susie_plot
produces a per-variable summary of
the SuSiE credible sets. susie_plot_iteration
produces a
diagnostic plot for the susie model fitting. For
susie_plot_iteration
, several plots will be created if
track_fit = TRUE
when calling susie
.
susie_plot(
model,
y,
add_bar = FALSE,
pos = NULL,
b = NULL,
max_cs = 400,
add_legend = NULL,
...
)
susie_plot_iteration(model, L, file_prefix, pos = NULL)
A SuSiE fit, typically an output from
susie
or one of its variants. For suse_plot
,
the susie fit must have model$z
, model$PIP
, and may
include model$sets
. model
may also be a vector of
z-scores or PIPs.
A string indicating what to plot: either "z_original"
for
z-scores, "z"
for z-score derived p-values on (base-10) log-scale,
"PIP"
for posterior inclusion probabilities,
"log10PIP"
for posterior inclusion probabiliities on the
(base-10) log-scale. For any other setting, the data are plotted as
is.
If add_bar = TRUE
, add horizontal bar to
signals in credible interval.
Indices of variables to plot. If pos = NULL
all
variables are plotted.
For simulated data, set b = TRUE
to highlight
"true" effects (highlights in red).
The largest credible set to display, either based on
purity (set max_cs
between 0 and 1), or based on size (set
max_cs > 1
).
If add_legend = TRUE
, add a legend to
annotate the size and purity of each CS discovered. It can also be
specified as location where legends should be added, e.g.,
add_legend = "bottomright"
(default location is
"topright"
).
Additional arguments passed to
plot
.
An integer specifying the number of credible sets to plot.
Prefix to path of output plot file. If not
specified, the plot, or plots, will be saved to a temporary
directory generated using tempdir
.
Invisibly returns NULL
.
set.seed(1)
n = 1000
p = 1000
beta = rep(0,p)
beta[sample(1:1000,4)] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))
res = susie(X,y,L = 10)
susie_plot(res,"PIP")
susie_plot(res,"PIP",add_bar = TRUE)
susie_plot(res,"PIP",add_legend = TRUE)
susie_plot(res,"PIP", pos=1:500, add_legend = TRUE)
# Plot selected regions with adjusted x-axis position label
res$genomic_position = 1000 + (1:length(res$pip))
susie_plot(res,"PIP",add_legend = TRUE,
pos = list(attr = "genomic_position",start = 1000,end = 1500))
# True effects are shown in red.
susie_plot(res,"PIP",b = beta,add_legend = TRUE)
set.seed(1)
n = 1000
p = 1000
beta = rep(0,p)
beta[sample(1:1000,4)] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))
res = susie(X,y,L = 10)
susie_plot_iteration(res, L=10)
#> Iterplot saved to /var/folders/9b/ck4lp8s140lcksryyh4dppdr0000gn/T//Rtmpj3YNjC/susie_plot.pdf