| Title: | Performing Monte Carlo Expectation Maximization Random Forest Imputation for Biological Data |
| Version: | 0.1.2 |
| Author: | Mohamed Soudy [aut, cre] |
| Maintainer: | Mohamed Soudy <MohmedSoudy2009@gmail.com> |
| Description: | Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | missForest, ggpubr, progress, doParallel, foreach |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Packaged: | 2023-02-24 16:27:57 UTC; Soudy |
| Date/Publication: | 2023-02-24 16:40:02 UTC |
Evaluate the imputed data sets and select the best data set
Description
The function is evaluate the imputed data sets based on the mean and standard deviation
Usage
EvalImp(Originaldata, ImputedSets ,Imputed.mean, Imputed.sd)
Arguments
Originaldata |
data frame of original data containing the missing values |
ImputedSets |
list of imputed data frames |
Imputed.mean |
data frame of the means of the imputed data sets |
Imputed.sd |
data frame of the standard deviations of the imputed data sets |
Value
The best data frame which mean and standard deviation are close to the original data
Author(s)
Mohamed Soudy Mohmedsoudy2009@gmail.com
Perform Monte Carlo Expectation Maximization Random Forest Imputation
Description
The function is used to impute the missing data using Monte Carlo Expectation Maximization Random Forest Imputation
Usage
MERO(Data, ntree = 100, Nsets = 5)
Arguments
Data |
a data matrix with missing values. The columns correspond to the variables and the rows to the observations. |
ntree |
number of trees to grow in each forest. |
Nsets |
number of simulations/ data sets to be generated. |
Value
A list containing data sets and imputed means, and imputed standard deviation.
Author(s)
Mohamed Soudy Mohmedsoudy2009@gmail.com
Plot the correlation in scatter plot between original mean and imputed mean
Description
The function is used to plot the correlation between the imputed mean and original mean
Usage
PlotCorrelateMean(OriginalMean, ImputedMean)
Arguments
OriginalMean |
means of the original data |
ImputedMean |
means of the imputed data |
Value
The scatter plot
Calculate Root Mean Square Error 'RMSE' between vectors
Description
The function is used to calculate the root mean square error between two vectors
Usage
RMSE(Actual, Predicted)
Arguments
Actual |
Vector of actual data |
Predicted |
vector of predicted data |
Value
The root mean square error between the two input vectors
Author(s)
Mohamed Soudy Mohmedsoudy2009@gmail.com
Examples
RMSE(c(1,2,3), c(10,20,30))