Regression - Ordered Logit

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This feature is only available in Q5.

Estimates an ordered logit model. Also known as Ordinal Logistic Regression, and the cumulative link model.

Contents

Options

Outcome The variable to be predicted by the predictor variables.

Predictors The variable(s) to predict the outcome.

Type:

Linear See Regression - Linear Regression.
Binary Logit See Regression - Binary Logit.
Ordered Logit.
Multinomial Logit See Regression - Multinomial Logit.
Poisson See Regression - Poisson Regression.
Quasi-Poisson See Regression - Quasi-Poisson Regression.
NBD See Regression - NBD Regression.

Robust standard errors Computes standard errors that are robust to violations of the assumption of constant variance (i.e., heteroscedasticity). See Robust Standard Errors. This is only available when Type is Linear.

Missing data See Missing Data Options.

Output

Summary
R Typical R output.
ANOVA
Relative Importance Analysis See here for more information. This option is not available for Multinomial Logit. Note that categorical predictors are not converted to be numeric, unlike in Driver (Importance) Analysis - Relative Importance Analysis.

Correction The multiple comparisons correction applied when computing the p-values of the post-hoc comparisons

Variable names Displays Variable Names in the output.

Absolute importance scores Whether the absolute value of Relative Importance Analysis scores should be displayed.

Auxiliary variables Variables to be used when imputing missing values (in addition to all the other variables in the model).

Weight. Where a weight has been set for the R Output, it will automatically applied when the model is estimated. By default, the weight is assumed to be a sampling weight, and the standard errors are estimated using Taylor series linearization (by contrast, in the Legacy Regression, weight calibration is used). See Weights, Effective Sample Size and Design Effects.

Filter The data is automatically filtered using any filters prior to estimating the model.

Crosstab Interaction Optional variable to test for interaction with other variables in the model. See Linear Regression for more details.

Additional options are available by editing the code.

Diagnostics

See Regression Diagnostics.

Acknowledgements

See Regression - Generalized Linear Model.

Code

form.dropBox({label: "Outcome", 
            types:["Variable: Numeric, Date, Money, Categorical, OrderedCategorical"], 
            name: "formOutcomeVariable"});
form.dropBox({label: "Predictor(s)",
            types:["Variable: Numeric, Date, Money, Categorical, OrderedCategorical"], 
            name: "formPredictorVariables", 
            multi:true});
var formType = form.comboBox({label: "Type", 
              alternatives: ["Linear", "Binary Logit", "Ordered Logit", "Multinomial Logit", "Poisson", "Quasi-Poisson", "NBD"], 
              name: "formType", default_value: "Ordered Logit"});
var altMissing = formType.getValue() == "Linear" ? ["Error if missing data", "Exclude cases with missing data", "Use partial data (pairwise correlations)", "Multiple imputation"] : ["Error if missing data", "Exclude cases with missing data", "Multiple imputation"]; 
var formMissing = form.comboBox({label: "Missing data", 
              alternatives: altMissing, 
              name: "formMissing", default_value: "Exclude cases with missing data"});
if (formMissing.getValue() == "Multiple imputation")
    form.dropBox({label: "Auxiliary variables",
            types:["Variable: Numeric, Date, Money, Categorical, OrderedCategorical"], 
            name: "formAuxiliaryVariables", 
            required: false, 
            multi:true});
var alt = formType.getValue() != "Multinomial Logit" ? ["Summary", "R", "ANOVA", "Relative Importance Analysis"] : ["Summary", "R", "ANOVA"];
var formOutput = form.comboBox({label: "Output", alternatives: alt, name: "formOutput", default_value: "Summary"});
form.comboBox({label: "Correction", alternatives: ["None", "False Discovery Rate", "Bonferroni"], name: "formCorrection", default_value: "None"});
var is_RIA = formOutput.getValue() == "Relative Importance Analysis";
form.checkBox({label: "Variable names", name: "formNames", default_value: false});
if (formType.getValue() == "Linear" &&
    formMissing.getValue() != "Use partial data (pairwise correlations)" &&
    formMissing.getValue() != "Multiple imputation")
    form.checkBox({label: "Robust standard errors", name: "formRobustSE", default_value: false});
if (is_RIA)
    form.setHeading("Relative Importance Analysis: " + formType.getValue());
else
    form.setHeading((formOutput.getValue() == "ANOVA" ? "ANOVA" : "Generalized Linear Model" ) +": " + formType.getValue());
if (formOutput.getValue() == "Relative Importance Analysis")
    form.checkBox({label: "Absolute importance scores", name: "formAbsoluteImportance", default_value: false});
if (formType.getValue() != "Multinomial Logit" && (is_RIA || formOutput.getValue() == "Summary"))
    form.dropBox({label: "Crosstab interaction", name: "formInteraction", types:["Variable: Numeric, Date, Money, Categorical, OrderedCategorical"], required: false});
library(flipRegression)
if (formType != "Linear" || formMissing == "Use partial data (pairwise correlations)" || formMissing == "Multiple imputation")
    formRobustSE <- FALSE
if (formMissing != "Multiple imputation")
    formAuxiliaryVariables <- NULL
importance.absolute <- if (formOutput == "Relative Importance Analysis") formAbsoluteImportance else FALSE
glm <- Regression(QFormula(formOutcomeVariable ~ formPredictorVariables),
                  weights = QPopulationWeight,
                  subset = QFilter,
                  missing = formMissing,
                  output = formOutput,
                  robust.se = formRobustSE,
                  type = formType,
                  show.labels = !formNames,
                  auxiliary = formAuxiliaryVariables,
                  correction = formCorrection,
                  interaction = if (exists("formInteraction")) formInteraction else NULL,
                  importance.absolute = importance.absolute)
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