sem_appl function

Structural equation modeling approach

Function which uses the sem function in the lavaan package to fit the model [REMOVE_ME]$L = \alpha_0 + \alpha_1 \cdot X + \epsilon_1, \epsilon_1 \sim N(0,\sigma_1^2)L = \alpha0 + \alpha1*X + \epsilon1, \epsilon1 ~ N(0,\sigma1^2) [REMOVE_ME_2]$

[REMOVE_ME]$K = \alpha_2 + \alpha_3 \cdot X + \alpha_4 \cdot L + \epsilon_2, \epsilon_2 \sim~ N(0,\sigma_2^2)K = \alpha2 + \alpha3*X + \alpha4*L + \epsilon2, \epsilon2 ~ N(0,\sigma2^2) [REMOVE_ME_2]$

[REMOVE_ME]$Y = \alpha_5 + \alpha_6 \cdot K + \alpha_{XY} \cdot X + \epsilon_3, \epsilon_3 \sim N(0,\sigma_3^2)Y = \alpha5 + \alpha6*K + \alphaXY*X + \epsilon3, \epsilon3 ~ N(0,\sigma3^2) [REMOVE_ME_2]$

in order to obtain point and standard error estimates of the parameters $\alpha1, \alpha3, \alpha4, \alpha6, \alphaXY$

for the GLM setting. See the vignette for more details.

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.

Returns

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).

Description

Function which uses the sem function in the lavaan package to fit the model

in order to obtain point and standard error estimates of the parameters $\alpha1, \alpha3, \alpha4, \alpha6, \alphaXY$

for the GLM setting. See the vignette for more details.

Examples

dat <- generate_data(setting = "GLM")
sem_appl(Y = dat$Y, X = dat$X, K = dat$K, L = dat$L)