gen_response_surf_y: gen_response_surf_y: Generate DV under response surface model

In jimmyrigby94/rrs: rrs: Robust Response Surface Analysis

Description

gen_response_surf_y: Generate DV under response surface model

Usage

gen_response_surf_y(x_data, beta, sigma = NULL, y_name = NULL)

Arguments

x_data	IV data typically generated by gen_response_surf_x()
beta	Vector of regression weights in the order of x_data. If generated by gen_response_surf_x(), this order is X1, X2, X1^2, X2^2, X1*X2.
sigma	Error variance of outcome variable (optional). If NULL taken to be 0.
y_name	name of y variable (optional).

Value

data frame with x and y variables given simulation parameters.

Examples

# Importing for magrittr pipe (%>%)
library(tidyverse)
# Defining Correlation Matrix describing how x1 and x2 are related
# Covarince and variance of x1^2, x2^2, and x1*x2 follow from this matrix
cov_mat<-matrix(c(1, 0,
                  0, 1), byrow = TRUE, 2, 2)

# Defining betas x1, x2, x1^2, x2^2, and x1*x2
beta<-c(0, 0, -.075, -.075, .15)

# Simulating 10,000 draws of size 1000 assuming the correlation structure and regression weights defined above.
sig_hat <- find_sig(n = 1000, cov_mat = cov_mat, beta = beta, target_var_y = 1)    

# Generating data frame for response suface examining leaders and follower agreeableness
simmed_df<-gen_response_surf_x(1000, cov_mat, x_names = c("L_Agree", "F_Agree"))%>%
  gen_response_surf_y(beta = beta, sigma = sig_hat, y_name = "Satisfaction")

head(simmed_df)

# Variance of simmed_df approximates target_var_y (defined in find_sig above)
# Population level variance will be even closer
var(simmed_df$Satisfaction)

jimmyrigby94/rrs documentation built on May 12, 2020, 3:41 p.m.