README.md

In hughesevoanth/glsmr: GAM and linear stratified observational and MR estimates of exposure on outcome

glsmr

GAM and linear stratified MR

This is an R package to aid in determining if observational or two-stage least square (instrument variable or in genetics Mendelian randomization) analysis have a non-linear relationship between expoosure and outcome.

installation

devtools::install_github("hughesevoanth/glsmr")

use

• There are two functions that are most useful

  • glsmr()
  • plot_glsmr()

• an example for using glsmr

  myexample = glsmr( wdata = mydata,
        outcome = "trait",
        exposure = "bmi",
        instrument = "bmi_grs",
        linear_covariates = c("batch", "sex"),
        smooth_covariates =  c("age"),
        # strata = 4, ## for quartiles
        strata = c(10,18.5,25,30,45),
        rnt_outcome = TRUE,
        weights_variable = NA,
        outlier_method = "iqr",
        outlier_cutoff = 5,
        messages = FALSE,
        return_models = TRUE)
  

  • NOTE: 'smooth_covariates' will be modeled as smooths or non-linear variables in the GAM, but as typical parametric variables in the linear and tsls analyses.

• an example for using plot_glsmr()

  plot_glsmr(myexample,
      add_strata_2_curves = FALSE,
      add_strata_2_points = TRUE,
      brewer_col = "Set1",
      old_plot_scheme = TRUE,
      old_GAM_smooths = TRUE,
      plot_obs_res_betas = FALSE,
      pval_thresh = 0.05)
  

An example figure from plot_glsmr()

See this repo's wiki for more information

wiki page here

hughesevoanth/glsmr documentation built on May 14, 2023, 3:41 p.m.