graph_model_q: Graph interactions for fitted models.

In jeff-hughes/reghelper: Helper Functions for Regression Analysis

Graph interactions for fitted models.

Description

graph_model_q provides an easy way to graph interactions in fitted models (linear, generalized linear, hierarchical linear, or ANOVA). Selected variables will be graphed at +/- 1 SD (if continuous) or at each level of the factor (if categorical).

Usage

graph_model_q(model, ...)

## S3 method for class 'lm'
graph_model_q(
  model,
  y,
  x,
  lines = NULL,
  split = NULL,
  errorbars = c("CI", "SE", "none"),
  ymin = NULL,
  ymax = NULL,
  labels = NULL,
  bargraph = FALSE,
  draw.legend = TRUE,
  dodge = 0,
  exp = FALSE,
  ...
)

## S3 method for class 'aov'
graph_model_q(
  model,
  y,
  x,
  lines = NULL,
  split = NULL,
  errorbars = c("CI", "SE", "none"),
  ymin = NULL,
  ymax = NULL,
  labels = NULL,
  bargraph = FALSE,
  draw.legend = TRUE,
  dodge = 0,
  exp = FALSE,
  ...
)

## S3 method for class 'glm'
graph_model_q(
  model,
  y,
  x,
  lines = NULL,
  split = NULL,
  type = c("link", "response"),
  errorbars = c("CI", "SE", "none"),
  ymin = NULL,
  ymax = NULL,
  labels = NULL,
  bargraph = FALSE,
  draw.legend = TRUE,
  dodge = 0,
  exp = FALSE,
  ...
)

## S3 method for class 'lme'
graph_model_q(
  model,
  y,
  x,
  lines = NULL,
  split = NULL,
  errorbars = c("CI", "SE", "none"),
  ymin = NULL,
  ymax = NULL,
  labels = NULL,
  bargraph = FALSE,
  draw.legend = TRUE,
  dodge = 0,
  exp = FALSE,
  ...
)

## S3 method for class 'merMod'
graph_model_q(
  model,
  y,
  x,
  lines = NULL,
  split = NULL,
  errorbars = c("CI", "SE", "none"),
  ymin = NULL,
  ymax = NULL,
  labels = NULL,
  bargraph = FALSE,
  draw.legend = TRUE,
  dodge = 0,
  exp = FALSE,
  ...
)

Arguments

model	A fitted linear model of type 'lm', 'aov', 'glm', 'lme', or 'merMod'.
...	Not currently implemented; used to ensure consistency with S3 generic.
y	The variable to be plotted on the y-axis. This variable is required for the graph.
x	The variable to be plotted on the x-axis. This variable is required for the graph.
lines	The variable to be plotted using separate lines (optional).
split	The variable to be split among separate graphs (optional).
errorbars	A string indicating what kind of error bars to show. Acceptable values are "CI" (95% confidence intervals), "SE" (+/-1 standard error of the predicted means), or "none".
ymin	Number indicating the minimum value for the y-axis scale. Default NULL value will adjust position to the lowest y value.
ymax	Number indicating the maximum value for the y-axis scale. Default NULL value will adjust position to the highest y value.
labels	A named list with strings for the various plot labels: 'title' will set the graph title, 'y' sets the y-axis label, 'x' sets the x-axis label, 'lines' sets the legend label, and 'split' sets the label for the facet. If any label is not set, the names of the variables will be used. Setting a label explicitly to NA will set a label with an empty string.
bargraph	Logical. TRUE will draw a bar graph of the results; FALSE will draw a line graph of the results.
draw.legend	Logical. Whether or not to draw legend on the graph.
dodge	A numeric value indicating the amount each point on the graph should be shifted left or right, which can help for readability when points are close together. Default value is 0, with .1 or .2 probably sufficient in most cases.
exp	Logical. If TRUE, the exponential function exp() will be used to transform the y-axis (i.e., e to the power of y). Useful for logistic regressions or for converting log-transformed y-values to their original units.
type	The type of prediction required. The default 'link' is on the scale of the linear predictors; the alternative 'response' is on the scale of the response variable. For more information, see predict.glm.

Details

If there are additional covariates in the model other than what is indicated to be graphed by the function, these variables will be plotted at their respective means. In the case of a categorical covariate, the results will be averaged across all its levels.

Note that in most cases it is easier to use graph_model and pass variable names in directly instead of strings of variable names. graph_model_q uses standard evaluation in cases where such evaluation is easier.

Value

A ggplot2 graph of the plotted variables in the model.

Methods (by class)

• graph_model_q(lm): Graphing linear models.

• graph_model_q(aov): Graphing ANOVA.

• graph_model_q(glm): Graphing generalized linear models.

• graph_model_q(lme): Graphing hierarchical linear models (nlme).

• graph_model_q(merMod): Graphing hierarchical linear models (lme4).

See Also

graph_model

Examples

# iris data
model <- lm(Sepal.Width ~ Sepal.Length * Species, data=iris)
graph_model_q(model, y='Sepal.Width', x='Sepal.Length', lines='Species')

# Orthodont data
if (require(nlme, quietly=TRUE)) {
    model <- lme(distance ~ age * Sex, data=Orthodont, random=~1|Subject)
    graph_model_q(model, y='distance', x='age', lines='Sex')
}

# Arabidopsis data
if (require(lme4, quietly=TRUE)) {
    model <- lmer(total.fruits ~ nutrient * amd + rack + (1|gen), data=Arabidopsis)
    graph_model_q(model, y='total.fruits', x='nutrient', lines='amd')
}

jeff-hughes/reghelper documentation built on Sept. 9, 2023, 1:52 p.m.