r - R中的多元线性回归问题

Question

在我的 lm 回归和回归表中，即使输入似乎是正确的，输出也不是相应列的单个变量。

您有什么想法，为什么要在一列中生成回归表中的多个变量？

太感谢了

使用的代码：

for (i in c(1:116))
{
  id <- (Ad1_users[i])
  x <- c(1:length(as.numeric(Ad1_users_35sec[Ad1_users_35sec$Users == Ad1_users[[i]], 10])))
  y <- as.numeric(Ad1_users_35sec[Ad1_users_35sec$Users == Ad1_users[[i]], 10])
  res<-testmax(1,0.4)
  ###if length over 4, use NA
  number_of_peaks<-length(res)
  if(length(res)>4){
    peaks<-c(rep(NA,4))
  }else{
    diff_length<-4-length(res)
    ###bind NA to produce same length
    peaks<-c(res,rep(NA,diff_length))
  }
  result <- c(peaks, id,number_of_peaks)
  Results_aropos1[[i]] <- result 
}


Results_aropos1<-t(matrix(unlist(Results_aropos1), nrow=6))
Results_aropos1<-data.frame(Results_aropos1)
colnames(Results_aropos1)[c(1:6)]<-c('peak1aropos1','peak2aropos1','peak3aropos1','peak4aropos1','user','number_of_peaksaropos1')

reg_Attitudetowardsthebrandmodel6_1peak1 = lm(brandappeal1 ~ positioning10 + Enjoyment1 + Appreciation1 + credibility1 + comprehensability1 + number_of_peaksaropos1, na.action = na.omit, data = totaldf1moodpeaks)
regressiontableAttitudetowardsthebrandmodel6_1peak1 <- str_squish(stargazer(reg_Attitudetowardsthebrandmodel6_1peak1, type = "text", align = TRUE, no.space = TRUE))
regressiontableAttitudetowardsthebrandmodel6_1peak1gl <- glance(reg_Attitudetowardsthebrandmodel6_1peak1)

output:
===================================================
                            Dependent variable:    
                        ---------------------------
                               brandappeal1        
---------------------------------------------------
positioning10                      0.074           
                                  (0.099)          
Enjoyment1                       0.351***          
                                  (0.082)          
Appreciation1                    0.213***          
                                  (0.077)          
credibility1                       0.098           
                                  (0.065)          
comprehensability1                 0.029           
                                  (0.051)          
number_of_peaksaropos11           -0.017           
                                  (0.175)          
number_of_peaksaropos12           -0.073           
                                  (0.186)          
number_of_peaksaropos13           -0.242           
                                  (0.388)          
Constant                          0.739**          
                                  (0.348)          
---------------------------------------------------
Observations                        114            
R2                                 0.550           
Adjusted R2                        0.516           
Residual Std. Error          0.483 (df = 105)      
F Statistic               16.068*** (df = 8; 105)  
===================================================
Note:                   *p<0.1; **p<0.05; ***p<0.01

标签： rlinear-regressionlm