Linear regression using OLS
regress( dataset, rvar, evar, int = "", check = "", form, data_filter = "", envir = parent.frame() )
| dataset | Dataset |
|---|---|
| rvar | The response variable in the regression |
| evar | Explanatory variables in the regression |
| int | Interaction terms to include in the model |
| check | Use "standardize" to see standardized coefficient estimates. Use "stepwise-backward" (or "stepwise-forward", or "stepwise-both") to apply step-wise selection of variables in estimation. Add "robust" for robust estimation of standard errors (HC1) |
| form | Optional formula to use instead of rvar, evar, and int |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
| envir | Environment to extract data from |
A list of all variables used in the regress function as an object of class regress
See https://radiant-rstats.github.io/docs/model/regress.html for an example in Radiant
summary.regress to summarize results
plot.regress to plot results
predict.regress to generate predictions
#> Linear regression (OLS) #> Data : diamonds #> Response variable : price #> Explanatory variables: carat, clarity #> Null hyp.: the effect of x on price is zero #> Alt. hyp.: the effect of x on price is not zero #> **Standardized coefficients shown (2 X SD)** #> #> coefficient std.error t.value p.value #> (Intercept) -0.504 0.025 -20.379 < .001 *** #> carat 1.010 0.006 165.125 < .001 *** #> clarity|SI2 0.353 0.025 13.857 < .001 *** #> clarity|SI1 0.456 0.025 17.997 < .001 *** #> clarity|VS2 0.537 0.025 21.080 < .001 *** #> clarity|VS1 0.564 0.026 21.809 < .001 *** #> clarity|VVS2 0.646 0.027 24.307 < .001 *** #> clarity|VVS1 0.635 0.027 23.466 < .001 *** #> clarity|IF 0.665 0.030 22.534 < .001 *** #> #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> R-squared: 0.904, Adjusted R-squared: 0.904 #> F-statistic: 3530.024 df(8,2991), p.value < .001 #> Nr obs: 3,000 #>#> List of 12 #> $ coeff :'data.frame': 9 obs. of 6 variables: #> ..$ label : chr [1:9] "(Intercept)" "carat" "clarity|SI2" "clarity|SI1" ... #> ..$ coefficient: num [1:9] -6781 8438 2791 3609 4250 ... #> ..$ std.error : num [1:9] 205 51.1 201.4 200.5 201.6 ... #> ..$ t.value : num [1:9] -33.1 165.1 13.9 18 21.1 ... #> ..$ p.value : num [1:9] 7.76e-205 0.00 2.28e-42 7.76e-69 4.38e-92 ... #> ..$ sig_star : chr [1:9] "***" "***" "***" "***" ... #> $ model :List of 13 #> ..$ coefficients : Named num [1:9] -6781 8438 2791 3609 4250 ... #> .. ..- attr(*, "names")= chr [1:9] "(Intercept)" "carat" "claritySI2" "claritySI1" ... #> ..$ residuals : Named num [1:3000] 199 954 630 -576 236 ... #> .. ..- attr(*, "names")= chr [1:3000] "1" "2" "3" "4" ... #> ..$ effects : Named num [1:3000] -214005 200946 -26340 -18929 7642 ... #> .. ..- attr(*, "names")= chr [1:3000] "(Intercept)" "carat" "claritySI2" "claritySI1" ... #> ..$ rank : int 9 #> ..$ fitted.values: Named num [1:3000] 381.133 -303.531 0.322 1281.793 844.125 ... #> .. ..- attr(*, "names")= chr [1:3000] "1" "2" "3" "4" ... #> ..$ assign : int [1:9] 0 1 2 2 2 2 2 2 2 #> ..$ qr :List of 5 #> .. ..$ qr : num [1:3000, 1:9] -54.7723 0.0183 0.0183 0.0183 0.0183 ... #> .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. ..$ : chr [1:3000] "1" "2" "3" "4" ... #> .. .. .. ..$ : chr [1:9] "(Intercept)" "carat" "claritySI2" "claritySI1" ... #> .. .. ..- attr(*, "assign")= int [1:9] 0 1 2 2 2 2 2 2 2 #> .. .. ..- attr(*, "contrasts")=List of 1 #> .. .. .. ..$ clarity: chr "contr.treatment" #> .. ..$ qraux: num [1:9] 1.02 1.02 1 1.01 1.03 ... #> .. ..$ pivot: int [1:9] 1 2 3 4 5 6 7 8 9 #> .. ..$ tol : num 0.0000001 #> .. ..$ rank : int 9 #> .. ..- attr(*, "class")= chr "qr" #> ..$ df.residual : int 2991 #> ..$ contrasts :List of 1 #> .. ..$ clarity: chr "contr.treatment" #> ..$ xlevels :List of 1 #> .. ..$ clarity: chr [1:8] "I1" "SI2" "SI1" "VS2" ... #> ..$ call : language lm(formula = form_upper, data = dataset) #> ..$ terms :Classes 'terms', 'formula' language price ~ carat + clarity #> .. .. ..- attr(*, "variables")= language list(price, carat, clarity) #> .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 #> .. .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. .. ..$ : chr [1:3] "price" "carat" "clarity" #> .. .. .. .. ..$ : chr [1:2] "carat" "clarity" #> .. .. ..- attr(*, "term.labels")= chr [1:2] "carat" "clarity" #> .. .. ..- attr(*, "order")= int [1:2] 1 1 #> .. .. ..- attr(*, "intercept")= int 1 #> .. .. ..- attr(*, "response")= int 1 #> .. .. ..- attr(*, ".Environment")=<environment: 0xb8682b0> #> .. .. ..- attr(*, "predvars")= language list(price, carat, clarity) #> .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "factor" #> .. .. .. ..- attr(*, "names")= chr [1:3] "price" "carat" "clarity" #> ..$ model :'data.frame': 3000 obs. of 3 variables: #> .. ..$ price : int [1:3000] 580 650 630 706 1080 3082 3328 4229 1895 3546 ... #> .. ..$ carat : num [1:3000] 0.32 0.34 0.3 0.35 0.4 0.6 0.88 0.93 0.51 1.01 ... #> .. ..$ clarity: Factor w/ 8 levels "I1","SI2","SI1",..: 5 3 4 6 4 7 3 3 6 2 ... #> .. ..- attr(*, "terms")=Classes 'terms', 'formula' language price ~ carat + clarity #> .. .. .. ..- attr(*, "variables")= language list(price, carat, clarity) #> .. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 #> .. .. .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. .. .. ..$ : chr [1:3] "price" "carat" "clarity" #> .. .. .. .. .. ..$ : chr [1:2] "carat" "clarity" #> .. .. .. ..- attr(*, "term.labels")= chr [1:2] "carat" "clarity" #> .. .. .. ..- attr(*, "order")= int [1:2] 1 1 #> .. .. .. ..- attr(*, "intercept")= int 1 #> .. .. .. ..- attr(*, "response")= int 1 #> .. .. .. ..- attr(*, ".Environment")=<environment: 0xb8682b0> #> .. .. .. ..- attr(*, "predvars")= language list(price, carat, clarity) #> .. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "factor" #> .. .. .. .. ..- attr(*, "names")= chr [1:3] "price" "carat" "clarity" #> ..- attr(*, "class")= chr "lm" #> $ mmx :List of 2 #> ..$ min: tibble [1 × 2] (S3: tbl_df/tbl/data.frame) #> .. ..$ price: int 338 #> .. ..$ carat: num 0.2 #> ..$ max: tibble [1 × 2] (S3: tbl_df/tbl/data.frame) #> .. ..$ price: int 18791 #> .. ..$ carat: num 3 #> $ vars : chr [1:2] "carat" "clarity" #> $ not_vary : chr(0) #> $ df_name : chr "diamonds" #> $ rvar : chr "price" #> $ evar : chr [1:2] "carat" "clarity" #> $ int : chr "" #> $ check : chr "" #> $ form : symbol #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:3] "regress" "model" "list"