confidence-interval

I am trying to apply PCA for Multi variant Analysis and plot the score plot for first two components with Hotelling T2 confidence ellipse in python. I was able to get the scatter plot and I want to add 95% confidence ellipse to the scatter plot. It would be great if anyone know how it can be done in python. Sample picture of expected output: This was bugging me, so I adopted an answer from PCA and Hotelling's T^2 for confidence intervall in R in python (and using some source code from the ggbiplot R package) from sklearn import decomposition from sklearn.preprocessing import StandardScaler

I have some data showing a long list of regions, the population of each region and the number of people in each region with a certain disease. I'm trying to show the confidence intervals for each proportion (but I'm not testing whether the proportions are statistically different). One approach is to manually calculate the standard errors and confidence intervals but I'd like to use a built-in tool like prop.test, because it has some useful options. However, when I use prop.test with vectors, it runs a chi-square test across all the proportions. I've solved this with a while loop (see dummy

Is there a way to add confidence intervals to a qqplot? I have a dataset of gene expression values, which I've visualized using PCA: pca1 = prcomp(data, scale. = TRUE) I'm now looking for outliers by checking the distribution of the data against the normal distribution through: qqnorm(pca1$x,pch = 20, col = c(rep("red", 73), rep("blue", 33))) qqline(pca1$x) This is my data: data = [2.48 104 4.25 219 0.682 0.302 1.09 0.586 90.7 344 13.8 1.17 305 2.8 79.7 3.18 109 0.932 562 0.958 1.87 0.59 114 391 13.5 1.41 208 2.37 166 3.42] I would now like to plot 95% confidence intervals to check which data

I'm trying to plot a exponential curve (nls object), and its confidence bands. I could easily did in ggplot following the Ben Bolker reply in this post . But I'd like to plot it in the basic graphics style, (also with the shaped polygon) df <- structure(list(x = c(0.53, 0.2, 0.25, 0.36, 0.46, 0.5, 0.14, 0.42, 0.53, 0.59, 0.58, 0.54, 0.2, 0.25, 0.37, 0.47, 0.5, 0.14, 0.42, 0.53, 0.59, 0.58, 0.5, 0.16, 0.21, 0.33, 0.43, 0.46, 0.1, 0.38, 0.49, 0.55, 0.54), y = c(63, 10, 15, 26, 34, 32, 16, 31,26, 37, 50, 37, 7, 22, 13, 21, 43, 22, 41, 43, 26, 53, 45, 7, 12, 25, 23, 31, 19, 37, 24, 50, 40)),

I am sure this is realted to Bootstrapping Krippendorff's Alpha . But I didn't understand the question nor the answers there. And it looks like that even the answers and comments are contradicting each other. set.seed(0) df <- data.frame(a = rep(sample(1:4),10), b = rep(sample(1:4),10)) kripp.alpha(t(df)) This is the output. Krippendorff's alpha Subjects = 40 Raters = 2 alpha = 0.342 How can I compute the confidence interval here? You are right it is connected to bootstrapping. You could compute the confidence interval the following way: library(irr) library(boot) alpha.boot <- function(d,w) {