View source: R/plot.metadiag.R
plot.metadiag | R Documentation |
This function plots the observe data in the ROC (Receiving Operating Charachteristics) space with the posterior predictive contours. The predictive curves are approximated using a non-parametric smoother or with a parametric model. For the parametric model the current implementation supports only a logistic link function. The marginal posterior predictive distributions are ploted outside the ROC space.
## S3 method for class 'metadiag' plot( x, parametric.smooth = TRUE, level = c(0.5, 0.75, 0.95), limits.x = c(0, 1), limits.y = c(0, 1), kde2d.n = 25, color.line = "red", title = paste("Posterior Predictive Contours (50%, 75% and 95%)"), marginals = TRUE, bin.hist = 30, color.hist = "lightblue", S = 500, color.pred.points = "lightblue", color.data.points = "blue", ... )
x |
The object generated by the metadiag function. |
parametric.smooth |
Indicates if the predictive curve is a parametric or non-parametric. |
level |
Credibility levels of the predictive curve. If parametric.smooth = FALSE, then the probability levels are estimated from the nonparametric surface. |
limits.x |
Numeric vector of length 2 specifying the x-axis limits. The default value is c(0, 1). |
limits.y |
Numeric vector of length 2 specifying the x-axis limits. The default value is c(0, 1). |
kde2d.n |
The number of grid points in each direction for the non-parametric density estimation. Can be scalar or a length-2 inter vector. |
color.line |
Color of the predictive contour line. |
title |
Optional parameter for setting a title in the plot. |
marginals |
Plot the posterior marginal predictive histograms. |
bin.hist |
Number of bins of the marginal histograms. |
color.hist |
Color of the histograms. |
S |
Number of predictive rates to be plotted. |
color.pred.points |
Color of the posterior predictive rates. |
color.data.points |
Color of the data points. |
... |
... |
metadiag
.
## Not run: library(bamdit) data("glas") glas.t <- glas[glas$marker == "Telomerase", 1:4] glas.m1 <- metadiag(glas.t, # Data frame re = "normal", # Random effects distribution re.model = "DS", # Random effects on D and S link = "logit", # Link function sd.Fisher.rho = 1.7, # Prior standard deviation of correlation nr.burnin = 1000, # Iterations for burnin nr.iterations = 10000, # Total iterations nr.chains = 2, # Number of chains r2jags = TRUE) # Use r2jags as interface to jags plot(glas.m1, # Fitted model level = c(0.5, 0.75, 0.95), # Credibility levels parametric.smooth = TRUE) # Parametric curve # Plot results: based on a non-parametric smoother of the posterior predictive rates ....... plot(glas.m1, # Fitted model level = c(0.5, 0.75, 0.95), # Credibility levels parametric.smooth = FALSE) # Non-parametric curve # Using the pipe command in the package dplyr and changing some colors ....... library(dplyr) glas.t %>% metadiag(re = "normal", re.model ="SeSp") %>% plot(parametric.smooth = FALSE, S = 100, color.data.points = "green", color.pred.points = "blue", color.line = "black") ## End(Not run)
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