| sem_appl {CIEE} | R Documentation |
Structural equation modeling approach
Description
Function which uses the sem function in the
lavaan package to fit the model
L = \alpha_0 + \alpha_1 \cdot X + \epsilon_1, \epsilon_1 \sim N(0,\sigma_1^2)
K = \alpha_2 + \alpha_3 \cdot X + \alpha_4 \cdot L + \epsilon_2, \epsilon_2 \sim~ N(0,\sigma_2^2)
Y = \alpha_5 + \alpha_6 \cdot K + \alpha_{XY} \cdot X + \epsilon_3, \epsilon_3 \sim N(0,\sigma_3^2)
in order to obtain point and standard error estimates
of the parameters
\alpha_1, \alpha_3, \alpha_4, \alpha_6, \alpha_{XY}
for the GLM setting.
See the vignette for more details.
Usage
sem_appl(Y = NULL, X = NULL, K = NULL, L = NULL)
Arguments
Y |
Numeric input vector for the primary outcome. |
X |
Numeric input vector for the exposure variable. |
K |
Numeric input vector for the intermediate outcome. |
L |
Numeric input vector for the observed confounding factor. |
Value
Returns a list with point estimates of the parameters
(point_estimates), standard error estimates
(SE_estimates) and p-values from large-sample
Wald-type tests (pvalues).
Examples
dat <- generate_data(setting = "GLM")
sem_appl(Y = dat$Y, X = dat$X, K = dat$K, L = dat$L)