Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

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Documentation for package ‘doMIsaul’ version 1.0.1

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cleanUp_partition Remove small clusters (i.e. unclassified observations for which no consensus was obtained)
CVE_LP Cross-validation for cox regression using the linear predictor estimator with wrapper for warnings handling
evaluate_partition_semisup Evaluation of a semisupervised obtained partition in comparison to reference partitions
evaluate_partition_unsup Comparison of an unsupervised obtained partition to a reference partition.
initiate_centers Initiate centers for clustering algorithm
mice.impute.cens Impute left censored data with MICE
MImpute Wrapper functions for multivariate imputation with survival data or left-censored data
MImpute_lcens Wrapper functions for multivariate imputation with survival data or left-censored data
MImpute_lcenssurv Wrapper functions for multivariate imputation with survival data or left-censored data
MImpute_surv Wrapper functions for multivariate imputation with survival data or left-censored data
MultiCons MultiCons Consensus Clustering Algorithm
partition_generation Unsupervised partition with K selection
plot_boxplot 'ggplot' type boxplots for each vars.cont by partition level.
plot_frequency 'ggplot' type barplots representing frequencies for each vars.cat by partition level.
plot_MIpca Plot a PCA from a multiply imputed dataset.
plot_MIpca_all Plot a PCA from a multiply imputed dataset.
seMIsupcox Semisupervised learning for a right censored endpoint
table_categorical Display table with comparison of the partition with categorical variables.
table_continuous Display table with comparison of the partition with continuous variables.
unsupMI Unsupervised learning for incomplete dataset