plot.trioGxE uses the calculations made in trioGxE and
plots the point- and interval-estimates of gene-environment interaction
between a single nucleotide polymorphism (SNP) and a continuously varying environmental or non-genetic
covariate in case-parent trio data."plot"(x, se = TRUE, seWithGxE.only = TRUE, ylim = NULL, yscale = TRUE, xlab = NULL, ylab = NULL, rugplot = TRUE, ...)trioGxE function.
TRUE (default), upper and lower lines are added to the plots
at 2 standard errors above and below the fitted values of the interaction functions.
When it is a positive number, lines are added at se standard errors above
and below the fitted interaction values.
When FALSE, no standard error lines are plotted.
TRUE, the associated standard errors reflect the uncertainty in the estimates of the
gene-environment interaction functions only.
If FALSE, the standard errors include the uncertainty in the genetic main effect estimates.
TRUE (default), the same y-axis scale is chosen for each plot.
Ignored if ylim is supplied.
TRUE) adds rugs.
plot, such as col, lwd, etc.
When object$penmod="codominant" (with se=TRUE),
confidence intervals are plotted for both interaction curves that are related to ${\rm GRR}_1$ and ${\rm GRR}_2$.
When object$penmod="dominant", the confidence intervals are plotted only in the left panel,
but not in the right panel because ${\rm GRR}_2$ is not estimated but set to be 1 under this penetrance mode.
Similarly, when object$penmod="recessive", the confidence intervals are plotted only in the right panel,
but not in the right panel because ${\rm GRR}_1$ is not estimated but set to be 1 under this penetrance mode.
When object$penmod="additive", equivalent confidence intervals
are plotted in both panels, which display equivalent fitted curves.
This is because ${\rm GRR}_1$ and ${\rm GRR}_2$ are set to be equivalent
under the log-additive or multiplicative penetrance mode.
When se is TRUE or a positive number, standard error lines are plotted
based on the calculations of the Bayesian posterior variance estimates of the generalized
additive model parameters for GRRs (Wood, 2006).
Wood, S. (2006): Generalized Additive Models: An Introduction with R, Boca Raton, FL: Chapman & Hall/CRC.
trioGxE, test.trioGxE, trioSimdata(hypoTrioDat)
## fitting a co-dominant model to the hypothetical data
simfit <- trioGxE(data=hypoTrioDat,pgenos=c("parent1","parent2"),cgeno="child",cenv="attr",
k=c(5,5),knots=NULL,sp=NULL)
## produce the graphical display of the point- and interval-estimates of GxE curve
plot.trioGxE(simfit) # or just plot(simfit)
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