# plotFunctionDraws: Plot matrix of function draws evaluated on a set of x In rbart: Bayesian Trees for Conditional Mean and Variance

## Description

Given draws of a function fd, d=1,2,...D and a set of x vectors x(j), j=1,2,...J, we have a D x J matrix of evaluations whose (d,j) element is fd(x(j)), the d th draw of the function evaluated at the j th x. This function plots the draws by plotting estimates of f(x_j) versus intervals for f(x_j). The estimates are the mean of the j^{th} column and the intervals are two quantiles of the j^{th} column (e.g 5% and 95%).

## Usage

 ```1 2 3 4``` ```plotFunctionDraws(fd,complevel=mean(fd),probs=c(.025,.975), xlab="posterior mean of function",ylab="posterior intervals", intervalcol="green",linecol="red", pts=NA,ptscol="blue", ptspch=1, ptscex=1, ...) ```

## Arguments

 `fd` D times J matrix whose (d,j) element is the d^th function draw evaluated at the j^{th} x. `complevel` A horizontal line is drawn a complevel to compare the intervals to. `probs` The two quantiles used to construct the intervals. `xlab` Label for x axis. `ylab` Label for y axis. `intervalcol` Color to draw the intervals with. `linecol` Color to draw the comparizon horizontal line with. `pts` Add (x_j,pts_j) to the plot. For example pts could be fitted values from and alternative model such as the linear model. `ptscol` Color to draw the points pts with. `ptspch` plot charactor to plot the points pts with. `ptscex` cex to plot the points pts with. `...` Arguments passed on to call to graphics::plot.

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## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```################################################## ## please see vignette and/or www.rob-mcculloch.org for more realistic examples ################################################## ## get simulated data data(simdat) ##get rbart run on the simulated data data(rbartonsimd) ## plot function (f and s) draws shat = sqrt(mean((simdat\$yp-rbartonsimd\$mmean)^2)) #overall estimate of sigma lmfit = lm(y~x,data.frame(x=simdat\$x,y=simdat\$y)) yhatlm = predict(lmfit,data.frame(x=simdat\$xp)) #fits from a linear model #Now we use plotFunctionDraws to look at mdraws (left panel) and sdraws (right panel). ## in the mean inference, you can see that the linear model seem unlikely ## in the variance inference, you can see that the posteriors of s(x) are far from a constant value par(mfrow=c(1,2)) ## look at mean inference plotFunctionDraws(rbartonsimd\$mdraws,complevel=mean(simdat\$y), probs=c(.05,.95), xlab=expression(hat(f)(x)), pts=yhatlm, ptscol="black", cex.lab=1.2, cex.axis=1.4, main="intervals for f(x)") ##look at the standard deviation inference plotFunctionDraws(rbartonsimd\$sdraws, complevel=shat, xlab=expression(hat(s)(x)), intervalcol="magenta", linecol="blue", cex.lab=1.2, cex.axis=1.4, main="intervals for s(x)") ```

rbart documentation built on Aug. 1, 2019, 5:04 p.m.