# 假设num_col是包含数值的数据框列 boxplot(df$num_col, main="Boxplot to Identify Outliers", ylab="Value", xlab="Variable") 习题26: 题目:编写一个函数,该函数使用IQR方法检测一个数值向量中的异常值,并返回这些异常值。 使用IQR方法检测数值向量中的异常值: iqr_outlier_func <- function(vec) { ...
identify_outliers(): 使用boxplot鉴别离群值; mahalanobis_distance(): 计算Mahalanobi距离和离群点; shapiro_test()andmshapiro_test(): 正态性检验. 比较均值 t_test(): 单样本、配对样本、独立样本t检验; wilcox_test(): 单样本、配对样本、独立样本秩和检验; sign_test(): 符号检验; anova_test(): ...
to.data.frame = T) df$group <- c(rep('阿卡波糖',20),rep('拜糖平',20)) attributes(df)[3] <- NULL head(df) ## no x group ## 1 1 -0.7 阿卡波糖 ## 2 2 -5.6 阿卡波糖 ## 3 3 2.0 阿卡波糖 ## 4 4 2.8 阿卡波糖 ## 5 5 0.7 阿卡波糖...
Let’s take an example of this univariate dataset [10,4,6,8,9,8,7,6,12,14,11,9,8,4,5,10,14,12,15,7,10,14,24,28] and identify outliers using visual approaches (all of the R code mentioned in this article are implemented in RStudio), # RStudio consolelibrary(gridExtra)x=c(...
To identify the natural clusters in this dataset we will be using a technique called “k-mean clustering." This could easily be done in a calculated field with a single line of R code. SCRIPT_INT(‘kmeans(data.frame(.arg1,.arg2,.arg3,.arg4),3)$cluster;', SUM([Petal length]), ...
Identifying these points in R is very simply when dealing with only one boxplot and a few outliers. That can easily be done using the “identify” function in R. For example, running the code bellow will plot a boxplot of a hundred observation sampled from a normal distribution, and will...
An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set.An outlier can cause serious problems in statistical analyses.Identifying outliers: There are several methods we can use to identify outliers. In Exp...
After we find the distances, we use chi-square value as cut-off in order to identify outliers. This is the same as the radius of the ellipse in the above example.The mahalanobis function that comes with R in the stats package returns distances between each point and the given center ...
It neatly shows two distinct outliers which I’ll be working with in this tutorial. You can load this dataset on R using the data function. data("warpbreaks") Once loaded, you can begin working on it. Visualizing Outliers in R One of the easiest ways to identify outliers in R ...
data screening (to identify data range, outliers and missing data;screen_*functions), calculating summary statistics (long-term, annual, monthly and daily statistics;calc_*functions), computing analyses (volume frequency analyses and annual trending;compute_*functions), and, ...