Standardized A Matrix

Source: R/ram.R

Matrix of regression slopes from the standardized simple mediation model.

A.std(taudotprime, betaprime, alphaprime, lambdax, lambdam, lambday)

Arguments

taudotprime

Numeric. Standardized slope of path from x to y \(\left( \dot{\tau}^{\prime} \right)\).
betaprime

Numeric. Standardized slope of path from m to y \(\left( \beta^{\prime} \right)\) .
alphaprime

Numeric. Standardized slope of path from x to m \(\left( \alpha^{\prime} \right)\) .
lambdax

Numeric. Factor loading xlatent ~ x \( \left( \lambda_x \right)\) . Numerically equivalent to the standard deviation of x.
lambdam

Numeric. Factor loading mlatent ~ m \( \left( \lambda_m \right)\) . Numerically equivalent to the standard deviation of m.
lambday

Numeric. Factor loading ylatent ~ y \( \left( \lambda_y \right)\) . Numerically equivalent to the standard deviation of y.

See also

Other reticular action model functions: A(), Mfrommu(), M(), S.std(), Sfromsigma2(), Sigmatheta.std(), Sigmathetafromsigma2(), Sigmatheta(), S(), mutheta()

Author

Ivan Jacob Agaloos Pesigan

Examples

A.std(
  taudotprime = 0.2080748,
  betaprime = 0.4126006,
  alphaprime = 0.3708979,
  lambdax = 1.137308,
  lambdam = 1.038248,
  lambday = 1.134973
)

#>         x m y   xlatent   mlatent  ylatent
#> x       0 0 0 1.1373080 0.0000000 0.000000
#> m       0 0 0 0.0000000 1.0382480 0.000000
#> y       0 0 0 0.0000000 0.0000000 1.134973
#> xlatent 0 0 0 0.0000000 0.0000000 0.000000
#> mlatent 0 0 0 0.3708979 0.0000000 0.000000
#> ylatent 0 0 0 0.2080748 0.4126006 0.000000