plotNorm function

Plot manifest and fitted norm scores

This function plots the manifest norm score against the fitted norm score from the inverse regression model per group. This helps to inspect the precision of the modeling process. The scores should not deviate too far from the regression line. Applicable for Taylor polynomial models.

plotNorm(
  model,
  age = NULL,
  score = NULL,
  width = NULL,
  weights = NULL,
  group = FALSE,
  minNorm = NULL,
  maxNorm = NULL,
  type = 0
)

Arguments

• model: The regression model, usually from the 'cnorm' or 'cnorm.betabinomial' function
• age: In case of beta binomial model, please provide the age vector
• score: In case of beta binomial model, please provide the score vector
• width: In case of beta binomial model, please provide the width for the sliding window. If null, the function tries to determine a sensible setting.
• weights: Vector or variable name in the dataset with weights for each individual case. If NULL, no weights are used.
• group: On optional grouping variable, use empty string for no group, the variable name for Taylor polynomial models or a vector with the groups for beta binomial models
• minNorm: lower bound of fitted norm scores
• maxNorm: upper bound of fitted norm scores
• type: Type of display: 0 = plot manifest against fitted values, 1 = plot manifest against difference values

Returns

A ggplot object representing the norm scores plot.

Examples

## Not run:

# Load example data set, compute model and plot results

# Taylor polynomial model
model <- cnorm(raw = elfe$raw, group = elfe$group)
plot(model, "norm")

# Beta binomial models; maximum number of items in elfe is n = 28
model.bb <- cnorm.betabinomial(elfe$group, elfe$raw, n = 28)
plotNorm(model.bb, age = elfe$group, score = elfe$raw)
## End(Not run)

See Also

Other plot: compare(), plot.cnorm(), plot.cnormBetaBinomial(), plot.cnormBetaBinomial2(), plotDensity(), plotDerivative(), plotNormCurves(), plotPercentileSeries(), plotPercentiles(), plotRaw(), plotSubset()