regression model is solved numerically with brute force. Please specify the range of raw values, you want to cover. With higher precision and smaller stepping, this function becomes computational intensive. In case a confidence coefficient (CI) and the reliability is specified, confidence intervals are computed as well, including a correction for regression to the mean.
rawTable( A, model, minRaw = NULL, maxRaw = NULL, minNorm = NULL, maxNorm = NULL, step = 1, covariate = NULL, monotonuous = TRUE, CI = 0.9, reliability = NULL )
A |
the age, either single value or vector with age values |
model |
The regression model or a cnorm object |
minRaw |
The lower bound of the raw score range |
maxRaw |
The upper bound of the raw score range |
minNorm |
Clipping parameter for the lower bound of norm scores (default 25) |
maxNorm |
Clipping parameter for the upper bound of norm scores (default 25) |
step |
Stepping parameter for the raw scores (default 1) |
covariate |
In case, a covariate has been used, please specify the degree of the covariate / the specific value here. |
monotonuous |
corrects for decreasing norm scores in case of model inconsistencies (default) |
CI |
confidence coefficient, ranging from 0 to 1, default .9 |
reliability |
coefficient, ranging between 0 to 1 |
either data.frame with raw scores and the predicted norm scores in case of simple A value or a list of norm tables if vector of A values was provided
normTable
Other predict:
derivationTable()
,
getNormCurve()
,
normTable()
,
predictNorm()
,
predictRaw()
# Generate cnorm object from example data cnorm.elfe <- cnorm(raw = elfe$raw, group = elfe$group) # generate a norm table for the raw value range from 0 to 28 for the time point month 7 of grade 3 table <- rawTable(3 + 7 / 12, cnorm.elfe, minRaw = 0, maxRaw = 28) # generate several raw tables table <- rawTable(c(2.5, 3.5, 4.5), cnorm.elfe, minRaw = 0, maxRaw = 28) # additionally compute confidence intervals table <- rawTable(c(2.5, 3.5, 4.5), cnorm.elfe, minRaw = 0, maxRaw = 28, CI = .9, reliability = .94)