PLMset-class {affyPLM} | R Documentation

## Class PLMset

### Description

This is a class representation for Probe level Linear Models fitted to Affymetrix GeneChip probe level data.

### Objects from the Class

Objects can be created using the function `fitPLM`

### Slots

`probe.coefs`:
  Object of class "matrix". Contains model coefficients related to probe effects
`se.probe.coefs`:
  Object of class "matrix". Contains standard error estimates for the probe coefficients
`chip.coefs`:
  Object of class "matrix". Contains model coefficients related to chip (or chip level) effects for each fit.
`se.chip.coefs`:
  Object of class "matrix". Contains standard error estimates for the chip coefficients
`model.description`:
  Object of class "character". This string describes the probe level model fitted.
`weights`:
  Object of class "matrix". Contains probe weights for each fit. The matrix has columns for chips and rows are probes
`phenoData`:
  Object of class "phenoData" This is an instance of class `phenoData` containing the patient (or case) level data. The columns of the pData slot of this entity represent variables and the rows represent patients or cases.
`annotation`
  A character string identifying the annotation that may be used for the `exprSet` instance.
`description`:
  Object of class "MIAME". For compatibility with previous version of this class description can also be a "character". The class `characterOrMIAME` has been defined just for this.
`cdfName`:
  A character string giving the name of the cdfFile
`nrow`:
  Object of class "numeric". Number of rows in chip
`ncol`:
  Object of class "numeric". Number of cols in chip
`notes`:
  Object of class "character" Vector of explanatory text
`varcov`:
  Object of class "list". A list of variance/covariance matrices
`residualSE`:
  Object of class "matrix". Contains residual standard error and df
`residuals`:
  Object of class "matrix". Contains residuals from model fit (if stored)

### Extends

Class `"exprSet"`, directly.

### Methods

weights<-
  `signature(object = "PLMset")`: replaces the weights
weights
  `signature(object = "PLMset")`: extracts the model fit weights
coefs<-
  `signature(object = "PLMset")`: replaces the chip coefs.
coefs
  `signature(object = "PLMset")`: extracts the chip coefs.
se
  `signature(object = "PLMset")`: extracts the standard error estimates of the chip coefs.
se<-
  `signature(object = "PLMset")`: replaces the standard error estimates of the chip coefs.
coefs.probe
  `signature(object = "PLMset")`: extracts the probe coefs.
se.probe
  `signature(object = "PLMset")`: extracts the standard error estimates of the probe coefs.
coefs.const
  `signature(object = "PLMset")`: extracts the intercept coefs.
se.const
  `signature(object = "PLMset")`: extracts the standard error estimates of the intercept coefs.
getCdfInfo
  `signature(object = "PLMset")`: retrieve the environment that defines the location of probes by probe set.
image
  `signature(x = "PLMset")`: creates an image of the robust linear model fit weights for each sample.
indexProbes
  `signature(object = "PLMset", which = "character")`: returns a list with locations of the probes in each probe set. The list names defines the probe set names. `which` can be "pm", "mm", or "both". If "both" then perfect match locations are given followed by mismatch locations.
Mbox
  `signature(object = "PLMset")`: gives a boxplot of M's for each chip. The M's are computed relative to a "median" chip.
normvec
  `signature(x = "PLMset")`: will return the normalization vector (if it has been stored)
residSE
  `signature(x = "PLMset")`: will return the residual SE (if it has been stored)
boxplot
  `signature(x = "PLMset")`: Boxplot of Normalized Unscaled Standard Errors (NUSE)
NUSE
  `signature(x = "PLMset")` : Boxplot of Normalized Unscaled Standard Errors (NUSE) or NUSE values
RLE|
  `signature(x = "PLMset")` : Relative Log Expression boxplot or values

### Note

This class is better described in the vignette.

### Author(s)

B. M. Bolstad bmb@bmbolstad.com

### References

Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

### Examples

[Package affyPLM version 1.8.0 Index]