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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