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--- interaction_effects_in_regression_analysis [2023/06/14 08:50] – [Two continuous variables] 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>
-x2=1: 0.97 + *3.5 x1 + -1 x2
+# 0.97 = 1 로 보면
-x2=2: 0.97 + *5.0 x1 + -1 x2
+x2=1: 0.97 + *3.5 x1 + -1 (1=x2)
-x2=3: 0.97 + *6.5 x1 + -1 x2
++ 3.5 x1
-x2=4: 0.97 + *8.0 x1 + -1 x2
+x2=2: 0.97 + *5.0 x1 + -1 (2=x2)
-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.