| dlsem-package {dlsem} | R Documentation |
Distributed-lag linear structural equation models
Description
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018; Magrini et. al, 2019; Magrini, 2020). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic ('ecq'), quadratic decreasing ('qd'), linearly decreasing ('ld') and gamma ('gam') lag shapes are available. The main functions of the package are:
dlsem, to perform parameter estimation;
causalEff, to compute all the pathwise causal lag shapes and the overall one connecting two or more variables;
lagPlot, to display a pathwise or an overall causal lag shape.
Details
| Package: | dlsem |
| Type: | Package |
| Version: | 2.4.6 |
| Date: | 2020-03-22 |
| License: | GPL-2 |
Author(s)
Alessandro Magrini <alessandro.magrini@unifi.it>
References
A. Magrini (2018). Linear Markovian models for lag exposure assessment. Biometrical Letters, 55(2): 179-195. DOI: 10.2478/bile-2018-0012.
A. Magrini, F. Bartolini, A. Coli, B. Pacini (2019). A structural equation model to assess the impact of agricultural research expenditure on multiple dimensions. Quality and Quantity, 53(4): 2063-2080. DOI: 10.1007/s11135-019-00855-z
A. Magrini (2020). A family of theory-based lag shapes for distributed-lag linear regression. To be appeared on Italian Journal of Applied Statistics.