standard-deviation

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r

问题 This question is related to my previous one Average of multiple files having different row sizes I have few files with different row sizes, but number of columns in each file is same. e.g. ifile1.txt 1 1001 ? ? 2 1002 ? ? 3 1003 ? ? 4 1004 ? ? 5 1005 ? 0 6 1006 ? 1 7 1007 ? 3 8 1008 5 4 9 1009 3 11 10 1010 2 9 ifile2.txt 1 2001 ? ? 2 2002 ? ? 3 2003 ? ? 4 2004 ? ? 5 2005 ? 0 6 2006 6 12 7 2007 6 5 8 2008 9 10 9 2009 3 12 10 2010 5 7 11 2011 2 ? 12 2012 9 ? ifile3.txt 1 3001 ? ? 2 3002 ? 6 3

问题 Do somebody know what is wrong with my code? I edited the post, because i didn´t give you the data. I want to calculate the sd. The calculation of the mean worked. Here is the link to the cropped data: https://drive.google.com/drive/folders/1ljT1fzaDlSmn_3j7zHshS5lrV1wBvVQD library(raster) r <- brick("filename") #mean mean <- mean(r) #sd standard_dev <- sd(r) standard_dev2 <- sd(r, na.rm =TRUE) standard_deviation <- calc(r, sd) 回答1: You want the compute the sd for each cell in a RasterBrick.

问题 Say I have a batch of images in the form of tensors with dimensions (B x C x W x H) where B is the batch size, C is the number of channels in the image, and W and H are the width and height of the image respectively. I'm looking to use the transforms.Normalize() function to normalize my images with respect to the mean and standard deviation of the dataset across the C image channels , meaning that I want a resulting tensor in the form 1 x C. Is there a straightforward way to do this? I tried

问题 Bellow is a stock daily returns matrix example ( ret_matriz ) IBOV PETR4 VALE5 ITUB4 BBDC4 PETR3 [1,] -0.040630825 -0.027795652 -0.052643733 -0.053488685 -0.048455772 -0.061668282 [2,] -0.030463489 -0.031010237 -0.047439725 -0.040229625 -0.030552275 -0.010409016 [3,] -0.022668170 -0.027012078 -0.022668170 -0.050372843 -0.080732363 0.005218051 [4,] -0.057468428 -0.074922051 -0.068414670 -0.044130126 -0.069032911 -0.057468428 [5,] 0.011897277 -0.004705891 0.035489885 -0.005934736 -0.006024115

问题 Bellow is a stock daily returns matrix example ( ret_matriz ) IBOV PETR4 VALE5 ITUB4 BBDC4 PETR3 [1,] -0.040630825 -0.027795652 -0.052643733 -0.053488685 -0.048455772 -0.061668282 [2,] -0.030463489 -0.031010237 -0.047439725 -0.040229625 -0.030552275 -0.010409016 [3,] -0.022668170 -0.027012078 -0.022668170 -0.050372843 -0.080732363 0.005218051 [4,] -0.057468428 -0.074922051 -0.068414670 -0.044130126 -0.069032911 -0.057468428 [5,] 0.011897277 -0.004705891 0.035489885 -0.005934736 -0.006024115

问题 I have a DataFrame for a fast Fourier transformed signal. There is one column for the frequency in Hz and another column for the corresponding amplitude. I have read a post made a couple of years ago, that you can use a simple boolean function to exclude or only include outliers in the final data frame that are above or below a few standard deviations. df = pd.DataFrame({'Data':np.random.normal(size=200)}) # example dataset of normally distributed data. df[~(np.abs(df.Data-df.Data.mean())>(3

问题 I've followed the advice laid out here for calculating the average of circular data: https://en.wikipedia.org/wiki/Mean_of_circular_quantities But I'd also like to calculate standard deviation as well. #A vector of directional data (separated by 20 degrees each) Dir2<-c(350,20,40) #Degrees to Radians D2R<-0.0174532925 #Radians to Degrees Rad2<-Dir2 * D2R Sin2<-sin(Rad2) SinAvg<-mean(Sin2) Cos2<-cos(Rad2) CosAvg<-mean(Cos2) RADAVG<-atan2(SinAvg, CosAvg) DirAvg<-RADAVG * R2D The above gives me

问题 I am trying to calculate the standard deviation of a set of values in PowerBI and I am stuck. There are two columns in a table (days and count). This is a frequency distribution of a transportation lane. Days goes from 1 to 100, count is the number of shipments that took those number of days. The formula to calculate the standard deviation of a frequency distribution is pretty straight forward: sqrt(sum(fx * (x - avgx)^2))/sum(fx)) But the Dax is giving me a massive headache. Any help would