snp.effects.plot {CGEN} | R Documentation

## Effects plot

### Description

Creates a plot of the effects returned from snp.effects

### Usage

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

### Arguments

`obj.list` | Return object or list of return objects from `snp.effects`. No default.
`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.

### Value

Returns nothing. Creates a snp effect plot.

### See Also

`snp.effects`

### Examples

     # 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))

[Package CGEN version 3.30.0 Index]