plotNormCurves | R Documentation |
This function plots the norm curves based on the regression model. It supports both Taylor polynomial models and beta-binomial models.
plotNormCurves(
model,
normList = NULL,
minAge = NULL,
maxAge = NULL,
step = 0.1,
minRaw = NULL,
maxRaw = NULL
)
model |
The model from the bestModel function, a cnorm object, or a cnormBetaBinomial / cnormBetaBinomial2 object. |
normList |
Vector with norm scores to display. If NULL, default values are used. |
minAge |
Age to start with checking. If NULL, it's automatically determined from the model. |
maxAge |
Upper end of the age check. If NULL, it's automatically determined from the model. |
step |
Stepping parameter for the age check, usually 1 or 0.1; lower scores indicate higher precision. |
minRaw |
Lower end of the raw score range, used for clipping implausible results. If NULL, it's automatically determined from the model. |
maxRaw |
Upper end of the raw score range, used for clipping implausible results. If NULL, it's automatically determined from the model. |
Please check the function for inconsistent curves: The different curves should not intersect. Violations of this assumption are a strong indication of violations of model assumptions in modeling the relationship between raw and norm scores.
Common reasons for inconsistencies include: 1. Vertical extrapolation: Choosing extreme norm scores (e.g., scores <= -3 or >= 3). 2. Horizontal extrapolation: Using the model scores outside the original dataset. 3. The data cannot be modeled with the current approach, or you need another power parameter (k) or R2 for the model.
A ggplot object representing the norm curves.
checkConsistency
, plotDerivative
, plotPercentiles
Other plot:
compare()
,
plot.cnorm()
,
plot.cnormBetaBinomial()
,
plot.cnormBetaBinomial2()
,
plotDensity()
,
plotDerivative()
,
plotNorm()
,
plotPercentileSeries()
,
plotPercentiles()
,
plotRaw()
,
plotSubset()
## Not run:
# For Taylor continuous norming model
m <- cnorm(raw = ppvt$raw, group = ppvt$group)
plotNormCurves(m, minAge=2, maxAge=5)
# For beta-binomial model
bb_model <- cnorm.betabinomial(age = ppvt$age, score = ppvt$raw, n = 228)
plotNormCurves(bb_model)
## End(Not run)
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