cdf

in R, with ecdf I can plot a empirical cumulative distribution function plot(ecdf(mydata)) and with hist I can plot a histogram of my data hist(mydata) How I can plot the histogram and the ecdf in the same plot? EDIT I try make something like that https://mathematica.stackexchange.com/questions/18723/how-do-i-overlay-a-histogram-with-a-plot-of-cdf symbolrush Also a bit late, here's another solution that extends @Christoph 's Solution with a second y-Axis. par(mar = c(5,5,2,5)) set.seed(15) dt <- rnorm(500, 50, 10) h <- hist( dt, breaks = seq(0, 100, 1), xlim = c(0,100)) par(new = T) ec <- ecdf

对如下20个原始数据绘制cdf图 1. 对数据进行排序，从小到大 2. 计算累积分布： 计算公式为： 然后对C3下拉，得到累积分布数据。 3. 选中B、C两列绘制散点图： 文章来源: https://blog.csdn.net/u013431916/article/details/90729505

Here is needed to plot CDF for 8 different functions in one plot. The problem that it gives just 7 different colors and the 8 one gives just first blue color again. How to make 8 different colors? Here is the script: locerror_2d=[Scan_Around[1],Triangle_Around[1],M_shape_Around[1],Hilbert_Around[1],Scan_SbS[1],Triangle_SbS[1],M_shape_SbS[1],Hilbert_SbS[1]] # N = len(locerror_2d[0]) #same for all ( here, I hope so... ) # N1=len(locerror_2d[2]) H_cent,h_cent1 = np.histogram( locerror_2d[0], bins = 10, normed = True ) # Random Walk Centroid hy_cent = np.cumsum(H_cent)*(h_cent1[1] - h_cent1[0]) H

I need to read long file with timestamp in seconds, and plot of CDF using numpy or scipy. I did try with numpy but seems the output is NOT what it is supposed to be. The code below: Any suggestions appreciated. import numpy as np import matplotlib.pyplot as plt data = np.loadtxt('Filename.txt') sorted_data = np.sort(data) cumulative = np.cumsum(sorted_data) plt.plot(cumulative) plt.show() tmdavison You have two options: 1: you can bin the data first. This can be done easily with the numpy.histogram function: import numpy as np import matplotlib.pyplot as plt data = np.loadtxt('Filename.txt') #

借助MATLAB（R2017b）分布拟合工具箱distributionFitter，我们可以快速地检查数据的分布。 可以进行非参数密度估计（核平滑方法）和参数密度估计（Beta分布、gamma分布、Normal分布等十几个常用的参数模型）。 给出参数估计结果： Distribution: Generalized Extreme Value Log likelihood: 17147.7 Domain: -Inf < y < Inf Mean: 0.00159053 Variance: 3.21144e-07 Parameter Estimate Std. Err. k 0.0506112 0.0163658 sigma 0.000411493 6.76348e-06 mu 0.0013314 8.99602e-06 Estimated covariance of parameter estimates: k sigma mu k 0.00026784 -3.4765e-08 -5.72776e-08 sigma -3.4765e-08 4.57446e-11 2.81887e-11 mu -5.72776e-08 2.81887e-11 8.09283e-11 可视化pdf、cdf、inverse cdf、概率图（PP图）、Survivor func(互补累积分布函数