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
, trioSim
data(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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