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Deducer lets you build an R linear model through menus: load and verify your data, choose one continuous outcome, classify predictors as numeric or factors, assemble the formula in Analysis > Linear Model, run it, and inspect both the coefficient table and diagnostic plots. The workflow below applies to Deducer 0.9-2, the version listed on CRAN on May 6, 2026.

What a Deducer linear model does

Deducer’s documentation describes the linear model (also called linear regression) as a framework for analyzing relationships between two or more variables. A standard model has one continuous outcome and one or more predictors. It estimates the outcome’s average relationship with those predictors under the formula you specify.

Deducer supplies a graphical interface for constructing the R model specification. You still need to decide which variable is the outcome, how categorical variables should be coded, whether effects are additive or interactive, and whether the fitted relationship is credible after diagnostics.

Install Deducer and start the right environment

CRAN lists Deducer 0.9-2 with dependencies including R, ggplot2, JGR, car and MASS, plus imports such as rJava. Java and Java/JRI are system requirements. Deducer is designed to work best with the Java-based JGR environment.

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  1. In R, install JGR and Deducer:

install.packages(c("JGR", "Deducer"))

  1. Launch JGR using the procedure appropriate for your operating system.
  2. Load Deducer from JGR, then open your data through the Data Viewer or the R console.

R, Java, JRI and package compatibility can vary by platform. If JGR or Deducer fails to start, check the current CRAN package requirements and the project’s platform-specific installation notes rather than applying Linux shared-library instructions to another operating system.

Open and verify the dataset

Use the Data Viewer

The Data Viewer provides a data view and a variable view. Confirm that measurements such as age, income or test score are stored as numeric variables, while groups such as treatment, region or sex have the intended factor levels.

Check imports before modeling

When importing a delimited file, verify the field separator, quote handling and whether the first row is a header. A wrong import can change variable types or column names before Deducer ever builds the model.

Why the type check matters

A categorical predictor placed in the numeric role can be converted with as.numeric, which uses its internal level ordering rather than a meaningful measurement scale. That can silently fit a different relationship from the one you intended.

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Build the model in Deducer

1. Open the linear-model dialog

In JGR, choose Analysis > Linear Model. The dialogs are also documented for other R environments, but JGR is the environment Deducer is intended to support most closely.

2. Assign the outcome and predictors

Choose exactly one continuous outcome variable. Place quantitative predictors in As Numeric and categorical predictors in As Factor.

Deducer role Use for Example Check
Outcome One continuous response to explain or predict Exam score Confirm its units and missing-value handling
As Numeric Quantitative covariates Hours studied, age A one-unit increase has a meaningful interpretation
As Factor Categorical predictors Treatment group, region Review level names and the reference level

Use a sampling weight or a subset only when it matches the sampling design and the question you are answering.

3. Specify terms in Model Builder

For an additive model, add each predictor’s main effect. Add an interaction only when the question is whether one predictor’s association changes across levels or values of another predictor. The builder also supports nested terms and orthogonal polynomial terms. A quadratic or cubic term can represent curvature when the data and diagnostics justify it.

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Read the generated formula in the dialog preview. The formula is the model: button selections should implement a defensible question, not replace one.

4. Review Model Explorer options and run

Inspect the Model Explorer preview and its available assumptions, tests, plots, means and export options. Then run the model. If your data frame is called dat, an equivalent additive model in ordinary R is:

fit <- lm(outcome ~ predictor1 + predictor2, data = dat)
summary(fit)

Replace the example names with your columns. The left side of ~ is the single outcome; terms on the right are predictors and model components.

Read the coefficient output

Numeric predictors

A numeric coefficient is the estimated change in the outcome associated with a one-unit increase in that predictor, holding the other included predictors fixed. Interpret the number in the outcome’s units and at the predictor’s actual scale.

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Factor predictors

A factor coefficient compares a level with the model’s reference level under the factor coding used by R. Identify that reference level before describing the direction or size of a group difference.

Uncertainty and practical size

Deducer’s summarylm reference documents coefficient estimates, standard errors, t values and p values. Treat these as uncertainty and inference summaries. A small p value does not by itself establish that an effect is large, useful or causally important; report the coefficient’s sign, units and magnitude alongside the uncertainty information.

Robust standard errors when variance is unequal

For heteroskedasticity concerns, Deducer documents summarylm(..., white.adjust=TRUE); its documented adjustment assumes HC3. This changes the summary’s uncertainty estimates. It does not correct a wrong mean relationship, dependence between observations, influential data errors or confounding.

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Check assumptions and influential observations

Use the diagnostic plots available through the model options and Model Explorer. Look for patterns and structure, not a single pass/fail signal.

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Residual distribution

Review the residual distribution for strong skew, heavy tails or other features that make the fitted errors questionable for the intended inference.

Residuals versus fitted values

A systematic curve or changing spread indicates that the mean relationship or variance may not be adequately represented. Deducer’s manual notes that a non-flat residual trend can indicate nonlinearity or a model breakdown in a subset of the data.

Scale-location plot

A non-horizontal trend in the scale-location plot can indicate unequal residual variance. Consider whether a transformation, a different specification or robust uncertainty estimates is appropriate.

Term plots and curvature

Term plots can expose nonlinear predictor relationships. Where a curved pattern is supported by the subject matter and diagnostics, consider a justified transformation or polynomial term rather than adding higher-order terms automatically.

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Cook’s distance

Inspect Cook’s distance together with the observation’s data quality, leverage and scientific context. A value above 1 is a prompt to investigate an observation, not an automatic deletion rule.

Residuals versus leverage

Look for observations that combine unusual predictor values with large residuals. Check the underlying record, entry errors, measurement conditions and whether the observation represents a real part of the target population before deciding how to handle it.

Choose a specification that answers the question

  • Additive main-effects model: estimates each predictor’s association while holding the others fixed, assuming effects do not depend on one another.
  • Interaction model: tests whether the association for one predictor differs across another predictor’s levels or values.
  • Polynomial model: represents curvature for a numeric predictor when a straight-line term is inadequate and the added shape is scientifically defensible.
  • Nested terms: represent hierarchical structures when one term is meaningfully defined within another.

Compare these choices by the question they answer and by the residual and influence patterns they produce. An automated plot can reveal a concern, but it cannot prove that all assumptions hold.

Common mistakes to avoid

  • Putting a factor in As Numeric and interpreting its internal level codes as measured quantities.
  • Skipping the generated-formula preview and losing an intended main effect or adding an unintended interaction.
  • Calling a coefficient “important” solely because its p value crosses a threshold.
  • Using HC3 robust standard errors as if they repaired nonlinearity, dependence or confounding.
  • Deleting every case with Cook’s distance above 1 without checking the record and reporting the decision.
  • Assuming a clean-looking diagnostic plot guarantees a correct model.

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