Type: Package
Title: Automated Mendelian Randomization Workflows and Visualizations
Version: 0.1.1
Description: Provides tools to summarize, analyze, and visualize results from Mendelian randomization studies using summarized genetic association data. The package includes functions for generating forest plots and scatter plots at the single-nucleotide polymorphism and Mendelian randomization method levels, and for fitting multiple estimators in a unified workflow, including inverse-variance weighted estimation, Mendelian randomization Egger regression, the weighted median estimator, the robust adjusted profile score, Mendelian randomization pleiotropy residual sum and outlier, Mendelian randomization with the genotype recoding invariance property, and a Bayesian horseshoe method. Related methods are described by Burgess (2013) <doi:10.1002/gepi.21758>, Bowden (2015) <doi:10.1093/ije/dyv080>, Bowden (2016) <doi:10.1002/gepi.21965>, Zhao (2020) <doi:10.1214/19-AOS1866>, Verbanck (2018) <doi:10.1038/s41588-018-0099-7>, Dudbridge (2025) <doi:10.1371/journal.pgen.1011967>, and Grant and Burgess (2024) <doi:10.1016/j.ajhg.2023.12.002>. Related open-source software includes 'TwoSampleMR' https://github.com/MRCIEU/TwoSampleMR, 'mr.raps' https://github.com/qingyuanzhao/mr.raps, 'MR-PRESSO' https://github.com/rondolab/MR-PRESSO, and 'MR-Horse' https://github.com/aj-grant/mrhorse.
Depends: R (≥ 4.2)
Imports: ggplot2 (≥ 4.0.1), dplyr (≥ 1.1.4), tidyr (≥ 1.3.1), MendelianRandomization (≥ 0.10.0), R2jags (≥ 0.8-9), coda (≥ 0.19-4.1), nortest (≥ 1.0-4), grDevices, graphics, stats, utils, methods
URL: https://github.com/KelinZhong/autoMR
BugReports: https://github.com/KelinZhong/autoMR/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-03-24 14:14:31 UTC; Colin
Author: Kelin Zhong [aut, cre], Chia-Ling Kuo [aut]
Maintainer: Kelin Zhong <kelin.zhong@uconn.edu>
Repository: CRAN
Date/Publication: 2026-03-29 15:00:10 UTC

S4 class to store instrument-level MR forest plots

Description

A GWASForestPlots object is returned by GWAS_forest(). It holds one ggplot object per outcome-exposure pair — each plot shows per-instrument (SNP-level) causal estimates alongside a pooled IVW estimate — together with the metadata needed to filter and export subsets.

Slots

plots

Named list of ggplot objects. Names follow the pattern "Outcome::Exposure".

outcomes

Character vector of outcome labels, parallel to plots.

exposures

Character vector of exposure labels, parallel to plots.

Methods

export_forest_plots(object, save_dir, file_type, width, height, dpi, outcome, exposure)

Saves plots to disk. Optionally filter by outcome, exposure, or both to export a subset.

Generate Instrument-level Forest Plots for Mendelian Randomization

Description

Creates a forest plot for each instrument (SNP) within the MR analysis, including a pooled IVW estimate at the top for comparison.

Usage

GWAS_forest(
  MR_input_data,
  report_form,
  save_plot = FALSE,
  file_type = "jpeg",
  save_dir = tempdir(),
  xlim_custom = NULL,
  dot_size = 2,
  axis_text_size = 10,
  axis_title_size = 12,
  digits = 2,
  plot_width = 8,
  plot_height = 6,
  label_text_size = 3
)

Arguments

MR_input_data

Harmonised MR input data frame. Must contain Outcome, Exposure, Instrument, beta_exposure, se_exposure, beta_outcome, and se_outcome columns.

report_form

Character string or vector indicating the standard output scale for each outcome (e.g., "Beta", "OR", "HR"). Defaults to "Beta".

save_plot

Deprecated. Plots are no longer saved inside the function. Use export_forest_plots() on the returned object instead. Ignored with a warning if supplied as TRUE.

file_type

Deprecated. Passed to export_forest_plots() instead. Ignored with a warning if supplied.

save_dir

Deprecated. Passed to export_forest_plots() instead. Ignored with a warning if supplied.

xlim_custom

Optional numeric vector of length 2 providing custom limits for the x-axis. If NULL, limits are determined by the data.

dot_size

Numeric value specifying the size of the points. Default is 2.

axis_text_size

Numeric value specifying the font size for axis labels. Default is 10.

axis_title_size

Numeric value specifying the font size for axis titles. Default is 12.

digits

Integer specifying the number of decimal places for labels. Default is 2.

plot_width

Numeric value specifying the width of exported plot file. Default is 8.

plot_height

Numeric value specifying the height of exported plot file. Default is 6.

label_text_size

Numeric value specifying the size of estimate labels (Beta/OR/HR and 95 percent CI) shown on the plot. Default is 3.

Value

A GWASForestPlots object containing one ggplot per outcome-exposure pair, with instrument-level (SNP) causal estimates and a pooled IVW estimate. Use export_forest_plots() to write plots to disk with optional filtering.

Examples


data("fi_49item")
input1 <- harmonize_mr_data(df = fi_49item)

gwas_plots <- GWAS_forest(
  MR_input_data   = input1,
  report_form     = "Beta",
  xlim_custom     = NULL,
  dot_size        = 2,
  axis_text_size  = 10,
  axis_title_size = 12,
  digits          = 2,
  plot_width      = 8,
  plot_height     = 6,
  label_text_size = 3
)

# Retrieve the exact outcome/exposure labels stored in the object
out_name <- gwas_plots@outcomes[1]   # "fi_49item"
exp_name <- gwas_plots@exposures[1]  # "Zn"

# Export all instrument-level plots as PDF (commented — writes to disk)
export_forest_plots(gwas_plots, save_dir = tempdir(), file_type = "pdf")

# Export plots for one outcome only
export_forest_plots(gwas_plots, save_dir = tempdir(), outcome = out_name)

# Export plots for one exposure only
export_forest_plots(gwas_plots, save_dir = tempdir(), exposure = exp_name)

# Export one specific outcome-exposure pair
export_forest_plots(gwas_plots, save_dir = tempdir(), outcome = out_name, exposure = exp_name)

S4 class to store method-level MR forest plots

Description

An MRForestPlots object is returned by MR_forest(). It holds one ggplot object per outcome-exposure pair — each plot compares causal estimates across MR methods (IVW, RAPS, Egger, etc.) — together with the metadata needed to filter and export subsets.

Slots

plots

Named list of ggplot objects. Names follow the pattern "Outcome::Exposure".

outcomes

Character vector of outcome labels, parallel to plots.

exposures

Character vector of exposure labels, parallel to plots.

Methods

export_forest_plots(object, save_dir, file_type, width, height, dpi, outcome, exposure)

Saves plots to disk. Optionally filter by outcome, exposure, or both to export a subset.

S4 class to store MR scatter plots

Description

An MRScatterPlots object is returned by plot_mr_scatter(). It holds one plot-parameter list per outcome-exposure pair, together with the metadata needed to filter and export subsets of plots.

Slots

plots

Named list of plot-parameter lists. Names follow the pattern "Outcome::Exposure".

outcomes

Character vector of outcome labels, parallel to plots.

exposures

Character vector of exposure labels, parallel to plots.

Methods

export_scatter_plots(object, save_dir, file_type, width, height, outcome, exposure)

Saves plots to disk. Optionally filter by outcome, exposure, or both to export a subset.

Generate Forest Plots across Multiple MR Methods

Description

Creates a forest plot comparing causal estimates across different MR methods (e.g., IVW, RAPS, Egger).

Usage

MR_forest(
  summary_df,
  effect,
  save_plot = FALSE,
  file_type = "jpeg",
  save_dir = tempdir(),
  custom_xlim = NULL,
  dot_size = 3,
  axis_text_size = 10,
  axis_title_size = 12,
  pval_text_size = 3,
  plot_width = 8,
  plot_height = 6,
  clamp_nonpositive = FALSE
)

Arguments

summary_df

MR results data frame, typically the output from run_mr_analysis().

effect

Character string or vector indicating the effect scale ("Beta", "OR", or "HR").

save_plot

Deprecated. Plots are no longer saved inside the function. Use export_forest_plots() on the returned object instead. Ignored with a warning if supplied as TRUE.

file_type

Deprecated. Passed to export_forest_plots() instead. Ignored with a warning if supplied.

save_dir

Deprecated. Passed to export_forest_plots() instead. Ignored with a warning if supplied.

custom_xlim

Optional numeric vector of length 2 for x-axis limits.

dot_size

Numeric value specifying the point size. Default is 3.

axis_text_size

Numeric value for axis font size.

axis_title_size

Numeric value for title font size.

pval_text_size

Numeric value for p-value label size.

plot_width

Width of exported plot.

plot_height

Height of exported plot.

clamp_nonpositive

Logical; whether non-positive estimates should be clamped to a small positive value before log-transformation.

Value

An MRForestPlots object containing one ggplot per outcome-exposure pair, with causal estimates compared across MR methods. Use export_forest_plots() to write plots to disk with optional filtering.

Examples


data("fi_49item")
input1   <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(
  MR_input_data     = input1,
  outcome.form      = NULL,
  use_ivw           = TRUE,
  use_raps          = TRUE,
  use_median        = TRUE,
  use_egger         = TRUE,
  use_mr_presso     = TRUE,
  use_mr_horse      = TRUE,
  use_mr_grip       = TRUE,
  NbDistribution    = 1000,
  SignifThreshold   = 0.05,
  mr_horse_n_iter   = 5000,
  mr_horse_n_burnin = 1000,
  mr_grip_parameters = NULL
)

mr_plots <- MR_forest(
  summary_df        = outcome1,
  effect            = "Beta",
  custom_xlim       = NULL,
  dot_size          = 3,
  axis_text_size    = 10,
  axis_title_size   = 12,
  pval_text_size    = 3,
  plot_width        = 8,
  plot_height       = 6,
  clamp_nonpositive = FALSE
)

# Retrieve the exact outcome/exposure labels stored in the object
out_name <- mr_plots@outcomes[1]   # "fi_49item"
exp_name <- mr_plots@exposures[1]  # "Zn"

# Export all method-level plots as PDF (commented — writes to disk)
export_forest_plots(mr_plots, save_dir = tempdir(), file_type = "pdf")

# Export plots for one outcome only
export_forest_plots(mr_plots, save_dir = tempdir(), outcome = out_name)

# Export plots for one exposure only
export_forest_plots(mr_plots, save_dir = tempdir(), exposure = exp_name)

# Export one specific outcome-exposure pair
export_forest_plots(mr_plots, save_dir = tempdir(), outcome = out_name, exposure = exp_name)

Export MR Forest Plots to Disk

Description

Saves plots stored in a GWASForestPlots or MRForestPlots object to a directory using ggplot2::ggsave(). You can export all plots or filter to a specific outcome, a specific exposure, or a specific outcome-exposure pair.

Usage

export_forest_plots(
  object,
  save_dir = tempdir(),
  file_type = "pdf",
  width = 8,
  height = 6,
  dpi = 300,
  outcome = NULL,
  exposure = NULL
)

## S4 method for signature 'GWASForestPlots'
export_forest_plots(
  object,
  save_dir = tempdir(),
  file_type = "pdf",
  width = 8,
  height = 6,
  dpi = 300,
  outcome = NULL,
  exposure = NULL
)

## S4 method for signature 'MRForestPlots'
export_forest_plots(
  object,
  save_dir = tempdir(),
  file_type = "pdf",
  width = 8,
  height = 6,
  dpi = 300,
  outcome = NULL,
  exposure = NULL
)

Arguments

object

A GWASForestPlots or MRForestPlots object returned by GWAS_forest() or MR_forest().

save_dir

Character string; directory to write files into. Must already exist. Defaults to tempdir().

file_type

Character string; output format passed to ggplot2::ggsave(). One of "pdf", "png", "jpeg", or "tiff". Default is "pdf".

width

Numeric; plot width in inches. Default is 8.

height

Numeric; plot height in inches. Default is 6.

dpi

Integer; resolution in dots per inch (ignored for PDF). Default is 300.

outcome

Optional character string; if supplied, only plots whose outcome matches this value are exported.

exposure

Optional character string; if supplied, only plots whose exposure matches this value are exported.

Details

Filtering logic:

File names are prefixed with Instrument_forest_ for GWASForestPlots objects and Method_forest_ for MRForestPlots objects, followed by <Outcome>_<Exposure>.<file_type> with spaces replaced by underscores.

Value

Invisibly returns object so calls can be chained.

Methods (by class)

Examples


data("fi_49item")
input1   <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(MR_input_data = input1)

gwas_plots <- GWAS_forest(MR_input_data = input1, report_form = "Beta")
mr_plots   <- MR_forest(summary_df = outcome1, effect = "Beta")

# Inspect what is stored; for fi_49item both objects print:
#   [1] fi_49item :: Zn

# Retrieve the exact outcome/exposure labels stored in the object
gwas_out <- gwas_plots@outcomes[1]   # "fi_49item"
gwas_exp <- gwas_plots@exposures[1]  # "Zn"
mr_out   <- mr_plots@outcomes[1]     # "fi_49item"
mr_exp   <- mr_plots@exposures[1]    # "Zn"

# Export all instrument-level plots as PDF (commented — writes to disk)
export_forest_plots(gwas_plots, save_dir = tempdir())

# Export one outcome only
export_forest_plots(gwas_plots, save_dir = tempdir(), outcome = gwas_out)

# Export one exposure only
export_forest_plots(mr_plots, save_dir = tempdir(), exposure = mr_exp)

# Export one specific pair
export_forest_plots(mr_plots, save_dir = tempdir(), outcome = mr_out, exposure = mr_exp)

Export MR Scatter Plots to Disk

Description

Saves plots stored in an MRScatterPlots object to a directory. You can export all plots or filter to a specific outcome, a specific exposure, or a specific outcome-exposure pair.

Usage

export_scatter_plots(
  object,
  save_dir = tempdir(),
  file_type = "pdf",
  width = 8,
  height = 6,
  outcome = NULL,
  exposure = NULL
)

## S4 method for signature 'MRScatterPlots'
export_scatter_plots(
  object,
  save_dir = tempdir(),
  file_type = "pdf",
  width = 8,
  height = 6,
  outcome = NULL,
  exposure = NULL
)

Arguments

object

An MRScatterPlots object returned by plot_mr_scatter().

save_dir

Character string; directory to write files into. Must exist. Defaults to tempdir().

file_type

Character string; output format passed to the corresponding grDevices device. One of "pdf", "png", "jpeg", or "tiff". Default is "pdf".

width

Numeric; plot width in inches. Default is 8.

height

Numeric; plot height in inches. Default is 6.

outcome

Optional character string; if supplied, only plots whose outcome matches this value are exported.

exposure

Optional character string; if supplied, only plots whose exposure matches this value are exported.

Details

Filtering logic:

File names follow the pattern Scatter_<Outcome>_<Exposure>.<file_type> with spaces replaced by underscores.

Value

Invisibly returns object so calls can be chained.

Methods (by class)

Examples


data("fi_49item")
input1   <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(MR_input_data = input1)
plots    <- plot_mr_scatter(MR_input_data = input1, summary_df = outcome1)

# Inspect what is stored; for fi_49item this prints:
#   [1] fi_49item :: Zn
#' # Retrieve the exact outcome/exposure labels stored in the object
out_name <- plots@outcomes[1]   # "fi_49item"
exp_name <- plots@exposures[1]  # "Zn"

# Export all plots to tempdir() (commented — writes to disk)
export_scatter_plots(plots, save_dir = tempdir())

# Export one outcome only
export_scatter_plots(plots, save_dir = tempdir(), outcome = out_name)

# Export one exposure only
export_scatter_plots(plots, save_dir = tempdir(), exposure = exp_name)

# Export one specific pair
export_scatter_plots(plots, save_dir = tempdir(), outcome = out_name, exposure = exp_name)

Frailty Index Dataset (49-items)

Description

A dataset containing variables used to compute a 49-item frailty index (FI). The exact variables/columns depend on your project definitions.

Format

A data frame.

Format vectors into a Mendelian Randomization input data frame

Description

Format vectors into a Mendelian Randomization input data frame

Usage

format_mr_input(
  Instrument,
  beta_exposure,
  se_exposure,
  beta_outcome,
  se_outcome,
  Outcome,
  Exposure,
  ALLELE1 = NULL,
  ALLELE0 = NULL,
  A1FREQ = NULL
)

Arguments

Instrument

Character vector of instrument/SNP identifiers.

beta_exposure

Numeric vector of exposure effects.

se_exposure

Numeric vector of exposure standard errors.

beta_outcome

Numeric vector of outcome effects.

se_outcome

Numeric vector of outcome standard errors.

Outcome

Character string or vector for outcome names (Mandatory).

Exposure

Character string or vector for exposure names (Mandatory).

ALLELE1

Optional character vector for non-effect alleles.

ALLELE0

Optional character vector for effect alleles.

A1FREQ

Optional numeric vector for effect allele frequencies.

Value

A data frame formatted for MR analysis.

Examples

data("fi_49item")

input_test1 <- format_mr_input(
  Instrument = fi_49item$Instrument,
  beta_exposure = fi_49item$beta_exposure,
  se_exposure = fi_49item$se_exposure,
  beta_outcome = fi_49item$beta_outcome,
  se_outcome = fi_49item$se_outcome,
  Outcome = fi_49item$Outcome,
  Exposure = fi_49item$Exposure
)
head(input_test1)

input_test2 <- format_mr_input(
  Instrument = fi_49item$Instrument,
  beta_exposure = fi_49item$beta_exposure,
  se_exposure = fi_49item$se_exposure,
  beta_outcome = fi_49item$beta_outcome,
  se_outcome = fi_49item$se_outcome,
  Outcome = fi_49item$Outcome,
  Exposure = fi_49item$Exposure,
  ALLELE1 = fi_49item$NEA_outcome,
  ALLELE0 = fi_49item$EA_outcome,
  A1FREQ = fi_49item$A1FREQ_outcome
)
head(input_test2)

Fried Frailty Phenotype Dataset

Description

A dataset containing variables used to compute Fried frailty phenotype measures. The exact variables/columns depend on your project definitions.

Format

A data frame.

Harmonize exposure and outcome SNP data

Description

Harmonizes alleles and ensures consistent effect direction between exposure and outcome datasets. Standardizes exposure effects to be positive.

Usage

harmonize_mr_data(df, Outcome = NULL, Exposure = NULL)

Arguments

df

Data frame containing required columns (beta_outcome, se_outcome, beta_exposure, se_exposure) and metadata (Outcome, Exposure).

Outcome

Optional; Character string to set as the Outcome name.

Exposure

Optional; Character string to set as the Exposure name.

Value

A harmonized data frame with 'Instrument' and allele columns.

Examples

input_test3 <- harmonize_mr_data(df = fi_49item)
head(input_test3)

Plot MR Scatter Plots for Multiple Outcomes and Exposures

Description

Generates one scatter plot per outcome-exposure pair using base R graphics, with a regression line overlaid for each requested Mendelian Randomization (MR) method. Plot parameters are stored in an MRScatterPlots S4 object and rendered on demand at export time, so no files are written to disk during this call.

Usage

plot_mr_scatter(
  MR_input_data,
  plot.xlab = "Exposure",
  plot.ylab = "Outcome",
  methods.plot = c("IVW", "RAPS", "Egger", "PRESSO", "Horse", "GRIP"),
  NbDistribution_presso = 1000,
  SignifThreshold_presso = 0.05,
  mr_horse_n_iter = 5000,
  mr_horse_n_burnin = 1000,
  show.legend = TRUE,
  summary_df = NULL,
  effect_scale = "Beta",
  use_df_results = TRUE
)

Arguments

MR_input_data

Harmonised MR input data frame. Must contain Outcome and Exposure columns.

plot.xlab

Character string; prefix for the x-axis label. Default is "Exposure".

plot.ylab

Character string; prefix for the y-axis label. Default is "Outcome".

methods.plot

Character vector of MR methods to overlay as regression lines. Supported values: "IVW", "RAPS", "Egger", "PRESSO", "Horse", "GRIP".

NbDistribution_presso

Integer; number of simulated distributions for on-the-fly MR-PRESSO calculation. Default is 1000.

SignifThreshold_presso

Numeric; significance threshold for on-the-fly MR-PRESSO outlier test. Default is 0.05.

mr_horse_n_iter

Integer; number of Markov chain Monte Carlo (MCMC) iterations for on-the-fly MR-Horse. Default is 5000.

mr_horse_n_burnin

Integer; number of MCMC burn-in samples for on-the-fly MR-Horse. Default is 1000.

show.legend

Logical; whether to annotate each plot with method labels, beta estimates, and p-values. Default is TRUE.

summary_df

Optional data frame of pre-calculated results from run_mr_analysis(). When supplied together with use_df_results = TRUE, avoids re-running the analysis.

effect_scale

Character string matching the scale used in summary_df: "Beta", "OR", or "HR". Default is "Beta".

use_df_results

Logical; if TRUE and summary_df is provided, method slopes are read from summary_df instead of being re-calculated. Default is TRUE.

Value

An MRScatterPlots object containing one recordedplot per outcome-exposure pair, together with outcome and exposure metadata. Use export_scatter_plots() to write plots to disk with optional filtering by outcome, exposure, or both.

Examples

data("fi_49item")
input1  <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(
  MR_input_data     = input1,
  outcome.form      = "Beta",
  use_ivw           = TRUE,
  use_raps          = FALSE,
  use_median        = FALSE,
  use_egger         = FALSE,
  use_mr_presso     = FALSE,
  use_mr_horse      = FALSE,
  use_mr_grip       = FALSE,
  NbDistribution    = 1000,
  SignifThreshold   = 0.05,
  mr_horse_n_iter   = 5000,
  mr_horse_n_burnin = 1000,
  mr_grip_parameters = NULL
)

# Pass pre-calculated results to avoid rerunning the analysis
plots <- plot_mr_scatter(
  MR_input_data  = input1,
  summary_df     = outcome1,
  use_df_results = TRUE
)

# Inspect the object; for fi_49item this prints:
#   [1] fi_49item :: Zn

# Retrieve the exact outcome/exposure labels stored in the object
out_name <- plots@outcomes[1]   # "fi_49item"
exp_name <- plots@exposures[1]  # "Zn"

# Export all plots as PDF (commented — writes to disk)
export_scatter_plots(plots, save_dir = tempdir(), file_type = "pdf")

# Export one outcome only
export_scatter_plots(plots, save_dir = tempdir(), outcome = out_name)

# Export one exposure only
export_scatter_plots(plots, save_dir = tempdir(), exposure = exp_name)

# Export one specific pair
# export_scatter_plots(plots, save_dir = tempdir(), outcome = out_name, exposure = exp_name)

# Export as PNG instead
export_scatter_plots(plots, save_dir = tempdir(), file_type = "png")

Run MR Analysis for Multiple Outcomes

Description

Performs causal inference analysis using multiple Mendelian Randomization (MR) methods across one or more outcomes and exposures. Returns a combined results data frame. To save the output, use standard R functions such as write.csv() or saveRDS() on the returned object.

Usage

run_mr_analysis(
  MR_input_data,
  outcome.form = NULL,
  use_ivw = TRUE,
  use_raps = TRUE,
  use_median = TRUE,
  use_egger = TRUE,
  use_mr_presso = TRUE,
  use_mr_horse = TRUE,
  use_mr_grip = TRUE,
  NbDistribution = 1000,
  SignifThreshold = 0.05,
  mr_horse_n_iter = 5000,
  mr_horse_n_burnin = 1000,
  mr_grip_parameters = NULL
)

Arguments

MR_input_data

Harmonised MR input data frame. Must contain Outcome and Exposure columns.

outcome.form

Character vector indicating the effect scale for each outcome: "Beta", "OR" (odds ratio), or "HR" (hazard ratio). A single value is recycled across all outcomes. Defaults to "Beta".

use_ivw

Logical; whether to run the Inverse Variance Weighted (IVW) method. Default is TRUE.

use_raps

Logical; whether to run the Robust Adjusted Profile Score (MR-RAPS) method. Default is TRUE.

use_median

Logical; whether to run the Weighted Median method. Default is TRUE.

use_egger

Logical; whether to run MR-Egger regression. Default is TRUE.

use_mr_presso

Logical; whether to run the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method. Default is TRUE.

use_mr_horse

Logical; whether to run the MR-Horse method. Default is TRUE.

use_mr_grip

Logical; whether to run the Generalized Regression with Instrument Pairs (MR-GRIP) method. Default is TRUE.

NbDistribution

Integer; number of simulated distributions for MR-PRESSO. Default is 1000.

SignifThreshold

Numeric; significance threshold for the MR-PRESSO outlier test. Default is 0.05.

mr_horse_n_iter

Integer; number of Markov chain Monte Carlo (MCMC) iterations for MR-Horse. Default is 5000.

mr_horse_n_burnin

Integer; number of MCMC burn-in samples for MR-Horse. Default is 1000.

mr_grip_parameters

List of additional parameters passed to MR-GRIP. If NULL, default parameters are used.

Value

A data frame combining results across all outcomes and exposures. Each row represents one outcome-exposure pair. Columns include estimates, confidence intervals (CI), and p-values for each method, together with diagnostic flags (e.g., F-statistic below 10, significant heterogeneity). Use write.csv() or saveRDS() to save the returned object.

Examples

data("fi_49item")
input1 <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(
  MR_input_data     = input1,
  outcome.form      = "Beta",
  use_ivw           = TRUE,
  use_raps          = FALSE,
  use_median        = FALSE,
  use_egger         = FALSE,
  use_mr_presso     = FALSE,
  use_mr_horse      = FALSE,
  use_mr_grip       = FALSE,
  NbDistribution    = 1000,
  SignifThreshold   = 0.05,
  mr_horse_n_iter   = 5000,
  mr_horse_n_burnin = 1000,
  mr_grip_parameters = NULL
)


data("fried_frailty")
input2 <- harmonize_mr_data(df = fried_frailty)
outcome2 <- run_mr_analysis(
  MR_input_data     = input2,
  outcome.form      = "OR",
  use_ivw           = TRUE,
  use_raps          = TRUE,
  use_median        = TRUE,
  use_egger         = TRUE,
  use_mr_presso     = TRUE,
  use_mr_horse      = TRUE,
  use_mr_grip       = TRUE,
  NbDistribution    = 1000,
  SignifThreshold   = 0.05,
  mr_horse_n_iter   = 5000,
  mr_horse_n_burnin = 1000,
  mr_grip_parameters = NULL
)

Display MR Plots on Screen

Description

Renders plots stored in an MRScatterPlots, GWASForestPlots, or MRForestPlots object to the active graphics device. You can display all plots or filter to a specific outcome, exposure, or outcome-exposure pair.

Usage

showplot(object, outcome = NULL, exposure = NULL)

## S4 method for signature 'MRScatterPlots'
showplot(object, outcome = NULL, exposure = NULL)

## S4 method for signature 'GWASForestPlots'
showplot(object, outcome = NULL, exposure = NULL)

## S4 method for signature 'MRForestPlots'
showplot(object, outcome = NULL, exposure = NULL)

Arguments

object

An MRScatterPlots, GWASForestPlots, or MRForestPlots object.

outcome

Optional character string; if supplied, only plots whose outcome matches this value are displayed.

exposure

Optional character string; if supplied, only plots whose exposure matches this value are displayed.

Details

Filtering logic:

Value

Invisibly returns object.

Methods (by class)

Examples


data("fi_49item")
input1   <- harmonize_mr_data(df = fi_49item)
outcome1 <- run_mr_analysis(MR_input_data = input1)

# Scatter plots
scatter <- plot_mr_scatter(MR_input_data = input1, summary_df = outcome1)
showplot(scatter)
showplot(scatter, outcome = "fi_49item")
showplot(scatter, outcome = "fi_49item", exposure = "Zn")

# GWAS forest plots
gwas_plots <- GWAS_forest(MR_input_data = input1, report_form = "Beta")
showplot(gwas_plots)
showplot(gwas_plots, exposure = "Zn")

# MR forest plots
mr_plots <- MR_forest(summary_df = outcome1, effect = "Beta")
showplot(mr_plots)
showplot(mr_plots, outcome = "fi_49item", exposure = "Zn")