Function reference • simulateGP

All functions
`add_u()`	Add confounder variables and their instruments
`allele_frequency()`	Estimate allele frequency from SNP
`arbitrary_map()`	Create an arbitrary map
`ascertain_samples()`	Ascertain some proportion of cases and controls from binary phenotype
`choose_effects()`	Get vector of effects that explain some amount of variance
`contingency()`	Obtain 2x2 contingency table from marginal parameters and odds ratio
`correlated_binomial()`	Simulate two correlated binomial variables
`create_system()`	Wrapper for simulation pipeline
`estimate_system_effects()`	Estimate the effects of all SNPs on all phenotypes
`expected_mse()`	Calculate expected MSE
`expected_se()`	Expected se given beta, af, n and vy
`expected_ssx()`	Calculate expected SSX
`fast_assoc()`	Get summary statistics in simple linear regression
`generate_gwas_params()`	Generate SNP effects given MAF, h2 and selection
`generate_gwas_ss()`	Modify SNP effects to account for LD
`generate_gwas_ss_1()`	Create a GWAS summary dataset
`generate_ldobj()`	Generate LD matrix objects from reference panel
`get_effs()`	Get effs for two traits and make dat format
`get_ld()`	Get LD matrix for a specified region from bfile reference panel
`get_population_allele_frequency()`	Estimate the allele frequency in population from case/control summary data
`get_regions_from_ldobjdir()`	Determine regions from LD file
`gwas()`	Perform association of many SNPs against phenotype
`gx_to_gp()`	Translate risk from liability to probability scale
`hap_freqs()`	Estimate haplotype frequencies for two loci
`init_parameters()`	Choose initial parameters for direct effects on X and Y
`ldetect`	Data frame of independent LD regions
`lor_to_rsq()`	Estimate proportion of variance of liability explained by SNP in general population
`make_geno()`	Create genotype matrix
`make_mvdat()`	Take several exposures and one outcome and make the data required for multivariable MR
`make_phen()`	Simulate variable based on the influences of other variables
`merge_exp_out()`	Organise outputs from `gwas` into harmonised dat format
`range01()`	Scale variable to have range between 0 and 1
`read_ldobjdir()`	Read in LD objects into list
`recode_dat()`	Recode data to make every effect on x positive
`recode_dat_intercept()`	Intercept recoding to have every effect on x positive
`recode_dat_simple()`	Simple recoding to have every effect on x positive
`risk_cross_plot()`	Plot liability vs probability disease risk
`risk_simulation()`	Make simulation to compare disease and liability scales
`sample_beta()`	Sample beta values given standard errors
`sample_system_effects()`	Sample the actual effects based on initial parameters
`simulateGP-package` `simulateGP` `simulategp`	simulateGP: Functions for Simulating Genotype-Phenotype Relationships
`simulate_geno()`	Simulate genotypes from haplotypes
`simulate_haplotypes()`	Simulate haplotypes of two loci
`simulate_population()`	Simulate individual level data from initial parameters
`stuff`	General funcs
`summary_set()`	Wrapper for generating a summary set
`test_ldobj()`	Create test LD object
`test_system()`	Apply MR tests to system
`y_to_binary()`	Convert continuous trait to binary

Reference

All functions