View source: R/reportingheterogeneity.R
latentIndex | R Documentation |
Calculate the latent index from the fitted model. The latent index is a standardized latent measure that takes values from 0 to 1, where 0 refers to the worst predicted state (the maximal observed value for the latent measure) and 1 refers to the best predicted state (the minimal observed value for the latent measure).
latentIndex(model, subset = NULL)
healthIndex(model, subset = NULL)
model |
a fitted |
subset |
an optional vector that specifies a subset of observations. |
a vector with a latent index for each individual.
Maciej J. Danko
Jurges2007hopit
\insertRefOKSUZYAN2019hopit
standardizeCoef
, getCutPoints
, getLevels
, hopit
.
# 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
hi <- latentIndex(model1)
summary(hi)
# plot a simple histogram of the function output
hist(hi, col='deepskyblue3')
#plot the reported health status versus the health index.
plot(hi, response = "data", ylab = 'Health index',
col='deepskyblue3', main = 'Reported health levels')
# plot the model-predicted health levels versus the health index.
plot(hi, response = "fitted", ylab = 'Health index',
col='deepskyblue3', main = 'Model-predicted health levels')
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