# Effects plot

### Description

Creates a plot of the effects returned from snp.effects

### Usage

1 | ```
snp.effects.plot(obj.list, op=NULL)
``` |

### Arguments

`obj.list` |
Return object or list of return objects from |

`op` |
List of options (see details). The default is NULL. |

### Details

Plots the effects returned from `snp.effects`

. By default, the effects in `StratEffects`

for each method will be plotted. The side of the effect will have a sawtooth edge if the effect goes beyond
the limits of the plot.

**Options list op:**
Below are the names for the options list `op`

. All names have default values
if they are not specified.

`method`

Character vector of the values "UML", "CML", "EB", "HCL", "CCL", "CLR". The default is all methods will be plotted.`type`

One of "JointEffects", "StratEffects", "StratEffects.2". The default is StratEffects.`ylim`

NULL or a 2-element numeric vector specifying the y-axis limits for all plots. If not specified, different plots will be on different scales. The default is NULL.`legend`

See`legend`

. Set to NA for no legend to appear. The default is NULL.`split.screen`

NULL or a 2-element vector for partitioning the plot window. This option is only valid for inputing a list of objects. The default is NULL.`colors`

Character vector of colors to use in the plot. See`colors`

for all possible colors. The default is NULL.`levels1`

Vector of levels for the SNP variable to plot. When plotting more than one method,`levels1`

has the default value of 1. Otherwise, the default is NULL.`levels2`

Vector of levels to plot for the variable`var`

(in`snp.effects`

). The default is NULL.`addCI`

0 or 1 to add 95% confidence intervals to the plot. The confidence intervals appear as un-filled boxes around each odds-ratio. The default is 0.

### See Also

`snp.effects`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
# Use the ovarian cancer data
data(Xdata, package="CGEN")
# Add some fake SNPs
set.seed(636)
Xdata[, "rs123"] <- rbinom(nrow(Xdata), 1, 0.4)
Xdata[, "rs456"] <- rbinom(nrow(Xdata), 1, 0.4)
Xdata[, "rs789"] <- rbinom(nrow(Xdata), 1, 0.4)
snpVars <- c("BRCA.status", "rs123", "rs456", "rs789")
objects <- list()
for (i in 1:length(snpVars)) {
fit <- snp.logistic(Xdata, "case.control", snpVars[i],
main.vars=c("oral.years", "n.children"),
int.vars=c("oral.years", "n.children"),
strata.var="ethnic.group")
# Compute the effects
objects[[i]] <- snp.effects(fit, "oral.years", var.levels=0:4)
}
# Plot
snp.effects.plot(objects)
# Plot all on the same scale
#snp.effects.plot(objects, op=list(ylim=c(0.9, 1.4), legend=list(x="bottom")))
# Plot all the joint effects of rs789 for the CML method and add confidence intervals
#snp.effects.plot(objects[[4]], op=list(method="CML", type="JointEffects",
# legend=list(x="bottomleft", inset=0), ylim=c(0.45, 1.3),
# colors=c("blue", "aquamarine", "skyblue"), addCI=1))
``` |