hopit (version 0.11.6)

getCutPoints: Calculate the threshold cut-points and individual adjusted responses using Jurges' method

Description

Calculate the threshold cut-points and individual adjusted responses using Jurges' method

Usage

getCutPoints(model, decreasing.levels = model$decreasing.levels, subset = NULL)

Value

a list with the following components:

cutpoints

cut-points for the adjusted categorical response levels with the corresponding percentiles of the latent index.

adjusted.levels

adjusted categorical response levels for each individual.

Arguments

model

a fitted hopit model.

decreasing.levels

a logical indicating whether self-reported health classes are ordered in increasing order.

subset

an optional vector specifying a subset of observations.

Author

Maciej J. Danko

References

Jurges2007hopit

OKSUZYAN2019hopit

See Also

latentIndex, standardiseCoef, getLevels, hopit.

Examples

# DATA
data(healthsurvey)

# the order of response levels decreases from the best health to
# the worst health; hence the hopit() parameter decreasing.levels
# is set to TRUE
levels(healthsurvey$health)

# Example 1 ---------------------

# fit a model
model1 <- hopit(latent.formula = health ~ hypertension + high_cholesterol +
                heart_attack_or_stroke + poor_mobility + very_poor_grip +
                depression + respiratory_problems +
                IADL_problems + obese + diabetes + other_diseases,
              thresh.formula = ~ sex + ageclass + country,
              decreasing.levels = TRUE,
              control = list(trace = FALSE),
              data = healthsurvey)

# calculate the health index cut-points
z <- getCutPoints(model = model1)
z$cutpoints

plot(z)

# tabulate the adjusted health levels for individuals (Jurges method):
rev(table(z$adjusted.levels))

# tabulate the original health levels for individuals
table(model1$y_i)

# tabulate the predicted health levels
table(model1$Ey_i)