pairwise_env_int.calc {gnonadd}

Pairwise environmental interaction effects

Description

Given a set of variants and environmental traits, and a single quantitative trait, this function calculates the interaction effect of all possible variant-environmental pairs

Usage

pairwise_env_int.calc(
  qt,
  g,
  env,
  round_imputed = FALSE,
  dominance_term = FALSE,
  square_env = FALSE,
  covariates = as.data.frame(matrix(0, nrow = 0, ncol = 0)),
  variant_names = paste(rep("variant", ncol(g)), as.character(1:ncol(g)), sep = "_"),
  env_names = paste(rep("env", ncol(env)), as.character(1:ncol(env)), sep = "_")
)

Arguments

qt

A numeric vector

g

A matrix, where each colomn represents a variant

env

A matrix, where each row represents an environmental variable

round_imputed

A boolian variable determining whether imputed genotype values should be rounded to the nearest integer in the analysis.

dominance_term

A boolian variable determining whether a dominance term for the variant should be included as a covariates in the analysis

square_env

A boolian variable determining whether the square of the environmental trait should be included as a covariate in the analysis

covariates

A dataframe containing any other covariates that should be used; one column per covariate

variant_names

A list of the names of the variants

env_names

A list of the names of the environmental variables

Value

A dataframe with all possible variant-environmental pairs and their estimated interaction effect

Examples

g_vec <- matrix(0, nrow = 100000, ncol = 3)
freqs <- runif(ncol(g_vec), min = 0, max = 1)
env_vec <- matrix(0, nrow = 100000, ncol = 3)
for(i in 1:ncol(g_vec)){
 g_vec[, i] <- rbinom(100000, 2, freqs[i])
}
for( i in 1:ncol(env_vec)){
 env_vec[, i] <- round(runif(100000,min=0,max=6))
}

qt_vec <- rnorm(100000) + 0.1 * g_vec[, 1] + 0.2 *
          g_vec[, 2] -0.1 * env_vec[, 3] + 0.1 *
          env_vec[, 1] + 0.1 * g_vec[, 1] * env_vec[, 1]
res <- pairwise_env_int.calc(qt_vec, g_vec, env_vec)