View source: R/reportingheterogeneity.R
getCutPoints | R Documentation |
Calculate the threshold cut-points and individual adjusted responses using Jurges' method
getCutPoints(model, decreasing.levels = model$decreasing.levels, subset = NULL)
model |
a fitted |
decreasing.levels |
a logical indicating whether self-reported health classes are ordered in increasing order. |
subset |
an optional vector specifying a subset of observations. |
a list with the following components:
cutpoints |
cut-points for the adjusted categorical response levels with the corresponding percentiles of the latent index. |
adjusted.levels |
adjusted categorical response levels for each individual. |
Maciej J. Danko
Jurges2007hopit
\insertRefOKSUZYAN2019hopit
latentIndex
, standardiseCoef
, getLevels
, hopit
.
# DATA data(healthsurvey) # the order of response levels decreases from the best health to # the worst health; hence the hopit() parameter decreasing.levels # is set to TRUE levels(healthsurvey$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 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)
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