Description Usage Arguments Value Examples
resp_surf: Robust Response Surface Analysis
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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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # 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)
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