plot.medsens: Plotting Results from Sensitivity Analysis for Causal...
In mediation: Causal Mediation Analysis

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.

## 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 `medsens`.
`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 `lowess` before being plotted. Default is 'FALSE'.
`smooth.ci`	a logical value indicating whether the confidence bands are smoothed via `lowess` before being plotted. Default is 'FALSE'.
`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 `contour.default` are used.
`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 pre-treatment 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 pre-treatment 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 lower-left corner of the plot), the unobserved pre-treatment 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 Waddell-Boie, 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. 1-38.

Imai, K., Keele, L. and Tingley, D. (2010) A General Approach to Causal Mediation Analysis, Psychological Methods, Vol. 15, No. 4 (December), pp. 309-334.

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. 51-71.

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.

medsens, plot, contour.

mediation documentation built on Oct. 9, 2019, 1:04 a.m.