getCutPoints: Calculate the threshold cut-points and individual adjusted...

In hopit: Hierarchical Ordered Probit Models with Application to Reporting Heterogeneity

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)

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.

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.

Author(s)

Maciej J. Danko

References

\insertRef

Jurges2007hopit

\insertRefOKSUZYAN2019hopit

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)

hopit documentation built on June 22, 2024, 6:55 p.m.