aravindhebbali
commented
6 years ago hi @elielw , are you suggesting something similar to this issue. May be ols_stepwise() can return a tibble with F and Sig. for each predictor after each step.
Closed elielw closed 6 years ago
aravindhebbali
commented
6 years ago hi @elielw , are you suggesting something similar to this issue. May be ols_stepwise() can return a tibble with F and Sig. for each predictor after each step.
elielw
commented
6 years ago Yes I think it's similar to this issue
aravindhebbali
commented
6 years ago hi @elielw, let me know if the below fix works.
library(olsrr)
#>
#> Attaching package: 'olsrr'
#> The following object is masked from 'package:datasets':
#>
#> rivers
# model
fit1 <- lm(y ~ ., olsrr::stepdata)
# stepwise
k <- ols_stepwise(fit1)
#> Stepwise Selection Method
#> ---------------------------
#>
#> Candidate Terms:
#>
#> 1. x1
#> 2. x2
#> 3. x3
#> 4. x4
#> 5. x5
#> 6. x6
#>
#> We are selecting variables based on p value...
#>
#> Variables Entered/Removed:
#>
#> - x6 added
#> - x1 added
#> - x3 added
#> - x2 added
#> - x6 added
#> - x4 added
#>
#> No more variables to be added/removed.
#>
#>
#> Final Model Output
#> ------------------
#>
#> Model Summary
#> ----------------------------------------------------------------
#> R 0.866 RMSE 0.503
#> R-Squared 0.750 Coef. Var 6430.859
#> Adj. R-Squared 0.750 MSE 0.253
#> Pred R-Squared 0.749 MAE 0.402
#> ----------------------------------------------------------------
#> RMSE: Root Mean Square Error
#> MSE: Mean Square Error
#> MAE: Mean Absolute Error
#>
#> ANOVA
#> ---------------------------------------------------------------------------
#> Sum of
#> Squares DF Mean Square F Sig.
#> ---------------------------------------------------------------------------
#> Regression 15163.528 4 3790.882 14966.061 0.0000
#> Residual 5064.705 19995 0.253
#> Total 20228.233 19999
#> ---------------------------------------------------------------------------
#>
#> Parameter Estimates
#> ---------------------------------------------------------------------------------------
#> model Beta Std. Error Std. Beta t Sig lower upper
#> ---------------------------------------------------------------------------------------
#> (Intercept) -0.005 0.004 -1.496 0.135 -0.012 0.002
#> x1 0.255 0.003 0.362 84.140 0.000 0.249 0.261
#> x3 0.253 0.003 0.356 82.604 0.000 0.247 0.259
#> x2 0.249 0.003 0.346 80.544 0.000 0.243 0.255
#> x4 -0.007 0.004 -0.007 -1.872 0.061 -0.014 0.000
#> ---------------------------------------------------------------------------------------
# betas and pvalues for each step
k$beta_pval
#> # A tibble: 23 x 4
#> model predictor beta pval
#> <int> <chr> <dbl> <dbl>
#> 1 1 (Intercept) -0.00365 3.56e- 1
#> 2 1 x6 0.233 0
#> 3 2 (Intercept) -0.00418 2.83e- 1
#> 4 2 x6 0.201 0
#> 5 2 x1 0.103 3.36e-120
#> 6 3 (Intercept) -0.00465 2.21e- 1
#> 7 3 x6 0.142 0
#> 8 3 x1 0.148 4.41e-230
#> 9 3 x3 0.146 5.39e-224
#> 10 4 (Intercept) -0.00535 1.33e- 1
#> # ... with 13 more rowsthank you!
Is it possible to retrieve these values? I can only find R-square, AIC, etc, but not F and Sig.