confidence-interval

I have used smooth.spline to estimate a cubic spline for my data. But when I calculate the 90% point-wise confidence interval using equation, the results seems to be a little bit off. Can someone please tell me if I did it wrongly? I am just wondering if there is a function that can automatically calculate a point-wise interval band associated with smooth.spline function. boneMaleSmooth = smooth.spline( bone[males,"age"], bone[males,"spnbmd"], cv=FALSE) error90_male = qnorm(.95)*sd(boneMaleSmooth$x)/sqrt(length(boneMaleSmooth$x)) plot(boneMaleSmooth, ylim=c(-0.5,0.5), col="blue", lwd=3, type=

How to obtain a confidence interval or a measure of prediction dispersion when using xgboost for classification? So for example, if xgboost predicts a probability of an event is 0.9, how can the confidence in that probability be obtained? Also is this confidence assumed to be heteroskedastic? To produce confidence intervals for xgboost model you should train several models (you can use bagging for this). Each model will produce a response for test sample - all responses will form a distribution from which you can easily compute confidence intervals using basic statistics. You should produce

I've following data: I want to plot a graph shows for all x the mean connected with lines (linespoint) and also showing all the confidence interval [ci_lower, ci_upper]. I managed to do that using 2 plots. set xrange [0:1100000] set yrange [0:5000] plot \ '2011-06-16_1n_result.txt' using 1:7 title '1 node' with lines, \ '2011-06-16_1n_result.txt' using 1:7:10:11 notitle with errorbars However it doesn't look nice with two colors and I have the feeling that there should be a way to do that with a single plot. What's the solution code to that? Best, Will I think you are looking for the

I am trying to summarise data from a household survey and as such most of my data is categorical (factor) data. I was looking to summarise it with plots of frequencies of responses to certain questions (e.g., a bar plot of percentages of households answering certain questions, with error bars showing confidence intervals). I found this excellent tutorial which I had thought was the answer to my prayers ( http://www.cookbook-r.com/Manipulating_data/Summarizing_data/ ) but turns out this is only going to help with continuous data. What I need is something similar that will allow me to calculate

We're teaching a stats class for biology students and trying to use R as the computing and data visualization platform. As much as possible, we'd like to avoid using extra packages and doing anything terribly "fancy" in R; the focus of the course is on the statistics, not the programming. Nevertheless, we haven't found a very good way of generating an errorbar plot in R for a two factor ANOVA design. We're using the ggplot2 package to make the plot, and while it does have a built-in stat_summary method of generating 95% CI errorbars, the way these are calculated may not always be the right way

I would like to get 95% confidence intervals for the regression coefficients of a quantile regression. You can calculate quantile regressions using the rq function of the quantreg package in R (compared to an OLS model): library(quantreg) LM<-lm(mpg~disp, data = mtcars) QR<-rq(mpg~disp, data = mtcars, tau=0.5) I am able to get 95% confidence intervals for the linear model using the confint function: confint(LM) When I use quantile regression I understand that the following code produces bootstrapped standard errors: summary.rq(QR,se="boot") But actually I would like something like 95%