Package index • TwoSampleMR

All functions

Isq(): I-squared calculation

add_metadata(): Add meta data to extracted data

add_rsq(): Estimate r-square of each association

allele_frequency(): Estimate allele frequency from SNP

available_outcomes(): Get list of studies with available GWAS summary statistics through API

clump_data(): Perform LD clumping on SNP data

combine_all_mrresults(): Combine all mr results

combine_data(): Combine data

contingency(): Obtain 2x2 contingency table from marginal parameters and odds ratio

convert_outcome_to_exposure(): Convert outcome data to exposure data

dat_to_MRInput(): Convert TwoSampleMR format to MendelianRandomization format

dat_to_RadialMR(): Convert dat to RadialMR format

default_parameters(): List of parameters for use with MR functions

directionality_test(): Perform MR Steiger test of directionality

effective_n(): Estimate the effective sample size in a case control study

enrichment(): Perform enrichment analysis

enrichment_method_list(): Get list of available p-value enrichment methods

estimate_trait_sd(): Estimate trait SD by obtaining beta estimates from z-scores and finding the ratio with original beta values

extract_instruments(): Find instruments for use in MR from the MR Base database

extract_outcome_data(): Supply the output from read_exposure_data() and all the SNPs therein will be queried against the requested outcomes in remote database using API.

fishers_combined_test(): Fisher's combined test

forest_plot(): Forest plot for multiple exposures and multiple outcomes

forest_plot_1_to_many(): 1-to-many forest plot

forest_plot_basic2(): A basic forest plot

format_1_to_many(): Format MR results for a 1-to-many forest plot

format_aries_mqtl(): Get data from methylation QTL results

format_data(): Read exposure or outcome data

format_gtex_eqtl(): Get data from eQTL catalog into correct format

format_gwas_catalog(): Get data selected from GWAS catalog into correct format

format_metab_qtls(): Get data from metabolomic QTL results

format_mr_results(): Format MR results for forest plot

format_proteomic_qtls(): Get data from proteomic QTL results

generate_odds_ratios(): Generate odds ratios

get_p_from_r2n(): Calculate p-value from R-squared and sample size

get_population_allele_frequency(): Estimate the allele frequency in population from case/control summary data

get_r_from_bsen(): Estimate R-squared from beta, standard error and sample size

get_r_from_lor(): Estimate proportion of variance of liability explained by SNP in general population

get_r_from_pn(): Calculate variance explained from p-values and sample size

get_se(): Get SE from effect size and p-value

harmonise_data(): Harmonise the alleles and effects between the exposure and outcome

harmonise_ld_dat(): Harmonise LD matrix against summary data

ld_matrix(): Get LD matrix for list of SNPs

ldsc_h2(): Univariate LDSC

ldsc_rg(): Bivariate LDSC

make_dat(): Convenient function to create a harmonised dataset

mr(): Perform all Mendelian randomization tests

mr_density_plot(): Density plot

mr_egger_regression(): Egger's regression for Mendelian randomization

mr_egger_regression_bootstrap(): Run bootstrap to generate standard errors for MR

mr_forest_plot(): Forest plot

mr_funnel_plot(): Funnel plot

mr_heterogeneity(): Get heterogeneity statistics

mr_ivw(): Inverse variance weighted regression

mr_ivw_fe(): Inverse variance weighted regression (fixed effects)

mr_ivw_mre(): Inverse variance weighted regression (multiplicative random effects model)

mr_ivw_radial(): Radial IVW analysis

mr_leaveoneout(): Leave one out sensitivity analysis

mr_leaveoneout_plot(): Plot results from leaveoneout analysis

mr_median(): MR median estimators

mr_meta_fixed(): Perform 2 sample IV using fixed effects meta analysis and delta method for standard errors

mr_meta_fixed_simple(): Perform 2 sample IV using simple standard error

mr_meta_random(): Perform 2 sample IV using random effects meta analysis and delta method for standard errors

mr_method_list(): Get list of available MR methods

mr_mode(): MR mode estimators

mr_moe(): Mixture of experts

mr_penalised_weighted_median(): Penalised weighted median MR

mr_pleiotropy_test(): Test for horizontal pleiotropy in MR analysis

mr_raps(): Robust adjusted profile score

mr_report(): Generate MR report

mr_rucker(): MR Rucker framework

mr_rucker_bootstrap(): Run rucker with bootstrap estimates

mr_rucker_cooksdistance(): MR Rucker with outliers automatically detected and removed

mr_rucker_jackknife(): Run rucker with jackknife estimates

mr_scatter_plot(): Create scatter plot with lines showing the causal estimate for different MR tests

mr_sign(): MR sign test

mr_simple_median(): Simple median method

mr_simple_mode(): MR simple mode estimator

mr_simple_mode_nome(): MR simple mode estimator (NOME)

mr_singlesnp(): Perform 2 sample MR on each SNP individually

mr_steiger(): MR Steiger test of directionality

mr_steiger2(): MR Steiger test of directionality

mr_two_sample_ml(): Maximum likelihood MR method

mr_uwr(): Unweighted regression

mr_wald_ratio(): Perform 2 sample IV using Wald ratio.

mr_weighted_median(): Weighted median method

mr_weighted_mode(): MR weighted mode estimator

mr_weighted_mode_nome(): MR weighted mode estimator (NOME)

mr_wrapper(): Perform full set of MR analyses

mv_basic(): Perform basic multivariable MR

mv_extract_exposures(): Extract exposure variables for multivariable MR

mv_extract_exposures_local(): Attempt to perform MVMR using local data

mv_harmonise_data(): Harmonise exposure and outcome for multivariable MR

mv_ivw(): Perform IVW multivariable MR

mv_lasso_feature_selection(): Apply LASSO feature selection to mvdat object

mv_multiple(): Perform IVW multivariable MR

mv_residual(): Perform basic multivariable MR

mv_subset(): Perform multivariable MR on subset of features

power_prune(): Power prune

read_exposure_data(): Read exposure data

read_outcome_data(): Read outcome data

run_mr_presso(): Wrapper for MR-PRESSO

run_mrmix(): Perform MRMix analysis on harmonised dat object

size.prune(): Size prune

sort_1_to_many(): Sort results for 1-to-many forest plot

split_exposure(): Split exposure column

split_outcome(): Split outcome column

standardise_units(): Try to standardise continuous traits to be in standard deviation units

steiger_filtering(): Steiger filtering function

steiger_sensitivity(): Evaluate the Steiger test's sensitivity to measurement error

subset_on_method(): Subset MR-results on method

trim(): Trim function to remove leading and trailing blank spaces

weighted_median(): Weighted median method

weighted_median_bootstrap(): Calculate standard errors for weighted median method using bootstrap