miss_imp_PMM: Missing values imputation using PMM

In ICRISAT-GEMS/SpaTemHTP: Processing and utilization of HTP data

Description

Impute the missing values using predictive mean matching

Usage

miss_imp_PMM(data, plot = TRUE)

Arguments

data	data.frame of dimension (N_genotype * N_replicate) x N_days containing the measured phenotypic values.
plot	logical value. If plot = TRUE, plot an overview of the missing values pattern. Default = TRUE.

Details

Missing values are imputed sequentially from the first to the last day.

Value

Return:

data.frame with missing values imputed.

Author(s)

Soumyashree Kar, Vincent Garin

References

Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business & Economic Statistics, 4(1), 87-94.

Examples

data(SG_PH_data)

data <- outliers_det_boxplot(data = SG_PH_data[, 6:28])

## Not run: 

data <- miss_imp_PMM(data = data)


## End(Not run)

ICRISAT-GEMS/SpaTemHTP documentation built on March 9, 2021, 12:12 a.m.