| Title: | Visualize Outputs from the 'Johnson-Neyman' Technique |
|---|---|
| Description: | Aids in the calculation and visualization of regions of non-significance using the 'Johnson-Neyman' technique and its extensions as described by Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5> to assess the influence of categorical and continuous moderators. Allows correcting for phylogenetic relatedness. |
| Authors: | Ken Toyama [cre, aut] |
| Maintainer: | Ken Toyama <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.1 |
| Built: | 2025-01-29 04:23:10 UTC |
| Source: | https://github.com/kenstoyama/jnplots |
Data for redness and brightness of different plumage patches in Furnariidae species and associated climatic data
data(bird_colors)data(bird_colors)
An object of class data.frame
Marcondes et al. (2021) The American Naturalist 197:592-606 (PubMed)
Seeholzer et al. (2017) Evolution 71:702:715 (PubMed)
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.
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 )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 )
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'. |
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.
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.
data(microlophus) jnt_cat(X='svl', Y='hl', m='species', data=microlophus, xlab='log(SVL)', ylab='log(head length)')data(microlophus) jnt_cat(X='svl', Y='hl', m='species', data=microlophus, xlab='log(SVL)', ylab='log(head length)')
Produces a plot showing how changes in the moderator affect the slope and significance of the relationship between the dependent variable and the predictor.
jnt_cont( X, Y, m, data, alpha.sig = 0.05, correlation = NULL, res = 100, xlab = X, ylab = Y, col.gradient = TRUE, sig_color = "lightblue", nonsig_color = "grey", max_col_grad = "red", min_col_grad = "blue", legend = TRUE )jnt_cont( X, Y, m, data, alpha.sig = 0.05, correlation = NULL, res = 100, xlab = X, ylab = Y, col.gradient = TRUE, sig_color = "lightblue", nonsig_color = "grey", max_col_grad = "red", min_col_grad = "blue", legend = TRUE )
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 continuous moderator. Must be the same as the name of the variable in the dataset (see argument “data”). The variable must be continuous. |
data |
A dataframe containing the variables in the model. |
alpha.sig |
A value representing the significance value (alpha) to be considered. |
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. |
res |
A numerical value that aids in the plotting of regions of (non)significance. Default=100, higher numbers increase the number of fitted regression lines plotted (N=res-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. |
col.gradient |
A logical indicating whether the significant regression lines should be plotted with a gradient of colors representing moderator values. Defaults to 'TRUE'. |
sig_color |
If col.gradient = FALSE, a character string indicating the color of the significant regression lines. Defaults to 'lightblue'. |
nonsig_color |
If col.gradient = FALSE, a character string indicating the color of the non-significant regression lines. Defaults to 'grey'. |
max_col_grad |
If col.gradient = TRUE, a character string indicating the maximum color of the gradient. |
min_col_grad |
If col.gradient = TRUE, a character string indicating the minimum color of the gradient. |
legend |
A logical indicating whether a legend should appear on top of the plot. Defaults to 'TRUE'. |
List with six elements: (1) results from the linear model, (2) lower and (3) upper limits of (non)significance in the moderator, (4) lower and (5) upper data limit in the data, and (6) a graphical output.
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.
#### non-phylogenetic model #### data(lizard_home_range) jnt_cont(X='PHR95_overlap_z', Y='hrsize95', m='degree_z', data=lizard_home_range, xlab = 'home range overlap 95', ylab='home range size 95') #### phylogenetic model #### jnt_cont(X='bio12', Y='back_bright', m='bio1', data=bird_colors, correlation=corPagel(1, tree_Furnariidae),xlab='precipitation (mm)', ylab='back brightness (scaled)',res=200)#### non-phylogenetic model #### data(lizard_home_range) jnt_cont(X='PHR95_overlap_z', Y='hrsize95', m='degree_z', data=lizard_home_range, xlab = 'home range overlap 95', ylab='home range size 95') #### phylogenetic model #### jnt_cont(X='bio12', Y='back_bright', m='bio1', data=bird_colors, correlation=corPagel(1, tree_Furnariidae),xlab='precipitation (mm)', ylab='back brightness (scaled)',res=200)
Dataset includes data on the determinants of home range size in the lizard Tiliqua rugosa
data(lizard_home_range)data(lizard_home_range)
An object of class data.frame
Payne et al. (2021) Ecological Monographs 92:e1519 (doi:10.1002/ecm.1519)
Data for body size and head morphology of Microlophus lizards
data(microlophus)data(microlophus)
An object of class data.frame
Toyama et al. (2018) Zoology 129:45-53 (PubMed)
Phylogeny of Furnariidae
data(tree_Furnariidae)data(tree_Furnariidae)
An object of class phylo
Harvey et al. (2020) Science 370:1343-1348 (PubMed)