resp_surf: resp_surf: Robust Response Surface Analysis
In jimmyrigby94/rrs: rrs: Robust Response Surface Analysis

Description Usage Arguments Value Examples

View source: R/resp_surf.R

resp_surf: Robust Response Surface Analysis

resp_surf(
  dep_var = NULL,
  fit_var = NULL,
  control = NULL,
  data = NULL,
  robust = FALSE,
  cluster = NULL
)

`dep_var`	Character value containing the name of response surface analysis (character value).
`fit_var`	Character vector containing the names of X1 and X2 for response surface analysis.
`control`	Character vector containing the names of the covariates included in the model.
`data`	data frame containing all variables in the model.
`robust`	Logical value answering "Should robust standard errors be used?"
`cluster`	If clustered, what is the ID variable associated with the cluster?

dif_tab: a table of counting the frequency that observations fall into congruent and noncongruent quadrants.

results: A tidy data frame containing the regression model results

loi: A tidy data frame containing the linear and quadratic terms for lines of interest

stat_pnt: The stationary point on the surface.

princ_axis: the principle axes of the response surface

model: the raw lm model used to generate the above information.

equation: the equation generated based on the users arguments.

# 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)

# Defining sig_hat directly to save time for example
sig_hat <- 0.9549575  

# 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")

# Fitting a Response Surface Model

model_1<-resp_surf(dep_var = "Satisfaction", 
                   fit_var = c("L_Agree", "F_Agree"), 
                   data = simmed_df, 
                   robust = FALSE)