mean

In R I have created a simple matrix of one column yielding a list of numbers with a set mean and a given standard deviation. rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) } r <- rnorm2(100,4,1) I now would like to plot how these numbers differ from the mean. I can do this in Excel as shown below: But I would like to use ggplot2 to create a graph in R. in the Excel graph I have cheated by using a line graph but if I could do this as columns it would be better. I have tried using a scatter plot but I cant work out how to turn this into deviations from the mean. Perhaps you want: rnorm2

转自： https://www.cnblogs.com/arnoldlu/p/7406441.html 1.前言 了解Linux中断子系统，同时也需要了解ARM体系结构中断处理流程；在熟悉整个软硬件架构和流程基础上，才能对流程进行细化，然后找出问题的瓶颈。《 2. 梳理中断处理子系统 》 但是所有的优化都离不开一个量化的过程，有个可靠、高效、可读性强的度量必不可少。《 3. 一种测量中断性能手段 》 最后基于此，进行中断性能的优化。《 4.中断性能优化 》 2. 梳理中断处理子系统 中断系统涉及到软硬件两部分，具体到ARM系统和Linux涉及到很多相关点。 硬件以Cortex-A53为基础，整个GIC架构包括两部分：CPU内部的GIC CPU Interface( Cortex-A53 Chapter 9 )和CPU外部的GIC external distributor component。 《ARM Cortex-A53 MPCore Processor Technical Reference Manual》简单介绍了A53核内部的GIC CPU Interface。 《ARM Generic Interrupt Controller Architecture Specification v3/v4》详细介绍了整个GIC架构的方方面面，具体实现比如GIC-600在《GIC-600

Say, i have three columns x <- c(-10, 1:6, 50) x1<- c(-20, 1:6, 60) z<- c(1,2,3,4,5,6,7,8) check outliers for x bx <- boxplot(x) bx$out check outliers for x1 bx1 <- boxplot(x1) bx1$out now we must delete outliers x <- x[!(x %in% bx$out)] x x1 <- x1[!(x1 %in% bx1$out)] x1 but we have variable Z(nominal) and we must remove observations, which correspond to the outlier of variables x and x1, in our case it is 1 and 8 obs. of Z How to do it? in output we must have x x1 z Na Na Na 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 Na Na Na Try this solution: x_to_remove<-which(x %in% bx$out) x <- x[!(x %in% bx

I am trying to find a function in R which smooths a 2D matrix which is already padded by zero around the matrix depending on the length of the window. If the window size is 3 then we pas 3 zeros from the top, bottom, right and left and then we do the averaging to smooth it. I found rollmean in SO and other tutorials but it doesn't exactly do what I want. I need (say if the window size is 3) we consider a window of 3*3 and take the average and replace it by the current elements of the current window and then move the window to the right by one unit (pixel). And for example when we reach the

I have a dataset of 100 samples, each of which has 195 mutations with their corresponding known clinical significance ("RealClass") and predicted value according to some prediction tool ("PredictionValues") For the demonstration, this is a random dataset that has the same structure as my dataset: predictions_100_samples<-as.data.frame(matrix(nrow=19500,ncol=3)) colnames(predictions_100_samples)<-c("Sample","PredictionValues","RealClass") predictions_100_samples$Sample<-rep(c(1:100), each = 195) predictions_100_samples$PredictionValues<-sample(seq(0,1,length.out=19500)) predictions_100_samples

本文参考《数据科学家联盟》饼干文章。 一、无量纲化：最值归一化、均值方差归一化及sklearn中的Scaler 在量纲不同的情况下，不能反映样本中每一个特征的重要程度时，将需要使用归一化方法。 一般来说解决方法为 把所有的数据都映射到同一个尺度（量纲）上。 1、常用的数据归一化有两种： 最值归一化(normalization) ： 把所有数据映射到0-1之间。最值归一化的使用范围是特征的分布具有明显边界的(分数0～100分、灰度0～255)，受outlier的影响比较大 均值方差归一化(standardization)： 把所有数据归一到均值为0方差为1的分布中。适用于数据中没有明显的边界，有可能存在极端数据值的情况. 2、sklearn中的Scaler 建模时要将数据集划分为训练数据集&测试数据集。 训练数据集进行归一化处理，需要计算出训练数据集的均值mean_train和方差std_train。 问题是：我们在对测试数据集进行归一化时，要计算测试数据的均值和方差么？ 答案是否定的。在对测试数据集进行归一化时，仍然要使用训练数据集的均值train_mean和方差std_train。这是因为测试数据是模拟的真实环境，真实环境中可能无法得到均值和方差，对数据进行归一化。只能够使用公式 (x_test - mean_train) / std_train 并且，数据归一化也是算法的一部分