tests/testthat/_snaps/bp-output.md

In rsquaredacademy/olsrr: Tools for Building OLS Regression Models

when fitted.values == TRUE, fitted vals from reg are used

Code
  ols_test_breusch_pagan(model)
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: fitted values of mpg

          Test Summary         
   ----------------------------
   DF            =    1 
   Chi2          =    1.255517 
   Prob > Chi2   =    0.2625014

Code
  ols_test_breusch_pagan(model, rhs = TRUE)
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

          Test Summary         
   ----------------------------
   DF            =    5 
   Chi2          =    2.489028 
   Prob > Chi2   =    0.7781466

Code
  ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

          Test Summary (Unadjusted p values)       
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.3364977 
    hp               0.7652006     1    0.3817059 
    wt               0.7748714     1    0.3787143 
    drat             0.7751270     1    0.3786356 
    qsec             1.2902861     1    0.2559952 
   ----------------------------------------------
    simultaneous     2.4890277     5    0.7781466 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, fitted.values = FALSE, rhs = TRUE, multiple = TRUE)
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

          Test Summary (Unadjusted p values)       
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.3364977 
    hp               0.7652006     1    0.3817059 
    wt               0.7748714     1    0.3787143 
    drat             0.7751270     1    0.3786356 
    qsec             1.2902861     1    0.2559952 
   ----------------------------------------------
    simultaneous     2.4890277     5    0.7781466 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = "bonferroni")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

          Test Summary (Bonferroni p values)       
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    1.0000000 
    hp               0.7652006     1    1.0000000 
    wt               0.7748714     1    1.0000000 
    drat             0.7751270     1    1.0000000 
    qsec             1.2902861     1    1.0000000 
   ----------------------------------------------
    simultaneous     2.4890277     5    0.7781466 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = "holm")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

            Test Summary (Holm's p values)         
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    1.0000000 
    hp               0.7652006     1    0.3817059 
    wt               0.7748714     1    0.7574285 
    drat             0.7751270     1    1.0000000 
    qsec             1.2902861     1    1.0000000 
   ----------------------------------------------
    simultaneous     2.4890277     5    0.7781466 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = "sidak")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

               Data               
   -------------------------------
   Response : mpg 
   Variables: disp hp wt drat qsec

            Test Summary (Sidak p values)          
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.8714086 
    hp               0.7652006     1    0.9096401 
    wt               0.7748714     1    0.9074328 
    drat             0.7751270     1    0.9073743 
    qsec             1.2902861     1    0.7720295 
   ----------------------------------------------
    simultaneous     2.4890277     5    0.7781466 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, vars = c("disp"))
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

       Data       
   ---------------
   Response : mpg 
   Variables: disp

          Test Summary         
   ----------------------------
   DF            =    1 
   Chi2          =    0.9237291 
   Prob > Chi2   =    0.3364977

Code
  ols_test_breusch_pagan(model, multiple = TRUE, rhs = FALSE, vars = c("disp",
    "hp"))
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

         Data        
   ------------------
   Response : mpg 
   Variables: disp hp

          Test Summary (Unadjusted p values)       
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.3364977 
    hp               0.7652006     1    0.3817059 
   ----------------------------------------------
    simultaneous     0.9587887     2    0.6191583 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, multiple = TRUE, vars = c("disp", "hp"), p.adj = "bonferroni")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

         Data        
   ------------------
   Response : mpg 
   Variables: disp hp

          Test Summary (Bonferroni p values)       
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.6729955 
    hp               0.7652006     1    0.7634118 
   ----------------------------------------------
    simultaneous     0.9587887     2    0.6191583 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, multiple = TRUE, vars = c("disp", "hp"), p.adj = "sidak")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

         Data        
   ------------------
   Response : mpg 
   Variables: disp hp

            Test Summary (Sidak p values)          
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.5597648 
    hp               0.7652006     1    0.6177124 
   ----------------------------------------------
    simultaneous     0.9587887     2    0.6191583 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, multiple = TRUE, vars = c("disp", "hp"), p.adj = "holm")
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

         Data        
   ------------------
   Response : mpg 
   Variables: disp hp

            Test Summary (Holm's p values)         
   ----------------------------------------------
    Variable           chi2       df        p     
   ----------------------------------------------
    disp             0.9237291     1    0.6729955 
    hp               0.7652006     1    0.3817059 
   ----------------------------------------------
    simultaneous     0.9587887     2    0.6191583 
   ----------------------------------------------

Code
  ols_test_breusch_pagan(model, multiple = TRUE, rhs = FALSE, vars = c("disp"))
Output

   Breusch Pagan Test for Heteroskedasticity
   -----------------------------------------
   Ho: the variance is constant            
   Ha: the variance is not constant

       Data       
   ---------------
   Response : mpg 
   Variables: disp

          Test Summary         
   ----------------------------
   DF            =    1 
   Chi2          =    0.9237291 
   Prob > Chi2   =    0.3364977

rsquaredacademy/olsrr documentation built on Nov. 8, 2024, 5:01 a.m.