Description Usage Arguments Details Warning Author(s) References See Also
This function is used to plot results from the 'medsens' function. Causal average mediation effects (as well as average direct effects and proportions mediated for selected models) can be plotted against two alternative sensitivity parameters.
1 2 3 4 5 6 7  ## S3 method for class 'medsens'
plot(x, sens.par = c("rho", "R2"),
r.type = c("residual", "total"), sign.prod = c("positive",
"negative"), pr.plot = FALSE, smooth.effect = FALSE,
smooth.ci = FALSE, ask = prod(par("mfcol")) < nplots,
levels = NULL, xlab = NULL, ylab = NULL, xlim = NULL,
ylim = NULL, main = NULL, lwd = par("lwd"), ...)

x 
'medsens' object, typically output from 
sens.par 
a character string indicating the sensitivity parameter to be used. Default plots effects as functions of "rho". See Details. 
r.type 
type of the R square parameter to be used in "R2" plots. If "residual", effects are plotted against the proportions of the residual variances that are explained by the unobserved confounder. If "total", the proportions of the total variances are used as sensitivity parameters. Only relevant if 'sens.par' is "R2". 
sign.prod 
a value indicating the direction of hypothesized confounding in the sensitivity analysis. If "positive", the confounder is assumed to affect the mediator and outcome variable in the same direction; if "negative" the effects are assumed to be in opposite directions. Only relevant if sens.par is set to "R2". 
pr.plot 
a logical value. If 'TRUE', the "proportions mediated" will be plotted instead of the average causal mediation effects or direct effects. Currently only available if the object 'medsens' is based on the linear mediator and binary probit outcome models. Default is 'FALSE'. 
smooth.effect 
a logical value indicating whether the estimated
mediation effects are smoothed via 
smooth.ci 
a logical value indicating whether the confidence bands are
smoothed via 
ask 
a logical value. If 'TRUE', the user is asked for input before a new figure is plotted. Default is to ask only if the number of plots on current screen is fewer than necessary. 
levels 
vector of levels at which to draw contour lines. Only relevant
if 'sens.par' is set to "R2". If 'NULL', default values in

xlab 
label for the x axis. Default labels are used if 'NULL'. 
ylab 
label for the y axis. Default labels are used if 'NULL'. 
xlim 
limits of the x axis. If 'NULL' default values are used. 
ylim 
limits of the y axis. If 'NULL' default values are used. 
main 
main title for the plot. If 'NULL', default titles are used. 
lwd 
width of the lines used in graphs. 
... 
additional arguments to be passed to plotting functions. 
The sensitivity analysis for causal mediation effects can be conducted in terms of two alternative sensitivity parameters, which both quantify the degree of violation of the sequential ignorability assumption. The "rho" parameter represents the correlation between the two error terms of the (latent) linear models for the mediator and outcome variables. A large value of rho indicates the existence of important common unobserved predictors for both the mediator and outcome and therefore a high degree of sequential ignorability violation, while a value close to zero implies there is no such confounders.
The resulting "rho" figures plot the estimated true values of ACME (or ADE,
proportion mediated) against rho, along with the confidence intervals. When
rho is zero, sequantial ignorability holds, so the estimated value at that
point will be equal to the estimate returned by the mediate
.
The confidence level is determined by the 'conf.level' value of the
original mediate
object.
The "R2" parameters represent the proportions of the mediator and outcome
variances that are explained by an unobserved pretreatment confounder,
thereby indicating the importance of such a confounder in each model. When
'r.type' is "residual", the R2 parameters represent the proportions of the
residual variances of the mediator and outcome models that become explained
by the inclusion of the hypothetical pretreatment confounder. These are
denoted as "R square stars" in Imai, Keele and Yamamoto (2010) and can also
be specified as "star" or using a numeric value 1 in medsens.plot
.
When 'r.type' is "total", the R2s represent the total mediator and outcome
variances the unobserved confounder would explain. This option can also be
specified using "tilde" or a numeric value 2.
For both types of the "R2" parameters, 'sign.prod' indicates the hypothesized direction in which the unobserved confounder affects the mediator and outcome. (The name derives from the fact that this direction is mathematically represented by the sign of the product of two regression coefficients.) If "positive" (or a numeric value 1) is given, the confounder is assumed to affect the mediator and outcome in the same direction. If "negative" (or a numeric value 1), the effect is assumed to be in opposite directions.
The resulting contours in the "R2" plots represent the values of the ACME (or ADE) for different combinations of the mediator R2 and outcome R2 values. When both values are zero (the lowerleft corner of the plot), the unobserved pretreatment confounder has no effect on either mediator or outcome and therefore sequantial ignorability is satisfied.
The 'smooth.effect' and 'smooth.ci' options should be used with caution since the smoothing could affect substantive implications of the graphical analysis in a significant way.
Dustin Tingley, Harvard University, dtingley@gov.harvard.edu; Teppei Yamamoto, Massachusetts Institute of Technology, teppei@mit.edu; Jaquilyn WaddellBoie, Princeton University, jwaddell@princeton.edu; Luke Keele, Penn State University, ljk20@psu.edu; Kosuke Imai, Princeton University, kimai@princeton.edu.
Tingley, D., Yamamoto, T., Hirose, K., Imai, K. and Keele, L. (2014). "mediation: R package for Causal Mediation Analysis", Journal of Statistical Software, Vol. 59, No. 5, pp. 138.
Imai, K., Keele, L. and Tingley, D. (2010) A General Approach to Causal Mediation Analysis, Psychological Methods, Vol. 15, No. 4 (December), pp. 309334.
Imai, K., Keele, L. and Yamamoto, T. (2010) Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects, Statistical Science, Vol. 25, No. 1 (February), pp. 5171.
Imai, K., Keele, L., Tingley, D. and Yamamoto, T. (2009) "Causal Mediation Analysis Using R" in Advances in Social Science Research Using R, ed. H. D. Vinod New York: Springer.
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