mean

问题 I need help with taking the following data which is organized in a large matrix and averaging all of the values that have a matching ID (index) and outputting another matrix with just the ID and the averaged value that trail it. File with data format: (This is the StarData variable) ID>>>>Values 002141865 3.867144e-03 742.000000 0.001121 16.155089 6.297494 0.001677 002141865 5.429278e-03 1940.000000 0.000477 16.583748 11.945627 0.001622 002141865 4.360715e-03 1897.000000 0.000667 16.863406 13

问题 Supposing a dataset as following: id date var1 001 20170101 1 001 20170101 2 001 20170101 3 001 20170102 1 001 20170102 2 002 20170101 1 002 20170101 2 002 20170102 1 002 20170102 2 I to calculate the mean for each id in each date through following code. proc summary data=HAVE nway; class id date; var var1 ; output out=WANT(drop=_:) mean=mean std=std; run; However, the WANT only represents date, mean, and std, but does not contain ID. How could I solve this problem? 回答1: I wasn't able to

问题 I am doing a data analysis on wall thickness measurements of circular tubes. I have the following matrix: > head(datIn, 12) Component Tube.number Measurement.location Sub.location Interval Unit Start 1 In 1 1 A 121 U6100 7/25/2000 2 In 1 1 A 122 U6100 5/24/2001 3 In 1 1 A 222 U6200 1/19/2001 4 In 1 1 A 321 U6300 6/1/2000 5 In 1 1 A 223 U6200 5/22/2002 6 In 1 1 A 323 U6300 6/18/2002 7 In 1 1 A 21 U6200 10/1/1997 8 In 1 1 A 221 U6200 6/3/2000 9 In 1 1 A 322 U6300 12/11/2000 10 In 1 1 B 122

问题 How do i find mean , median of a set of numbers without using arrays in C? Question is rather not the way to find mean or median but how to store a set of numbers and perform some operations on them if use of arrays is not allowed ? 回答1: A fun problem. The key to to find a place to store all the numbers. Code could use a file, a linked list, etc. The below uses a linked list and recursion. Leave it to OP for refinements about multiple modes or median when count is even. typedef struct num_S {

问题 I have the following matrix (let's call it df), for which I would like to create bootstrapped means and 95% confidence intervals for each column, due to the heavily 0 weighted distribution. I would like the mean and CI's to be added to the bottom of the matrix as new rows. This is a small subset of the data, the true data has >600 rows which will make the bootstrapping much more effective. row.names V183 V184 V185 V186 V187 V188 V189 V190 V191 V192 V193 V194 V195 V196 V197 V198 V199 V200 V201

问题 I have a set of data looks like this CHROM POS GT DIFF 1 chr01 14653 CT 254 2 chr01 14907 AG 254 3 chr01 14930 AG 23 4 chr01 15190 GA 260 5 chr01 15211 TG 21 6 chr01 16378 TC 1167 Where POS range from 1xxxx to 1xxxxxxx. And CHROM is a categorical variable that contains values of "chr01" to "chr22" and "chrX". I want to plot a scatterplot: y(DIFF) vs. X(POS) having panels separated by CHROM grouped by GT (different colors by GT) I'm creating a ggplot with running average (though not time

问题 EDIT : Since I wasn't able to find a correct solution, I changed the application's structure a bit and posted another question: Mongoose - find documents not in a list I have a MEAN app with three models: User , Task , and for keeping track of which task is assigned to which user I have UserTask , which looks like this: const mongoose = require("mongoose"); const autopopulate = require("mongoose-autopopulate"); const UserTaskSchema = mongoose.Schema({ completed: { type: Boolean, default:

问题 I have a structure named 'data' with 100 entries, each corresponding to a participant from an experiment. Each of the 100 entries contains multiple 6x6 matrices giving different values. For instance, an example of a matrix from my first participant is: data.p001.matrixCB 18.9737 17.0000 14.2829 12.4499 11.7898 10.0995 18.1384 16.0000 13.4907 11.7898 11.2250 10.3441 14.7986 12.5300 11.7898 11.7473 12.2066 9.3808 14.3527 13.4536 12.9615 13.3417 12.7279 11.7047 18.0278 17.8885 17.6068 17.4642 17

问题 Hi guys I asked a similar question about this here previously and I got some really awesome answers. But it turns out I have more data I need to work with sadly. So I have sample data in this format which is generated using head(data). So I have 3 specimens with their own Time and speed data....I'm not exactly using speed for my actual data Time Speed Time.1 Speed.1 Time.2 Speed.2 Error in unique.default(x) : unique() applies only to vectors Would it be possible for you guys to tell me what

问题 I've asked many questions about this and all the answers were really helpful...but once again my data is weird and I need help...Basically, what I want to do is find the average speed at a certain range of intervals...lets say from 6 s to 40 s my average speed would be 5 m/s...etc etc.. So it was pointed out to me to use this code... library(IRanges) idx <- seq(1, ncol(data), by=2) # idx is now 1, 3, 5. It will be passed one value at a time to `i`. # that is, `i` will take values 1 first,