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This vignette introduces the use of the refundBayes
package with simple examples for: (1) Bayesian Scalar-on-Function
Regression (SoFR), (2) Bayesian Functional Cox Rergession (FCox), and
(3) Bayesian Function-on-Scalar Rergession (FoSR). The
refundBayes package provides users with a convenient way to
perform Bayesian functional data analysis using Stan, a state-of-the-art
Bayesian analysis language. The syntax of refundBayes is
designed to resemble that of mgcv::gam, but it employs
Bayesian posterior inference and includes additional arguments specific
to the Stan implementation.
library(remotes)
remotes::install_github("https://github.com/ZirenJiang/refundBayes")## Skipping install of 'refundBayes' from a github remote, the SHA1 (5a1f14ca) has not changed since last install.
## Use `force = TRUE` to force installation## Read the example data
data.SoFR = readRDS("data/example_data_sofr.rds")
## Run Bayesian SOFR with the sofr_bayes() function
refundBayes_SoFR = refundBayes::sofr_bayes(y ~ X1+s(tmat, by=lmat*wmat, bs="cc", k=10),
data = data.SoFR,
family = binomial(),
runStan = TRUE, # Whether automatically run Stan program.
niter = 1500, # Total number of posterior sampling.
nwarmup = 500, # Burn-in value.
nchain = 3, # Number of parallel computed chains for posterior sampling.
ncores = 3
)
## Visualization of the fitted functional coefficient
library(ggplot2)
plot(refundBayes_SoFR)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the refundBayes package.
## Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## [[1]]
mean.curve.est = apply(refundBayes_SoFR$func_coef[[1]], 2, mean)
upper.curve.est = apply(refundBayes_SoFR$func_coef[[1]], 2, function(x){quantile(x, prob = 0.975)})
lower.curve.est = apply(refundBayes_SoFR$func_coef[[1]], 2, function(x){quantile(x, prob = 0.025)})
library(mgcv)
## This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.fit_freq1 = gam(y ~ s(tmat, by=lmat*wmat, bs="cc", k=10)+X1, data=data.SoFR, family="binomial")
fit_freq2 = gam(y ~ s(tmat, by=lmat*wmat, bs="cc", k=10)+X1, data=data.SoFR, family="binomial", method = "REML")
plotfot=plot(fit_freq1)
plotfot2=plot(fit_freq2,unconditional = TRUE)
T_num = 100
plotdata=data.frame(value=c(mean.curve.est,
upper.curve.est,
lower.curve.est,
plotfot[[1]]$fit,
plotfot[[1]]$fit+plotfot[[1]]$se,
plotfot[[1]]$fit-plotfot[[1]]$se,
plotfot2[[1]]$fit,
plotfot2[[1]]$fit+plotfot2[[1]]$se,
plotfot2[[1]]$fit-plotfot2[[1]]$se),
xmat=c(rep(1: T_num,3),
rep(plotfot[[1]]$x*100,6)),
Method=c(rep("Bayesian",T_num),
rep("Bayesian",T_num),
rep("Bayesian",T_num),
rep("Frequentist - GCV",100),
rep("Frequentist - GCV",100),
rep("Frequentist - GCV",100),
rep("Frequentist - REML",100),
rep("Frequentist - REML",100),
rep("Frequentist - REML",100)),
type=c(rep("Estimate",T_num),
rep("CI_upper",T_num),
rep("CI_lower",T_num),
rep("Estimate",100),
rep("CI_upper",100),
rep("CI_lower",100),
rep("Estimate",100),
rep("CI_upper",100),
rep("CI_lower",100)))
library(ggplot2)
ggplot(plotdata,aes(y=value,x=xmat))+geom_line(aes(linetype=type,color=Method))+
ylab("Functional effect")+xlab("Time index")+
#scale_x_continuous(breaks=seq(0,1,by=3))+
scale_linetype_manual(values=c("twodash", "longdash","solid"),name="Line Type")+
theme_minimal()
## Read the example data
Func_Cox_Data = readRDS("data/example_data_Cox.rds")
Func_Cox_Data$wmat = Func_Cox_Data$MIMS
Func_Cox_Data$cens = 1 - Func_Cox_Data$event
## Run Bayesian SOFR with the sofr_bayes() function
refundBayes_FCox = refundBayes::fcox_bayes(survtime ~ X1+s(tmat, by=lmat*wmat, bs="cc", k=10),
data = Func_Cox_Data,
cens = Func_Cox_Data$cens,
runStan = TRUE, # Whether automatically run Stan program.
niter = 1500, # Total number of posterior sampling.
nwarmup = 500, # Burn-in value.
nchain = 3, # Number of parallel computed chains for posterior sampling.
ncores = 3
)## Warning: There were 6 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems## Visualization of the fitted functional coefficient
library(ggplot2)
plot(refundBayes_FCox)## [[1]]
## Read the example data
FoSR_exp_data = readRDS("data/example_data_FoSR.rds")
FoSR_exp_data$y = FoSR_exp_data$MIMS
## Run Bayesian SOFR with the sofr_bayes() function
refundBayes_FoSR = refundBayes::fosr_bayes(y ~ X,
data = FoSR_exp_data,
runStan = TRUE, # Whether automatically run Stan program.
niter = 1500, # Total number of posterior sampling.
nwarmup = 500, # Burn-in value.
nchain = 1, # Number of parallel computed chains for posterior sampling.
ncores = 1
)##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.001614 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 16.14 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 1500 [ 0%] (Warmup)
## Chain 1: Iteration: 150 / 1500 [ 10%] (Warmup)
## Chain 1: Iteration: 300 / 1500 [ 20%] (Warmup)
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## Chain 1: Iteration: 501 / 1500 [ 33%] (Sampling)
## Chain 1: Iteration: 650 / 1500 [ 43%] (Sampling)
## Chain 1: Iteration: 800 / 1500 [ 53%] (Sampling)
## Chain 1: Iteration: 950 / 1500 [ 63%] (Sampling)
## Chain 1: Iteration: 1100 / 1500 [ 73%] (Sampling)
## Chain 1: Iteration: 1250 / 1500 [ 83%] (Sampling)
## Chain 1: Iteration: 1400 / 1500 [ 93%] (Sampling)
## Chain 1: Iteration: 1500 / 1500 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 51.492 seconds (Warm-up)
## Chain 1: 9.381 seconds (Sampling)
## Chain 1: 60.873 seconds (Total)
## Chain 1:
## Warning: The largest R-hat is NA, indicating chains have not mixed.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#r-hat
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess
## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Visualization of the fitted functional coefficient
library(ggplot2)
plot(refundBayes_FoSR)## Warning in ggplot2::geom_line(aes(type = .data$type, color = .data$type)):
## Ignoring unknown aesthetics: type
## [[1]]
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They may not be fully stable and should be used with caution. We make no claims about them.
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