Create the PIP plot and accompanying effect plot showing the effect estimates and significance of the effects for all the traits. A z-scores plot is also created when z-scores are available.
mvsusie_plot(
fit,
chr = 1,
pos = seq(1, length(fit$variable_names)),
markers = fit$variable_names,
conditions = fit$condition_names,
poslim = range(pos),
lfsr_cutoff = 0.01,
sentinel_only = TRUE,
cs_plot = names(fit$sets$cs),
add_cs = FALSE,
conditional_effect = TRUE,
cs_colors = c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a", "#b15928",
"#a6cee3", "#b2df8a", "#fb9a99", "#fdbf6f", "#cab2d6", "#ffff99", "gray", "cyan")
)The mvSuSiE fitted model.
The chromosome number.
The positions of the genetic markers. It should have the
same length as fit$variable_names.
The names of the genetic markers (usually SNPs).
The names of the conditions.
The range of positions to show in the PIP plot.
The significance level for lfsr. The default is 0.01.
If TRUE, only plot the sentinel marker
for each CS. If FALSE, plot all markers in each CS.
The CSs included in the plot. The default is to show all CSs.
If TRUE, add colored dots to the top of the
effect plot showing CS membership.
If TRUE, plot the conditional
effect. If FALSE, plot the marginal effect.
conditional_effect = TRUE is recommended.
The color palette for CSs.
The output includes the PIP plot, effect plot, z-scores
plot (if z-scores are available in fit), and the table of
effect estimates at sentinel markers.
# See the "mvsusie_intro" vignette for examples.