`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)
```

- model
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.- y
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.- add_bar
If

`add_bar = TRUE`

, add horizontal bar to signals in credible interval.- pos
Indices of variables to plot. If

`pos = NULL`

all variables are plotted.- b
For simulated data, set

`b = TRUE`

to highlight "true" effects (highlights in red).- max_cs
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`

).- add_legend
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`

.- L
An integer specifying the number of credible sets to plot.

- file_prefix
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
```