crplot {PSEA} | R Documentation

## Component-plus-residual (CR) plot

### Description

Component-plus-residual (CR) plot for quantitative variables and group-specific effects.

### Usage

    crplot(lm,quantv,g=NULL,newplot=TRUE,col=c(1,2,4),xlab=NULL,ylab='CR',...)

### Arguments

`lm` | object of class "lm". Fitted model.
`quantv` | character. Name of the quantitative independent variable ("component" to be plotted).
`g` | character vector. Interaction regressors coding for group-specific effects in the model.
`newplot` | logical. If TRUE (default) a new plot (graphics device) is opened.
`col` | integer vector. Colors of groups. Defaults to 1 (black), 2 (red) and 4 (blue) for group 1, 2 and 3, respectively.
`xlab` | character. x-axis label. Absent by default.
`ylab` | character. y-axis label. "CR" by default.
`...` | Further arguments passed to plot()

### Details

In the fitted model (lm), group-specific effects are specified by interaction regressors. In this case, an interaction regressor is a quantitative variable multiplied by a binary variable (i.e. where 0s represent samples of group 1 and 1s represent samples of group 2).

Predicted components are not centered and the intercept of the CR is 0 by definition. This function thus differs from cr.plot in the car package.

### Value

`NULL` | This function is used for plotting.

### Author(s)

Alexandre Kuhn alexandre.m.kuhn@gmail.com

### References

Kuhn A, Thu D, Waldvogel HJ, Faull RL, Luthi-Carter R. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods 2011, 8(11):945-7

### Examples

    ## Load example expression data (variable "expression")
    ## for 23 transcripts and 41 samples, and associated
    ## phenotype (i.e. group) information (variable "groups")
    data("example")

    ## The group data is encoded as a binary vector where
    ## 0s represent control samples (first 29 samples) and
    ## 1s represent disease samples (last 12 samples)
    groups

    ## Neuronal reference signals (i.e. quantitative variable)
    ## and group-specific change in neuronal expression
    ## (i.e. interaction regressor)
    neuron_probesets <- list(c("221805_at", "221801_x_at", "221916_at"),
                    "201313_at", "210040_at", "205737_at", "210432_s_at")
    neuron_reference <- marker(expression, neuron_probesets)
    neuron_difference <- groups * neuron_reference

    ## Fit an expression model containing neuronal expression and
    ## neuron-specific change in expression between control and
    ## disease samples
    model <- lm(expression["202429_s_at",] ~ neuron_reference +
    		neuron_difference)

    ## Visualize the dependence on the neuronal reference signal and
    ## the group-specific effect (decreased neuronal expression in
    ## disease samples). Black and red dots represent control and
    ## disease samples, respectively.
    crplot(model, "neuron_reference", g="neuron_difference")

[Package PSEA version 1.26.0 Index]