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--- interaction_effects_in_regression_analysis [2023/06/12 08:50] – [E.g. 3] hkimscil
+++ interaction_effects_in_regression_analysis [2025/06/16 13:00] (current) – [E.g.2] hkimscil
@@ Line 96: / Line 96: @@
 ====== Two category variables ======
-<code>> set.seed(12)
+<code>
+> set.seed(12)
 > f1<-gl(n=2,k=30,labels=c("Low","High"))
 > f2<-as.factor(rep(c("A","B","C"),times=20))
@@ Line 272: / Line 273: @@
   - f1High:f2C : 질소가 High이고 온도도 High인 상태 -1.16 감소한다.
-<code>interact_plot(mod2, pred = "f1", modx = "f2")</code>
+<code>
-{{:r:interaction.effects.2.jpeg}}
+> interact_plot(mod2, pred = "f1", modx = "f2")
+</code>
+{{:pasted:20250616-072703.png?400}}
+<code>
+> interact_plot(mod2, pred = "f2", modx = "f1")
+</code>
+{{:pasted:20250616-072946.png?400}}
+:r:interaction.effects.2.jpeg
 ====== Two continuous variables ======
 <code># third case interaction between two continuous variables
@@ Line 379: / Line 387: @@
   - (위의 마지막 식에서) x1:x2 = x1*x2 : 질소량이 1씩 증가할 때 마다, 온도의 영향력은 1.5식 증가한다. 예를 들면 질소량이 0일 경우, 온도와 작물 간의 기울기는 약 2인데, 질소의 양이 1 증가하고 온도가 1 증가하면 기울기는 2 + 1.5 = 3.5가 된다.
   - <WRAP box><code>
-x1=1,x2=1: 0.97 + *3.5 x1 + -1 x2
+# 0.97 = 1 로 보면
-x1=2,x2=2: 0.97 + *5.0 x1 + -1 x2
+x2=1: 0.97 + *3.5 x1 + -1 (1=x2)
-x1=3,x2=3: 0.97 + *6.5 x1 + -1 x2
++ 3.5 x1
-x1=4,x2=4: 0.97 + *8.0 x1 + -1 x2
+x2=2: 0.97 + *5.0 x1 + -1 (2=x2)
-x1=5,x2=5: 0.97 + *9.5 x1 + -1 x2</code>
+      -1 + 5.0 x1
+x2=3: 0.97 + *6.5 x1 + -1 (3=x2)
+      -2 + 6.5 x1
+x2=4: 0.97 + *8.0 x1 + -1 (4=x2)
+      -3 + 8.0 x1
+x2=5: 0.97 + *9.5 x1 + -1 (5=x2)
+      -4 + 9.5 x1
+</code>
 <code>
 *(1.995115 + 1.499595*x2):
@@ Line 405: / Line 420: @@
 ====== E.g.2  ======
 {{:r:states.rds}}
-Download the data file to c:/Rstatistics first. Then
+<code>
-do
+# states.data <- readRDS("c:/Rstatistics/dataSets/states.rds")
-<code>states.data <- readRDS("c:/Rstatistics/dataSets/states.rds") </code>
+states.data <- readRDS(url("http://commres.net/wiki/_media/r/states.rds"))
+</code>
 Or, read the above data file directly
@@ Line 455: / Line 471: @@
 </code>
 <code>
-> data.info <- data.frame(attributes(data)[c("names", "var.labels")])
+> data.info <- data.frame(attributes(states.data)[c("names", "var.labels")])
 > # attributes(data) reveals various attributes of the data file,
 > # which contains variable names and labels.
@@ Line 909: / Line 925: @@
 </code>
 <code>
+> fit <- lm(Income ~ Illiteracy + Murder, data = as.data.frame(state.x77))
+> fiti <- lm(Income ~ Illiteracy * Murder, data = as.data.frame(state.x77))
+> summary(fit)
+Call:
+lm(formula = Income ~ Illiteracy + Murder, data = as.data.frame(state.x77))
+Residuals:
+   Min     1Q Median     3Q    Max
+-880.9 -397.3  -51.3  333.1 1960.7
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)
+(Intercept)   4890.5      187.6   26.06   <2e-16 ***
+Illiteracy    -548.7      184.6   -2.97   0.0046 **
+Murder          25.4       30.5    0.83   0.4089
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Residual standard error: 560 on 47 degrees of freedom
+Multiple R-squared:  0.203,	Adjusted R-squared:  0.169
+F-statistic: 5.98 on 2 and 47 DF,  p-value: 0.00486
+> summary(fiti)
+Call:
+lm(formula = Income ~ Illiteracy * Murder, data = as.data.frame(state.x77))
+Residuals:
+   Min     1Q Median     3Q    Max
+-955.2 -326.0   10.7  300.0 1892.1
+Coefficients:
+                  Estimate Std. Error t value Pr(>|t|)
+(Intercept)         3822.6      405.3    9.43  2.5e-12 ***
+Illiteracy           617.3      434.9    1.42   0.1624
+Murder               146.8       50.3    2.92   0.0054 **
+Illiteracy:Murder   -117.1       40.1   -2.92   0.0054 **
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Residual standard error: 520 on 46 degrees of freedom
+Multiple R-squared:  0.327,	Adjusted R-squared:  0.283
+F-statistic: 7.46 on 3 and 46 DF,  p-value: 0.000359
+>
 </code>
 <code>> interact_plot(fiti, pred = "Illiteracy", modx = "Murder")</code>