jnt_cat {JNplots}

Calculation and visualization of regions of non-significance to assess the influence of categorical moderators

Description

Produces a plot showing regions of non-significance defined by predictor values for which no significant differences in the dependent variable are found between categories.

Usage

jnt_cat(
  X,
  Y,
  m,
  data,
  alpha.sig = 0.05,
  plot.full = FALSE,
  correlation = NULL,
  cols = c("black", "black"),
  pch = c(16, 1),
  cex = 1,
  xlab = X,
  ylab = Y,
  lty = c(1, 2),
  line.col = c("black", "black"),
  lwd = c(1, 1),
  legend = TRUE
)

Arguments

X

A character string defining the name of the covariate (e.g., size in an allometry analysis). Must be the same as the name of the variable in the dataset (see argument “data”).

Y

A character string defining the name of the dependent variable. Must be the same as the name of the variable in the dataset (see argument “data”).

m

A character string defining the name of a categorical moderator (e.g., males and females, herbivorous and carnivorous, etc). Must be the same as the name of the variable in the dataset (see argument “data”). The variable must have two levels.

data

A dataframe containing the variables in the model.

alpha.sig

A value representing the significance value (alpha) to be considered.

plot.full

A logical. It indicates whether the plot should show the JN non-significance regions even if they don’t or just partially overlap the data. The default option is 'FALSE', meaning that the plot limits will depend only on the range of predictor values

correlation

an optional corStruct object describing the within-group correlation structure. See the documentation of corClasses for a description of the available corStruct classes. If a grouping variable is to be used, it must be specified in the form argument to the corStruct constructor. Defaults to NULL, corresponding to uncorrelated errors.

cols

A vector of strings defining the colors of the symbols to be used in the plot. By default, c('black', 'black') is used, which combines with the default in the argument 'pch' to present two groups of datapoints as open and close.

pch

A vector of strings defining the symbols to be used to represent distinct groups in the plot. Use same symbol codes as in the argument 'pch' in the R base function 'plot'. By default, c(16, 1) is used, which combines with the default in the argument 'cols' to present two groups of datapoints as open and close.

cex

number indicating the amount by which plotting symbols should be scaled relative to the default (1).

xlab

A title for the X axis. Defaults to the name of the predictor variable in the data.

ylab

A title for the Y axis. Defaults to the name of the dependent variable in the data.

lty

A vector defining the line type of the regression lines for each category. Defaults to c(1,2).

line.col

A vector of strings defining the line colors of the regression lines for each category. Defaults to c('black','black').

lwd

A vector defining the line width of the regression lines for each category. Defaults to c(1,1).

legend

A logical indicating whether a legend should appear on top of the plot. Defaults to 'TRUE'.

Value

List with four elements: (1) results from the linear model, (2) lower and (3) upper limits of non-significance in the predictor, and (4) a graphical output.

References

Toyama, K. S. (2023). JNplots: an R package to visualize outputs from the Johnson-Neyman technique for categorical and continuous moderators, including options for phylogenetic regressions. bioRxiv, 2023-05.

Examples

data(microlophus)
jnt_cat(X='svl', Y='hl', m='species', data=microlophus,
xlab='log(SVL)', ylab='log(head length)')