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Impute missing values with random numbers based on the row minimum

Source: R/functions-imputation.R
imputeRowMinRand.Rd

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.

Usage

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

Arguments

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.

Details

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.

See also

imputeLCMD package for more left censored imputation functions.

Other imputation functions: imputeRowMin()

Author

Johannes Rainer, Mar Garcia-Aloy

Examples


library(faahKO)
library(MSnbase)
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)
#>                 ko15      ko16       ko18      ko19       ko21      ko22
#> 200.1/2926  147887.5  451600.7   65290.38        NA   91635.45  162012.4
#> 205/2791   1778568.9 1567038.1 1482796.38 1039129.8 1223132.35 1072037.7
#> 206/2790    237993.6  269714.0  201393.42  150107.3  176989.65  156797.0
#> 207.1/2719  380873.0  460629.7  351750.14  219288.0  286848.56  235022.6
#> 219.1/2525  235544.9  173623.4         NA        NA  185792.43  174458.8
#> 231/2517          NA        NA  222609.07  286232.1  435094.49        NA
#>                 wt15       wt16       wt18       wt19      wt21       wt22
#> 200.1/2926  175177.1   82619.48         NA   69198.22  153273.5   98144.28
#> 205/2791   1950287.5 1466780.60 1572679.16 1275312.76 1356014.3 1231442.16
#> 206/2790    276541.8  222366.15  211717.71  186850.88  188285.9  172348.76
#> 207.1/2719  417169.6  324892.46  277990.70  220972.35  252874.0  236728.16
#> 219.1/2525  244584.5  161184.05   72029.38         NA  238194.4  173829.95
#> 231/2517          NA         NA         NA  240261.21  201316.2  179437.72
head(mat_imp_meansd)
#>                 ko15       ko16       ko18       ko19       ko21       ko22
#> 200.1/2926  147887.5  451600.71   65290.38   25418.38   91635.45  162012.44
#> 205/2791   1778568.9 1567038.14 1482796.38 1039129.82 1223132.35 1072037.70
#> 206/2790    237993.6  269713.98  201393.42  150107.31  176989.65  156797.04
#> 207.1/2719  380873.0  460629.74  351750.14  219287.97  286848.56  235022.63
#> 219.1/2525  235544.9  173623.38   24880.01   32149.76  185792.43  174458.77
#> 231/2517    101238.9   76327.08  222609.07  286232.15  435094.49   85929.56
#>                 wt15       wt16       wt18       wt19      wt21       wt22
#> 200.1/2926  175177.1   82619.48   22707.20   69198.22  153273.5   98144.28
#> 205/2791   1950287.5 1466780.60 1572679.16 1275312.76 1356014.3 1231442.16
#> 206/2790    276541.8  222366.15  211717.71  186850.88  188285.9  172348.76
#> 207.1/2719  417169.6  324892.46  277990.70  220972.35  252874.0  236728.16
#> 219.1/2525  244584.5  161184.05   72029.38   49169.00  238194.4  173829.95
#> 231/2517    124602.4   99186.90   79848.59  240261.21  201316.2  179437.72
head(mat_imp_fromto)
#>                  ko15      ko16       ko18        ko19       ko21       ko22
#> 200.1/2926  147887.53  451600.7   65290.38    7359.863   91635.45  162012.44
#> 205/2791   1778568.94 1567038.1 1482796.38 1039129.818 1223132.35 1072037.70
#> 206/2790    237993.62  269714.0  201393.42  150107.310  176989.65  156797.04
#> 207.1/2719  380873.05  460629.7  351750.14  219287.968  286848.56  235022.63
#> 219.1/2525  235544.92  173623.4   11044.23   27016.589  185792.43  174458.77
#> 231/2517     44911.84   49671.3  222609.07  286232.146  435094.49   11386.76
#>                  wt15       wt16        wt18       wt19      wt21       wt22
#> 200.1/2926  175177.08   82619.48    4396.818   69198.22  153273.5   98144.28
#> 205/2791   1950287.49 1466780.60 1572679.161 1275312.76 1356014.3 1231442.16
#> 206/2790    276541.85  222366.15  211717.713  186850.88  188285.9  172348.76
#> 207.1/2719  417169.58  324892.46  277990.701  220972.35  252874.0  236728.16
#> 219.1/2525  244584.47  161184.05   72029.378   15814.95  238194.4  173829.95
#> 231/2517     14954.11   74724.08    9623.735  240261.21  201316.2  179437.72