Logistic regression
logistic( dataset, rvar, evar, lev = "", int = "", wts = "None", check = "", form, ci_type, data_filter = "", envir = parent.frame() )
| dataset | Dataset |
|---|---|
| rvar | The response variable in the model |
| evar | Explanatory variables in the model |
| lev | The level in the response variable defined as _success_ |
| int | Interaction term to include in the model |
| wts | Weights to use in estimation |
| 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 |
| ci_type | To use the profile-likelihood (rather than Wald) for confidence intervals use "profile". For datasets with more than 5,000 rows the Wald method will be used, unless "profile" is explicitly set |
| 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 with all variables defined in logistic as an object of class logistic
See https://radiant-rstats.github.io/docs/model/logistic.html for an example in Radiant
summary.logistic to summarize the results
plot.logistic to plot the results
predict.logistic to generate predictions
plot.model.predict to plot prediction output
#> Logistic regression (GLM) #> Data : titanic #> Response variable : survived #> Level : Yes in survived #> Explanatory variables: pclass, sex #> Null hyp.: there is no effect of x on survived #> Alt. hyp.: there is an effect of x on survived #> #> OR OR% coefficient std.error z.value p.value #> (Intercept) 2.151 0.188 11.420 < .001 *** #> pclass|2nd 0.409 -59.1% -0.893 0.208 -4.290 < .001 *** #> pclass|3rd 0.181 -81.9% -1.712 0.191 -8.953 < .001 *** #> sex|male 0.080 -92.0% -2.522 0.163 -15.447 < .001 *** #> #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Pseudo R-squared: 0.281 #> Log-likelihood: -506.562, AIC: 1021.124, BIC: 1040.924 #> Chi-squared: 396.861 df(3), p.value < .001 #> Nr obs: 1,043 #>#> List of 16 #> $ coeff :'data.frame': 4 obs. of 8 variables: #> ..$ label : chr [1:4] "(Intercept)" "pclass|2nd" "pclass|3rd" "sex|male" #> ..$ OR : num [1:4] 8.5966 0.4093 0.1806 0.0803 #> ..$ OR% : num [1:4] 7.597 -0.591 -0.819 -0.92 #> ..$ coefficient: num [1:4] 2.151 -0.893 -1.712 -2.522 #> ..$ std.error : num [1:4] 0.188 0.208 0.191 0.163 #> ..$ z.value : num [1:4] 11.42 -4.29 -8.95 -15.45 #> ..$ p.value : num [1:4] 0.000000000000000000000000000003312134962983916736635286 0.000017893695745503967250034788039059208131220657378435| __truncated__ #> ..$ sig_star : chr [1:4] "***" "***" "***" "***" #> $ model :List of 30 #> ..$ coefficients : Named num [1:4] 2.151 -0.893 -1.712 -2.522 #> .. ..- attr(*, "names")= chr [1:4] "(Intercept)" "pclass2nd" "pclass3rd" "sexmale" #> ..$ residuals : Named num [1:1043] 1.12 2.45 -9.6 -1.69 -9.6 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ fitted.values : Named num [1:1043] 0.896 0.408 0.896 0.408 0.896 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ effects : Named num [1:1043] 4.269 0.522 -5.63 15.447 -2.89 ... #> .. ..- attr(*, "names")= chr [1:1043] "(Intercept)" "pclass2nd" "pclass3rd" "sexmale" ... #> ..$ R : num [1:4, 1:4] -12.8 0 0 0 -3.5 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : chr [1:4] "(Intercept)" "pclass2nd" "pclass3rd" "sexmale" #> .. .. ..$ : chr [1:4] "(Intercept)" "pclass2nd" "pclass3rd" "sexmale" #> ..$ rank : int 4 #> ..$ qr :List of 5 #> .. ..$ qr : num [1:1043, 1:4] -12.81 0.0384 0.0239 0.0384 0.0239 ... #> .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. ..$ : chr [1:1043] "1" "2" "3" "4" ... #> .. .. .. ..$ : chr [1:4] "(Intercept)" "pclass2nd" "pclass3rd" "sexmale" #> .. ..$ rank : int 4 #> .. ..$ qraux: num [1:4] 1.02 1.02 1.03 1.02 #> .. ..$ pivot: int [1:4] 1 2 3 4 #> .. ..$ tol : num 0.00000000001 #> .. ..- attr(*, "class")= chr "qr" #> ..$ family :List of 12 #> .. ..$ family : chr "binomial" #> .. ..$ link : chr "logit" #> .. ..$ linkfun :function (mu) #> .. ..$ linkinv :function (eta) #> .. ..$ variance :function (mu) #> .. ..$ dev.resids:function (y, mu, wt) #> .. ..$ aic :function (y, n, mu, wt, dev) #> .. ..$ mu.eta :function (eta) #> .. ..$ initialize: language { if (NCOL(y) == 1) { ... #> .. ..$ validmu :function (mu) #> .. ..$ valideta :function (eta) #> .. ..$ simulate :function (object, nsim) #> .. ..- attr(*, "class")= chr "family" #> ..$ linear.predictors: Named num [1:1043] 2.151 -0.371 2.151 -0.371 2.151 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ deviance : num 1013 #> ..$ aic : num 1021 #> ..$ null.deviance : num 1410 #> ..$ iter : int 4 #> ..$ weights : Named num [1:1043] 0.0933 0.2416 0.0933 0.2416 0.0933 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ prior.weights : Named num [1:1043] 1 1 1 1 1 1 1 1 1 1 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ df.residual : int 1039 #> ..$ df.null : int 1042 #> ..$ y : Named num [1:1043] 1 1 0 0 0 1 1 0 1 0 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ converged : logi TRUE #> ..$ boundary : logi FALSE #> ..$ model :'data.frame': 1043 obs. of 3 variables: #> .. ..$ survived: logi [1:1043] TRUE TRUE FALSE FALSE FALSE TRUE ... #> .. ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> .. ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> .. ..- attr(*, "terms")=Classes 'terms', 'formula' language survived ~ pclass + sex #> .. .. .. ..- attr(*, "variables")= language list(survived, pclass, sex) #> .. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 #> .. .. .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. .. .. ..$ : chr [1:3] "survived" "pclass" "sex" #> .. .. .. .. .. ..$ : chr [1:2] "pclass" "sex" #> .. .. .. ..- attr(*, "term.labels")= chr [1:2] "pclass" "sex" #> .. .. .. ..- attr(*, "order")= int [1:2] 1 1 #> .. .. .. ..- attr(*, "intercept")= int 1 #> .. .. .. ..- attr(*, "response")= int 1 #> .. .. .. ..- attr(*, ".Environment")=<environment: 0x13282680> #> .. .. .. ..- attr(*, "predvars")= language list(survived, pclass, sex) #> .. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "logical" "factor" "factor" #> .. .. .. .. ..- attr(*, "names")= chr [1:3] "survived" "pclass" "sex" #> ..$ call : language glm(formula = form_upper, family = binomial(link = "logit"), data = dataset, weights = wts) #> ..$ formula :Class 'formula' language survived ~ pclass + sex #> .. .. ..- attr(*, ".Environment")=<environment: 0x13282680> #> ..$ terms :Classes 'terms', 'formula' language survived ~ pclass + sex #> .. .. ..- attr(*, "variables")= language list(survived, pclass, sex) #> .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 #> .. .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. .. ..$ : chr [1:3] "survived" "pclass" "sex" #> .. .. .. .. ..$ : chr [1:2] "pclass" "sex" #> .. .. ..- attr(*, "term.labels")= chr [1:2] "pclass" "sex" #> .. .. ..- attr(*, "order")= int [1:2] 1 1 #> .. .. ..- attr(*, "intercept")= int 1 #> .. .. ..- attr(*, "response")= int 1 #> .. .. ..- attr(*, ".Environment")=<environment: 0x13282680> #> .. .. ..- attr(*, "predvars")= language list(survived, pclass, sex) #> .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "logical" "factor" "factor" #> .. .. .. ..- attr(*, "names")= chr [1:3] "survived" "pclass" "sex" #> ..$ data : tibble [1,043 × 3] (S3: tbl_df/tbl/data.frame) #> .. ..$ survived: logi [1:1043] TRUE TRUE FALSE FALSE FALSE TRUE ... #> .. ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> .. ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> .. ..- attr(*, "description")= chr "## Titanic\n\nThis dataset describes the survival status of individual passengers on the Titanic. The titanic d"| __truncated__ #> ..$ offset : NULL #> ..$ control :List of 3 #> .. ..$ epsilon: num 0.00000001 #> .. ..$ maxit : num 25 #> .. ..$ trace : logi FALSE #> ..$ method : chr "glm.fit" #> ..$ contrasts :List of 2 #> .. ..$ pclass: chr "contr.treatment" #> .. ..$ sex : chr "contr.treatment" #> ..$ xlevels :List of 2 #> .. ..$ pclass: chr [1:3] "1st" "2nd" "3rd" #> .. ..$ sex : chr [1:2] "female" "male" #> ..- attr(*, "class")= chr [1:2] "glm" "lm" #> $ mmx : tibble [1,043 × 3] (S3: tbl_df/tbl/data.frame) #> ..$ survived: logi [1:1043] TRUE TRUE FALSE FALSE FALSE TRUE ... #> ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> ..- attr(*, "description")= chr "## Titanic\n\nThis dataset describes the survival status of individual passengers on the Titanic. The titanic d"| __truncated__ #> $ rv : Factor w/ 2 levels "Yes","No": 1 1 2 2 2 1 1 2 1 2 ... #> $ not_vary : chr(0) #> $ df_name : chr "titanic" #> $ vars : chr [1:2] "pclass" "sex" #> $ rvar : chr "survived" #> $ evar : chr [1:2] "pclass" "sex" #> $ lev : chr "Yes" #> $ int : chr "" #> $ wts : NULL #> $ check : chr "" #> $ form : symbol #> $ ci_type : chr "profile" #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:3] "logistic" "model" "list"