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--- r:path_analysis [2022/11/15 22:17] – [Lavaan in R: explanation] hkimscil
+++ r:path_analysis [2023/11/27 16:57] (current) – [Lavaan in R: explanation] hkimscil
@@ Line 1: / Line 1: @@
 ====== Path Analysis ======
+{{:r:pasted:20230529-234519.png}}
 ====== Introduction ======
 {{youtube>UGIVPtFKwc0}}
@@ Line 114: / Line 115: @@
 # my own
 # pbt model
-specmod5 <- "
+specmod5 <- '
     # Directional relations (path)
-    intention ~ attitude + norms + control
+    intention ~ a*attitude + b*norms + c*control
-    behavior ~ intention + norms
+    behavior ~ d*intention
     # Covariances
     attitude ~~ norms + control
     norms ~~ control
-"
+    ad := a*d
+    bd := b*d
+    cd := c*d
+'
 fitmod5 <- sem(specmod5, data=df)
 summary(fitmod5, fit.measures=TRUE, rsquare=TRUE)
@@ Line 629: / Line 633: @@
   * Step 2: Use 'lavaan' function to run analysis. Here, I will be saving  the results in an R object called 'fit' (arbitrarily named). Inside  the parenthesis are arguments separated by commas. The first argument contains the name of the object containing the model syntax (see above). The object is named 'model' (again, arbitrarily named above). Next, we have the 'data' argument. This identifies the object (i.e., data frame)  containing the raw data.
 <code>
-fit<-lavaan(model,data=processdata)
+fit<-lavaan(model, data=processdata)
 </code>
   * The 'summary' function can be used to obtain various fit measures and the parameter estimates for the model
 <code>
-summary(fit,fit.measures=TRUE)
+summary(fit, fit.measures=TRUE)
 </code>
   * To obtain standardized estimates, use the 'standardized' argument (setting it to TRUE) when using the 'summary' function. You will need to interpret the Std.all column in the output, as it will provide standardized estimates for all measured variables in the model.
 <code>
-summary(fit,fit.measures=TRUE,standardized=TRUE,rsquare=TRUE)
+summary(fit, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)
 </code>
@@ Line 687: / Line 691: @@
   interest~~anxiety'
-  fit<-lavaan(model,data=processdata,auto.var=TRUE)
+  fit<-lavaan(model, data=processdata, auto.var=TRUE)
-  summary(fit,fit.measures=TRUE,standardized=TRUE,rsquare=TRUE)
+  summary(fit, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)
 </code>
@@ Line 747: / Line 751: @@
 CODING
 <code>
-processdata <- read.csv("http://commres.net/wiki/_media/r/path_analysis_datan_binw.csv")
+processdata<-read.csv("http://commres.net/wiki/_media/r/path_analysis_datan_binw.csv",
+                       header=TRUE, sep=",", fileEncoding="UTF-8-BOM")
 str(processdata)
 library(lavaan)
@@ Line 767: / Line 772: @@
     interest~~anxiety
 '
-fit <- lavaan(model. data=processdata)
+fit <- lavaan(model, data=processdata)
-fit <- sem(model. data=processdata)
+fit <- sem(model, data=processdata)
 summary(fit, fit.measures=TRUE)
@@ Line 910: / Line 915: @@
 see [[https://www.rensvandeschoot.com/tutorials/lme4/|lme4 tutorial]]
+===== Exercise =====
+Using mtcars in R
+<code>
+?mtcars
+mtcars
+str(mtcars)
+df <- mtcars
+</code>
+<code>
+# model specfication
+model <-'
+  mpg ~ hp + gear + cyl + disp + carb + am + wt
+  hp ~ cyl + disp + carb
+'
+# model fit
+fit <- cfa(model, data = mtcars)
+summary(fit, fit.measures = TRUE, standardized=T, rsquare=T)
+semPaths(fit, 'std', layout = 'circle')
+</code>