# ggplot2 extension for plotting diversity–stability and diversity–synchrony relationships. `ggiSTAY_analysis` is a graphical function based on the output from the functions `iSTAY_Single` or `iSTAY_Multiple`. It generates plots showing the relationships between stability (and synchrony if multiple time series are included) and an additional variable, such as diversity or another relevant factor. ```r ggiSTAY_analysis(output, x_variable, by_group = NULL, model = "LMM") ``` ## Arguments - `output`: The output obtained from `iSTAY_Single` or `iSTAY_Multiple`. It must include (or be combined with) a column corresponding to the variable specified in `x_variable`. If `by_group` is not `NULL`, must also include a column corresponding the variable specified in `by_group`. - `x_variable`: The name of the column representing the diversity (or other) variable to be used as the x-axis in the plot. - `by_group`: The name of the column representing a categorical variable used to color points by group. The argument is required if `model = "LMM"`, as the model uses it as random effect for both intercept and slope. Default is `NULL`. - `model`: Specifies the fitting model. Use `model = "lm"` for a linear model; or `model = "LMM"` for a linear mixed model with random effects for both intercept and slope. Default is `model = "LMM"`. ## Returns For an `iSTAY_Single` object, this function returns a figure showing the relationship between the diversity (or other) variable and stability. For an `iSTAY_Multiple` object, this function returns a figure showing the diversity (or other) variable and gamma, alpha, and beta stability, as well as synchrony. ## Examples ```r data("Data_Jena_20_metacommunities") data("Data_Jena_76_metapopulations") data("Data_Jena_462_populations") data("Data_Jena_hierarchical_structure") ## Single time series analysis # Analyze the stability of individual plots and diversity-stability # relationship based on 76 plots # See Example 2 in the iSTAY vignette for the output. individual_plots <- do.call(rbind, Data_Jena_20_metacommunities) output_individual_plots_div <- iSTAY_Single(data=individual_plots, order.q=c(1,2), Alltime=TRUE) output_individual_plots_div <- data.frame(output_individual_plots_div, log2_sowndiv = log2(as.numeric(do.call(rbind, strsplit(output_individual_plots_div[,1],"[._]+"))[,2])), block = do.call(rbind, strsplit(output_individual_plots_div[,1],"[._]+"))[,1]) ggiSTAY_analysis(output = output_individual_plots_div, x_variable = "log2_sowndiv", by_group="block", model="LMM") # Analyze the stability of individual populations and diversity-stability # relationships based on 462 populations # See Example 4 in the iSTAY vignette for the output. individual_populations <- do.call(rbind, Data_Jena_76_metapopulations) output_individual_populations_div <- iSTAY_Single(data = individual_populations, order.q=c(1,2), Alltime=TRUE) output_individual_populations_div <- data.frame(output_individual_populations_div, log2_sowndiv = log2(as.numeric(do.call(rbind, strsplit(output_individual_populations_div[,1],"[._]+"))[,3])), block=do.call(rbind, strsplit(output_individual_populations_div[,1],"[._]+"))[,2]) ggiSTAY_analysis(output = output_individual_populations_div, x_variable = "log2_sowndiv", by_group = "block", model = "LMM") ## Multiple time series analysis # Analyze the stability and synchrony within each metacommunity # and their relationships with diversity based on 20 metacommunities # See Example 6 in the iSTAY vignette for the output. metacommunities <- Data_Jena_20_metacommunities output_metacommunities_div <- iSTAY_Multiple(data = metacommunities, order.q = c(1,2), Alltime = TRUE) output_metacommunities_div <- data.frame(output_metacommunities_div, log2_sowndiv = log2(as.numeric(do.call(rbind, strsplit(output_metacommunities_div[, 1], "_"))[, 2])), block = do.call(rbind, strsplit(output_metacommunities_div[, 1], "_"))[, 1]) ggiSTAY_analysis(output = output_metacommunities_div, x_variable = "log2_sowndiv", by_group = "block", model = "LMM") # Analyze the stability and synchrony within each metapopulation # and their relationships with diversity based on 76 metapopulations # See Example 8 in the iSTAY vignette for the output. metapopulations <- Data_Jena_76_metapopulations output_metapopulations_div <- iSTAY_Multiple(data = metapopulations, order.q=c(1,2), Alltime=TRUE) output_metapopulations_div <- data.frame(output_metapopulations_div, log2_sowndiv = log2(as.numeric(do.call(rbind, strsplit(output_metapopulations_div[,1],"[._]+"))[,3])), block = do.call(rbind, strsplit(output_metapopulations_div[,1],"_"))[,2]) ggiSTAY_analysis(output = output_metapopulations_div, x_variable = "log2_sowndiv", by_group = "block", model = "LMM") ```

ggiSTAY_analysis function