flexreg: Flexible Regression Models for Bounded Continuous Responses
In FlexReg: Regression Models for Bounded Continuous and Discrete Responses
View source: R/flexreg_Augmentation.R
flexreg R Documentation
Flexible Regression Models for Bounded Continuous Responses
Description
The function fits some flexible regression models for bounded continuous responses (e.g., proportions and rates) via a Bayesian approach to inference based on Hamiltonian Monte Carlo algorithm.
Available regression models are the flexible beta regression model (type = "FB", default), the variance inflated beta (type = "VIB"), the beta (type = "Beta"), as well as their augmented versions.
Usage
flexreg(
formula,
zero.formula = NULL,
one.formula = NULL,
data,
type = "FB",
link.mu = "logit",
prior.beta = "normal",
hyperparam.beta = NULL,
prior.omega0 = "normal",
hyperparam.omega0 = NULL,
prior.omega1 = "normal",
hyperparam.omega1 = NULL,
hyperparam.p = NULL,
hyperparam.w = NULL,
hyperparam.k = NULL,
link.phi = NULL,
prior.phi = NULL,
hyperparam.phi = NULL,
prior.psi = NULL,
hyperparam.psi = NULL,
n.chain = 1,
n.iter = 5000,
warmup.perc = 0.5,
thin = 1,
verbose = TRUE,
...
)
Arguments
formula
an object of class "formula": a symbolic description of the mean model (y ~ x) or the mean and precision models (y ~ x | z) to be fitted (see Details).
zero.formula
an object of class "formula": a symbolic description of the zero augmented model to be fitted (see Details).
one.formula
an object of class "formula": a symbolic description of the one augmented model to be fitted (see Details).
data
an optional data.frame, list, or object that is coercible to a data.frame through as.data.frame containing the variables in the model. If not found in data, the variables in formula, zero.formula, and one.formula are taken from the environment from which the function flexreg is called.
type
a character specifying the type of regression model. Current options are "FB" (flexible beta, default), "VIB" (variance inflated beta), and "Beta".
link.mu
a character specifying the link function for the mean model (mu). Currently, "logit" (default), "probit", "cloglog", and "loglog" are supported.
prior.beta
a character specifying the prior distribution for the regression coefficients of the mean model, beta. Currently, "normal" (default) and "cauchy" are supported.
hyperparam.beta
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of beta regression coefficients. The default is 100 if the prior is "normal", 2.5 if it is "cauchy".
prior.omega0
a character specifying the prior distribution for the regression coefficients of the augmented model in zero, omega0. Currently, "normal" (default) and "cauchy" are supported.
hyperparam.omega0
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of omega0 regression coefficients. The default is 100 if the prior is "normal", 2.5 if it is "cauchy".
prior.omega1
a character specifying the prior distribution for the regression coefficients of the augmented model in one, omega1. Currently, "normal" (default) and "cauchy" are supported.
hyperparam.omega1
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of omega1 regression coefficients. The default is 100 if the prior is "normal", 2.5 if it is "cauchy".
hyperparam.p
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of p. The default is c(0.5,2), which corresponds to the uniform prior distribution.
hyperparam.w
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of w. The default is c(0.5,2), which corresponds to the uniform prior distribution.
hyperparam.k
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of k. The default is c(0.5,2), which corresponds to the uniform prior distribution.
link.phi
a character specifying the link function for the precision model (phi). Currently, "identity" (default), "log", and "sqrt" are supported.
prior.phi
a character specifying the prior distribution for precision parameter phi if
link.phi = "identity". Currently, "gamma" (default) and "unif" are supported.
hyperparam.phi
a positive numeric (vector of length 1) specifying the hyperprior parameter for the prior distribution of phi. If the prior is "gamma", the value identifies the gamma's shape and rate parameters (the default is 0.001). If the prior is "uniform" the hyperparameter must be specified to define the upper limit of the support of phi.
prior.psi
a character specifying the prior distribution for the regression coefficients of the precision model psi (not supported if link.phi = "identity"). Currently, "normal" (default) and "cauchy" are supported.
hyperparam.psi
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of psi regression coefficients. The default is 100 if the prior is "normal", 2.5 if it is "cauchy".
n.chain
a positive integer specifying the number of Markov chains. The default is 1.
n.iter
a positive integer specifying the number of iterations for each chain (including warm-up). The default is 5000.
warmup.perc
the percentage of iterations per chain to discard.
thin
a positive integer specifying the period for saving samples. The default is 1.
verbose
a logical (with default TRUE) indicating whether to print intermediate output.
...
additional arguments from sampling.
Details
Let Y be a continuous bounded random variable whose distribution can be specified in the type argument and \mu be the mean of Y.
The flexreg function links the parameter \mu to a linear predictor through a function g_1(\cdot) specified in link.mu:
g_1(\mu) = \bold{x}^t \bold{\beta},
where \bold{\beta} is the vector of regression coefficients for the mean model.
The prior distribution and the related hyperparameter of \bold{\beta} can be specified in prior.beta and hyperparam.beta, respectively.
By default, the precision parameter \phi is assumed to be constant.
The prior distribution and the related hyperparameter of \phi can be specified in prior.phi and hyperparam.phi.
It is possible to extend the model by linking \phi to an additional (possibly overlapping) set of covariates through a proper link
function g_2(\cdot) specified in the link.phi argument:
g_2(\phi) = \bold{z}^t \bold{\psi},
where \bold{\psi} is the vector of regression coefficients for the precision model.
The prior distribution and the related hyperparameter of \bold{\psi} can be specified in prior.psi and hyperparam.psi.
In the function flexreg, the regression model for the mean and, where appropriate, for the precision parameter can be specified in the
formula argument with a formula of type y ~ x1 + x2 | z1 + z2 where covariates on the left of "|" are included in the regression model
for the mean, whereas covariates on the right of "|" are included in the regression model for the precision.
If the second part is omitted, i.e., y ~ x1 + x2, the precision is assumed constant for each observation.
In presence of zero values in the response, one has to link the parameter q_0, i.e., the probability of augmentation in zero, to an additional (possibly overlapping) set of covariates through a logit link function:
g_3(q_{0}) = \bold{x}_{0}^t \bold{\omega_0},
where \bold{\omega_0} is the vector of regression coefficients for the augmented model in zero.
The prior distribution and the related hyperparameter of \bold{\omega_0} can be specified in prior.omega0 and hyperparam.omega0.
In presence of one values in the response, one has to link the parameter q_1, i.e., the probability of augmentation in one, to an additional (possibly overlapping) set of covariates through a logit link function:
g_4(q_{1}) = \bold{x}_{1}^t \bold{\omega_1},
where \bold{\omega_1} is the vector of regression coefficients for the augmented model in one.
The prior distribution and the related hyperparameter of \bold{\omega_1} can be specified in prior.omega1 and hyperparam.omega1.
If both the augmented models in zero and one are specified, the link function is a bivariate logit.
In flexreg function, the augmented models in zero and/or one can be specified in the
zero.formula and/or one.formula arguments with a formula of type ~ x.
Left hand side in zero.formula and one.formula can be omitted; if specified, they have to be the same as left hand side in formula.
Value
The flexreg function returns an object of class `flexreg`, i.e. a list with the following elements:
call
the function call.
type
the type of regression model.
formula
the overall formula.
aug
a character specifing the absence of the augmentation ("No") or the presence of augmentation in zero ("0"), one ("1"), or both ("01").
link.mu
a character specifing the link function in the mean model.
link.phi
a character specifing the link function in the precision model.
model
a list of objects of class `stanfit` containing the fitted model(s).
response
the response variable, assuming values in (0, 1).
design.X
the design matrix for the mean model.
design.Z
the design matrix for the precision model (if defined).
design.X0
the design matrix for the augmented model in zero (if defined).
design.X1
the design matrix for the augmented model in one (if defined).
References
Di Brisco, A. M., Migliorati, S. (2020). A new mixed-effects mixture model for constrained longitudinal data. Statistics in Medicine, 39(2), 129–145. doi:10.1002/sim.8406
Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020). Robustness against outliers: A new variance inflated regression model for proportions. Statistical Modelling, 20(3), 274–309. doi:10.1177/1471082X18821213
Ferrari, S.L.P., Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. doi:10.1080/0266476042000214501
Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) A New Regression Model for Bounded Responses. Bayesian Analysis, 13(3), 845–872. doi:10.1214/17-BA1079
Examples
## Not run:
data("Reading")
FB <- flexreg(accuracy.adj ~ iq, data = Reading, type="FB")
# Regression model with one augmentation:
AFB1 <- flexreg(accuracy ~ dyslexia | iq + dyslexia + iq:dyslexia,
one.formula = ~ iq + dyslexia, data = Reading, type="FB")
## End(Not run)
FlexReg documentation built on Sept. 9, 2025, 5:49 p.m.
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View source: R/flexreg_Augmentation.R
| flexreg | R Documentation |
Flexible Regression Models for Bounded Continuous Responses
Description
The function fits some flexible regression models for bounded continuous responses (e.g., proportions and rates) via a Bayesian approach to inference based on Hamiltonian Monte Carlo algorithm.
Available regression models are the flexible beta regression model (type = "FB", default), the variance inflated beta (type = "VIB"), the beta (type = "Beta"), as well as their augmented versions.
Usage
flexreg(
formula,
zero.formula = NULL,
one.formula = NULL,
data,
type = "FB",
link.mu = "logit",
prior.beta = "normal",
hyperparam.beta = NULL,
prior.omega0 = "normal",
hyperparam.omega0 = NULL,
prior.omega1 = "normal",
hyperparam.omega1 = NULL,
hyperparam.p = NULL,
hyperparam.w = NULL,
hyperparam.k = NULL,
link.phi = NULL,
prior.phi = NULL,
hyperparam.phi = NULL,
prior.psi = NULL,
hyperparam.psi = NULL,
n.chain = 1,
n.iter = 5000,
warmup.perc = 0.5,
thin = 1,
verbose = TRUE,
...
)
Arguments
formula |
an object of class " |
zero.formula |
an object of class " |
one.formula |
an object of class " |
data |
an optional |
type |
a character specifying the type of regression model. Current options are |
link.mu |
a character specifying the link function for the mean model (mu). Currently, |
prior.beta |
a character specifying the prior distribution for the regression coefficients of the mean model, |
hyperparam.beta |
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of |
prior.omega0 |
a character specifying the prior distribution for the regression coefficients of the augmented model in zero, |
hyperparam.omega0 |
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of |
prior.omega1 |
a character specifying the prior distribution for the regression coefficients of the augmented model in one, |
hyperparam.omega1 |
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of |
hyperparam.p |
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of |
hyperparam.w |
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of |
hyperparam.k |
a vector of length 2 with positive elements specifying the hyperparameters for the beta prior distribution of |
link.phi |
a character specifying the link function for the precision model (phi). Currently, |
prior.phi |
a character specifying the prior distribution for precision parameter |
hyperparam.phi |
a positive numeric (vector of length 1) specifying the hyperprior parameter for the prior distribution of |
prior.psi |
a character specifying the prior distribution for the regression coefficients of the precision model |
hyperparam.psi |
a positive numeric (vector of length 1) specifying the hyperprior scale parameter for the prior distribution of |
n.chain |
a positive integer specifying the number of Markov chains. The default is 1. |
n.iter |
a positive integer specifying the number of iterations for each chain (including warm-up). The default is 5000. |
warmup.perc |
the percentage of iterations per chain to discard. |
thin |
a positive integer specifying the period for saving samples. The default is 1. |
verbose |
a logical (with default |
... |
additional arguments from |
Details
Let Y be a continuous bounded random variable whose distribution can be specified in the type argument and \mu be the mean of Y.
The flexreg function links the parameter \mu to a linear predictor through a function g_1(\cdot) specified in link.mu:
g_1(\mu) = \bold{x}^t \bold{\beta},
where \bold{\beta} is the vector of regression coefficients for the mean model.
The prior distribution and the related hyperparameter of \bold{\beta} can be specified in prior.beta and hyperparam.beta, respectively.
By default, the precision parameter \phi is assumed to be constant.
The prior distribution and the related hyperparameter of \phi can be specified in prior.phi and hyperparam.phi.
It is possible to extend the model by linking \phi to an additional (possibly overlapping) set of covariates through a proper link
function g_2(\cdot) specified in the link.phi argument:
g_2(\phi) = \bold{z}^t \bold{\psi},
where \bold{\psi} is the vector of regression coefficients for the precision model.
The prior distribution and the related hyperparameter of \bold{\psi} can be specified in prior.psi and hyperparam.psi.
In the function flexreg, the regression model for the mean and, where appropriate, for the precision parameter can be specified in the
formula argument with a formula of type y ~ x1 + x2 | z1 + z2 where covariates on the left of "|" are included in the regression model
for the mean, whereas covariates on the right of "|" are included in the regression model for the precision.
If the second part is omitted, i.e., y ~ x1 + x2, the precision is assumed constant for each observation.
In presence of zero values in the response, one has to link the parameter q_0, i.e., the probability of augmentation in zero, to an additional (possibly overlapping) set of covariates through a logit link function:
g_3(q_{0}) = \bold{x}_{0}^t \bold{\omega_0},
where \bold{\omega_0} is the vector of regression coefficients for the augmented model in zero.
The prior distribution and the related hyperparameter of \bold{\omega_0} can be specified in prior.omega0 and hyperparam.omega0.
In presence of one values in the response, one has to link the parameter q_1, i.e., the probability of augmentation in one, to an additional (possibly overlapping) set of covariates through a logit link function:
g_4(q_{1}) = \bold{x}_{1}^t \bold{\omega_1},
where \bold{\omega_1} is the vector of regression coefficients for the augmented model in one.
The prior distribution and the related hyperparameter of \bold{\omega_1} can be specified in prior.omega1 and hyperparam.omega1.
If both the augmented models in zero and one are specified, the link function is a bivariate logit.
In flexreg function, the augmented models in zero and/or one can be specified in the
zero.formula and/or one.formula arguments with a formula of type ~ x.
Left hand side in zero.formula and one.formula can be omitted; if specified, they have to be the same as left hand side in formula.
Value
The flexreg function returns an object of class `flexreg`, i.e. a list with the following elements:
call |
the function call. |
type |
the type of regression model. |
formula |
the overall formula. |
aug |
a character specifing the absence of the augmentation ( |
link.mu |
a character specifing the link function in the mean model. |
link.phi |
a character specifing the link function in the precision model. |
model |
a list of objects of class |
response |
the response variable, assuming values in (0, 1). |
design.X |
the design matrix for the mean model. |
design.Z |
the design matrix for the precision model (if defined). |
design.X0 |
the design matrix for the augmented model in zero (if defined). |
design.X1 |
the design matrix for the augmented model in one (if defined). |
References
Di Brisco, A. M., Migliorati, S. (2020). A new mixed-effects mixture model for constrained longitudinal data. Statistics in Medicine, 39(2), 129–145. doi:10.1002/sim.8406
Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020). Robustness against outliers: A new variance inflated regression model for proportions. Statistical Modelling, 20(3), 274–309. doi:10.1177/1471082X18821213
Ferrari, S.L.P., Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. doi:10.1080/0266476042000214501
Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) A New Regression Model for Bounded Responses. Bayesian Analysis, 13(3), 845–872. doi:10.1214/17-BA1079
Examples
## Not run:
data("Reading")
FB <- flexreg(accuracy.adj ~ iq, data = Reading, type="FB")
# Regression model with one augmentation:
AFB1 <- flexreg(accuracy ~ dyslexia | iq + dyslexia + iq:dyslexia,
one.formula = ~ iq + dyslexia, data = Reading, type="FB")
## End(Not run)
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