standardizeCoef {hopit}

Standardization of the coefficients

Description

Calculate standardized the coefficients (e.g. disability weights for the health variables) using the predicted latent measure obtained from the model.
In the self-rated health example the standardized coefficients are called disability weights Jurges (2007) and are calculated for each health variable to provide information about the impact of a specific health measure on the latent index (see latentIndex). The disability weight for a health variable is equal to the ratio of the corresponding health coefficient and the difference between the lowest and the highest values of the predicted latent health. In other words, the disability weight reduces the latent index by some given amount or percentage (i.e., the latent index of every individual is reduced by the same amount if the person had a heart attack or other heart problems)(Jurges 2007).

Usage

standardizeCoef(model, namesf = identity)

standardiseCoef(model, namesf = identity)

disabilityWeights(model, namesf = identity)

Arguments

model

a fitted hopit model.

namesf

a vector of the names of coefficients or one argument function that modifies the names of coefficients.

Value

a vector with standardized coefficients.

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, getCutPoints, 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)

# a function that modifies the coefficient names.
txtfun <- function(x) gsub('_',' ',substr(x,1,nchar(x)-3))

# calculate and plot the disability weights
sc <- standardizeCoef(model1, namesf = txtfun)
sc

summary(sc)

plot(sc)