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--- sand_box [2025/09/30 12:59] – hkimscil
+++ sand_box [2025/12/29 04:46] (current) – hkimscil
@@ Line 1: / Line 1: @@
+{{pasted:20251229-044558.png}}
+<code>
+library(tidyverse)
+library(lavaan)
+cormat = lav_matrix_lower2full(c(1,
+.418613, 1,
+.4153223, 0.2652651, 1,
+.2670484, 0.4315334, 0.2709399, 1))
+colnames(cormat) <- rownames(cormat) <- c("X1","X2","X3","Y")
+cormat
+n_mean = matrix( c(rep(254,4),4.066, 4.024, 4.033, 3.919),
+                 ncol=4, nrow = 2, byrow = T )
+n_mean
+n_mean <- n_mean %>%
+  `colnames<-`(c("X1","X2","X3","Y")) %>%
+  `rownames<-`(c("n", "mean"))
+n_mean
+# Install the MASS package if not already installed
+# install.packages("MASS")
+# Load the package
+library(MASS)
+library(lavaan)
+#01 데이터 입력 ####
+#AC:개인이 성공 성취도,
+#IN:가계의 수입,
+#AB:개인의 능력,
+#SU:개인의성공열정
+#covMat  #공분산 행렬로 결과 확인
+# 1. Define the mean vector (for 3 variables)
+mns <- c(100, 120, 150, 130)
+# 2. Define the covariance matrix (must be symmetric and positive-definite)
+# Example: 3x3 covariance matrix
+cv_mat <- lav_matrix_lower2full(
+               c(26.5,
+.5, 37.9,
+.5, 25.2, 41.7,
+.5, 14.6, 14.5, 15.8))
+colnames(cvMat) <- rownames(cvMat) <- c("AC", "IN", "AB","SU")
+# 3. Simulate the data (e.g., 100 observations)
+n_observations <- 300
+sim_d <- mvrnorm(n = n_observations, mu = mns, Sigma = cv_mat)
+sim_data <- round(sim_d,0)
+# View the first few rows of the simulated data
+colnames(sim_data) <- c("AC", "IN", "AB","SU")
+head(sim_data)
+str(sim_data)
+PathModel <- '
+SU ~ a*AB +b*IN
+AC ~ c*SU + d*AB
+AB ~~ IN
+'
+#03 분석진행은 sem을 이용하여 한다. ####
+# sem(모델, sample.cov= 공분산 행렬, sample.nobs=샘플의 개수)
+#fitM <- sem(PathModel, sample.cov = covMat, sample.nobs = 300,
+#            fixed.x = FALSE)
+fitM <- sem(PathModel, data=sim_data)
+#04 결과보기 ####
+summary(fitM, standardized=TRUE,fit.measures=TRUE, rsquare=T)
+# install.packages("semPlot")
+library(semPlot)
+semPaths(fitM, whatLabels = "std", curveAdjacent = TRUE,
+         style = "lisrel",
+         intAtSide=TRUE, intercepts = TRUE, rotation = 2,
+         layout = "tree3",
+         edge.label.cex = 1,
+         edge.label.position=1, edge.color = "black",
+         color=list(lat="tomato", man="grey"),
+         curvature=3,
+         exoVar = TRUE,exoCov = TRUE)
+data(PoliticalDemocracy)
+PoliticalDemocracy
+sem.model <- "
+  # measurement model
+    ind60 =~ x1 + x2 + x3
+    dem60 =~ y1 + y2 + y3 + y4
+    dem65 =~ y5 + y6 + y7 + y8
+  # regressions
+    dem60 ~ ind60
+    dem65 ~ ind60 + dem60
+  # residual correlations
+    y1 ~~ y5
+    y2 ~~ y4 + y6
+    y3 ~~ y7
+    y4 ~~ y8
+    y6 ~~ y8
+"
+# Estimate model
+fit1 <- sem(model = sem.model, data = PoliticalDemocracy)
+# show summary result
+summary(fit1, fit.measures=TRUE, standardized=TRUE)
+library(lavaan)
+library(semPlot)
+library(OpenMx)
+library(tidyverse)
+library(knitr)
+library(kableExtra)
+library(GGally)
+model <-'
+mpg ~ hp + gear + cyl + disp + carb + am + wt
+hp ~ cyl + disp + carb
+'
+fit.mod <- sem(model, data=mtcars)
+summary(fit.mod, standardize=T, rsquare=T)
+dat <- read.csv("http://commres.net/_media/r/eating.disorder.sim.csv")
+head(dat)
+n <- nrow(dat)
+S <- cov(dat)
+cor <- cor(dat)
+#
+# xSelf-Efficacy = BMI + Self-Esteem + XDisturbance
+# Bulimic Symptoms = BMI + Self-Esteem + XSelf-Efficacy + XDisturbance
+# Restrictive Symptoms = BMI + Self-Esteem + xSelf-Efficacy + xDisturbance
+# Overall Risk = BMI + Self-Esteem + xSelf-Efficacy + Acculturation + xDisturbance
+#
+ex1 <- "           #opening a quote
+  # Tilda ~ : Regression
+  # M ~ X regression (X predicts M)
+  # Each line corresponds to an equation
+  # Disturbance is automatically included for each regression
+  # (i.e. no extra term needed)
+  DietSE ~ BMI + SelfEsteem      #DietSE is predicted by BMI and SelfEsteem
+  Bulimia ~ DietSE + BMI + SelfEsteem
+  Restrictive ~ DietSE + BMI + SelfEsteem
+  Risk ~ DietSE + BMI + SelfEsteem + Accu
+"
+ex1.fit <- sem(ex1, data=dat)
+summary(ex1.fit, standardize=T, rsquare=T)
+ex1b.fit <- sem(model = ex1, sample.cov = S, sample.nobs = n)
+summary(ex1b.fit)
+library(tidyverse)
+library(lavaan)
+cormat = lav_matrix_lower2full(c(1,
+.418613, 1,
+.4153223, 0.2652651, 1,
+.2670484, 0.4315334, 0.2709399, 1))
+colnames(cormat) <- rownames(cormat) <- c("X1","X2","X3","Y")
+cormat
+n_mean = matrix( c(rep(254,4),4.066, 4.024, 4.033, 3.919),
+                 ncol=4, nrow = 2, byrow = T )
+n_mean
+n_mean <- n_mean %>%
+  `colnames<-`(c("X1","X2","X3","Y")) %>%
+  `rownames<-`(c("n", "mean"))
+n_mean
+# Install the MASS package if not already installed
+# install.packages("MASS")
+# Load the package
+library(MASS)
+library(lavaan)
+#01 데이터 입력 ####
+#AC:개인이 성공 성취도,
+#IN:가계의 수입,
+#AB:개인의 능력,
+#SU:개인의성공열정
+#covMat  #공분산 행렬로 결과 확인
+# 1. Define the mean vector (for 3 variables)
+mns <- c(100, 120, 150, 130)
+# 2. Define the covariance matrix (must be symmetric and positive-definite)
+# Example: 3x3 covariance matrix
+cv_mat <- lav_matrix_lower2full(
+               c(26.5,
+.5, 37.9,
+.5, 25.2, 41.7,
+.5, 14.6, 14.5, 15.8))
+colnames(cvMat) <- rownames(cvMat) <- c("AC", "IN", "AB","SU")
+# 3. Simulate the data (e.g., 100 observations)
+n_observations <- 300
+sim_d <- mvrnorm(n = n_observations, mu = mns, Sigma = cv_mat)
+sim_data <- round(sim_d,0)
+# View the first few rows of the simulated data
+colnames(sim_data) <- c("AC", "IN", "AB","SU")
+head(sim_data)
+str(sim_data)
+PathModel <- '
+SU ~ a*AB +b*IN
+AC ~ c*SU + d*AB
+AB ~~ IN
+'
+#03 분석진행은 sem을 이용하여 한다. ####
+# sem(모델, sample.cov= 공분산 행렬, sample.nobs=샘플의 개수)
+#fitM <- sem(PathModel, sample.cov = covMat, sample.nobs = 300,
+#            fixed.x = FALSE)
+fitM <- sem(PathModel, data=sim_data)
+#04 결과보기 ####
+summary(fitM, standardized=TRUE,fit.measures=TRUE, rsquare=T)
+# install.packages("semPlot")
+library(semPlot)
+semPaths(fitM, whatLabels = "std", curveAdjacent = TRUE,
+         style = "lisrel",
+         intAtSide=TRUE, intercepts = TRUE, rotation = 2,
+         layout = "tree3",
+         edge.label.cex = 1,
+         edge.label.position=1, edge.color = "black",
+         color=list(lat="tomato", man="grey"),
+         curvature=3,
+         exoVar = TRUE,exoCov = TRUE)
+data(PoliticalDemocracy)
+PoliticalDemocracy
+sem.model <- "
+  # measurement model
+    ind60 =~ x1 + x2 + x3
+    dem60 =~ y1 + y2 + y3 + y4
+    dem65 =~ y5 + y6 + y7 + y8
+  # regressions
+    dem60 ~ ind60
+    dem65 ~ ind60 + dem60
+  # residual correlations
+    y1 ~~ y5
+    y2 ~~ y4 + y6
+    y3 ~~ y7
+    y4 ~~ y8
+    y6 ~~ y8
+"
+# Estimate model
+fit1 <- sem(model = sem.model, data = PoliticalDemocracy)
+# show summary result
+summary(fit1, fit.measures=TRUE, standardized=TRUE)
+library(lavaan)
+library(semPlot)
+library(OpenMx)
+library(tidyverse)
+library(knitr)
+library(kableExtra)
+library(GGally)
+model <-'
+mpg ~ hp + gear + cyl + disp + carb + am + wt
+hp ~ cyl + disp + carb
+'
+fit.mod <- sem(model, data=mtcars)
+summary(fit.mod, standardize=T, rsquare=T)
+dat <- read.csv("http://commres.net/_media/r/eating.disorder.sim.csv")
+head(dat)
+n <- nrow(dat)
+S <- cov(dat)
+cor <- cor(dat)
+#
+# xSelf-Efficacy = BMI + Self-Esteem + XDisturbance
+# Bulimic Symptoms = BMI + Self-Esteem + XSelf-Efficacy + XDisturbance
+# Restrictive Symptoms = BMI + Self-Esteem + xSelf-Efficacy + xDisturbance
+# Overall Risk = BMI + Self-Esteem + xSelf-Efficacy + Acculturation + xDisturbance
+#
+ex1 <- "           #opening a quote
+  # Tilda ~ : Regression
+  # M ~ X regression (X predicts M)
+  # Each line corresponds to an equation
+  # Disturbance is automatically included for each regression
+  # (i.e. no extra term needed)
+  DietSE ~ BMI + SelfEsteem      #DietSE is predicted by BMI and SelfEsteem
+  Bulimia ~ DietSE + BMI + SelfEsteem
+  Restrictive ~ DietSE + BMI + SelfEsteem
+  Risk ~ DietSE + BMI + SelfEsteem + Accu
+"
+ex1.fit <- sem(ex1, data=dat)
+summary(ex1.fit, standardize=T, rsquare=T)
+ex1b.fit <- sem(model = ex1, sample.cov = S, sample.nobs = n)
+summary(ex1b.fit)
+</code>
+<tabbed>
+  * sand box:code01
+  * *sand box:output01
+</tabbed>
 {{clock}}
+----
+  graph TD
+    A(**mermaid**)-->B((__plugin__))
+    A-->C(((//for//)))
+    B-->D[["[[https://www.dokuwiki.org/dokuwiki|Dokuwiki]]"]]
+    C-->D
 \begin{eqnarray*}
 & & P(A \mid B) = \dfrac{P(A \cap B)}{P(B)}\\
@@ Line 15: / Line 326: @@
 & & e^{i \pi} + 1 = 0\;
 \end{eqnarray*}
-<latex>\setlength{\unitlength}{1mm} \begin{picture}(93,46) \put( 0,14){\vector(1,0){60}} \put(61,13){$x$} \put(20,4){\vector(0,1){37}} \put(19,43){$y$} \put(50,34){\circle*{2}} \put(52,35){$P$} \multiput(20,34)(4,0){8}{\line(1,0){2}} \put(14.5,33.5){$y_P$} \multiput(50,14)(0,4){5}{\line(0,1){2}} \put(48,11){$x_P$} \put( 2,8){\vector(3,1){56}} \put(59,26.5){$x'$} \multiput(50,34)(1.9,-5.7){2} {\line(1,-3){1.2}} \put(52,22){$x_P'$} \multiput(50,34)(-5.8,-1.933){6} {\line(-3,-1){3.6}} \put(12,21){$y_P'$} \put(22,8){\vector(-1,3){10.5}} \put(10,41){$y'$} \end{picture}</latex>
 <WRAP tabs>
@@ Line 26: / Line 333: @@
 </WRAP>
-{{tabinclude>page1|Top page,page2|Second page,*page3}}
+{{tabinclude>sand_box:page1|Top page,sand_box:page2|Second page,*sand_box:page3}}
 <tabbed>
-  * page1|top page
+  * *sand_box:page1
-  * page2
+  * sand_box:page2
-  * *page3
+  * sand_box:page3
 </tabbed>