VisualizeDesign {ExploreModelMatrix} | R Documentation

## Visualize design matrix

### Description

Given a sample table and a design formula, generate a collection of static plots for exploring the resulting design matrix graphically. This function is called internally by `ExploreModelMatrix()`, but can also be used directly if interactivity is not required.

### Usage

    VisualizeDesign(
      sampleData,
      designFormula,
      flipCoordFitted = FALSE,
      flipCoordCoocc = FALSE,
      textSizeFitted = 5,
      textSizeCoocc = 5,
      textSizeLabsFitted = 12,
      textSizeLabsCoocc = 12,
      lineWidthFitted = 25,
      addColorFitted = TRUE,
      colorPaletteFitted = scales::hue_pal(),
      dropCols = NULL
    )

### Arguments

`sampleData` | A `data.frame` of `DataFrame` with sample information.
`designFormula` | A `formula`. All components of the terms must be present as columns in `sampleData`.
`flipCoordFitted, flipCoordCoocc` | A `logical`, whether to flip the coordinate axes in the fitted values/co-occurrence plot, respectively.
`textSizeFitted, textSizeCoocc` | A `numeric` scalar giving the text size in the fitted values/co-occurrence plot, respectively.
`textSizeLabsFitted, textSizeLabsCoocc` | A `numeric` scalar giving the text size for the axis labels in the fitted values/co-occurrence plot, respectively.
`lineWidthFitted` | A `numeric` scalar giving the maximal length of a row in the fitted values plot, before it is split and printed on multiple lines
`addColorFitted` | A `logical` scalar indicating whether the terms in the fitted values plot should be shown in different colors.
`colorPaletteFitted` | A `function` returning a color palette to use for coloring the model coefficients in the fitted values plot.
`dropCols` | A character vector with columns to drop from the design matrix, or NULL if no columns should be dropped.

### Value

A list with the following elements:

- `sampledata`: A `data.frame`, expanded from the input `sampleData`

- `plotlist`: A list of plots, displaying the fitted values for each combination of predictor values, in terms of the model coefficients.

- `designmatrix`: The design matrix, after removing any columns in `dropCols`

- `pseudoinverse`: The pseudoinverse of the design matrix

- `vifs`: A `data.frame` with calculated variance inflation factors

- `colors`: A vector with colors to use for different model coefficients

- `cooccurrenceplots`: A list of plots, displaying the co-occurrence pattern for the predictors (i.e., the number of observations for each combination of predictor values)

### Author(s)

Charlotte Soneson

### Examples

    VisualizeDesign(
      sampleData = data.frame(genotype = rep(c("A", "B"), each = 4),
                              treatment = rep(c("treated", "untreated"), 4)),
      designFormula = ~genotype + treatment
    )

[Package ExploreModelMatrix version 0.99.3 Index]