gen_stat: Calculation of imputation performance statistics

In aprodi/methyLImp: Missing Value Estimation of DNA Methylation Data

Description

Calculates the performances statistics of an imputed methylation array with respect to four metrics: Root Mean Square Error, Mean Absolute Error, Pearson Correlation Coefficient and Mean Absolute Percentage Error.

Usage

gen_stat(original, imputed) 

Arguments

original	Original matrix of betas or M-values before imputation
imputed	Matrix of betas or M-values after imputation

Value

Returns a detailed summary of the performance statistics of the imputed array.

Author(s)

Pietro Di Lena

References

Di Lena P et al: Missing value estimation methods for DNA methylation data. submitted to Bioinformatics

Examples

## Load the methyLImp dataset, containing no missing value
data(gse64495)
summary(gse64495)
## Artificially introduce 10% missing values in the first sample
## with the gen_randNA function
set.seed(50)
samp <- 1
frac <- 0.1
gse64495.mis <- gen_randNA(gse64495,samp,frac)
summary(gse64495.mis)
## Impute the missing values with the methyLImp routine methyLImp
## Note that variables need to be on the columns and
## samples on the rows.
gse64495.imp <- methyLImp(t(gse64495.mis),min=0,max=0)
gse64495.imp <- t(gse64495.imp)
## Compare imputed and original values
miss <- is.na(gse64495.mis[,samp])
orig <- gse64495[miss,samp]
pred <- gse64495.imp[miss,samp]
gen_stat(orig,pred)

aprodi/methyLImp documentation built on May 25, 2019, 2:20 p.m.