logistic_regression() performs binary logistic regression with
SPSS-compatible output. Wraps stats::glm(family = binomial) and adds
odds ratios, pseudo R-squared measures, classification table, and model tests
matching SPSS LOGISTIC REGRESSION output.
Supports two interface styles:
Formula interface:
logistic_regression(data, high_satisfaction ~ age + income)SPSS-style:
logistic_regression(data, dependent = high_satisfaction, predictors = c(age, income))
Usage
logistic_regression(
data,
formula = NULL,
dependent = NULL,
predictors = NULL,
weights = NULL,
conf.level = 0.95,
factors = c("dummy", "numeric")
)Arguments
- data
Your survey data (a data frame or tibble). If grouped (via
dplyr::group_by()), separate regressions are run for each group.- formula
A formula specifying the model (e.g.,
y ~ x1 + x2). If provided,dependentandpredictorsare ignored.- dependent
The dependent variable (unquoted). Used with
predictorswhen no formula is given. Must have exactly two distinct values (e.g. 0/1, 1/2, a two-level factor); see Technical Details.- predictors
Predictor variable(s) (unquoted, supports tidyselect). Used with
dependentwhen no formula is given. The dependent, weights and grouping variables are never used as predictors (a selection such aswhere(is.numeric)drops them with a message). Character predictors are entered as factors.- weights
Optional survey weights. When specified, weighted maximum likelihood estimation is used, matching SPSS WEIGHT BY behavior. Give a column name (unquoted or as a string), an expression such as
sampling_weight * 2, or a numeric vector with one weight per row.- conf.level
Confidence level for odds ratio intervals (default 0.95).
- factors
How factor predictors are entered into the model:
"dummy"(default, matches base Rglm()) expands a factor withLlevels intoL - 1contrasts;"numeric"silently coerces factor levels to their integer codes, matching SPSSLOGISTIC REGRESSIONdefault behavior when no/CATEGORICALsubcommand is given. The "numeric" mode emits a one-linecli::cli_inform()listing the coerced variables. Note that for ordered factors, "dummy" applies R's default polynomial contrasts (terms suffixed.L,.Q,.C), not treatment dummies; convert withfactor(x, ordered = FALSE)first if you want dummy coding.factorsapplies to factors only: labelled predictors from SPSS files (haven_labelled) are numeric and enter with their numeric codes, as in SPSS LOGISTIC REGRESSION without/CATEGORICAL;summary()notes them. Convert them withto_labelfirst to get dummy coding.
Value
For ungrouped data, an object of class
c("logistic_regression", "glm", "lm") — the fitted
glm itself, with mariposa-specific slots attached:
- coef_table
Tibble with B, S.E., Wald, df, Sig., Exp(B), CI_lower, CI_upper
- model_summary
List with minus2LL, cox_snell_r2, nagelkerke_r2, mcfadden_r2
- omnibus_test
List with chi_sq, df, p for overall model test
- classification
List with table, overall_pct, pct_correct_0, pct_correct_1
- hosmer_lemeshow
List with chi_sq, df, p (goodness-of-fit test)
- dv_encoding
Tibble Original -> Internal (0/1) of the outcome categories (SPSS "Dependent Variable Encoding")
- n
Sample size (listwise complete cases; weighted N when weighted)
- formula, dependent, predictor_names, weighted, weight_name, is_grouped, conf.level
Call metadata.
Because the object inherits from "glm", all standard
generics (predict(), anova(), vcov(),
confint(), residuals(), fitted(),
coef(), broom::tidy(), broom::glance(),
broom::augment()) dispatch natively without unwrapping.
summary() returns the SPSS-style mariposa summary; for
the raw glm summary use stats::summary.glm() on the same
object. confint() returns Wald intervals on the log-odds
scale (the SPSS method; exp(confint(model)) gives the
"C.I. for EXP(B)" of the summary), profile-likelihood intervals via
confint(model, method = "profile"); see
confint.logistic_regression.
For grouped data, returns a list of class "logistic_regression"
with $groups holding one fitted glm-inheriting result per group.
Details
Understanding the Results
The output includes five sections matching SPSS LOGISTIC REGRESSION output:
Omnibus Test: Tests whether the model as a whole is significant. A significant chi-square means the model predicts better than chance.
Model Summary: -2 Log Likelihood and pseudo R-squared values. Lower -2LL = better fit. Higher R-squared = more variance explained.
Hosmer-Lemeshow Test: Goodness-of-fit test. A non-significant result (p > 0.05) means the model fits the data well.
Classification Table: How well the model classifies cases. Shows percentage correctly predicted for each group and overall.
Coefficients: B, Wald test, odds ratios (Exp(B)), and CIs.
Interpreting odds ratios (Exp(B)):
Exp(B) > 1: Predictor increases the odds of the outcome
Exp(B) < 1: Predictor decreases the odds of the outcome
Exp(B) = 1: Predictor has no effect on the odds
When to Use This
Use logistic_regression() when:
Your dependent variable is binary (yes/no, 0/1, pass/fail)
You want to predict group membership from one or more predictors
You need odds ratios to interpret predictor effects
For continuous outcomes, use linear_regression instead.
Technical Details
Dependent Variable: Any variable with exactly two distinct
observed values, coded internally as 0/1 like SPSS LOGISTIC REGRESSION
does: for numeric and labelled variables the lower value becomes 0 and
the higher 1 (so 1/2 and 0/1 codings give the same model); for factors
unused levels are dropped and the first remaining level becomes 0;
character values are ordered alphabetically; logicals code
FALSE = 0, TRUE = 1. The model predicts the probability of
the category coded 1 - the compact print names it
([P(y = category)]), and summary() shows the SPSS
"Dependent Variable Encoding" table (also stored as
$dv_encoding). A grouped analysis uses one encoding for all
groups.
Missing Data: Listwise deletion is used (matching SPSS LOGISTIC REGRESSION default behavior).
Weights: When weights are specified, they are treated as frequency weights (matching SPSS WEIGHT BY behavior).
Pseudo R-squared: Three measures are reported:
Cox & Snell R-squared (bounded below 1)
Nagelkerke R-squared (adjusted to reach 1)
McFadden R-squared (1 - LL_model/LL_null)
Factor Predictors: By default (factors = "dummy"),
factor predictors are expanded into L - 1 contrasts via
R's stats::model.matrix(), matching base R glm(): unordered
factors get treatment (dummy) contrasts against the first level, while
ordered factors get R's default polynomial contrasts
(.L/.Q/.C terms). Pass factors = "numeric"
to silently coerce factor levels to their integer codes (SPSS
LOGISTIC REGRESSION default without an explicit
/CATEGORICAL subcommand).
Grouped Analysis: When data is grouped via
dplyr::group_by(), a separate regression is run for each group
(matching SPSS SPLIT FILE BY).
See also
linear_regression for continuous outcome variables.
chi_square for testing associations between categorical variables.
summary.logistic_regression for detailed output with toggleable sections.
Other regression:
linear_regression(),
marginal_effects()
Examples
library(dplyr)
data(survey_data)
# Create binary DV
survey_data$high_satisfaction <- ifelse(survey_data$life_satisfaction >= 4, 1, 0)
# Bivariate logistic regression
logistic_regression(survey_data, high_satisfaction ~ age)
#> Logistic Regression: high_satisfaction ~ age [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.000, chi2(1) = 0.28, p = 0.595, Accuracy = 57.7%, N = 2421
#> Use summary() for detailed output.
# Multiple logistic regression
logistic_regression(survey_data, high_satisfaction ~ age + income + education)
#> Logistic Regression: high_satisfaction ~ age + income + education [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.213, chi2(5) = 364.62, p < 0.001 ***, Accuracy = 68.3%, N = 2115
#> Use summary() for detailed output.
# SPSS-style interface
logistic_regression(survey_data,
dependent = high_satisfaction,
predictors = c(age, income))
#> Logistic Regression: high_satisfaction ~ age + income [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.209, chi2(2) = 357.43, p < 0.001 ***, Accuracy = 68.4%, N = 2115
#> Use summary() for detailed output.
# Weighted logistic regression
logistic_regression(survey_data, high_satisfaction ~ age,
weights = sampling_weight)
#> Logistic Regression: high_satisfaction ~ age [P(high_satisfaction = 1)] [Weighted]
#> Nagelkerke R2 = 0.000, chi2(1) = 0.30, p = 0.586, Accuracy = 57.6%, N = 2437
#> Use summary() for detailed output.
# Grouped by region
survey_data |>
dplyr::group_by(region) |>
logistic_regression(high_satisfaction ~ age)
#> Logistic Regression: high_satisfaction ~ age [P(high_satisfaction = 1)] [Grouped: region]
#> [region = East]
#> Nagelkerke R2 = 0.002, chi2(1) = 0.74, p = 0.390, Accuracy = 58.3%, N = 465
#> [region = West]
#> Nagelkerke R2 = 0.000, chi2(1) = 0.03, p = 0.860, Accuracy = 57.6%, N = 1956
#> Use summary() for detailed output.
# Factor predictors: dummy-coding (default, matches base R glm())
logistic_regression(survey_data, high_satisfaction ~ age + education)
#> Logistic Regression: high_satisfaction ~ age + education [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.084, chi2(4) = 156.25, p < 0.001 ***, Accuracy = 63.4%, N = 2421
#> Use summary() for detailed output.
# Factor predictors: SPSS-style ordinal-as-scale
logistic_regression(survey_data, high_satisfaction ~ age + education,
factors = "numeric")
#> ℹ Factor predictor(s) coerced to numeric (SPSS-style ordinal scaling):
#> • `education`
#> Logistic Regression: high_satisfaction ~ age + education [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.078, chi2(2) = 144.84, p < 0.001 ***, Accuracy = 63.4%, N = 2421
#> Use summary() for detailed output.
# --- Three-layer output ---
result <- logistic_regression(survey_data, high_satisfaction ~ age + income)
result # compact one-line overview
#> Logistic Regression: high_satisfaction ~ age + income [P(high_satisfaction = 1)]
#> Nagelkerke R2 = 0.209, chi2(2) = 357.43, p < 0.001 ***, Accuracy = 68.4%, N = 2115
#> Use summary() for detailed output.
summary(result) # full detailed SPSS-style output
#>
#> Logistic Regression Results
#> ---------------------------
#> - Formula: high_satisfaction ~ age + income
#> - Method: ENTER
#> - N: 2115
#>
#> Dependent Variable Encoding
#> ------------------------------
#> Original Value Internal Value
#> ------------------------------
#> 0 0
#> 1 1
#> ------------------------------
#>
#> Omnibus Tests of Model Coefficients
#> ---------------------------------
#> Chi-square df Sig.
#> ---------------------------------
#> Model 357.432 2 <.001 ***
#> ---------------------------------
#>
#> Model Summary
#> -------------------------------
#> -2 Log Likelihood 2520.010
#> Cox & Snell R Square 0.155
#> Nagelkerke R Square 0.209
#> McFadden R Square 0.124
#> -------------------------------
#>
#> Hosmer and Lemeshow Test
#> ------------------------
#> Chi-square df Sig.
#> ------------------------
#> 150.764 8 <.001
#> ------------------------
#>
#> Classification Table (cutoff = 0.50; rows: observed, columns: predicted)
#> ---------------------------------------
#> Observed 0 1 % Correct
#> ---------------------------------------
#> 0 508 380 57.2
#> 1 289 938 76.4
#> Overall Percentage 68.4
#> ---------------------------------------
#>
#> Variables in the Equation
#> ------------------------------------------------------------------------------------------
#> Term B S.E. Wald df Sig. Exp(B) 95% CI Lower 95% CI Upper
#> ------------------------------------------------------------------------------------------
#> (Intercept) -2.252 0.212 112.868 1 <.001 0.105 ***
#> age 0.001 0.003 0.174 1 .677 1.001 0.996 1.007
#> income 0.001 4.26e-05 268.118 1 <.001 1.0007 1.0006 1.0008 ***
#> ------------------------------------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
summary(result, classification = FALSE) # hide classification table
#>
#> Logistic Regression Results
#> ---------------------------
#> - Formula: high_satisfaction ~ age + income
#> - Method: ENTER
#> - N: 2115
#>
#> Dependent Variable Encoding
#> ------------------------------
#> Original Value Internal Value
#> ------------------------------
#> 0 0
#> 1 1
#> ------------------------------
#>
#> Omnibus Tests of Model Coefficients
#> ---------------------------------
#> Chi-square df Sig.
#> ---------------------------------
#> Model 357.432 2 <.001 ***
#> ---------------------------------
#>
#> Model Summary
#> -------------------------------
#> -2 Log Likelihood 2520.010
#> Cox & Snell R Square 0.155
#> Nagelkerke R Square 0.209
#> McFadden R Square 0.124
#> -------------------------------
#>
#> Hosmer and Lemeshow Test
#> ------------------------
#> Chi-square df Sig.
#> ------------------------
#> 150.764 8 <.001
#> ------------------------
#>
#> Variables in the Equation
#> ------------------------------------------------------------------------------------------
#> Term B S.E. Wald df Sig. Exp(B) 95% CI Lower 95% CI Upper
#> ------------------------------------------------------------------------------------------
#> (Intercept) -2.252 0.212 112.868 1 <.001 0.105 ***
#> age 0.001 0.003 0.174 1 .677 1.001 0.996 1.007
#> income 0.001 4.26e-05 268.118 1 <.001 1.0007 1.0006 1.0008 ***
#> ------------------------------------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05