mean

问题 I need help finding the monthly average of gold from 1950 to 2018 this is what my data looks like str(gold) 'data.frame': 827 obs. of 2 variables: $ Date : Factor w/ 827 levels "1950-01","1950-02",..: 1 2 3 4 5 6 7 8 9 10 ... $ Price: num 34.7 34.7 34.7 34.7 34.7 ... > head(gold) Date Price 1 1950-01 34.73 2 1950-02 34.73 3 1950-03 34.73 4 1950-04 34.73 5 1950-05 34.73 6 1950-06 34.73 > tail(gold) Date Price 822 2018-06 1282.13 823 2018-07 1238.06 824 2018-08 1201.86 825 2018-09 1199.20 826

问题 There are similar questions on the website, but I could not find an answer to this seemingly very simple problem. I fit a mixture of two gaussians on the Old Faithful Dataset: if(!require("mixtools")) { install.packages("mixtools"); require("mixtools") } data_f <- faithful plot(data_f$waiting, data_f$eruptions) data_f.k2 = mvnormalmixEM(as.matrix(data_f), k=2, maxit=100, epsilon=0.01) data_f.k2$mu # estimated mean coordinates for the 2 multivariate Gaussians data_f.k2$sigma # estimated

问题 I have a sorted array (as below), what methods can I use to sum equivalent target responses and therefore calculate a mean at each value? e.g. sum all values corresponding to target = 15.26 (not the same values in other trials). I considered looping through to look for similar numbers in the 3rd column but surely there is a simpler solution. Many thanks, Newbie response target 103 4.556049 15.260 55 10.549498 15.260 31 18.497221 15.260 130 13.275155 15.260 93 6.621331 15.260 35 7.653972 15

问题 I asked a question like this before but I decided to simplify my data format because I'm very new at R and didnt understand what was going on....here's the link for the question How to handle more than multiple sets of data in R programming? But I edited what my data should look like and decided to leave it like this..in this format... X1.0 X X2.0 X.1 0.9 0.9 0.2 1.2 1.3 1.4 0.8 1.4 As you can see I have four columns of data, The real data I'm dealing with is up to 2000 data points....

问题 I'm implementing an application using the full MEAN stack. I created a login page to signup with facebook to be able to show a profile page. But I discovered some problems. For this reason, I created a smaller version of my webapp, maintaining the same project structure. The complete code, executable (only replacing "client id" and "secret") with "npm install" and after "nodemon" is available here: https://github.com/Ks89/MEAN-OAuth_Example If I'll call (with a browser) the rest API that I

问题 I have a data frame named abc on which I'm doing moving average using rollapply . The following code works: forecast <- rollapply(abc, width=12, FUN=mean, align = "right", fill=NA) Now, I want to do the same thing with the width being variable, i.e. for the 1st month, it'll be empty, for the second month, first month's value will come. For the third month, it'll be (first month+second month/2), i.e. for the ith month, if i<=12 , the value will be (sum(1:i-1)/(i-1)) and for i>=12 it will be

问题 I have a cell array ( arr_new ) that includes both numbers and strings and I want to find the mean value of each column (and ignoring the strings because those are points that I want to ignore in my calculation) using Matlab. The array is a 200x200 cell array and it is a combination of numbers and strings. I tried to use this: for k = 1:cols Y(k) = mean(arr_new(k,:)); end But of course, it did not work because of the strings. Any help would be appreciated. 回答1: nCols = size(arr_new,2); Y =

问题 (Very) amateur coder and statistician working on a problem in R. I have four integer lists: A, B, C, D. A <- [1:133] B <- [1:266] C <- [1:266] D <- [1:133, 267-400] I want R to generate all of the permutations from picking 1 item from each of these lists (I know this code will take forever to run), and then take the mean of each of those permutations. So, for instance, [1, 100, 200, 400] -> 175.25. Ideally what I would have at the end is a list of all of these means then. Any ideas? 回答1: Here

问题 How I can calculate the arithmetic mean of a large vector(series) in distributed computing where I partition the data on multiple nodes. I do not want to use map reduce paradigm. Is there any distributed algorithm to efficiently compute the mean besides the trivial computation of individual sum on each node and then bringing the result at master node and dividing with the size of the vector(series). 回答1: distributed average consensus is an alternative. The problem with the trivial approach of

问题 I am working with GPS data and trying to figure out how to average the 11th-15th fixes for latitude and longitude. I have seen solutions in similar questions for how to average every n rows. The problem is that occasionally the satellites bomb out and the fixes stop at 13 or 14. So, in these cases, I only want to average 3 or 4 values instead of 5. So I am looking to average values for latitude and longitude starting from where the number in series is 11 until the number in series drops again