Logistic regression with SPSS-style output

Fits a binary logistic regression using stats::glm() with family = binomial and prints formatted output including an omnibus model test, model summary statistics, and a coefficients table with odds ratios (Exp(B)).

Usage

jlogistic(
  formula,
  data,
  subset = NULL,
  variable.id = NULL,
  numeric = NULL,
  categorical = NULL,
  count = NULL,
  ci = NULL,
  classification = FALSE,
  diagnostics = NULL,
  ref.categories = NULL,
  full = FALSE,
  case.processing.detail = NULL,
  digits = NULL,
  ...,
  value.id = NULL
)

Arguments

formula

A model formula, e.g. DV ~ IV1 + IV2. The DV must be a binary variable coded 0/1.

data

A data frame containing variables referenced in formula.

subset

An optional unquoted logical expression (e.g. Group == 1) to subset cases for this call only.

variable.id

Character or NULL. Variable label display mode: one of "both", "names", "labels", "legend", or "legend.bottom". "names" shows variable names only; "both" shows "name: label"; "labels" replaces each coefficient's variable name with its label in the Coefficients table (factor level decoration is preserved) – best for short labels; "legend" prints a label legend just below the Coefficients table (at the coefficients/fit seam); "legend.bottom" prints it at the very end. NULL (default) defers to joutput()'s variable.id setting. Not a logical.

numeric

Optional character vector of variable names to treat as continuous even if they have value labels.

categorical

Optional character vector of variable names to treat as categorical even if they lack value labels.

count

Optional character vector of independent-variable names to treat as counts for this call (the per-call counterpart of jcount()). A count predictor is numeric-like, so it enters the model exactly as numeric would; the argument is provided for symmetry with the other analysis functions. The binary dependent variable is fixed, so naming it here has no effect. A variable cannot be listed in both count and categorical.

ci

Logical or NULL. If TRUE, adds 95% confidence intervals for Exp(B). If NULL (default), defers to joutput().

classification

Logical. If TRUE, prints a classification table showing predicted vs observed outcomes. Default is FALSE.

diagnostics

Logical, character vector, or NULL. If TRUE, prints VIF table. If a character vector, vif is currently the only supported option. If NULL (default), defers to joutput().

ref.categories

Logical or NULL. Per-call override for showing the reference-categories block (the baseline level dropped from each set of dummy variables). NULL (default) defers to joutput()'s ref.categories setting. Applies to jlm() and jlogistic() only, since they are the functions that produce dummy-coded coefficient tables.

full

Logical. If TRUE, turns on ci, classification, and diagnostics. Does not override explicit FALSE values.

case.processing.detail

Per-call override of the Case Processing Summary detail tier: one of "none", "totals", or "per_code". NULL (default) uses the active joutput() level default.

digits

Integer or NULL. Number of decimal places for continuous statistics in the output tables (range 0-7; digits = 0 prints whole numbers with no trailing decimal point). Does not affect p-values, percentages, or integer quantities (counts, N, degrees of freedom), which keep their own fixed conventions. NULL (default) defers to joutput()'s digits setting (default 3).

...

Reserved for argument-name checking. Passing which, plots, or show will produce a helpful error suggesting diagnostics instead.

value.id

Character or NULL. Value-label display mode for the dummy category rows in the Coefficients table: one of "both" ("code: label", degrading to a bare code where a code has no label), "values" (the bare code), or "labels" (the value label, degrading to the bare code where none exists). The reference category folded into each grouped variable's header follows the same mode. "legend" and "legend.bottom" are not supported here: a coefficient table already pairs each value label with its row, so a separate legend block would only duplicate it. Passing either explicitly is an error; a joutput() default of "legend" or "legend.bottom" is tolerated and rendered as "both", so it does not break a bare call. Variables with no value labels render identically under all supported modes. NULL (default) defers to joutput()'s value.id setting. Applies only to multi-category dummy predictors; continuous and single-contrast (dichotomous) predictors are unaffected. Not a logical.

Value

Invisibly returns a list of class jst_logistic containing:

model

The fitted glm object.

model_type

Character string logistic.

model_frame

The model frame used to fit the model.

formula_used

The formula after dummy expansion.

coefficients

Formatted coefficient table (data frame).

coefficients_raw

Flat data frame of raw, full-precision coefficient statistics (one row per coefficient): term (machine key, shared with jlm), b, SE, Wald, df, p, exp_b, and exp_ci_lower / exp_ci_upper odds-ratio CI bounds (present regardless of the ci display toggle). Carries an outcome attribute.

fit_raw

List of raw, full-precision model-level fit statistics: ll_model, ll_null, deviance, null_deviance, the omnibus likelihood-ratio test (chi_sq, omnibus_df, omnibus_p), Cox & Snell and Nagelkerke pseudo R-squared (cox_snell_r2, nagelkerke_r2), aic, and n.

nagelkerke_r2

Nagelkerke pseudo R-squared.

cox_snell_r2

Cox & Snell pseudo R-squared.

neg2ll

-2 Log Likelihood.

aic

Akaike Information Criterion.

omnibus

Named vector: chi_square, df, p.

n

Number of observations.

predicts

Character string describing what the model predicts.

dummy_coef_names

Names of dummy variable columns.

ref_cats

Reference category descriptions.

vif

Named numeric vector of VIF values, or NULL.

sample_info

Pipeline and missing data counts.

Details

The dependent variable must be coded 0/1. If it is not, the function stops with a clear error message and suggests the appropriate jrecode() command.

Handles haven-labelled variables, registered dummy variables via jdummy(), and the numeric/categorical overrides in the same way as jlm().

See also

jstats for the package overview, workflow conventions, and complete function listing.

Examples

# With explicit data frame -- Volunteer is already coded 0/1
jlogistic(Volunteer ~ Income + Age, data = community)
#> Logistic Regression
#> 
#> Case Processing    Excluded  Remaining
#>     Original              —        100
#>     Auto-listwise         6         94
#>     Analysis N            —         94
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ──────────────────────────────────────
#> 
#> 
#> Coefficients
#>                b      SE    Wald   df   p    Exp(B)
#> -----------  ------  -----  -----  --  ----  ------
#> (Intercept)  -2.543  0.925  7.565   1  .006   0.079
#> Income        0.000  0.000  4.849   1  .028   1.000
#> Age           0.023  0.020  1.351   1  .245   1.023
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p   
#> ----------  --  ----
#>      8.548   2  .014
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           119.027           0.087          0.117  125.027
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# A 1/2-coded dichotomy (Yes = 1, No = 2) must be recoded to 0/1 first
df <- community
df$OwnsHome01 <- jrecode(df, OwnsHome,
                         map = "1=1; 2=0", labels = "0=No; 1=Yes")
#> 
#> Note: jrecode() returns the recoded values; assign them to a column to keep them:
#>   df$<name> <- jrecode(...)
#> To check the recode landed correctly, compare jfreq() on the original and the new column.
jlogistic(OwnsHome01 ~ Income + Age, data = df)
#> Logistic Regression
#> 
#> Case Processing    Excluded  Remaining
#>     Original              —        100
#>     Auto-listwise         6         94
#>     Analysis N            —         94
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ──────────────────────────────────────
#> 
#> 
#> Coefficients
#>                b      SE     Wald   df    p    Exp(B)
#> -----------  ------  -----  ------  --  -----  ------
#> (Intercept)  -5.586  1.268  19.421   1  <.001   0.004
#> Income        0.000  0.000   9.186   1   .002   1.000
#> Age           0.082  0.025  10.948   1  <.001   1.086
#> 
#> Outcome: OwnsHome01
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p    
#> ----------  --  -----
#>       30.4   2  <.001
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²     AIC
#> -----------------  --------------  -------------  ------
#>             99.23           0.276          0.369  105.23
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# Using juse() default
juse(community)
#> Default data frame set to: community
jlogistic(Volunteer ~ Income + Age)
#> Logistic Regression
#> Using default data frame: community
#> 
#> Case Processing    Excluded  Remaining
#>     Original              —        100
#>     Auto-listwise         6         94
#>     Analysis N            —         94
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ──────────────────────────────────────
#> 
#> 
#> Coefficients
#>                b      SE    Wald   df   p    Exp(B)
#> -----------  ------  -----  -----  --  ----  ------
#> (Intercept)  -2.543  0.925  7.565   1  .006   0.079
#> Income        0.000  0.000  4.849   1  .028   1.000
#> Age           0.023  0.020  1.351   1  .245   1.023
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p   
#> ----------  --  ----
#>      8.548   2  .014
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           119.027           0.087          0.117  125.027
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# CATEGORICAL PREDICTORS
#
# Per-call: categorical = ... applies for one call only and does not
# persist.
jlogistic(Volunteer ~ Region + Age, categorical = "Region")
#> Logistic Regression
#> Using default data frame: community
#> 
#> Coefficients
#>                     b      SE    Wald   df   p    Exp(B)
#> ----------------  ------  -----  -----  --  ----  ------
#> (Intercept)       -2.144  0.901  5.662   1  .017   0.117
#> Region_South (1)   0.155  0.642  0.058   1  .809   1.168
#> Region_East (1)    0.692  0.570  1.474   1  .225   1.998
#> Region_West (1)   -0.396  0.596  0.441   1  .507   0.673
#> Age                0.041  0.020  4.474   1  .034   1.042
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p   
#> ----------  --  ----
#>      8.544   4  .074
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           127.515           0.082           0.11  137.515
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# The recommended approach for repeated analyses: register the variable
# with jdummy() before running jlogistic(). This sets categorical
# treatment persistently across subsequent analyses.
jdummy(community, Region)
#> Dummy Variable Registration
#>   Variable: Region (haven_labelled)
#>   Reference category: Region_North
#>   Dummy variables: Region_South, Region_East, Region_West
#>   Cases: 100 (0 missing)
#> 
#> Note: this registration is stored for this session only.
#> To keep it across sessions, save the data frame in R format (.rds):
#>   jsave(community, "community.rds")
#> 
#> Next session, load that file to restore the registration:
#>   community <- jload("community.rds")
jlogistic(Volunteer ~ Region + Age)
#> Logistic Regression
#> Using default data frame: community
#> 
#> Coefficients
#>                     b      SE    Wald   df   p    Exp(B)
#> ----------------  ------  -----  -----  --  ----  ------
#> (Intercept)       -2.144  0.901  5.662   1  .017   0.117
#> Region_South (1)   0.155  0.642  0.058   1  .809   1.168
#> Region_East (1)    0.692  0.570  1.474   1  .225   1.998
#> Region_West (1)   -0.396  0.596  0.441   1  .507   0.673
#> Age                0.041  0.020  4.474   1  .034   1.042
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p   
#> ----------  --  ----
#>      8.544   4  .074
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           127.515           0.082           0.11  137.515
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# To choose a non-default reference category:
jdummy(community, Region, ref = "West")
#> Dummy Variable Registration
#>   Variable: Region (haven_labelled)
#>   Reference category: Region_West
#>   Dummy variables: Region_North, Region_South, Region_East
#>   Cases: 100 (0 missing)
#> 
#> Note: this registration is stored for this session only.
#> To keep it across sessions, save the data frame in R format (.rds):
#>   jsave(community, "community.rds")
#> 
#> Next session, load that file to restore the registration:
#>   community <- jload("community.rds")
jlogistic(Volunteer ~ Region + Age)
#> Logistic Regression
#> Using default data frame: community
#> 
#> Coefficients
#>                     b      SE    Wald   df   p    Exp(B)
#> ----------------  ------  -----  -----  --  ----  ------
#> (Intercept)       -2.540  0.984  6.656   1  .010   0.079
#> Region_North (1)   0.396  0.596  0.441   1  .507   1.485
#> Region_South (1)   0.551  0.669  0.677   1  .410   1.734
#> Region_East (1)    1.088  0.581  3.504   1  .061   2.967
#> Age                0.041  0.020  4.474   1  .034   1.042
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p   
#> ----------  --  ----
#>      8.544   4  .074
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           127.515           0.082           0.11  137.515
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# FORCING NUMERIC TREATMENT
#
# Use numeric = ... when a labelled variable should enter as a score.
jlogistic(Volunteer ~ Age + Education, numeric = "Education")
#> Logistic Regression
#> Using default data frame: community
#> 
#> Case Processing    Excluded  Remaining
#>     Original              —        100
#>     Auto-listwise         6         94
#>     Analysis N            —         94
#> 
#> Missing-data breakdown  From 100    %
#>     Education
#>       Missing              6      6.0
#> 
#> ──────────────────────────────────────
#> 
#> 
#> Coefficients
#>                b      SE     Wald   df    p    Exp(B)
#> -----------  ------  -----  ------  --  -----  ------
#> (Intercept)  -4.484  1.209  13.755   1  <.001   0.011
#> Age           0.042  0.022   3.654   1   .056   1.043
#> Education     0.841  0.197  18.137   1  <.001   2.318
#> 
#> Outcome: Volunteer
#> 
#> Omnibus Test of Model Coefficients
#> Chi-Square  df  p    
#> ----------  --  -----
#>     27.586   2  <.001
#> 
#> Model Summary
#> -2 Log Likelihood  Cox & Snell R²  Nagelkerke R²      AIC
#> -----------------  --------------  -------------  -------
#>           100.633           0.254          0.342  106.633
#> 
#> Dependent Variable Encoding
#>   Modeled (1):   Yes
#>   Reference (0): No

# Not normally needed. You'd clear a default or registration only to
# undo a mistake, or -- as in this example -- to reset state for testing.
jdummy(community, NULL)
#> Dummy registrations cleared for community: Region.
juse(NULL)
#> Default data frame cleared.