# impute.lowess

0th

Percentile

##### Imputing log2 ratios

Imputing log2 ratios

Keywords
models
##### Usage
impute.lowess(aCGH.obj, chrominfo = human.chrom.info.Jul03, maxChrom =
23, smooth = 0.1)
##### Arguments
aCGH.obj
Object of class aCGH.
chrominfo
a chromosomal information associated with the mapping of the data
maxChrom
Highest chromosome to impute.
smooth
smoothing parameter for the lowess procedure
##### Details

There are two main reasons to impute data. One is that given that imputation is reasonable, one can increase the analytical power and improve results. Another, more practical, is that at the moment many widely used fuctions in R do not support missing values. While procedures such as kNN imputations is widely used for gene expression data, it is more powerful to take advantage of the genomic structure of the array CGH data and use a smoother. Note that we perform only one pass os smoothing. If there still remain missing values, they are imputed by the median on the chromosome or chromosomal arm where applicable,

##### Value

Computes and returns the imputed log2 ratio matrix of the aCGH object.

aCGH, impute.HMM.

##### Aliases
• impute.lowess
##### Examples


clones.info <-
header = TRUE, sep = "\t", quote="", comment.char="")
log2.ratios <-