Ordinary least squares regression.

ols_regress(object, ...)

# S3 method for lm
ols_regress(object, ...)

Arguments

object

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted or class lm.

...

Other inputs.

Value

ols_regress returns an object of class "ols_regress". An object of class "ols_regress" is a list containing the following components:

r

square root of rsquare, correlation between observed and predicted values of dependent variable

rsq

coefficient of determination or r-square

adjr

adjusted rsquare

sigma

root mean squared error

cv

coefficient of variation

mse

mean squared error

mae

mean absolute error

aic

akaike information criteria

sbc

bayesian information criteria

sbic

sawa bayesian information criteria

prsq

predicted rsquare

error_df

residual degrees of freedom

model_df

regression degrees of freedom

total_df

total degrees of freedom

ess

error sum of squares

rss

regression sum of squares

tss

total sum of squares

rms

regression mean square

ems

error mean square

f

f statistis

p

p-value for f

n

number of predictors including intercept

betas

betas; estimated coefficients

sbetas

standardized betas

std_errors

standard errors

tvalues

t values

pvalues

p-value of tvalues

df

degrees of freedom of betas

conf_lm

confidence intervals for coefficients

title

title for the model

dependent

character vector; name of the dependent variable

predictors

character vector; name of the predictor variables

mvars

character vector; name of the predictor variables including intercept

model

input model for ols_regress

Interaction Terms

If the model includes interaction terms, the standardized betas are computed after scaling and centering the predictors.

References

https://www.ssc.wisc.edu/~hemken/Stataworkshops/stdBeta/Getting

Examples

ols_regress(mpg ~ disp + hp + wt, data = mtcars)
#> Model Summary #> -------------------------------------------------------------- #> R 0.909 RMSE 2.639 #> R-Squared 0.827 Coef. Var 13.135 #> Adj. R-Squared 0.808 MSE 6.964 #> Pred R-Squared 0.768 MAE 1.907 #> -------------------------------------------------------------- #> RMSE: Root Mean Square Error #> MSE: Mean Square Error #> MAE: Mean Absolute Error #> #> ANOVA #> -------------------------------------------------------------------- #> Sum of #> Squares DF Mean Square F Sig. #> -------------------------------------------------------------------- #> Regression 931.057 3 310.352 44.566 0.0000 #> Residual 194.991 28 6.964 #> Total 1126.047 31 #> -------------------------------------------------------------------- #> #> Parameter Estimates #> ---------------------------------------------------------------------------------------- #> model Beta Std. Error Std. Beta t Sig lower upper #> ---------------------------------------------------------------------------------------- #> (Intercept) 37.106 2.111 17.579 0.000 32.782 41.429 #> disp -0.001 0.010 -0.019 -0.091 0.929 -0.022 0.020 #> hp -0.031 0.011 -0.354 -2.724 0.011 -0.055 -0.008 #> wt -3.801 1.066 -0.617 -3.565 0.001 -5.985 -1.617 #> ----------------------------------------------------------------------------------------
# if model includes interaction terms set iterm to TRUE ols_regress(mpg ~ disp * wt, data = mtcars, iterm = TRUE)
#> Model Summary #> -------------------------------------------------------------- #> R 0.922 RMSE 2.455 #> R-Squared 0.850 Coef. Var 12.219 #> Adj. R-Squared 0.834 MSE 6.027 #> Pred R-Squared 0.801 MAE 1.897 #> -------------------------------------------------------------- #> RMSE: Root Mean Square Error #> MSE: Mean Square Error #> MAE: Mean Absolute Error #> #> ANOVA #> -------------------------------------------------------------------- #> Sum of #> Squares DF Mean Square F Sig. #> -------------------------------------------------------------------- #> Regression 957.299 3 319.100 52.947 0.0000 #> Residual 168.749 28 6.027 #> Total 1126.047 31 #> -------------------------------------------------------------------- #> #> Parameter Estimates #> ---------------------------------------------------------------------------------------- #> model Beta Std. Error Std. Beta t Sig lower upper #> ---------------------------------------------------------------------------------------- #> (Intercept) 44.082 3.123 14.115 0.000 37.685 50.479 #> disp -0.056 0.013 -0.385 -4.257 0.000 -0.083 -0.029 #> wt -6.496 1.313 -0.616 -4.946 0.000 -9.186 -3.805 #> disp:wt 0.012 0.003 0.278 3.596 0.001 0.005 0.018 #> ----------------------------------------------------------------------------------------