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--- r:path_analysis [2022/11/15 13:01] – hkimscil
+++ r:path_analysis [2023/05/29 23:45] – hkimscil
@@ Line 1: / Line 1: @@
 ====== Path Analysis ======
+{{:r:pasted:20230529-234519.png}}
 ====== Introduction ======
 {{youtube>UGIVPtFKwc0}}
@@ Line 110: / Line 111: @@
 </code>
+----
+<code>
+# my own
+# pbt model
+specmod5 <- '
+    # Directional relations (path)
+    intention ~ a*attitude + b*norms + c*control
+    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)
+</code>
 ====== Output ======
 <code>
@@ Line 439: / Line 458: @@
 </code>
+===== specmod5 =====
+<code>
+> specmod5 <- "
++     # Directional relations (path)
++     intention ~ attitude + norms + control
++     behavior ~ intention + norms
++     # Covariances
++     attitude ~~ norms + control
++     norms ~~ control
++ "
+> fitmod5 <- sem(specmod5, data=df)
+> summary(fitmod5, fit.measures=TRUE, rsquare=TRUE)
+lavaan 0.6-12 ended normally after 18 iterations
+  Estimator                                         ML
+  Optimization method                           NLMINB
+  Number of model parameters                        13
+  Number of observations                           199
+Model Test User Model:
+  Test statistic                                 1.781
+  Degrees of freedom                                 2
+  P-value (Chi-square)                           0.410
+Model Test Baseline Model:
+  Test statistic                               182.295
+  Degrees of freedom                                10
+  P-value                                        0.000
+User Model versus Baseline Model:
+  Comparative Fit Index (CFI)                    1.000
+  Tucker-Lewis Index (TLI)                       1.006
+Loglikelihood and Information Criteria:
+  Loglikelihood user model (H0)              -1258.396
+  Loglikelihood unrestricted model (H1)      -1257.506
+  Akaike (AIC)                                2542.792
+  Bayesian (BIC)                              2585.605
+  Sample-size adjusted Bayesian (BIC)         2544.421
+Root Mean Square Error of Approximation:
+  RMSEA                                          0.000
+Percent confidence interval - lower         0.000
+Percent confidence interval - upper         0.136
+  P-value RMSEA <= 0.05                          0.569
+Standardized Root Mean Square Residual:
+  SRMR                                           0.018
+Parameter Estimates:
+  Standard errors                             Standard
+  Information                                 Expected
+  Information saturated (h1) model          Structured
+Regressions:
+                   Estimate  Std.Err  z-value  P(>|z|)
+  intention ~
+    attitude          0.352    0.058    6.068    0.000
+    norms             0.153    0.059    2.577    0.010
+    control           0.275    0.058    4.740    0.000
+  behavior ~
+    intention         0.443    0.068    6.525    0.000
+    norms             0.034    0.068    0.493    0.622
+Covariances:
+                   Estimate  Std.Err  z-value  P(>|z|)
+  attitude ~~
+    norms             0.200    0.064    3.128    0.002
+    control           0.334    0.070    4.748    0.000
+  norms ~~
+    control           0.220    0.065    3.411    0.001
+Variances:
+                   Estimate  Std.Err  z-value  P(>|z|)
+   .intention         0.530    0.053    9.975    0.000
+   .behavior          0.698    0.070    9.975    0.000
+    attitude          0.928    0.093    9.975    0.000
+    norms             0.830    0.083    9.975    0.000
+    control           0.939    0.094    9.975    0.000
+R-Square:
+                   Estimate
+    intention         0.369
+    behavior          0.199
+</code>
 ===== Lavaan in R: explanation =====
@@ Line 460: / Line 573: @@
 <code>
-processdata<-read.csv("path analysis dataN BinW.csv", header=TRUE, sep=",")
+# processdata<-read.csv("path analysis dataN BinW.csv", header=TRUE, sep=",")
+processdata<-read.csv("http://commres.net/wiki/_media/r/path_analysis_datan_binw.csv",
+                       header=TRUE, sep=",", fileEncoding="UTF-8-BOM")
 </code>
@@ Line 636: / 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 799: / 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>