model3

조절된 조절 구해보자

#model 3
x<-rnorm(100)
w1<-rnorm(100)+ x^2
w2<-rnorm(100)+ abs(x)
y<-rnorm(100, 0,1)  + w1*w2*x
co1<-rnorm(100)

d<-data.frame(x,w1,w2,y,co1)

조절된 조절은 존내 방법으로 나타낼 수 있나..?모르겠다

boot3<-function(xxx,mmm, mmm2,yyy,d,bootnum){
  ###estimate a*m
  boot3_1<-function(xxx,mmm,mmm2, yyy,d){
    n<-sample(1:nrow(d),nrow(d),replace = T)
    nnk<-d[n,]
    nnk<-as.data.frame(nnk)
    k2<-lm(nnk[,yyy]~ nnk[,xxx]+nnk[,mmm] + nnk[,mmm2]+ nnk[,xxx]*nnk[,mmm] + nnk[,xxx]*nnk[,mmm2] + nnk[,xxx]*nnk[,mmm]*nnk[,mmm2], data=nnk)
    s2<-summary(k2)
    coem<-s2$coefficients
    eff<-as.data.frame(coem)
    eff1<-eff[nrow(eff)-2,1]
    eff2<-eff[nrow(eff)-1,1]
    eff3<-eff[nrow(eff),1]
    efff<-c(eff1, eff2, eff3)
    efff<-matrix(efff, ncol = 3)
    efff
  }
  k<-1
  l<-matrix(rep(NA,bootnum*3),ncol = 3)
  l<-as.data.frame(l)
  repeat{
    l[k,]<-boot3_1(xxx,mmm, mmm2,yyy,d)
    k<-k+1
    if(k>=bootnum+1) break
  }
  estimates<-list(l)
  ci1<-quantile(l[,1],probs = c(.001,0.01,0.05,0.10,0.90,0.95,0.99,.999))
  ci2<-quantile(l[,2],probs = c(.001,0.01,0.05,0.10,0.90,0.95,0.99,.999))
  ci3<-quantile(l[,3],probs = c(.001,0.01,0.05,0.10,0.90,0.95,0.99,.999))
  kmkmkmkm<-list(c(mean(l[,1]),sd(l[,1])),ci1, c(mean(l[,2]), sd(l[,2])),ci2,
                 c(mean(l[,3]),sd(l[,3])),ci3)
  names(kmkmkmkm)<-c("moderation_mean_BootSE_x*w1", "moderation_CI_x*w1","moderation_mean_BootSE_x*w2", "moderation_CI_x*w2", "moderation_mean_BootSE_x*w1*w2", "moderation_CI_x*w1*w2")
  kmkmkmkm
}

boot3(1,2,3,4,d,100)

## $`moderation_mean_BootSE_x*w1`
## [1] -0.1845618  0.2139238
## 
## $`moderation_CI_x*w1`
##        0.1%          1%          5%         10%         90%         95% 
## -0.62295476 -0.59395024 -0.51046297 -0.43806192  0.07310038  0.18495294 
##         99%       99.9% 
##  0.41518243  0.44515935 
## 
## $`moderation_mean_BootSE_x*w2`
## [1] -0.15311482  0.06318983
## 
## $`moderation_CI_x*w2`
##         0.1%           1%           5%          10%          90%          95% 
## -0.272177582 -0.270377175 -0.255456638 -0.242636026 -0.074623342 -0.045451186 
##          99%        99.9% 
## -0.009361005 -0.005457043 
## 
## $`moderation_mean_BootSE_x*w1*w2`
## [1] 1.01413084 0.06810441
## 
## $`moderation_CI_x*w1*w2`
##      0.1%        1%        5%       10%       90%       95%       99%     99.9% 
## 0.7337806 0.8532388 0.9120283 0.9299563 1.0870237 1.0911727 1.1623455 1.1768790