r - lm 用两种不同语言(英语和韩语)编写的相同数据集的不同结果

Question

lm 的结果应用在用两种不同语言(一种用英文编写，另一种用韩文编写)编写的两个数据集(数值变量 + 分类变量)上的函数是不同的。除了分类变量，数值变量完全一样。什么可以解释结果的差异？

#data 
df3 <- repmis::source_DropboxData("df3_v0.1.csv","gg30a74n4ew3zzg",header = TRUE)

#the one written in korean 
out1<-lm(YD~SANJI+TAmin8+TMINup18do6+typ_rain6+DTD9,data=df3)
summary(out1)

#the one written in eng 
df3$SANJI[df3$SANJI=="전북"]<-"JB"
df3$SANJI[df3$SANJI=="충북"]<-"CHB"
df3$SANJI[df3$SANJI=="경북"]<-"KB"
df3$SANJI[df3$SANJI=="전남"]<-"JN"
df3$SANJI2[df3$SANJI2=="고창"]<-"Gochang"
df3$SANJI2[df3$SANJI2=="괴산"]<-"Goesan"
df3$SANJI2[df3$SANJI2=="단양"]<-"Danyang"
df3$SANJI2[df3$SANJI2=="봉화"]<-"Fenghua"
df3$SANJI2[df3$SANJI2=="신안"]<-"Sinan"
df3$SANJI2[df3$SANJI2=="안동"]<-"Andong"
df3$SANJI2[df3$SANJI2=="영광"]<-"younggang"
df3$SANJI2[df3$SANJI2=="영양"]<-"youngyang"
df3$SANJI2[df3$SANJI2=="영주"]<-"youngju"
df3$SANJI2[df3$SANJI2=="예천"]<-"Yecheon"
df3$SANJI2[df3$SANJI2=="의성"]<-"Yusaeng"
df3$SANJI2[df3$SANJI2=="제천"]<-"Jechon"
df3$SANJI2[df3$SANJI2=="진안"]<-"Jinan"
df3$SANJI2[df3$SANJI2=="청송"]<-"Changsong"
df3$SANJI2[df3$SANJI2=="해남"]<-"Haenam"
out2<-lm(YD~SANJI+TAmin8+TMINup18do6+typ_rain6+DTD9,data=df3)
summary(out2)

#the one written in korean 
#Call:
#lm(formula = YD ~ SANJI + TAmin8 + TMINup18do6 + typ_rain6 + 
#    DTD9, data = df3)

#Residuals:
#    Min      1Q  Median      3Q     Max 
#-98.836 -23.173  -2.261  22.626 111.367 

#Coefficients:
#             Estimate Std. Error t value Pr(>|t|)    
#(Intercept) 970.33251   84.12479  11.534  < 2e-16 ***
#SANJI전남   -33.75664   12.53277  -2.693 0.008158 ** 
#SANJI전북   -44.17939   11.22274  -3.937 0.000144 ***
#SANJI충북   -44.09285    9.16736  -4.810 4.74e-06 ***
#TAmin8      -25.56618    3.36053  -7.608 9.37e-12 ***
#TMINup18do6   4.58052    0.96528   4.745 6.19e-06 ***
#typ_rain6    -0.19754    0.02862  -6.903 3.23e-10 ***
#DTD9        -16.15975    2.65128  -6.095 1.59e-08 ***
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Residual standard error: 37.2 on 112 degrees of freedom
#Multiple R-squared:   0.58,    Adjusted R-squared:  0.5538 
#F-statistic:  22.1 on 7 and 112 DF,  p-value: < 2.2e-16


#the one written in eng 
#Call:
#lm(formula = YD ~ SANJI + TAmin8 + TMINup18do6 + typ_rain6 + 
#    DTD9, data = df3)

#Residuals:
#    Min      1Q  Median      3Q     Max 
#-98.836 -23.173  -2.261  22.626 111.367 

#Coefficients:
#             Estimate Std. Error t value Pr(>|t|)    
#(Intercept) 926.23966   84.32621  10.984  < 2e-16 ***
#SANJIJB      -0.08654   12.32752  -0.007    0.994    
#SANJIJN      10.33620   13.09434   0.789    0.432    
#SANJIKB      44.09285    9.16736   4.810 4.74e-06 ***
#TAmin8      -25.56618    3.36053  -7.608 9.37e-12 ***
#TMINup18do6   4.58052    0.96528   4.745 6.19e-06 ***
#typ_rain6    -0.19754    0.02862  -6.903 3.23e-10 ***
#DTD9        -16.15975    2.65128  -6.095 1.59e-08 ***
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Residual standard error: 37.2 on 112 degrees of freedom
#Multiple R-squared:   0.58,    Adjusted R-squared:  0.5538 
#F-statistic:  22.1 on 7 and 112 DF,  p-value: < 2.2e-16

Answer 1

您的整体模型拟合是相同的，只是您的因子有不同的引用类(“SANJIJ”)。具有不同的引用水平也会影响您的截距，但不会改变您的连续协变量的估计。

您可以使用 relevel()强制使用特定的引用类(假设 SANJIJ 已经是一个因子)或显式创建具有 levels= 的 factor()参数，否则默认顺序按字母顺序排序，不同语言的级别排序方式可能不同。