AROC.bnp: Nonparametric Bayesian inference of the covariate-adjusted...
In ROCnReg: ROC Curve Inference with and without Covariates
AROC.bnp R Documentation
Nonparametric Bayesian inference of the covariate-adjusted ROC curve (AROC).
Description
This function estimates the covariate-adjusted ROC curve (AROC) using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018).
Usage
AROC.bnp(formula.h, group, tag.h, data, standardise = TRUE,
p = seq(0, 1, l = 101), ci.level = 0.95, compute.lpml = FALSE, compute.WAIC = FALSE,
compute.DIC = FALSE, pauc = pauccontrol(), density = densitycontrol.aroc(),
prior.h = priorcontrol.bnp(), mcmc = mcmccontrol(),
parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL)
Arguments
formula.h
A formula object specifying the regression function associated to each component of the single-weights linear dependent Dirichlet process mixture model used to estimate the conditional distribution function of the diagnostic test outcome in the healthy population. Regarding the modelling of continuous covariates, both linear and nonlinear effects are allowed, with nonlinear effects being modelled through B-spline basis expansions (see Note).
group
A character string with the name of the variable that distinguishes healthy/nondiseased from diseased individuals.
tag.h
The value codifying healthy individuals in the variable group.
data
A data frame representing the data and containing all needed variables.
standardise
A logical value. If TRUE both the test outcomes and the continuous covariates assumed to have a linear effect are standardised (i.e., the resulting variables have mean zero and standard deviation of one). The default is TRUE.
p
Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve.
ci.level
An integer value (between 0 and 1) specifying the level for the credible interval. The default is 0.95.
compute.lpml
A logical value. If TRUE, the log pseudo marginal likelihood (LPML, Geisser and Eddy, 1979) and the conditional predictive ordinates (CPO) are computed.
compute.WAIC
A logical value. If TRUE, the widely applicable information criterion (WAIC, Gelman et al., 2014; Watanabe, 2010) is computed.
compute.DIC
A logical value. If TRUE, the deviance information criterion (DIC)(Celeux et al., 2006, Spiegelhalter et al., 2002) is computed.
pauc
A list of control values to replace the default values returned by the function pauccontrol. This argument is used to indicate whether the partial area under the covariate-adjusted ROC curve (pAAUC) should be computed, and in case it is computed, whether the focus should be placed on restricted false positive fractions (FPFs) or on restricted true positive fractions (TPFs), and the upper bound for the FPF (if focus is FPF) or the lower bound for the TPF (if focus is TPF).
density
A list of control values to replace the default values returned by the function densitycontrol.aroc. This argument is used to indicate whether the conditional densities of the marker in the healthy population should be computed, and in case it is to be computed, at which grid of test outcomes the conditional densities should be evaluated, and at which covariate values they should be predicted.
prior.h
A list of control values to replace the default values returned by the function priorcontrol.bnp. See priorcontrol.bnp for details.
mcmc
A list of control values to replace the default values returned by the function mcmccontrol. See mcmccontrol for details.
parallel
A characters string with the type of parallel operation: either "no" (default), "multicore" (not available on Windows) or "snow".
ncpus
An integer with the number of processes to be used in parallel operation. Defaults to 1.
cl
An object inheriting from class cluster (from the parallel package), specifying an optional parallel or snow cluster if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the call.
Details
Estimates the covariate-adjusted ROC curve (AROC) defined as
AROC\left(p\right) = Pr\{1 - F_{\bar{D}}(Y_D | \mathbf{X}_{D}) \leq p\},
where F_{\bar{D}}(\cdot|\mathbf{X}_{\bar{D}}) denotes the distribution function of Y_{\bar{D}} conditional on the vector of covariates \mathbf{X}_{\bar{D}}.
The method implemented in this function combines a single-weights linear dependent Dirichlet process mixture model (De Iorio et al., 2009) to estimate F_{\bar{D}}(\cdot|\mathbf{X}_{\bar{D}}) and the Bayesian bootstrap (Rubin, 1981) to estimate the outside probability. More precisely, and letting \{(\mathbf{x}_{\bar{D}i},y_{\bar{D}i})\}_{i=1}^{n_{\bar{D}}} be a random sample from the nondiseased population, our postulated model for the conditional distribution function takes the following form
F_{\bar{D}}(y_{\bar{D}i}|\mathbf{X}_{\bar{D}}=\mathbf{x}_{\bar{D}i}) = \sum_{l=1}^{L}\omega_l\Phi(y_{\bar{D}i}\mid\mu_{l}(\mathbf{x}_{\bar{D}i}),\sigma_l^2),
where \Phi(y|\mu, \sigma^2) denotes the cumulative distribution function of the normal distribution, evaluated at y, with mean \mu and variance \sigma^2. The regression function \mu_{l}(\mathbf{x}_{\bar{D}i}) can incorportate both linear and nonlinear (through B-splines) effects of continuous covariates, categorical covariates (factors) as well as interactions. Interactions between categorical and (nonlinear) continuous covariates are also allowed (factor-by curve interactions). For the sake of simplicity we write \mu_{l}(\mathbf{x}_{\bar{D}i}) = \mathbf{z}_{\bar{D}i}^{T}\mathbf{\beta}_l (l=1,...,L), where \mathbf{z}_{\bar{D}i} is the ith column of the design matrix (possibly containing a basis representation of some/all continuous covariates). Here L is a pre-specified upper bound on the number of mixture components. The \omega_l's result from a truncated version of the stick-breaking construction (\omega_1=v_1; \omega_l=v_l\prod_{r<l}(1-v_r), l=2,\ldots,L; v_1,\ldots,v_{L-1}\sim Beta (1,\alpha); v_L=1, \alpha \sim \Gamma(a_{\alpha},b_{\alpha})), \mathbf{\beta}_l\sim N_{Q}(\mathbf{m},\mathbf{S}), and \sigma_l^{-2}\sim\Gamma(a,b). It is further assumed that \mathbf{m} \sim N_{Q}(\mathbf{m}_0,\mathbf{S}_0) and \mathbf{S}^{-1}\sim W(\nu,(\nu\Psi)^{-1}). Here \Gamma(a,b) denotes a Gamma distribution with shape parameter a and rate parameter b, W(\nu,(\nu\Psi)^{-1}) denotes a Wishart distribution with \nu degrees of freedom and expectation \Psi^{-1}, and Q denotes the dimension of the vector \mathbf{z}_{\bar{D}i}. It is worth mentioning that when L=1, the model for the conditional distribution of the test outcomes (in the healthy population) reduces to a normal regression model (where continuous covariates effects are modelled either parametrically or nonparametrically). For a detailed description, we refer to Inacio de Carvalho and Rodriguez-Alvarez (2018).
Regarding the area under the curve, we note that
AAUC = \int_{0}^{1}AROC(p)dp = 1 - E\{U_D\},
where U_D = 1 - F_{\bar{D}}(Y_D |\mathbf{X}_D). In our implementation, the expectation is computed using the Bayesian bootstrap (using the same weights as those used to estimate the AROC, see Inacio de Carvalho and Rodriguez-Alvarez (2018) for details). As far as the partial area under the curve is concerned, when focus = "FPF" and assuming an upper bound u_1 for the FPF, what it is computed is
pAAUC_{FPF}(u_1)=\int_0^{u_1} AROC(p)dp = u_1 - E\{U_{D,u_1}\},
where U_{D,u_1} = min\{u_1, 1 - F_{\bar{D}}(Y_D |\mathbf{X}_D)\}. Again, the expectation is computed using the Bayesian bootstrap. The returned value is the normalised pAAUC, pAAUC_{FPF}(u_1)/u_1 so that it ranges from u_1/2 (useless test) to 1 (perfect marker). Conversely, when focus = "TPF", and assuming a lower bound for the TPF of u_2, the partial area corresponding to TPFs lying in the interval (u_2,1) is computed as
pAAUC_{TPF}(u_2)=\int_{AROC^{-1}(u_2)}^{1}AROC(p)dp-\{1-AROC^{-1}(u_2)\}\times u_2.
Here, the computation of the integral is done numerically. The returned value is the normalised pAAUC, pAAUC_{TPF}(u_2)/(1-u_2), so that it ranges from (1-u_2)/2 (useless test) to 1 (perfect test).
Finally, it is important referring that with respect to the computation of the DIC, when L=1, it is computed as in Spiegelhalter et al. (2002), and when L>1, DIC3 as described in Celeux et al. (2006) is computed. Also, for the computation of the conditional predictive ordinates (CPO) we follow the stable version proposed by Gelman et al. (2014).
Value
As a result, the function provides a list with the following components:
call
The matched call.
data
The original supplied data argument.
missing.ind
A logical value indicating whether for each pair of observations (test outcomes and covariates) missing values occur.
marker
The name of the diagnostic test variable in the dataframe.
group
The value of the argument group used in the call.
tag.h
The value of the argument tag.h used in the call.
p
Set of false positive fractions (FPF) at which the covariate-adjusted ROC curve (AROC) has been estimated.
ci.level
The value of the argument ci.level used in the call.
prior
A list returning the hyperparameter values.
ROC
Estimated covariate-adjusted ROC curve (AROC) (posterior mean) and ci.level*100% pointwise credible band.
AUC
Estimated area under the covariate-adjusted ROC curve (AAUC) (posterior mean), and ci.level*100% credible interval.
pAUC
If computed, estimated partial area under the covariate-adjusted ROC curve (pAAUC) (posterior mean) and ci.level*100% credible
interval. Note that the returned values are normalised, so that the maximum value is one (see more on Details).
newdata
If compute is set to TRUE in the argument density, a data frame containing the values of the covariates at which the regression function and conditional densities were computed (see below).
reg.fun.h
If compute is set to TRUE in the argument density, a data frame containing the predicted regression function (posterior mean) and ci.level*100% pointwise credible band.
dens
If compute is set to TRUE in the argument density, a list with two components (only for the healthy population): grid (grid of test outcomes where the densities were evaluated) and dens (MCMC realisations of the corresponding conditional densities).
lpml
If computed, a list with two components: the log pseudo marginal likelihood (LPML) and the conditional predictive ordinates (CPO).
WAIC
If computed, widely applicable information criterion (WAIC) and associated complexity penalty (pW).
DIC
If computed, deviance information criterion (DIC) and associated complexity penalty (pD).
fit
Results of the fitting process. A list with the following components: (1) formula: the value of the argument formula.h used in the call. (2) mm: information needed to construct the model design matrix associated with the single weights linear dependent Dirichlet process mixture model. (3) beta: array of dimension nsavexLxQ with the sampled regression coefficients. (4) sd: matrix of dimension nsavexL with the sampled variances. (4) probs: matrix of dimension nsavexL with the sampled components' weights. Here, nsave is the number of Gibbs sampler iterations saved, L is the upper bound on the number of mixture components, and Q is the dimension of vector \mathbf{z}_{\bar{D}} (see also Details).
data_model
List with the data used in the fit: observed diagnostic test outcome and design matrices, separately for the healthy and diseased groups.
Note
The input argument formula.h is similar to that used for the glm function, except that flexible specifications can be added by means of the function f(). For instance, specification y \sim x1 + f(x2, K = 3) would assume a linear effect of x1 (if x1 continuous) and the effect of x2 would be modeled using B-splines basis functions. The argument K = 3 indicates that 3 internal knots will be used, with the quantiles of x2 used for their location. Categorical variables (factors) can be also incorporated, as well as interaction terms. For example, to include the factor-by-curve interaction between age and gender we need to specify, e.g., y \sim gender + f(age, by = gender, K = c(3, 5)). Note that, in this case, the number of knots can be different for each level of the factor. The order of the vector K of knots should match the levels of the factor.
References
Celeux, G., Forbes, F., Robert C. P., and Titerrington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis, 1, 651–674.
De Iorio, M., Johnson, W. O., Muller, P., and Rosner, G. L. (2009). Bayesian nonparametric nonproportional hazards survival modeling. Biometrics, 65, 762–775.
Geisser, S. and Eddy, W.F. (1979) A Predictive Approach to Model Selection, Journal of the American Statistical Association, 74, 153–160.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D.B. (2014). Bayesian Data Analysis, 3rd ed. CRC Press: Boca Raton, FL.
Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24, 997–1010.
Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.
Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9, 130–134.
Speigelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model comparison and fit. Journal of the Royal Statistical Society, Ser. B, 64, 583–639.
Watanabe, S. (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. Journal of Machine Learning Research, 11, 3571–3594.
See Also
AROC.bnp, AROC.sp, AROC.kernel, pooledROC.BB, pooledROC.emp, pooledROC.kernel, pooledROC.dpm, cROC.bnp, cROC.sp or AROC.kernel.
Examples
library(ROCnReg)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
AROC_bnp <- AROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
group = "status",
tag.h = 0,
data = newpsa,
standardise = TRUE,
p = seq(0,1,l=101),
compute.lpml = TRUE,
compute.WAIC = TRUE,
compute.DIC = TRUE,
pauc = pauccontrol(compute = TRUE, focus = "FPF", value = 0.5),
density = densitycontrol.aroc(compute = TRUE, grid.h = NA, newdata = NA),
prior.h = priorcontrol.bnp(m0 = rep(0, 4), S0 = 10*diag(4), nu = 6, Psi = diag(4),
a = 2, b = 0.5, alpha = 1, L =10),
mcmc = mcmccontrol(nsave = 500, nburn = 100, nskip = 1))
summary(AROC_bnp)
plot(AROC_bnp)
ROCnReg documentation built on March 31, 2023, 5:42 p.m.
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| AROC.bnp | R Documentation |
Nonparametric Bayesian inference of the covariate-adjusted ROC curve (AROC).
Description
This function estimates the covariate-adjusted ROC curve (AROC) using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018).
Usage
AROC.bnp(formula.h, group, tag.h, data, standardise = TRUE,
p = seq(0, 1, l = 101), ci.level = 0.95, compute.lpml = FALSE, compute.WAIC = FALSE,
compute.DIC = FALSE, pauc = pauccontrol(), density = densitycontrol.aroc(),
prior.h = priorcontrol.bnp(), mcmc = mcmccontrol(),
parallel = c("no", "multicore", "snow"), ncpus = 1, cl = NULL)
Arguments
formula.h |
A |
group |
A character string with the name of the variable that distinguishes healthy/nondiseased from diseased individuals. |
tag.h |
The value codifying healthy individuals in the variable |
data |
A data frame representing the data and containing all needed variables. |
standardise |
A logical value. If TRUE both the test outcomes and the continuous covariates assumed to have a linear effect are standardised (i.e., the resulting variables have mean zero and standard deviation of one). The default is TRUE. |
p |
Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve. |
ci.level |
An integer value (between 0 and 1) specifying the level for the credible interval. The default is 0.95. |
compute.lpml |
A logical value. If TRUE, the log pseudo marginal likelihood (LPML, Geisser and Eddy, 1979) and the conditional predictive ordinates (CPO) are computed. |
compute.WAIC |
A logical value. If TRUE, the widely applicable information criterion (WAIC, Gelman et al., 2014; Watanabe, 2010) is computed. |
compute.DIC |
A logical value. If TRUE, the deviance information criterion (DIC)(Celeux et al., 2006, Spiegelhalter et al., 2002) is computed. |
pauc |
A list of control values to replace the default values returned by the function |
density |
A list of control values to replace the default values returned by the function |
prior.h |
A list of control values to replace the default values returned by the function |
mcmc |
A list of control values to replace the default values returned by the function |
parallel |
A characters string with the type of parallel operation: either "no" (default), "multicore" (not available on Windows) or "snow". |
ncpus |
An integer with the number of processes to be used in parallel operation. Defaults to 1. |
cl |
An object inheriting from class |
Details
Estimates the covariate-adjusted ROC curve (AROC) defined as
AROC\left(p\right) = Pr\{1 - F_{\bar{D}}(Y_D | \mathbf{X}_{D}) \leq p\},
where F_{\bar{D}}(\cdot|\mathbf{X}_{\bar{D}}) denotes the distribution function of Y_{\bar{D}} conditional on the vector of covariates \mathbf{X}_{\bar{D}}.
The method implemented in this function combines a single-weights linear dependent Dirichlet process mixture model (De Iorio et al., 2009) to estimate F_{\bar{D}}(\cdot|\mathbf{X}_{\bar{D}}) and the Bayesian bootstrap (Rubin, 1981) to estimate the outside probability. More precisely, and letting \{(\mathbf{x}_{\bar{D}i},y_{\bar{D}i})\}_{i=1}^{n_{\bar{D}}} be a random sample from the nondiseased population, our postulated model for the conditional distribution function takes the following form
F_{\bar{D}}(y_{\bar{D}i}|\mathbf{X}_{\bar{D}}=\mathbf{x}_{\bar{D}i}) = \sum_{l=1}^{L}\omega_l\Phi(y_{\bar{D}i}\mid\mu_{l}(\mathbf{x}_{\bar{D}i}),\sigma_l^2),
where \Phi(y|\mu, \sigma^2) denotes the cumulative distribution function of the normal distribution, evaluated at y, with mean \mu and variance \sigma^2. The regression function \mu_{l}(\mathbf{x}_{\bar{D}i}) can incorportate both linear and nonlinear (through B-splines) effects of continuous covariates, categorical covariates (factors) as well as interactions. Interactions between categorical and (nonlinear) continuous covariates are also allowed (factor-by curve interactions). For the sake of simplicity we write \mu_{l}(\mathbf{x}_{\bar{D}i}) = \mathbf{z}_{\bar{D}i}^{T}\mathbf{\beta}_l (l=1,...,L), where \mathbf{z}_{\bar{D}i} is the ith column of the design matrix (possibly containing a basis representation of some/all continuous covariates). Here L is a pre-specified upper bound on the number of mixture components. The \omega_l's result from a truncated version of the stick-breaking construction (\omega_1=v_1; \omega_l=v_l\prod_{r<l}(1-v_r), l=2,\ldots,L; v_1,\ldots,v_{L-1}\sim Beta (1,\alpha); v_L=1, \alpha \sim \Gamma(a_{\alpha},b_{\alpha})), \mathbf{\beta}_l\sim N_{Q}(\mathbf{m},\mathbf{S}), and \sigma_l^{-2}\sim\Gamma(a,b). It is further assumed that \mathbf{m} \sim N_{Q}(\mathbf{m}_0,\mathbf{S}_0) and \mathbf{S}^{-1}\sim W(\nu,(\nu\Psi)^{-1}). Here \Gamma(a,b) denotes a Gamma distribution with shape parameter a and rate parameter b, W(\nu,(\nu\Psi)^{-1}) denotes a Wishart distribution with \nu degrees of freedom and expectation \Psi^{-1}, and Q denotes the dimension of the vector \mathbf{z}_{\bar{D}i}. It is worth mentioning that when L=1, the model for the conditional distribution of the test outcomes (in the healthy population) reduces to a normal regression model (where continuous covariates effects are modelled either parametrically or nonparametrically). For a detailed description, we refer to Inacio de Carvalho and Rodriguez-Alvarez (2018).
Regarding the area under the curve, we note that
AAUC = \int_{0}^{1}AROC(p)dp = 1 - E\{U_D\},
where U_D = 1 - F_{\bar{D}}(Y_D |\mathbf{X}_D). In our implementation, the expectation is computed using the Bayesian bootstrap (using the same weights as those used to estimate the AROC, see Inacio de Carvalho and Rodriguez-Alvarez (2018) for details). As far as the partial area under the curve is concerned, when focus = "FPF" and assuming an upper bound u_1 for the FPF, what it is computed is
pAAUC_{FPF}(u_1)=\int_0^{u_1} AROC(p)dp = u_1 - E\{U_{D,u_1}\},
where U_{D,u_1} = min\{u_1, 1 - F_{\bar{D}}(Y_D |\mathbf{X}_D)\}. Again, the expectation is computed using the Bayesian bootstrap. The returned value is the normalised pAAUC, pAAUC_{FPF}(u_1)/u_1 so that it ranges from u_1/2 (useless test) to 1 (perfect marker). Conversely, when focus = "TPF", and assuming a lower bound for the TPF of u_2, the partial area corresponding to TPFs lying in the interval (u_2,1) is computed as
pAAUC_{TPF}(u_2)=\int_{AROC^{-1}(u_2)}^{1}AROC(p)dp-\{1-AROC^{-1}(u_2)\}\times u_2.
Here, the computation of the integral is done numerically. The returned value is the normalised pAAUC, pAAUC_{TPF}(u_2)/(1-u_2), so that it ranges from (1-u_2)/2 (useless test) to 1 (perfect test).
Finally, it is important referring that with respect to the computation of the DIC, when L=1, it is computed as in Spiegelhalter et al. (2002), and when L>1, DIC3 as described in Celeux et al. (2006) is computed. Also, for the computation of the conditional predictive ordinates (CPO) we follow the stable version proposed by Gelman et al. (2014).
Value
As a result, the function provides a list with the following components:
call |
The matched call. |
data |
The original supplied data argument. |
missing.ind |
A logical value indicating whether for each pair of observations (test outcomes and covariates) missing values occur. |
marker |
The name of the diagnostic test variable in the dataframe. |
group |
The value of the argument |
tag.h |
The value of the argument |
p |
Set of false positive fractions (FPF) at which the covariate-adjusted ROC curve (AROC) has been estimated. |
ci.level |
The value of the argument |
prior |
A list returning the hyperparameter values. |
ROC |
Estimated covariate-adjusted ROC curve (AROC) (posterior mean) and |
AUC |
Estimated area under the covariate-adjusted ROC curve (AAUC) (posterior mean), and |
pAUC |
If computed, estimated partial area under the covariate-adjusted ROC curve (pAAUC) (posterior mean) and |
newdata |
If |
reg.fun.h |
If |
dens |
If |
lpml |
If computed, a list with two components: the log pseudo marginal likelihood (LPML) and the conditional predictive ordinates (CPO). |
WAIC |
If computed, widely applicable information criterion (WAIC) and associated complexity penalty (pW). |
DIC |
If computed, deviance information criterion (DIC) and associated complexity penalty (pD). |
fit |
Results of the fitting process. A list with the following components: (1) |
data_model |
List with the data used in the fit: observed diagnostic test outcome and design matrices, separately for the healthy and diseased groups. |
Note
The input argument formula.h is similar to that used for the glm function, except that flexible specifications can be added by means of the function f(). For instance, specification y \sim x1 + f(x2, K = 3) would assume a linear effect of x1 (if x1 continuous) and the effect of x2 would be modeled using B-splines basis functions. The argument K = 3 indicates that 3 internal knots will be used, with the quantiles of x2 used for their location. Categorical variables (factors) can be also incorporated, as well as interaction terms. For example, to include the factor-by-curve interaction between age and gender we need to specify, e.g., y \sim gender + f(age, by = gender, K = c(3, 5)). Note that, in this case, the number of knots can be different for each level of the factor. The order of the vector K of knots should match the levels of the factor.
References
Celeux, G., Forbes, F., Robert C. P., and Titerrington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis, 1, 651–674.
De Iorio, M., Johnson, W. O., Muller, P., and Rosner, G. L. (2009). Bayesian nonparametric nonproportional hazards survival modeling. Biometrics, 65, 762–775.
Geisser, S. and Eddy, W.F. (1979) A Predictive Approach to Model Selection, Journal of the American Statistical Association, 74, 153–160.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D.B. (2014). Bayesian Data Analysis, 3rd ed. CRC Press: Boca Raton, FL.
Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24, 997–1010.
Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.
Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9, 130–134.
Speigelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model comparison and fit. Journal of the Royal Statistical Society, Ser. B, 64, 583–639.
Watanabe, S. (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. Journal of Machine Learning Research, 11, 3571–3594.
See Also
AROC.bnp, AROC.sp, AROC.kernel, pooledROC.BB, pooledROC.emp, pooledROC.kernel, pooledROC.dpm, cROC.bnp, cROC.sp or AROC.kernel.
Examples
library(ROCnReg)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
AROC_bnp <- AROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
group = "status",
tag.h = 0,
data = newpsa,
standardise = TRUE,
p = seq(0,1,l=101),
compute.lpml = TRUE,
compute.WAIC = TRUE,
compute.DIC = TRUE,
pauc = pauccontrol(compute = TRUE, focus = "FPF", value = 0.5),
density = densitycontrol.aroc(compute = TRUE, grid.h = NA, newdata = NA),
prior.h = priorcontrol.bnp(m0 = rep(0, 4), S0 = 10*diag(4), nu = 6, Psi = diag(4),
a = 2, b = 0.5, alpha = 1, L =10),
mcmc = mcmccontrol(nsave = 500, nburn = 100, nskip = 1))
summary(AROC_bnp)
plot(AROC_bnp)
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