Computes predicted probabilities for integer scores on the original scale \(\{0, 1, \ldots, K\}\) implied by the fitted beta interval model.
Usage
brs_predict_scoreprob(
object,
newdata = NULL,
scores = NULL,
format = c("matrix", "long"),
id_col = "id"
)Value
If format = "matrix", a numeric matrix with one row per
observation and one column per requested score.
If format = "long", a long data frame with columns
id_col, score, and prob.
Details
For a score \(s\) and \(K =\) ncuts, probabilities are computed as:
\(P(Y=s)=F(\mathrm{lim}/K)\) for \(s=0\),
\(P(Y=s)=1-F((K-\mathrm{lim})/K)\) for \(s=K\),
\(P(Y=s)=F((s+\mathrm{lim})/K)-F((s-\mathrm{lim})/K)\) for \(s \in \{1,\ldots,K-1\}\),
where \(F\) is the beta CDF under the fitted \((\mu_i,\phi_i)\).
References
Lopes, J. E. (2023). Modelos de regressao beta para dados de escala. Master's dissertation, Universidade Federal do Parana, Curitiba. URI: https://hdl.handle.net/1884/86624.
Hawker, G. A., Mian, S., Kendzerska, T., and French, M. (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care and Research, 63(S11), S240-S252. doi:10.1002/acr.20543
Hjermstad, M. J., Fayers, P. M., Haugen, D. F., et al. (2011). Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. Journal of Pain and Symptom Management, 41(6), 1073-1093. doi:10.1016/j.jpainsymman.2010.08.016
Examples
# \donttest{
dat <- data.frame(
y = c(
0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
10, 40, 55, 70, 85, 25, 35, 65, 80, 15
),
x1 = rep(c(1, 2), 10),
x2 = rep(c(0, 0, 1, 1), 5)
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brs(y ~ x1, data = prep)
pmat <- brs_predict_scoreprob(fit)
head(pmat[, 1:5])
#> score_0 score_1 score_2 score_3 score_4
#> [1,] 0.01415135 0.01827163 0.01528009 0.01384051 0.01292160
#> [2,] 0.02412204 0.02639973 0.02075481 0.01816032 0.01654409
#> [3,] 0.01415135 0.01827163 0.01528009 0.01384051 0.01292160
#> [4,] 0.02412204 0.02639973 0.02075481 0.01816032 0.01654409
#> [5,] 0.01415135 0.01827163 0.01528009 0.01384051 0.01292160
#> [6,] 0.02412204 0.02639973 0.02075481 0.01816032 0.01654409
plong <- brs_predict_scoreprob(fit, scores = 0:10, format = "long")
head(plong)
#> id score prob
#> 1 1 0 0.01415135
#> 2 1 1 0.01827163
#> 3 1 2 0.01528009
#> 4 1 3 0.01384051
#> 5 1 4 0.01292160
#> 6 1 5 0.01226162
# }