getCutPoints {hopit}

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

Jurges H (2007). “True health vs response styles: exploring cross-country differences in self-reported health.” Health Economics, 16(2), 163-178. doi:10.1002/hec.1134.

Oksuzyan A, Danko MJ, Caputo J, Jasilionis D, Shkolnikov VM (2019). “Is the story about sensitive women and stoical men true? Gender differences in health after adjustment for reporting behavior.” Social Science & Medicine, 228, 41-50. doi:10.1016/j.socscimed.2019.03.002.

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)