Introduction to betaregscale

Overview

The betaregscale package provides maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. Common examples include pain intensity scales (NRS-11, NRS-21, NRS-101), Likert-type scales, product quality ratings, and any instrument whose response can be mapped to the open interval \((0,1)\).

The key idea is that a discrete score recorded on a bounded scale carries measurement uncertainty inherent to the instrument. For instance, a pain score of \(y=6\) on a 0–10 NRS is not an exact value but rather represents a range: after rescaling to \((0,1)\), the observation is treated as interval-censored in \([0.55,0.65]\). The package uses the beta distribution to model such data, building a complete likelihood that supports mixed censoring types within the same dataset.

Installation

# Development version from GitHub:
# install.packages("remotes")
remotes::install_github("evandeilton/betaregscale")

library(betaregscale)

Censoring types

The complete likelihood supports four censoring types, automatically classified by brs_check():

\(\delta\)	Type	Likelihood contribution
0	Exact (uncensored)	\(f(y_i;a_i,b_i)\)
1	Left-censored (\(y=0\))	\(F(u_i;a_i,b_i)\)
2	Right-censored (\(y=K\))	\(1-F(l_i;a_i,b_i)\)
3	Interval-censored	\(F(u_i;a_i,b_i)-F(l_i;a_i,b_i)\)

where \(f(\cdot)\) and \(F(\cdot)\) are the beta density and CDF, \([l_i,u_i]\) are the interval endpoints, and \((a_i,b_i)\) are the beta shape parameters derived from \(\mu_i\) and \(\phi_i\) via the chosen reparameterization.

Interval construction

Scale observations are mapped to \((0,1)\) with midpoint uncertainty intervals:

\[y_t=y/K,\quad\text{interval }[y_t-h/K,y_t+h/K]\]

where \(K\) is the number of scale categories (ncuts) and \(h\) is the half-width (lim, default 0.5).

# Illustrate brs_check with a 0-10 NRS scale
y_example <- c(0, 3, 5, 7, 10)
cr <- brs_check(y_example, ncuts = 10)
kbl10(cr)

left	right	yt	y	delta
0.00	0.05	0.0	0	1
0.25	0.35	0.3	3	3
0.45	0.55	0.5	5	3
0.65	0.75	0.7	7	3
0.95	1.00	1.0	10	2

The delta column shows that \(y=0\) is left-censored (\(\delta=1\)), \(y=10\) is right-censored (\(\delta=2\)), and all interior values are interval-censored (\(\delta=3\)).

Data preparation with `brs_prep()`

In practice, analysts may want to supply their own censoring indicators or interval endpoints rather than relying on the automatic classification of brs_check(). The brs_prep() function provides a flexible, validated bridge between raw analyst data and brs().

It supports four input modes:

Mode 1: Score only (automatic)

# Equivalent to brs_check - delta inferred from y
d1 <- data.frame(y = c(0, 3, 5, 7, 10), x1 = rnorm(5))
kbl10(brs_prep(d1, ncuts = 10))

left	right	yt	y	delta	x1
0.00	0.05	0.0	0	1	0.5769
0.25	0.35	0.3	3	3	1.1427
0.45	0.55	0.5	5	3	-0.7074
0.65	0.75	0.7	7	3	-0.6186
0.95	1.00	1.0	10	2	-0.0083

Mode 2: Score + explicit censoring indicator

# Analyst specifies delta directly
d2 <- data.frame(
  y     = c(50, 0, 99, 50),
  delta = c(0, 1, 2, 3),
  x1    = rnorm(4)
)
kbl10(brs_prep(d2, ncuts = 100))

left	right	yt	y	delta	x1
0.500	0.500	0.50	50	0	0.3389
0.000	0.005	0.00	0	1	1.4054
0.985	1.000	0.99	99	2	-0.8653
0.495	0.505	0.50	50	3	-0.8740

Mode 3: Interval endpoints with NA patterns

When the analyst provides left and/or right columns, censoring is inferred from the NA pattern:

d3 <- data.frame(
  left  = c(NA, 20, 30, NA),
  right = c(5, NA, 45, NA),
  y     = c(NA, NA, NA, 50),
  x1    = rnorm(4)
)
kbl10(brs_prep(d3, ncuts = 100))

left	right	yt	y	delta	x1
0.0	0.05	0.025	NA	1	0.7743
0.2	1.00	0.600	NA	2	-0.4019
0.3	0.45	0.375	NA	3	-0.2151
0.5	0.50	0.500	50	0	0.6053

Mode 4: Analyst-supplied intervals

When the analyst provides y, left, and right simultaneously, their endpoints are used directly (rescaled by \(K\)):

d4 <- data.frame(
  y     = c(50, 75),
  left  = c(48, 73),
  right = c(52, 77),
  x1    = rnorm(2)
)
kbl10(brs_prep(d4, ncuts = 100))

left	right	yt	y	delta	x1
0.48	0.52	0.50	50	3	-0.6146
0.73	0.77	0.75	75	3	-0.7244

Using brs_prep with `brs()`

Data processed by brs_prep() is automatically detected by brs() - the internal brs_check() step is skipped:

set.seed(42)
n <- 200
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n))
sim <- brs_sim(
  formula = ~ x1 + x2, data = dat,
  beta = c(0.2, -0.5, 0.3), phi = 0.3,
  link = "logit", link_phi = "logit",
  repar = 2
)
# Remove left, right, yt so brs_prep can rebuild them
prep <- brs_prep(sim[-c(1:3)], ncuts = 100)
fit_prep <- brs(y ~ x1 + x2,
  data = prep, repar = 2,
  link = "logit", link_phi = "logit"
)
summary(fit_prep, digits = 4)
#> 
#> Call:
#> brs(formula = y ~ x1 + x2, data = prep, link = "logit", link_phi = "logit", 
#>     repar = 2)
#> 
#> Quantile residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3.3566 -0.7310 -0.0537  0.6635  2.9618 
#> 
#> Coefficients (mean model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)   0.1946     0.0996   1.954   0.0507 .  
#> x1           -0.5456     0.1079  -5.057 4.26e-07 ***
#> x2            0.2204     0.1067   2.066   0.0389 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Phi coefficients (precision model with logit link):
#>       Estimate Std. Error z value Pr(>|z|)    
#> (phi)   0.3326     0.0910   3.655 0.000257 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-likelihood: -804.9904 on 4 Df | AIC: 1617.9809 | BIC: 1631.1741 
#> Pseudo R-squared: 0.1372  (midpoint approx.; interpret with caution for heavily censored data) 
#> Number of iterations: 28 (BFGS) 
#> Censoring: 152 interval | 19 left | 29 right

Example 1: Fixed dispersion model

Simulating data

We simulate observations from a beta regression model with fixed dispersion, two covariates, and a logit link for the mean.

set.seed(4255)
n <- 250
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n))

sim_fixed <- brs_sim(
  formula  = ~ x1 + x2,
  data     = dat,
  beta     = c(0.3, -0.6, 0.4),
  phi      = 1 / 10,
  link     = "logit",
  link_phi = "logit",
  ncuts    = 100,
  repar    = 2
)

kbl10(head(sim_fixed, 8))

left	right	yt	y	delta	x1	x2
0.805	0.815	0.81	81	3	1.9510	-1.3482
0.585	0.595	0.59	59	3	0.7725	1.2966
0.245	0.255	0.25	25	3	0.7264	1.1585
0.965	0.975	0.97	97	3	0.0487	0.6334
0.285	0.295	0.29	29	3	-0.5445	0.1161
0.795	0.805	0.80	80	3	0.3600	-0.2545
0.355	0.365	0.36	36	3	0.7136	0.7694
0.745	0.755	0.75	75	3	-0.7274	1.0397

Each observation is centered in its interval. For example, a score of 67 on a 0–100 scale yields \(y_t=0.67\) with the interval \([0.665,0.675]\).

Fitting the model

fit_fixed <- brs(
  y ~ x1 + x2,
  data     = sim_fixed,
  link     = "logit",
  link_phi = "logit",
  repar    = 2
)
summary(fit_fixed)
#> 
#> Call:
#> brs(formula = y ~ x1 + x2, data = sim_fixed, link = "logit", 
#>     link_phi = "logit", repar = 2)
#> 
#> Quantile residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.8953 -0.6209  0.0858  0.6729  3.4008 
#> 
#> Coefficients (mean model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  0.35460    0.08730   4.062 4.87e-05 ***
#> x1          -0.65902    0.09482  -6.950 3.64e-12 ***
#> x2           0.32880    0.09221   3.566 0.000363 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Phi coefficients (precision model with logit link):
#>       Estimate Std. Error z value Pr(>|z|)  
#> (phi)  0.15143    0.08202   1.846   0.0649 .
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-likelihood: -989.6966 on 4 Df | AIC: 1987.3932 | BIC: 2001.4790 
#> Pseudo R-squared: 0.2264  (midpoint approx.; interpret with caution for heavily censored data) 
#> Number of iterations: 29 (BFGS) 
#> Censoring: 198 interval | 14 left | 38 right

The summary output follows the betareg package style, showing separate coefficient tables for the mean and precision submodels, with Wald z-tests and \(p\)-values based on the standard normal distribution.

Goodness of fit

kbl10(brs_gof(fit_fixed))

logLik	AIC	BIC	pseudo_r2
-989.6966	1987.393	2001.479	0.2264

Comparing link functions

The package supports several link functions for the mean submodel. We can compare them using information criteria:

links <- c("logit", "probit", "cauchit", "cloglog")
fits <- lapply(setNames(links, links), function(lnk) {
  brs(y ~ x1 + x2, data = sim_fixed, link = lnk, repar = 2)
})

# Estimates
est_table <- do.call(rbind, lapply(names(fits), function(lnk) {
  e <- brs_est(fits[[lnk]])
  e$link <- lnk
  e
}))
kbl10(est_table)

variable	estimate	se	z_value	p_value	ci_lower	ci_upper	link
(Intercept)	0.3546	0.0873	4.0619	0.0000	0.1835	0.5257	logit
x1	-0.6590	0.0948	-6.9505	0.0000	-0.8449	-0.4732	logit
x2	0.3288	0.0922	3.5657	0.0004	0.1481	0.5095	logit
(phi)	0.1514	0.0820	1.8463	0.0649	-0.0093	0.3122	logit
(Intercept)	0.2180	0.0532	4.0969	0.0000	0.1137	0.3223	probit
x1	-0.3964	0.0548	-7.2312	0.0000	-0.5038	-0.2890	probit
x2	0.1955	0.0547	3.5745	0.0004	0.0883	0.3026	probit
(phi)	0.1539	0.0819	1.8794	0.0602	-0.0066	0.3144	probit
(Intercept)	0.3062	0.0824	3.7164	0.0002	0.1447	0.4677	cauchit
x1	-0.6286	0.1100	-5.7153	0.0000	-0.8441	-0.4130	cauchit


# Goodness of fit
gof_table <- do.call(rbind, lapply(fits, brs_gof))
kbl10(gof_table)

	logLik	AIC	BIC	pseudo_r2
logit	-989.6966	1987.393	2001.479	0.2264
probit	-989.8231	1987.646	2001.732	0.2229
cauchit	-989.7611	1987.522	2001.608	0.1804
cloglog	-989.7198	1987.439	2001.525	0.1437

Residual diagnostics

The plot() method provides six diagnostic panels. By default, the first four are shown:

plot(fit_fixed, caption = NULL, gg = TRUE, title = NULL, theme = ggplot2::theme_bw())

For ggplot2 output (requires the ggplot2 package):

plot(fit_fixed, gg = TRUE)

Predictions

# Fitted means
kbl10(
  data.frame(mu_hat = head(predict(fit_fixed, type = "response"))),
  digits = 4
)

mu_hat
0.2019
0.5675
0.5638
0.6297
0.6795
0.5084


# Conditional variance
kbl10(
  data.frame(var_hat = head(predict(fit_fixed, type = "variance"))),
  digits = 4
)

var_hat
0.0867
0.1320
0.1323
0.1254
0.1171
0.1344


# Quantile predictions
kbl10(predict(fit_fixed, type = "quantile", at = c(0.10, 0.25, 0.5, 0.75, 0.90)))

q_0.1	q_0.25	q_0.5	q_0.75	q_0.9
0.0000	0.0006	0.0329	0.3130	0.7400
0.0335	0.2024	0.6326	0.9348	0.9943
0.0322	0.1974	0.6255	0.9323	0.9940
0.0624	0.2999	0.7476	0.9688	0.9982
0.0979	0.3959	0.8302	0.9855	0.9995
0.0168	0.1306	0.5167	0.8863	0.9865
0.0231	0.1596	0.5678	0.9096	0.9905
0.1981	0.5958	0.9385	0.9980	1.0000
0.1001	0.4013	0.8341	0.9862	0.9995
0.0088	0.0860	0.4218	0.8340	0.9755

Confidence intervals

Wald confidence intervals based on the asymptotic normal approximation:

kbl10(confint(fit_fixed))

	2.5 %	97.5 %
(Intercept)	0.1835	0.5257
x1	-0.8449	-0.4732
x2	0.1481	0.5095
(phi)	-0.0093	0.3122

kbl10(confint(fit_fixed, model = "mean"))

	2.5 %	97.5 %
(Intercept)	0.1835	0.5257
x1	-0.8449	-0.4732
x2	0.1481	0.5095

Censoring structure

The brs_cens() function provides a visual and tabular overview of the censoring types in the fitted model:

brs_cens(fit_fixed, gg = TRUE, inform = TRUE)

Example 2: Variable dispersion model

In many applications, the dispersion parameter \(\phi\) may depend on covariates. The package supports variable-dispersion models using the Formula package notation: y ~ x1 + x2 | z1 + z2, where the terms after | define the linear predictor for \(\phi\). The same brs_sim() function is used for fixed and variable dispersion; the second formula part activates the precision submodel in simulation.

Simulating data

set.seed(2222)
n <- 250
dat_z <- data.frame(
  x1 = rnorm(n),
  x2 = rnorm(n),
  x3 = rbinom(n, size = 1, prob = 0.5),
  z1 = rnorm(n),
  z2 = rnorm(n)
)

sim_var <- brs_sim(
  formula = ~ x1 + x2 + x3 | z1 + z2,
  data = dat_z,
  beta = c(0.2, -0.6, 0.2, 0.2),
  zeta = c(0.2, -0.8, 0.6),
  link = "logit",
  link_phi = "logit",
  ncuts = 100,
  repar = 2
)

kbl10(head(sim_var, 10))

left	right	yt	y	delta	x1	x2	x3	z1	z2
0.000	0.005	0.00	0	1	-0.3381	0.7980	1	-0.1859	1.7165
0.000	0.005	0.00	0	1	0.9392	-2.2067	0	-1.1473	0.8539
0.985	0.995	0.99	99	3	1.7377	0.0134	0	0.3364	0.9417
0.535	0.545	0.54	54	3	0.6963	0.2765	1	2.1178	0.1317
0.515	0.525	0.52	52	3	0.4623	-0.8707	1	0.5450	-0.3795
0.005	0.015	0.01	1	3	-0.3151	-1.0969	1	-0.4672	-0.8259
0.005	0.015	0.01	1	3	0.1927	1.8391	0	-1.2001	-0.1784
0.035	0.045	0.04	4	3	1.1307	-1.3548	0	-0.6740	-0.9092
0.315	0.325	0.32	32	3	1.9764	0.6912	1	0.9831	0.0286
0.825	0.835	0.83	83	3	1.2071	0.2571	0	0.6295	-0.2415

Fitting the model

fit_var <- brs(
  y ~ x1 + x2 | z1,
  data     = sim_var,
  link     = "logit",
  link_phi = "logit",
  repar    = 2
)
summary(fit_var)
#> 
#> Call:
#> brs(formula = y ~ x1 + x2 | z1, data = sim_var, link = "logit", 
#>     link_phi = "logit", repar = 2)
#> 
#> Quantile residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3.2736 -0.5585  0.0259  0.6191  2.5197 
#> 
#> Coefficients (mean model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  0.31884    0.08541   3.733 0.000189 ***
#> x1          -0.48246    0.08961  -5.384 7.28e-08 ***
#> x2           0.23905    0.08270   2.890 0.003847 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Phi coefficients (precision model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  0.33336    0.08621   3.867  0.00011 ***
#> z1          -0.81256    0.09204  -8.829  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-likelihood: -936.0499 on 5 Df | AIC: 1882.0998 | BIC: 1899.7071 
#> Pseudo R-squared: 0.1164  (midpoint approx.; interpret with caution for heavily censored data) 
#> Number of iterations: 34 (BFGS) 
#> Censoring: 181 interval | 23 left | 46 right

Notice the (phi)_ prefix in the precision coefficient names, following the betareg convention.

Accessing coefficients by submodel

# Full parameter vector
round(coef(fit_var), 4)
#>       (Intercept)                x1                x2 (phi)_(Intercept) 
#>            0.3188           -0.4825            0.2390            0.3334 
#>          (phi)_z1 
#>           -0.8126

# Mean submodel only
round(coef(fit_var, model = "mean"), 4)
#> (Intercept)          x1          x2 
#>      0.3188     -0.4825      0.2390

# Precision submodel only
round(coef(fit_var, model = "precision"), 4)
#> (phi)_(Intercept)          (phi)_z1 
#>            0.3334           -0.8126

# Variance-covariance matrix for the mean submodel
kbl10(vcov(fit_var, model = "mean"))

	(Intercept)	x1	x2
(Intercept)	0.0073	-3e-04	0.0002
x1	-0.0003	8e-03	0.0001
x2	0.0002	1e-04	0.0068

Comparing link functions (variable dispersion)

links <- c("logit", "probit", "cauchit", "cloglog")
fits_var <- lapply(setNames(links, links), function(lnk) {
  brs(y ~ x1 + x2 | z1, data = sim_var, link = lnk, repar = 2)
})

# Estimates
est_var <- do.call(rbind, lapply(names(fits_var), function(lnk) {
  e <- brs_est(fits_var[[lnk]])
  e$link <- lnk
  e
}))
kbl10(est_var)

variable	estimate	se	z_value	p_value	ci_lower	ci_upper	link
(Intercept)	0.3188	0.0854	3.7332	0.0002	0.1514	0.4862	logit
x1	-0.4825	0.0896	-5.3841	0.0000	-0.6581	-0.3068	logit
x2	0.2390	0.0827	2.8904	0.0038	0.0770	0.4011	logit
(phi)_(Intercept)	0.3334	0.0862	3.8668	0.0001	0.1644	0.5023	logit
(phi)_z1	-0.8126	0.0920	-8.8286	0.0000	-0.9930	-0.6322	logit
(Intercept)	0.1972	0.0524	3.7601	0.0002	0.0944	0.3000	probit
x1	-0.2980	0.0541	-5.5038	0.0000	-0.4041	-0.1919	probit
x2	0.1477	0.0505	2.9237	0.0035	0.0487	0.2467	probit
(phi)_(Intercept)	0.3326	0.0862	3.8571	0.0001	0.1636	0.5015	probit
(phi)_z1	-0.8131	0.0920	-8.8379	0.0000	-0.9934	-0.6327	probit


# Goodness of fit
gof_var <- do.call(rbind, lapply(fits_var, brs_gof))
kbl10(gof_var)

	logLik	AIC	BIC	pseudo_r2
logit	-936.0499	1882.100	1899.707	0.1164
probit	-935.9539	1881.908	1899.515	0.1211
cauchit	-936.7522	1883.504	1901.112	0.0957
cloglog	-935.6783	1881.357	1898.964	0.0667

Diagnostics for variable dispersion

plot(fit_var)

Advanced analyst functions

The package includes analyst-facing helpers for uncertainty quantification, effect interpretation, score-scale communication, and predictive validation.

Parametric bootstrap confidence intervals

set.seed(101)
boot_ci <- brs_bootstrap(
  fit_fixed,
  R = 30,
  level = 0.95,
  ci_type = "bca",
  keep_draws = TRUE
)
kbl10(head(boot_ci, 10))

parameter	estimate	se_boot	ci_lower	ci_upper	mcse_lower	mcse_upper	wald_lower	wald_upper	level
(Intercept)	0.3546	0.0982	0.1799	0.5024	0.0318	0.0217	0.1835	0.5257	0.95
x1	-0.6590	0.1044	-0.8166	-0.4950	0.0227	0.0378	-0.8449	-0.4732	0.95
x2	0.3288	0.1088	0.1484	0.5288	0.0259	0.0321	0.1481	0.5095	0.95
(phi)	0.1514	0.0913	0.0439	0.3120	0.0113	0.0194	-0.0093	0.3122	0.95

autoplot.brs_bootstrap(
  boot_ci,
  type = "ci_forest",
  title = "Bootstrap (BCa) vs Wald intervals"
)

Average marginal effects (AME)

set.seed(202)
ame <- brs_marginaleffects(
  fit_fixed,
  model = "mean",
  type = "response",
  interval = TRUE,
  n_sim = 60,
  keep_draws = TRUE
)
kbl10(ame)

variable	ame	std.error	ci.lower	ci.upper	model	type	n
x1	-0.1425	0.0170	-0.1735	-0.1058	mean	response	250
x2	0.0711	0.0203	0.0310	0.1029	mean	response	250

autoplot.brs_marginaleffects(ame, type = "forest")

Score probabilities on the original scale

prob_scores <- brs_predict_scoreprob(fit_fixed, scores = 0:10)
kbl10(prob_scores[1:6, 1:6])

score_0	score_1	score_2	score_3	score_4	score_5
0.3600	0.0758	0.0406	0.0289	0.0228	0.0190
0.0393	0.0280	0.0192	0.0157	0.0136	0.0122
0.0403	0.0285	0.0195	0.0159	0.0137	0.0123
0.0252	0.0205	0.0147	0.0122	0.0108	0.0098
0.0171	0.0155	0.0115	0.0097	0.0087	0.0080
0.0587	0.0363	0.0240	0.0191	0.0163	0.0145

kbl10(
  data.frame(row_sum = rowSums(prob_scores)[1:6]),
  digits = 4
)

row_sum
0.6136
0.1774
0.1802
0.1340
0.1044
0.2261

Repeated k-fold cross-validation

set.seed(303) # For cross-validation reproducibility
cv_res <- brs_cv(
  y ~ x1 + x2,
  data = sim_fixed,
  k = 5,
  repeats = 2,
  repar = 2,
)
kbl10(cv_res)

repeat	fold	n_train	n_test	log_score	rmse_yt	mae_yt	converged	error
1	1	200	50	-4.0896	0.3338	0.2852	TRUE	NA
1	2	200	50	-4.0065	0.3482	0.3080	TRUE	NA
1	3	200	50	-3.7595	0.3069	0.2693	TRUE	NA
1	4	200	50	-3.9121	0.3660	0.3281	TRUE	NA
1	5	200	50	-4.0921	0.3834	0.3422	TRUE	NA
2	1	200	50	-4.2336	0.3669	0.3191	TRUE	NA
2	2	200	50	-3.8826	0.3231	0.2829	TRUE	NA
2	3	200	50	-3.8884	0.3151	0.2787	TRUE	NA
2	4	200	50	-4.0835	0.3745	0.3272	TRUE	NA
2	5	200	50	-3.8069	0.3697	0.3366	TRUE	NA

kbl10(
  data.frame(
    metric = c("log_score", "rmse_yt", "mae_yt"),
    mean = c(
      mean(cv_res$log_score, na.rm = TRUE),
      mean(cv_res$rmse_yt, na.rm = TRUE),
      mean(cv_res$mae_yt, na.rm = TRUE)
    )
  ),
  digits = 4
)

metric	mean
log_score	-3.9755
rmse_yt	0.3488
mae_yt	0.3077

S3 methods reference

The following standard S3 methods are available for objects of class "brs":

Method	Description
`print()`	Compact display of call and coefficients
`summary()`	Detailed output with Wald tests and goodness-of-fit
`coef(model=)`	Extract coefficients (full, mean, or precision)
`vcov(model=)`	Variance-covariance matrix (full, mean, or precision)
`confint(model=)`	Wald confidence intervals
`logLik()`	Log-likelihood value
`AIC()`, `BIC()`	Information criteria
`nobs()`	Number of observations
`formula()`	Model formula
`model.matrix(model=)`	Design matrix (mean or precision)
`fitted()`	Fitted mean values
`residuals(type=)`	Residuals: response, pearson, rqr, weighted, sweighted
`predict(type=)`	Predictions: response, link, precision, variance, quantile
`plot(gg=)`	Diagnostic plots (base R or ggplot2)

Reparameterizations

The package supports three reparameterizations of the beta distribution, controlled by the repar argument:

Direct (repar = 0): Shape parameters \(a=\mu\) and \(b=\phi\) are used directly. This is rarely used in practice.

Precision (repar = 1, Ferrari & Cribari-Neto, 2004): The mean \(\mu\in(0,1)\) and precision \(\phi>0\) yield \(a=\mu\phi\) and \(b=(1-\mu)\phi\). Higher \(\phi\) means less variability.

Mean–variance (repar = 2): The mean \(\mu\in(0,1)\) and dispersion \(\phi\in(0,1)\) yield \(a=\mu(1-\phi)/\phi\) and \(b=(1-\mu)(1-\phi)/\phi\). Here \(\phi\) acts as a coefficient of variation: smaller \(\phi\) means less variability.

# Precision parameterization: mu = 0.5, phi = 10 (high precision)
brs_repar(mu = 0.5, phi = 10, repar = 1)
#>   shape1 shape2
#> 1      5      5

# Mean-variance parameterization: mu = 0.5, phi = 0.1 (low dispersion)
brs_repar(mu = 0.5, phi = 0.1, repar = 2)
#>   shape1 shape2
#> 1    4.5    4.5

References

Ferrari, S. L. P., and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815. DOI: 10.1080/0266476042000214501. Validated online via: https://doi.org/10.1080/0266476042000214501.
Smithson, M., and Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods, 11(1), 54–71. DOI: 10.1037/1082-989X.11.1.54. Validated online via: https://doi.org/10.1037/1082-989X.11.1.54.
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. Validated online via: https://doi.org/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. Validated online via: https://doi.org/10.1016/j.jpainsymman.2010.08.016.

Introduction to betaregscale

Overview

Installation

Censoring types

Interval construction

Data preparation with brs_prep()

Mode 1: Score only (automatic)

Mode 2: Score + explicit censoring indicator

Mode 3: Interval endpoints with NA patterns

Mode 4: Analyst-supplied intervals

Using brs_prep with brs()

Example 1: Fixed dispersion model

Simulating data

Fitting the model

Goodness of fit

Comparing link functions

Residual diagnostics

Predictions

Confidence intervals

Censoring structure

Example 2: Variable dispersion model

Simulating data

Fitting the model

Accessing coefficients by submodel

Comparing link functions (variable dispersion)

Diagnostics for variable dispersion

Advanced analyst functions

Parametric bootstrap confidence intervals

Average marginal effects (AME)

Score probabilities on the original scale

Repeated k-fold cross-validation

S3 methods reference

Reparameterizations

References

Data preparation with `brs_prep()`

Using brs_prep with `brs()`