统计分析中重要得非参数检验.docxVIP

重要的非参数检验拟合优度检验(goodness of fit)正态性检验利用shapiro-wilk的统计量做正态性检验。R语言命令：shapiro.text(x)x-- a numeric vector of data values. Missing values are allowed, but the number of non-missing values must be between 3 and 5000.经验分布的Kolmogorov-Smirnov检验方法利用总体分布函数和经验分布函数之间的距离来建立统计量。理论上可以检验任何分布。(1).单个总体的检验ks.test(x,”pexp”,1/1500)(2).两个总体的检验假设是来自分布的总体的样本，且未知，是来自的总体的样本，未知。那么检验两个分布是否相同，即原假设为。R语言命令：ks.test(x,y,…,alternative=c(“two.sided”,”less”,”greater”),exact=NULL)x-- a numeric vector of data values.y-- either a numeric vector of data values, or a character string naming a cumulative distribution function or an actual cumulative distribution function such as pnorm. Only continuous CDFs are valid....-- parameters of the distribution specified (as a character string) by y.alternative-- indicates the alternative hypothesis and must be one of two.sided (default), less, or greater. You can specify just the initial letter of the value, but the argument name must be give in full. See ‘Details’ for the meanings of the possible values.exact-- NULL or a logical indicating whether an exact p-value should be computed. See ‘Details’ for the meaning of NULL. Not available in the two-sample case for a one-sided test or if ties are present.Pearson拟合优度检验(1).理论分布完全已知的情况，那么零假设，被择假设，上述问题的检验方法是将数轴分成m个区间：，记这些区间的理论概率分布为。为中落在区间内的个数，则在原假设成立下，的期望值为，与的差距可视为理论与观察之间的偏离的衡量。那么统计量就是将这些衡量结合起来得到的。记统计量。在原假设成立的条件下，当时，依分布收敛于自由度为的分布。当给定显著性水平，，则拒绝原假设。进一步讨论，计算出值后，可以计算出值，，可将值成为所得数据与原假设的拟合优度，值越大，支持原假设的证据就越强。给定一个显著性水平，当时就拒绝原假设。R语言命令：chisq.test(x, y = NULL, correct = TRUE,p = rep(1/length(x), length(x)), rescale.p = FALSE,simulate.p.value = FALSE, B = 2000)x-- a numeric vector or matrix. x and y can also both be factors.y-- a numeric vector; ignored if x is a matrix. If x is a factor, y should be a factor of the same length.correct-- a logical indicating whether to apply continuity correction when computing the test statistic for 2 by 2 tables: one half is subtracted from all |O - E| differences. No correction is done if simulate