Plot Conditional Effects

Plot the conditional effects for different levels of moderators.

Usage

# S3 method for class 'cond_indirect_effects'
plot(
  x,
  x_label,
  w_label = "Moderator(s)",
  y_label,
  title,
  x_from_mean_in_sd = 1,
  x_method = c("sd", "percentile"),
  x_percentiles = c(0.16, 0.84),
  x_sd_to_percentiles = NA,
  note_standardized = TRUE,
  no_title = FALSE,
  line_width = 1,
  point_size = 5,
  graph_type = c("default", "tumble"),
  use_implied_stats = TRUE,
  facet_grid_cols = NULL,
  facet_grid_rows = NULL,
  facet_grid_args = list(as.table = FALSE, labeller = "label_both"),
  digits = 4,
  ...
)

Arguments

x: The output of cond_indirect_effects(). (Named x because it is required in the naming of arguments of the plot generic function.)
x_label: The label for the X-axis. Default is the value of the predictor in the output of cond_indirect_effects().
w_label: The label for the legend for the lines. Default is "Moderator(s)".
y_label: The label for the Y-axis. Default is the name of the response variable in the model.
title: The title of the graph. If not supplied, it will be generated from the variable names or labels (in x_label, y_label, and w_label). If "", no title will be printed. This can be used when the plot is for manuscript submission and figures are required to have no titles.
x_from_mean_in_sd: How many SD from mean is used to define "low" and "high" for the focal variable. Default is 1.
x_method: How to define "high" and "low" for the focal variable levels. Default is in terms of the standard deviation of the focal variable, "sd". If equal to "percentile", then the percentiles of the focal variable in the dataset is used. If the focal variable is a latent variable, only "sd" can be used.
x_percentiles: If x_method is "percentile", then this argument specifies the two percentiles to be used, divided by 100. It must be a vector of two numbers. The default is c(.16, .84), the 16th and 84th percentiles, which corresponds approximately to one SD below and above mean for a normal distribution, respectively.
x_sd_to_percentiles: If x_method is "percentile" and this argument is set to a number, this number will be used to determine the percentiles to be used. The lower percentile is the percentile in a normal distribution that is x_sd_to_percentiles SD below the mean. The upper percentile is the percentile in a normal distribution that is x_sd_to_percentiles SD above the mean. Therefore, if x_sd_to_percentiles is set to 1, then the lower and upper percentiles are 16th and 84th, respectively. Default is NA.
note_standardized: If TRUE, will check whether a variable has SD nearly equal to one. If yes, will report this in the plot. Default is TRUE.
no_title: If TRUE, title will be suppressed. Default is FALSE.
line_width: The width of the lines as used in ggplot2::geom_segment(). Default is 1.
point_size: The size of the points as used in ggplot2::geom_point(). Default is 5.
graph_type: If "default", the typical line-graph with equal end-points will be plotted. If "tumble", then the tumble graph proposed by Bodner (2016) will be plotted. Default is "default" for single-group models, and "tumble" for multigroup models.
use_implied_stats: For a multigroup model, if TRUE, the default, model implied statistics will be used in computing the means and SDs, which take into equality constraints, if any. If FALSE, then the raw data is used to compute the means and SDs. For latent variables, model implied statistics are always used.
facet_grid_cols, facet_grid_rows: If either or both of them are set to character vector(s) of moderator names, then ggplot2::facet_grid() will be used to plot the graph, with facet_grid_cols used as cols and facet_grid_rows used as rows when calling ggplot2::facet_grid().
facet_grid_args: The list of arguments to be used in calling ggplot2::facet_grid(). Ignored if ggplot2::facet_grid() is not used.
digits: The number of decimal places to be printed for numerical moderators when facet_grid is used. Default is 4.
...: Additional arguments. Ignored.

Value

A ggplot2 graph. Plotted if not assigned to a name. It can be further modified like a usual ggplot2 graph.

Details

This function is a plot method of the output of cond_indirect_effects(). It will use the levels of moderators in the output.

It plots the conditional effect from x to y in a model for different levels of the moderators. For multigroup models, the group will be the 'moderator' and one line is drawn for each group.

It does not support conditional indirect effects. If there is one or more mediators in x, it will raise an error.

Multigroup Models

Since Version 0.1.14.2, support for multigroup models has been added for models fitted by lavaan. If the effect for each group is drawn, the graph_type is automatically switched to "tumble" and the means and SDs in each group will be used to determine the locations of the points.

If the multigroup model has any equality constraints, the implied means and/or SDs may be different from those of the raw data. For example, the mean of the x-variable may be constrained to be equal in this model. To plot the tumble graph using the model implied means and SDs, set use_implied_stats to TRUE.

Latent Variables

A path that involves a latent x-variable and/or a latent y-variable can be plotted. Because the latent variables have no observed data, the model implied statistics will always be used to get the means and SDs to compute values such as the low and high points of the x-variable.

References

Bodner, T. E. (2016). Tumble graphs: Avoiding misleading end point extrapolation when graphing interactions from a moderated multiple regression analysis. Journal of Educational and Behavioral Statistics, 41(6), 593-604. doi:10.3102/1076998616657080

Examples

library(lavaan)
dat <- modmed_x1m3w4y1
n <- nrow(dat)
set.seed(860314)
dat$gp <- sample(c("gp1", "gp2", "gp3"), n, replace = TRUE)
dat <- cbind(dat, factor2var(dat$gp, prefix = "gp", add_rownames = FALSE))

# Categorical moderator

mod <-
"
m3 ~ m1 + x + gpgp2 + gpgp3 + x:gpgp2 + x:gpgp3
y ~ m2 + m3 + x
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE)
out_mm_1 <- mod_levels(c("gpgp2", "gpgp3"),
                       sd_from_mean = c(-1, 1),
                       fit = fit)
out_1 <- cond_indirect_effects(wlevels = out_mm_1, x = "x", y = "m3", fit = fit)
plot(out_1)

plot(out_1, graph_type = "tumble")


# Numeric moderator

dat <- modmed_x1m3w4y1
mod2 <-
"
m3 ~ m1 + x + w1 + x:w1
y ~ m3 + x
"
fit2 <- sem(mod2, dat, meanstructure = TRUE, fixed.x = FALSE)
out_mm_2 <- mod_levels("w1",
                       w_method = "percentile",
                       percentiles = c(.16, .84),
                       fit = fit2)
out_mm_2
#>            w1
#> 84%  1.157084
#> 16% -0.626876
out_2 <- cond_indirect_effects(wlevels = out_mm_2, x = "x", y = "m3", fit = fit2)
plot(out_2)

plot(out_2, graph_type = "tumble")


# Multigroup models

dat <- data_med_mg
mod <-
"
m ~ x + c1 + c2
y ~ m + x + c1 + c2
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE,
           group = "group")

# For a multigroup model, group will be used as
# a moderator
out <- cond_indirect_effects(x = "m",
                             y = "y",
                             fit = fit)
out
#> 
#> == Conditional effects ==
#> 
#>  Path: m -> y
#>  Conditional on group(s): Group A[1], Group B[2]
#> 
#>     Group Group_ID   ind
#> 1 Group A        1 0.465
#> 2 Group B        2 1.110
#> 
#>  - The 'ind' column shows the direct effects.
#>  
plot(out)