imputeRowMinRand: Impute missing values with random numbers based on the row...
In yclement/xcms: LC-MS and GC-MS Data Analysis

Description Usage Arguments Details Author(s) See Also Examples

Replace missing values with random numbers. When using the method = "mean_sd", random numbers will be generated from a normal distribution based on (a fraction of) the row min and a standard deviation estimated from the linear relationship between row standard deviation and mean of the full data set. Parameter sd_fraction allows to further reduce the estimated standard deviation. When using the method method = "from_to", random numbers between 2 specific values will be generated.

imputeRowMinRand(
  x,
  method = c("mean_sd", "from_to"),
  min_fraction = 1/2,
  min_fraction_from = 1/1000,
  sd_fraction = 1,
  abs = TRUE
)

`x`	`matrix` with abundances, rows being features/metabolites and columns samples.
`method`	method `character(1)` defining the imputation method. See description for details. Defaults to `method = "mean_sd"`.
`min_fraction`	`numeric(1)` with the fraction of the row minimum that should be used to replace `NA` values in that row in case that `mean_sd` method is specified. When using `from_to` method, this value will be the one used to calculate the maximum value for replace `NA` values in that row.
`min_fraction_from`	`numeric(1)` with the fraction of the row minimum that should be used to calculate the minimum value for replace `NA` values in that row. This parameter is used only in case that `from_to` method is specified.
`sd_fraction`	`numeric(1)` factor to reduce the estimated standard deviation. This parameter is used only in case that `mean_sd` method is specified.
`abs`	`logical(1)` to force imputed values to be strictly positive.

For method mean_sd, imputed values are taken from a normal distribution with mean being a user defined fraction of the row minimum and the standard deviation estimated for that mean based on the linear relationship between row standard deviations and row means in the full matrix x.

To largely avoid imputed values being negative or larger than the real values, the standard deviation for the random number generation is estimated ignoring the intercept of the linear model estimating the relationship between standard deviation and mean. If abs = TRUE NA values are replaced with the absolute value of the random values.

For method from_to, imputed values are taken between 2 user defined fractions of the row minimum.

Johannes Rainer, Mar Garcia-Aloy

imputeLCMD package for more left censored imputation functions.

Other imputation functions: imputeRowMin()

library(faahKO)
data("faahko")

xset <- group(faahko)
mat <- groupval(xset, value = "into")

## Estimate the relationship between row sd and mean. The standard deviation
## of the random distribution is estimated on this relationship.
mns <- rowMeans(mat, na.rm = TRUE)
sds <- apply(mat, MARGIN = 1, sd, na.rm = TRUE)
plot(mns, sds)
abline(lm(sds ~ mns))

mat_imp_meansd <- imputeRowMinRand(mat, method = "mean_sd")
mat_imp_fromto <- imputeRowMinRand(mat, method = "from_to")

head(mat)
head(mat_imp_meansd)
head(mat_imp_fromto)