umx_make_MR_data {umx}

Simulate Mendelian Randomization data

Description

umx_make_MR_data returns a dataset containing 4 variables: A variable of interest (Y), a putative cause (X), a qtl (quantitative trait locus) influencing X, and a confounding variable (U) affecting both X and Y.

Usage

umx_make_MR_data(
  nSubjects = 1000,
  Vqtl = 0.02,
  bXY = 0.1,
  bUX = 0.5,
  bUY = 0.5,
  pQTL = 0.5,
  seed = 123
)

Arguments

nSubjects

Number of subjects in sample

Vqtl

Variance of QTL affecting causal variable X (Default 0.02)

bXY

Causal effect of X on Y (Default 0.1)

bUX

Confounding effect of confounder 'U' on X (Default 0.5)

bUY

Confounding effect of confounder 'U' on Y (Default 0.5)

pQTL

Decreaser allele frequency (Default 0.5)

seed

value for the random number generator (Default 123)

Details

The code to make these Data. Modified from Dave Evans 2016 Boulder workshop talk.

Value

- data.frame

See Also

umx_make_TwinData

Other Data Functions: noNAs(), prolific_anonymize(), prolific_check_ID(), prolific_read_demog(), umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polychoric(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric(), umx

Examples

df = umx_make_MR_data(10000)
str(df)
## Not run: 
m1 = umxTwoStage(Y ~ X, ~qtl, data = df)
plot(m1)

## End(Not run)