创建虚拟变量以进行双向ANOVA

2 回答

• 1

  lm() 将为您创建虚拟变量 . 无需自己创建它们：

  m <- lm(Time ~ Bact*Temperature, data = d)
  anova(m)
  

  Edit

  如果你想在 lm() 的引擎下同行，你可以看到 model.matrix(m) 的设计矩阵

  回复于 2024-04-24T04:55:13+08:00
• 0

  我也和你做同样的事情 . 我希望能够控制住，所以每当我有时间，我都会用以下方法自行设计假人：

  d = data.frame(
    Temperature = c(rep("Cool", 6), rep("Warm", 6)),
    Bact = c(rep("Bact 1", 2), rep("Bact 2", 2), rep("Bact 3", 2), rep("Bact 1", 2), rep("Bact 2", 2), rep("Bact 3", 2)),
    Time = c(15.23,14.32,14.77,15.12,14.05,15.48,14.13,16.13,16.44,14.82,17.96,16.65)
  )
  

  这是：

  > d
     Temperature   Bact  Time
  1         Cool Bact 1 15.23
  2         Cool Bact 1 14.32
  3         Cool Bact 2 14.77
  4         Cool Bact 2 15.12
  5         Cool Bact 3 14.05
  6         Cool Bact 3 15.48
  7         Warm Bact 1 14.13
  8         Warm Bact 1 16.13
  9         Warm Bact 2 16.44
  10        Warm Bact 2 14.82
  11        Warm Bact 3 17.96
  12        Warm Bact 3 16.65
  

  因此，您只需要对因子（温度，温度）进行虚拟化，以便以下过程有效：

  xfactors <- Filter(is.factor,d) #filter only the factors to dummify
  b <- data.frame(matrix(NA,nrow=nrow(xfactors),ncol=1)) #make empty data.frame to initiate b
  for ( i in 1:ncol(xfactors)) { #start loop
    a <- data.frame(model.matrix(~xfactors[,i])) #make dummies here
    b <- cbind(b, a[-1]) #remove intercept and combine dummies
  }
  b <- data.frame(b[-1]) #make a data.frame
  #the reference dummy gets excluded automatically by model.matrix
  colnames(b) <- c('warm' , 'bact2' , 'bact3') #you will probably want to change the names to sth smaller
  
  > b
     warm bact2 bact3
  1     0     0     0
  2     0     0     0
  3     0     1     0
  4     0     1     0
  5     0     0     1
  6     0     0     1
  7     1     0     0
  8     1     0     0
  9     1     1     0
  10    1     1     0
  11    1     0     1
  12    1     0     1
  

  然后运行模型：

  new_data <- cbind(b, Time=d$Time) #add time to the data
  mymod <- lm(Time ~ warm*bact2+warm*bact3, data=new_data) #compute lm with interactions
  #you shouldn't compute the interactions between dummy variables because they come from the same variable
  

  输出：

  > summary(mymod)
  
  Call:
  lm(formula = Time ~ warm * bact2 + warm * bact3, data = new_data)
  
  Residuals:
     Min     1Q Median     3Q    Max 
   -1.00  -0.67   0.00   0.67   1.00 
  
  Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
  (Intercept)  14.7750     0.6873  21.498 6.61e-07 ***
  warm          0.3550     0.9719   0.365    0.727    
  bact2         0.1700     0.9719   0.175    0.867    
  bact3        -0.0100     0.9719  -0.010    0.992    
  warm:bact2    0.3300     1.3745   0.240    0.818    
  warm:bact3    2.1850     1.3745   1.590    0.163    
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Residual standard error: 0.9719 on 6 degrees of freedom
  Multiple R-squared:  0.6264,    Adjusted R-squared:  0.3151 
  F-statistic: 2.012 on 5 and 6 DF,  p-value: 0.2097
  

  回复于 2024-04-24T04:55:13+08:00