[required] A fitted model object (e.g., model = your.model). Supported models include a wide range of regression types, including linear, robust linear, nonlinear, generalized least squares, generalized linear, mixed-effects, and generalized additive (mixed) models. Compatible model-fitting functions include: stats::lm, MASS::rlm, stats::nls, nlme::gls, stats::glm, MASS::glm.nb, lme4::lmer, lme4::glmer, lme4::glmer.nb, glmmTMB::glmmTMB, and mgcv::gam.

[required] The data frame used to fit the model (e.g., data = your.data). This data frame is used internally for generating predictions. All variables used in the model formula (including predictors, offsets, grouping variables, and interaction terms) must be present in this data frame. If the model was fitted without using a data argument (e.g., using variables from the global environment), you must ensure that data includes all required variables. Otherwise, prediction may fail or produce incorrect results.

[required] The name of the target explanatory variable to be plotted (e.g., predictor = "x1").

The name of an interaction or additional variable for conditioning (e.g., by = "x2"). If the variable is:

• continuous, predictions are shown for cross-sections at the 10th, 50th, and 90th percentiles.

• categorical, a separate prediction line or point will be plotted for each level.

• used as a grouping variable in a random effect term (e.g., (1|group) or s(group, bs = "re")) and re.form = NULL, predictions are conditional on each group's estimated random effect.

Although easyViz does not natively support direct visualization of three-way interactions in a multi-panel plot, this can be easily achieved by combining the by and fix_values arguments. For example, if your model includes a term like x1*x2*x3, you can visualize the effect of x1 across levels of x2 by setting predictor = "x1", by = "x2", and fixing x3 at a specific value using fix_values = c(x3 = ...). Repeating this with different values of x3 produces multiple plots that can be arranged to visualize the full three-way interaction. See the Examples section for a demonstration of how to apply this approach.

Character string indicating the type of predictions to plot. Either "response" (default), which returns predictions on the original outcome scale by applying the inverse of the model's link function (e.g., probabilities for binary models), or "link", which returns predictions on the linear predictor (link) scale (e.g., log-odds, log-counts, or other transformed scales depending on the model).

Logical. Applies only when the predictor is numeric and a categorical by variable is specified. If TRUE (default), the prediction range for each level of the by variable is limited to the range of the predictor observed within that level. This avoids extrapolating predictions beyond the available data for each subgroup. If FALSE, predictions span the entire range of the predictor across all levels of the by variable. If the by variable is numeric, pred_range_limit is automatically set to FALSE, since numeric by values are treated as continuous rather than grouping factors.

Logical. If TRUE, prediction lines (and their confidence intervals) for numeric predictors are drawn after raw data, so they appear on top. Default is FALSE, which draws predictions underneath the data. This has no effect for categorical predictors — for those, predictions are always drawn on top of raw data.

Number of prediction points to use for numeric predictors. Defaults to 101, consistent with visreg. The default should work well in most cases. Increasing pred_resolution may be particularly helpful when the predictor spans a wide range or when visualizing nonlinear relationships (e.g., splines or polynomials), to ensure smooth and accurate rendering of the effect. Note: A higher value may slightly increase computation time, especially when combined with many levels of a by variable.

How to condition non-target numeric predictors. Either "median" (default) or "mean". This determines how numeric variables that are not directly plotted are held constant during prediction, while varying the predictor of interest — a common approach when visualizing effects in multivariable models. To fix specific variables at custom values instead, use the fix_values argument.

How to condition non-target categorical predictors. Either "mode" (default) or "reference". As for "num_conditioning", conditioning means holding these variables constant while varying the predictor of interest. If multiple levels are equally frequent when "mode" is selected, the level chosen will be the first in the factor's level order (which by default is alphabetical and typically coincides with the reference level, unless explicitly re-leveled). This behavior also applies to grouping variables used as random effects when re.form = NULL. To fix categorical variables (including grouping variables) at specific levels, use fix_values.

A named vector or named list specifying fixed values for one or more variables during prediction. Supports both numeric and categorical variables. For numeric variables, specify a fixed value (e.g., fix_values = c(x = 1)). For categorical variables (factors), provide the desired level as a character string or factor (e.g., fix_values = c(group = "levelA") or fix_values = list(group = levels(data$group)[1])). This overrides the default conditioning behavior specified via num_conditioning and cat_conditioning. Note: This argument also applies to grouping variables used as random effects: when re.form = NULL, predictions are conditional on the level specified in fix_values; if not specified, the level is chosen based on cat_conditioning. This argument is useful for setting offsets, forcing predictions at specific values, or ensuring consistent conditioning across models. For example, it is particularly useful when you want to visualize the effect of a predictor at a specific level of an interacting variable, without conditioning on all levels. E.g., to plot the conditional effect of a continuous predictor x1 at a specific value of another variable x2 (numeric or categorical), simply set fix_values = c(x2 = ...) and omit the by argument. This creates a clean single-effect plot for x1 at the desired level of x2, without plotting multiple lines or groups as by would. This argument can also be used to visualize three-way interactions when combined with by. See the by argument for details, and the Examples section for a demonstration of how to apply this approach.

A formula specifying which random effects to include when generating predictions. This argument is relevant for mixed-effects models only (e.g., from lme4, glmmTMB, or mgcv::gam()).

• re.form = NULL (default): produces group-specific predictions, conditional on the random-effect levels present in the data. By default, easyViz fixes grouping variables at their mode (i.e., the most frequent level), so the prediction reflects the conditional estimate for that group. You can override this by explicitly fixing the grouping variable via fix_values (e.g., fix_values = c(group = "levelA")). If all levels are equally frequent and no value is specified, the first level (in factor order) is used, which is usually alphabetical unless re-leveled. If by corresponds to a grouping variable used in a random effect, predictions are visualized for all group levels (i.e., conditional predictions).

• re.form = NA or re.form = ~0: produces population-level (i.e., marginal) predictions by excluding random effects from the prediction step. The random effects are still part of the fitted model and influence the estimation of fixed effects and their uncertainty, but they are not included when computing predicted values. This is equivalent to assuming random effects are zero — representing an "average" group or subject.

For mgcv::gam() models, random effects are typically modeled using smooth terms like s(group, bs = "re"). Although predict.gam() does not support a re.form argument, easyViz emulates its behavior: re.form = NULL includes random-effect smooths, while re.form = NA or ~0 excludes them via the exclude argument in predict.gam(). Note: For models fitted with lme4 (e.g., lmer(), glmer()), standard errors are not available when re.form = NULL.

A custom function to back-transform predictions for transformed response variables (e.g., exp for log-transformed responses, or function(x) x^2 for square root-transformed responses). Note: If you wish to model a transformed response, it is recommended to apply the transformation directly in the model formula (e.g., log(y)), rather than modifying the response variable in the data set. This ensures that observed data points are correctly plotted on the original (back-transformed) scale. Otherwise, raw data and predicted values may not align properly in the plot.

x-axis limits for the plot (e.g., xlim = c(0, 10)). Defaults to automatic scaling based on the data range. Applies to both numeric and categorical predictors. For categorical variables, x-axis positions are treated as integer values (e.g., 1, 2, ..., k), and adjusting xlim (e.g., xlim = c(0.5, k + 0.5)) can control spacing and margins around the plotted levels.

y-axis limits for the plot (e.g., ylim = c(10, 20)). Defaults to automatic scaling based on the data and prediction range.

x-axis labels (e.g., xlab = "x"). Defaults to "predictor".

y-axis labels (e.g., ylab = "y"). Defaults to "response".

Custom labels for levels of a categorical predictor (e.g., cat_labels = c("Level A", "Level B", "Level C")).

Font family for the plot. E.g., "sans" (default), "serif", "mono".

Text orientation for axis labels (default: 1).

Box type around the plot. E.g., "o" (default), "n", "L".

A named list of additional graphical parameters passed to base R's plot() function. These arguments allow users to override default appearance settings in a flexible way. Common options include axis label size, color, label text, tick mark spacing, and coordinate scaling. Note: Only arguments recognized by plot.default() are supported. Parameters that must be set via par() (such as mar, oma, mfrow, mgp) are not applied through plot_args. If you wish to adjust those settings, set them directly using par() before calling easyViz(). Many valid parameters are documented in both ?plot.default and ?par. In plot_args, they are passed to plot(), not to par(). Common plot() parameters you may override:

• Label/Text size and style: cex.lab, cex.axis, cex.main, font.lab, font.axis, font.main

• Colors: col.lab, col.axis, col.main, col.sub, col, bg, fg

• Label/Text content: xlab, ylab, main, sub

• Box and axis rendering: bty, axes, frame.plot, ann

• Coordinate settings and tick spacing: xlim, ylim, xaxs, yaxs, xaxp, yaxp, asp, xlog, ylog

For a full list of supported parameters, see ?plot.default and ?par. Example usage:
plot_args = list(main = "Title", cex.lab = 1.2, col.axis = "gray40", xaxp = c(0, 10, 5)).

Logical. Whether to display raw data points (default: TRUE). For binomial models where the response is expressed in the formula as cbind(successes, failures) or as successes / trials, the raw data points plotted on the y-axis are based on the calculated proportions: successes / (successes + failures) or successes / trials, respectively. These proportions are computed internally from the original data and temporarily added to the data set for visualization purposes.

For binary responses, how to display raw data points in the plot. Either "plain" (default), which plots each individual 0/1 observation as-is, or "binned", which groups observations into intervals (bins) of the predictor and plots the proportion of 0s and 1s within each bin. This makes it easier to visualize trends in binary outcomes, especially when many points overlap.

Number of bins for displaying binary response raw data when binary_data_type = "binned" (default: 10).

Logical. If TRUE, raw data points are jittered horizontally to reduce overplotting. Applies to both categorical and numeric predictors. Default is FALSE. For categorical predictors, jittering helps distinguish overlapping points. For numeric predictors, it can be useful when many data points share the same x-value (e.g., integers or rounding).

Point color for raw data (default: rgb(0,0,0, alpha = 0.4)). Can be specified as a color name (e.g., "gray"), an integer (e.g., 1), or an RGB (e.g., rgb(0,0,0,alpha=0.4)) or hex string (e.g., "#808080"). Dynamic: accepts multiple values when points are plotted for different values/levels of a variable.
Tip: For large data sets with many overlapping data points, it is recommended to use semi-transparent colors to reduce overplotting. You can achieve this by setting a low alpha value (e.g., rgb(1,0,0, alpha = 0.1), or by using adjustcolor() with the argument alpha.f (e.g., adjustcolor("red", alpha.f = 0.1)). In such cases, consider setting pred_on_top = TRUE to ensure that prediction lines and confidence intervals remain clearly visible above the dense cloud of raw points.

Point shape for raw data (default: 16). Dynamic: accepts multiple values when points are plotted for different values/levels of a variable.

Point size for raw data (default: 0.75). Dynamic: accepts multiple values when points are plotted for different values/levels of a variable.

Color of the predicted line for numerical predictors (default: "black"). Can be specified as a color name, number or RGB/hex string. Dynamic: accepts multiple values (e.g., c("red", "green", "blue")) when multiple lines are plotted (i.e., when by is specified).

Type of the predicted line for numerical predictors (default: 1). Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when multiple lines are plotted (i.e., when by is specified).

Width of the predicted line for numerical predictors (default: 2). Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when multiple lines are plotted (i.e., when by is specified).

Type of 95 percent confidence intervals for numeric predictors. Either "polygon" (default) to draw shaded confidence bands, "lines" to draw lines, or NULL to suppress confidence intervals for numeric predictors. Note: ci_type = NULL does not suppress confidence bars for categorical predictors; these are always shown unless manually suppressed via custom logic (e.g., by setting ci_bar_lwd = 0).

Color for 95 percent confidence interval polygon (default: "gray"). Requires ci_type = "polygon". Can be specified as a color name, number or RGB/hex string. Dynamic: accepts multiple values (e.g., c("red", "green", "blue")) when 95 percent CIs are plotted for multiple lines (i.e., when by is specified).

Color for 95 percent confidence interval lines (default: "black"). Requires ci_type = "lines". Can be specified as a color name, number or RGB/hex string. Dynamic: accepts multiple values (e.g., c("red", "green", "blue")) when 95 percent CIs are plotted for multiple lines (i.e., when by is specified).

Type for 95 percent confidence interval lines (default: 1). Requires ci_type = "lines". Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when 95 percent CIs are plotted for multiple lines (i.e., when by is specified).

Width for 95 percent confidence interval lines (default: 1). Requires ci_type = "lines". Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when 95 percent CIs are plotted for multiple lines (i.e., when by is specified).

Color for predicted point values of categorical predictors (default: "black"). Can be specified as a color name, number or RGB/hex string. Dynamic: accepts multiple values (e.g., c("red", "green", "blue")) when points are plotted for an interaction (i.e., when by is specified).

Shape for predicted point values of categorical predictors (default: 16). Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when points are plotted for an interaction (i.e., when by is specified).

Size for predicted point values of categorical predictors (default: 1). Dynamic: accepts multiple values (e.g., c(1, 2, 3)) when points are plotted for an interaction (i.e., when by is specified).

Color for 95 percent confidence interval bars (default: "black"). Applies only when the predictor is categorical. Can be specified as a color name, number, or RGB/hex string.

Type for 95 percent confidence interval bars (default: 1). Applies only when the predictor is categorical.

Width for 95 percent confidence interval bars (default: 1). Applies only when the predictor is categorical. To suppress confidence interval bars, set ci_bar_lwd = 0 (line width of zero).

Size of the caps on 95 percent confidence interval bars (default: 0.1). Increase for more visible caps, set to 0 to remove caps and draw plain vertical bars.

Logical. Whether to add a legend for by variable levels (default: FALSE).

Legend position. Either a named position string ("top", "bottom", "left", "right", "topleft", "topright", "bottomleft", "bottomright"), or a numeric vector c(x, y) specifying exact coordinates for manual placement. In base plot() in R, there is no direct way to place the legend truly outside the plot area (e.g., to the right of the axis and tick marks). A workaround, especially effective when bty = "n", is to expand the xlim or ylim range, optionally adjust tick spacing using plot_args = list(xaxp = ...) or yaxp = ..., and set legend_position = c(x, y) to manually locate the legend within the expanded plotting coordinates. When placing the legend in an expanded region, note that axis labels may become misaligned; for best results, suppress them with xlab = "" or ylab = "" and add them manually using text() after the plot is drawn. See the Examples section for a demonstration of this approach. Alternatively, you can enable drawing outside the plot region by setting par(xpd = TRUE) and increasing the plot margins using par(mar = ...). In this case, the legend must be added manually after the call to easyViz(), as it will fall outside the plot area managed by the function.

Optional character string. If specified, this will appear as the title of the legend. In this case, the legend labels will correspond to the levels of the by variable (e.g., "A", "B", "C"). If left as NULL (default), the legend labels will follow standard behavior (e.g., "by = level"). In any case, they can be manually specified via legend_labels.

Custom labels for the legend (e.g., legend_labels = c("Level A", "Level B", "Level C")).

Numeric. Text size for the legend title (default: 1).

Numeric. Text size for the legend labels (default: 0.9).

Logical. If TRUE, the legend is drawn horizontally (side-by-side) instead of vertically (stacked). Defaults to FALSE. Useful for placing the legend above or below the plot in a compact layout.

A named list of additional arguments passed to base R's legend() function. These allow fine-tuned control over the appearance and placement of the legend and override the high-level options provided by legend_position, legend_title and other legend_* arguments. For example, you can adjust the legend's box style, border color, spacing, point size, or background color. Common options include:

• Point and line appearance: pch, col, pt.cex, pt.lwd, lty, lwd

• Layout and spacing: ncol, x.intersp, y.intersp, inset, xjust, yjust

• Text style and color: cex, text.col, font, adj

• Box and background: bty, box.lwd, box.col, bg

• Title control: title, title.col, title.cex, title.adj

For a full list of supported parameters, see ?legend. Example usage:
legend_args = list(bty = "o", box.col = "black", pt.cex = 1.5). Tip: If you're adding a legend outside the plot region using par(xpd = TRUE), you must call legend() manually after easyViz() to place it correctly. Use legend_args only for legends drawn inside the plot area.