######################################################
## data file: PlannedBehavior.csv
######################################################
df <- read.csv("http://commres.net/wiki/_media/r/plannedbehavior.csv")
head(df)
str(df)
######################################################
# attitude
# norms
# control
# intention
# behavior
######################################################
# install.packages("lavaan")
library(lavaan)

# Specify model
specmod <- "
intention ~ attitude + norms + control 
"
# Estimate model
fitmod <- sem(specmod, data=df)

# Summarize model
summary(fitmod, fit.measures=TRUE, rsquare=TRUE)

######################################################
> ## data file: PlannedBehavior.csv
> ######################################################
> df <- read.csv("http://commres.net/wiki/_media/r/plannedbehavior.csv")
> head(df)
  attitude norms control intention behavior
1     2.31  2.31    2.03      2.50     2.62
2     4.66  4.01    3.63      3.99     3.64
3     3.85  3.56    4.20      4.35     3.83
4     4.24  2.25    2.84      1.51     2.25
5     2.91  3.31    2.40      1.45     2.00
6     2.99  2.51    2.95      2.59     2.20
> str(df)
'data.frame':	199 obs. of  5 variables:
 $ attitude : num  2.31 4.66 3.85 4.24 2.91 2.99 3.96 3.01 4.77 3.67 ...
 $ norms    : num  2.31 4.01 3.56 2.25 3.31 2.51 4.65 2.98 3.09 3.63 ...
 $ control  : num  2.03 3.63 4.2 2.84 2.4 2.95 3.77 1.9 3.83 5 ...
 $ intention: num  2.5 3.99 4.35 1.51 1.45 2.59 4.08 2.58 4.87 3.09 ...
 $ behavior : num  2.62 3.64 3.83 2.25 2 2.2 4.41 4.15 4.35 3.95 ...
> ######################################################
> # attitude
> # norms
> # control
> # intention
> # behavior
> ######################################################
> # install.packages("lavaan")
> library(lavaan)
This is lavaan 0.6-9
lavaan is FREE software! Please report any bugs.
Warning message:
패키지 ‘lavaan’는 R 버전 4.1.2에서 작성되었습니다 
> 
> # Specify model
> specmod <- "
+ intention ~ attitude + norms + control 
+ "
> # Estimate model
> fitmod <- sem(specmod, data=df)
> 
> # Summarize model
> summary(fitmod, fit.measures=TRUE, rsquare=TRUE)
lavaan 0.6-9 ended normally after 11 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                         4
                                                      
  Number of observations                           199
                                                      
Model Test User Model:
                                                      
  Test statistic                                 0.000
  Degrees of freedom                                 0

Model Test Baseline Model:

  Test statistic                                91.633
  Degrees of freedom                                 3
  P-value                                        0.000

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    1.000
  Tucker-Lewis Index (TLI)                       1.000

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)               -219.244
  Loglikelihood unrestricted model (H1)       -219.244
                                                      
  Akaike (AIC)                                 446.489
  Bayesian (BIC)                               459.662
  Sample-size adjusted Bayesian (BIC)          446.990

Root Mean Square Error of Approximation:

  RMSEA                                          0.000
  90 Percent confidence interval - lower         0.000
  90 Percent confidence interval - upper         0.000
  P-value RMSEA <= 0.05                             NA

Standardized Root Mean Square Residual:

  SRMR                                           0.000

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

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)
   .intention         0.530    0.053    9.975    0.000

R-Square:
                   Estimate
    intention         0.369