Structural Equation Modeling for Field Experiments

Performs SEM analysis for agricultural field experiments. Allows testing of hypothesized causal relationships among variables.

Usage

sem_analysis(data, model, plot = TRUE, verbose = TRUE)

Arguments

data: Data frame with variables
model: Model specification in lavaan syntax
plot: Logical, whether to generate path diagram
verbose: Logical. If TRUE (default), prints formatted output to console.

Value

SEM results including fit indices and parameter estimates

Author

Lalit Kumar Rolaniya, ICAR-IIPR, Bikaner

Examples

# \donttest{
if (requireNamespace("lavaan", quietly = TRUE)) {
  set.seed(42)
  n <- 100
  nitrogen <- rnorm(n, 60, 10)
  phosphorus <- rnorm(n, 30, 5)
  yield <- 0.5 * nitrogen + 0.3 * phosphorus + rnorm(n, 0, 5)
  df <- data.frame(yield = yield, nitrogen = nitrogen, phosphorus = phosphorus)
  model <- 'yield ~ nitrogen + phosphorus'
  result <- sem_analysis(df, model, plot = FALSE)
}
#> 
#> ==========================================================
#>           STRUCTURAL EQUATION MODEL ANALYSIS
#> ==========================================================
#> 
#> --- Model Fit Indices ---
#> Chi-square: 0 (df = 0 , p = NA )
#> CFI: 1 
#> TLI: 1 
#> RMSEA: 0 
#> SRMR: 0 
#> 
#> --- Fit Assessment ---
#> Model fit: GOOD
#> 
#> --- Parameter Estimates ---
#> 
#>          lhs op        rhs     est    se     z pvalue ci.lower ci.upper  std.lv
#> 1      yield  ~   nitrogen   0.428 0.048 8.883  0.000    0.334    0.523   0.428
#> 2      yield  ~ phosphorus   0.385 0.111 3.470  0.001    0.168    0.603   0.385
#> 3      yield ~~      yield  24.916 3.524 7.071  0.000   18.010   31.822  24.916
#> 4   nitrogen ~~   nitrogen 107.358 0.000    NA     NA  107.358  107.358 107.358
#> 5   nitrogen ~~ phosphorus   1.458 0.000    NA     NA    1.458    1.458   1.458
#> 6 phosphorus ~~ phosphorus  20.234 0.000    NA     NA   20.234   20.234  20.234
#>   std.all std.nox
#> 1   0.640   0.062
#> 2   0.250   0.056
#> 3   0.518   0.518
#> 4   1.000 107.358
#> 5   0.031   1.458
#> 6   1.000  20.234
# }