c:ma:regression_r_note
############################ dvar <- read.csv("http://commres.net/wiki/_media/elemapi2_.csv", sep="\t", fileEncoding="UTF-8-BOM") dvar mod <- lm(api00 ~ ell + acs_k3 + avg_ed + meals, data=dvar) summary(mod) anova(mod) mod01 <- lm(api00 ~ acs_k3 + ell + avg_ed + meals, data=dvar) summary(mod01) anova(mod01) mod02 <- lm(api00 ~ ell + avg_ed + acs_k3 + meals, data=dvar) summary(mod02) anova(mod02) mod03 <- lm(api00 ~ ell + avg_ed + meals + acs_k3, data=dvar) summary(mod03) anova(mod03) ################################################# #setwd("user location") #Working directory set.seed(123) #Standardizes the numbers generated by rnorm; see Chapter 5 n <- 100 #Number of participants; graduate students x <- rnorm(N, 175, 7) #IV; hours since dawn m <- 0.7*X + rnorm(N, 0, 5) #Suspected mediator; coffee consumption y <- 0.4*M + rnorm(N, 0, 5) #DV; wakefulness md <- data.frame(x, m, y) lm.yx <- lm(y~x,data=md) summary(lm.yx) lm.mx <- lm(m~x, data=md) summary(lm.mx) lm.ymx <- lm(y~m+x, data=md) summary(lm.ymx) ##################################### # x와 m이 상관관계가 높고 # 이론적으로 혹은 논리적으로 # 인과관계가 분명할 때, # x의 (y로 가는) 영향력이 # m으로 옮겨가서 영향력을 # 간접적으로 주는 것으로 해석할 # 수 있다. 이 때 # mediation effect를 계산한다 ##################################### lm.xm <- lm(x~m, data = md) summary(lm.xm) lm.xm.res <- lm.xm$residuals lm.yxmres <- lm(y~lm.xm.res, data=md) summary(lm.yxmres) lm.reg <- lm.xm$fitted.values lm.yreg <- lm(y~lm.reg,data=md) summary(lm.yreg) lm.reg2 <- lm.mx$fitted.values lm.yreg2 <- lm(y~lm.reg2,data=md) summary(lm.yreg2) cor(lm.reg, lm.reg2) ##################################### attach(md) lma <- lm(x~m, data=md) lmb <- lm(m~x, data=md) summary(lma) summary(lmb) reg <- lma$fitted.values yreg <- lm(y~reg, data = md) summary(yreg) res <- lma$residuals yres <- lm(y~res, data=md) summary(yres) ss.x <- var(x)*(length(x)-1) ss.m <- var(m)*(length(m)-1) var(x) ss.x ss.reg <- sum(reg^2) rsa <- ss.reg/ss.x rsa reg2 <- lmb$fitted.values var(m) ss.m ss.reg2 <- sum(reg2^2) rsb <- ss.reg2/ss.m rsb ########################### options(digits = 4) HSGPA <- c(3.0, 3.2, 2.8, 2.5, 3.2, 3.8, 3.9, 3.8, 3.5, 3.1) FGPA <- c(2.8, 3.0, 2.8, 2.2, 3.3, 3.3, 3.5, 3.7, 3.4, 2.9) SATV <- c(500, 550, 450, 400, 600, 650, 700, 550, 650, 550) GREV <- c(600, 670, 540, 800, 750, 820, 830, 670, 690, 600) ##GREV <- c(510, 670, 440, 800, 750, 420, 830, 470, 690, 600) scholar <- data.frame(HSGPA, FGPA, SATV, GREV) # collect into a data frame describe(scholar) # provides descrptive information about each variable attach(scholar) m00 <- lm(FGPA ~ SATV, data=scholar) summary(m00) m01 <- lm(HSGPA ~ SATV, data=scholar) summary(m01) m03 <- lm(FGPA ~ HSGPA + SATV, data=scholar) summary(m03) pcor.test(FGPA, SATV, HSGPA) pcor.test(FGPA, HSGPA, SATV) # proof of other variables controlled ########################### a <- spcor.test(FGPA, SATV, HSGPA)$estimate^2 b <- spcor.test(FGPA, HSGPA, SATV)$estimate^2 c <- cor(FGPA, SATV)^2 d <- cor(FGPA, HSGPA)^2 c-a d-b # common affected area proof ###########################
c/ma/regression_r_note.txt · Last modified: 2023/10/23 19:38 by hkimscil