R语言讲义(包括各种回归).ppt

R 语言讲义 吴喜之 免费(没有权力和铜臭) 资源公开, 可改变代码(不是黑盒子,也不是 吝啬鬼, 透明是防止“腐败”的最好方式) 容易学习。可编程以实行复杂的课题 可扩展: 通过数千个网上提供的适用于不同 领域、不同目的、不同方法的软件包来实 现你的目标。也可以把你的方法贡献出来 功能强大(绘图功能, 优秀的内在帮助系统, R社区的支持，不断更新，不断修正) 没有任何一个商业软件有如此多和如此新 的算法 世界应用统计学家大都把自己的方法首先以R来 实现，并尽量放到R 网站上 一年多，R网站的软件包数量增加了两倍，从近 1000个到近3000多个。大都都有关于计算、演 示和输入输出方法的函数和例子数据 除非得到巨额资助(或者永远使用盗版软件), 没 有理由在公立学校教授商业软件 绝大多数美国统计研究生都会的语言(Berkeley 统计和应用数学本科都开设R语言课) 我的很大一部分数据分析知识的来源就是R. 我都能学会, 并且到处宣传和普及, 相信你们会 做得更好! 点击CRAN得到一批镜像网站 下载R(http:/www.r- /) 点击镜像网站比如Berkeley 选择这个,下载安装文件 选择这个,下载软件包 选择 base Packages （每个都有大量数据和可以读写修改的 函数/程序） base The R Base Package boot Bootstrap R (S-Plus) Functions (Canty) class Functions for Classification cluster Cluster Analysis Extended Rousseeuw et al. concord Concordance and reliability datasets The R Datasets Package exactRankTests Exact Distributions for Rank and Permutation Tests foreign Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, . graphics The R Graphics Package grDevices The R Graphics Devices and Support for Colours and Fonts grid The Grid Graphics Package KernSmooth Functions for kernel smoothing for Wand all(x!=0);any(x0);(1:10)x0 diff(x) diff(x,lag=2) x=matrix(1:20,4,5);x x=matrix(1:20,4,5,byrow=T);x t(x) x=matrix(sample(1:100,20),4,5) 2*x x+5 y=matrix(sample(1:100,20),5,4) x+t(y) (z=x%*%y) z1=solve(z) # solve(a,b)可以解ax=b方程 z1%*%z round(z1%*%z,14) 矩阵 nrow(x); ncol(x);dim(x)#行列数目 x=matrix(rnorm(24),4,6) xc(2,1),#第2和第1行 x,c(1,3) #第1和第3列 x2,1 #第2,1元素 xx,10,1 #第1列大于0的元素 sum(x,10) #第1列大于0的元素的个数 sum(x,10data(consumption) library(perturb);data(consumption) head(consumption) library(MASS) ct1-c(NA,c-length(c); a-lm.ridge(cct1+dpi+r+d_dpi, lambda=seq(0, 0.1,length=100), model =TRUE) names(a)# “coef“ “scales“ “Inter“ “lambda“ “ym“ “xm“ “GCV“ “kHKB“ “kLW“ a$lambdawhich.min(a$GCV) #找到GCV 最小时的lambdaGCV= 0.014 a$coef,which.min(a$GCV) #找到GCV 最小时对应的系数 a$coef,which.min(a$GCV) par(mfrow=c(1,2) plot(a) #画出图形，并作出lambda 取0.01 时的那条线，以红线表示。 abline(v=a$lambdawhich.min(a$GCV),col=“red“) plot(a$lambda,a$GCV,type=“l“)#lamda 同GCV 之间关系的图形 abline(v=a$lambdawhich.min(a$GCV),col=“green“) 对于正交数据（独立） #另一个例子 longley # not the same as the S-PLUS dataset names(longley)1 - “y“ a0=lm.ridge(y ., longley)#lambda = 0 plot(lm.ridge(y ., longley,lambda = seq(0,0.1,0.001) select(lm.ridge(y ., longley,lambda = seq(0,0.1,0.0001) a1=lm.ridge(y ., longley,lambda=0.0057) a1$coe 偏最小二乘回归 PLSR (Partial Least Squares and Principal Component Regression) oliveoil pls Sensory and physico-chemical data of olive oils Description A data set with scores on 6 attributes from a sensory panel and measurements of 5 physico- chemical quality parameters on 16 olive oil samples. The first five oils are Greek, the next five are Italian and the last six are Spanish. data(oliveoil) Format: A data frame with 16 observations on the following 2 variables. Sensory: a matrix with 6 columns. Scores for attributes yellow, green, brown, glossy, transp, and syrup. Chemical: a matrix with 5 columns. Measurements of acidity, peroxide, K232, K270, and DK. Source Massart, D. L., Vandeginste, B. G. M., Buydens, L. M. C., de Jong, S., Lewi, P. J., Smeyers- Verbeke, J. (1998) Handbook of Chemometrics and Qualimetrics: Part B. Elsevier. Tables 35.1 and 35.4. Package pls version 2.1-0 Index SensoryChemical sensory chemical #偏最小二乘回归(先主成份回归) library(pls); data(oliveoil);head(oliveoil);dim(oliveoil) oliveoil$sensory#是一个16x6矩阵 oliveoil$chemical #是一个16x5矩阵 #PCR sens.pcr - pcr(sensory chemical, ncomp = 4, scale = TRUE, data = oliveoil) summary(sens.pcr);names(sens.pcr) 1 “coefficients“ “scores“ “loadings“ “Yloadings“ “projection“ “Xmeans“ 7 “Ymeans“ “fitted.values“ “residuals“ “Xvar“ “Xtotvar“ “ncomp“ 13 “method“ “scale“ “call“ “terms“ “model“ sens.pcr$loadings sens.pcr$coefficients sens.pcr$scores sens.pcr$Yloadings sens.pcr$projection sens.pcr$residuals #PLSR sens.pls - plsr(sensory chemical, ncomp = 4, scale = TRUE, data = oliveoil) summary(sens.pls);names(sens.pls) 1 “coefficients“ “scores“ “loadings“ “loading.weights“ “Yscores“ 6 “Yloadings“ “projection“ “Xmeans“ “Ymeans“ “fitted.values“ 11 “residuals“ “Xvar“ “Xtotvar“ “ncomp“ “method“ 16 “scale“ “call“ “terms“ “model“ sens.pls$loadings sens.pls$coef sens.pls$scores sens.pls$loading.weights sens.pls$Yscores sens.pls$Yloadings sens.pls$Xvar sens.pls$Xtotvar library(pls);data(yarn) Consisting of 21 NIR spectra of PET yarns, measured at 268 wavelengths, and 21 corresponding densities. (Erik Swierenga). library(pls);data(yarn) names(yarn)#1 “NIR“ “density“ “train“ dim(yarn$NIR)#28 268 自变量 yarn$density #因变量 summary(yarn$train) yarn$train Mode FALSE TRUE NAs logical 7 21 0 yarn.pls - plsr(density NIR, ncomp = 4, scale = TRUE, data = yarn) summary(yarn.pls);names(yarn.pls) 1 “coefficients“ “scores“ “loadings“ “loading.weights“ “Yscores“ 6 “Yloadings“ “projection“ “Xmeans“ “Ymeans“ “fitted.values“ 11 “residuals“ “Xvar“ “Xtotvar“ “ncomp“ “method“ 16 “scale“ “call“ “terms“ “model“ yarn.pls$loadings yarn.pls$coef yarn.pls$scores yarn.pls$loading.weights yarn.pls$Yscores yarn.pls$Yloadings yarn.pls$Xvar yarn.pls$Xtotvar 118 LASSO方法 在线性模型中，人们必须选择 合适的变量；比如 常用的逐步回归法就是选择显 著的变量而抛弃那些不显 著的。Tibshirani(1996)1提出了一个新的方法来处理变量 选择的问题。该方法在模型系数绝对值 的和小于某常数 的条件下，谋求残差平方和最小。该方法既提供了如子 集选择方法那样的可以解释的模型，也具有岭回归那样 的稳定性。它不删除变量，但使得一些回归系数收缩、 变小，甚至为0。因而，该方法被称为lasso(least absolute shrinkage and selection operator，最小绝对值 收缩和选择 算子2)。 1 Tibshirani, R. (1996). “Regression shrinkage and selection via the lasso”, J. R. Statist. Soc. B, 58(1) 267-288. 2 作为非缩写的英文词，lasso还有套马索的意义，和这里的缩写 lasso没有任何关联。把它翻译成“套马索”是不恰当的。 119 library(lars) data(diabetes);attach(diabetes);names(diabetes);dim(diabetes);head(diabetes)