R 语言stats包中的median函数用来计算样本的中位数。该函数接受一个数值向量x,以及一个逻辑值na.rm用来设置在计算中位数之前是否去除NA值。该函数的语法如下: median(x, na.rm = FALSE, ...) 下面是使用该函数的两个例子: > median(3:5) 4 > median(c(3:5, 50, 150)) [1] 5 现在我们可以计算银...
该函数接受一个数值向量x,以及一个逻辑值na.rm用来设置在计算中位数之前是否去除NA值。该函数的语法如下: median(x, na.rm = FALSE, ...) 下面是使用该函数的两个例子: > median(3:5) 4 > median(c(3:5, 50, 150)) [1] 5 现在我们可以计算银行数据中age列的中位数: > median(bank$age) 39 ...
2. Median, Range, Quartiles and Boxplot [Graph] (Median, Maximum, Minimum, Quartiles, Quartile Deviation, Boxplot) B. Test for Mean,Variance,etc. (Parametric) 1. One-Sample t-Test (to Compared Mean) [Graph] 2. Two-Sample t-Test (Student's and Welch's t-Tests) [Graph] ...
2. Median, Range, Quartiles and Boxplot. 1) Median 2) Maximum 3) Minimum 4) 1st Quartile 5) 3rd Quartile 6) Quartile Deviation 7) Boxplot Graph 3. One-Sample t-Test (to Specified Mean) 1) Mean 2) Standard deviation (2)
介绍几个数据描述性统计的R包。 psych::describe() psych::describe(iris) ## 带星号的是因子 # vars n mean sd median trimmed mad min max range skew kurtosis se # Sepal.Length 1 150 5.84 0.83 5.80 5.81 1.04 4.3 7.9 3.6 0.31 -0.61 0.07 # Sepal.Width 2 150 3.06 0.44 3.00 3.04 0.44 2.0...
data(iris) describe_distribution(iris) #> Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing #> --- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] | 0.31 | -0.55 | 150 | 0 #> Sepal.Width | 3.06 | 0.44 | 0.52 | [2.00, 4.40] | 0.32 | ...
> median(LungCap) [1] 8 > sd(LungCap) [1] 2.662008 > sqrt(var(LungCap)) [1] 2.662008 > > sd(LungCap)^2 [1] 7.086288 > min(LungCap) [1] 0.507 > max(LungCap) [1] 14.675 > range(LungCap) [1] 0.507 14.675 > quantile function (x, ...) UseMethod("quantile") <bytecod...
After looking around, I found this interesting paper which derived (and validated through simple simulations), simple formulas that can be used to convert the median/range into a mean and a variance in a distribution free fashion. With a = min of the data b = max of the data m = ...
results = pickle.load(fd)forkeyinresults.keys():print'='*80printkey,':'printstr(results[key])print'='*80ifoptions.catalysisisNone:returnlog_q = results['log_q']print'Median\t95\% Interval'foriinxrange(log_q.mu.shape[1]):
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