plot.cond_indirect_effects: Plot Conditional Effects

In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

Plot Conditional Effects

Description

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.

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.

Value

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

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3102/1076998616657080")}

See Also

cond_indirect_effects()

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
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
plot(out)

manymome documentation built on Oct. 4, 2024, 5:10 p.m.