Add new raw data ("Dane/raw244.csv")
Delete all old datasets
Add new raw data on places ("Dane/Places244.csv")
Add occupation,education and sport activites classification datasets
"eduprog2.csv", "occupation2.csv", "trainfav2.csv"
Change: "Relabelling..." to incorporate new data
Add Dane/DataInd231.csv - new preprocessed data on individuals
Update "MissImpute/BasicImpute.R" after changes in "Relabelling..."
"Add Dane/DatIndIm231.csv"
Correction in "OLsToNAs.R" - now it works!
Add outliers removal in book12 and movie30
Some cosmetic changes in "BasicImpute.R"
Update "LimitData.R" and "LimDatInd231.csv"
after changes in "Relabelling..." and incorporating new raw data
Update ImputeGenMICE.R after changes in "Relabelling"
and incorporating new raw data
Update description in "ImputeGenMICE.R"
Now it refers to the problem in press data too
Add standard error computing function
in "ComputingMisc.R"
Update "ImputeHelper.R"
Update "ImputeGenMICE.R" up to social capital
Update "ImputeHelper.R"
add function computing RMSE of an imputation
Update "ImputeGenMICE.R" (up to cultural preferenses)
but the plotting is not finished yet!
Add "MissImpute/MICEimput.RData"
this is an R workspace file with the outcomes of MICE
thanks to this there is no need to repeat the analysis,
which is very computationaly heavy, over and over again
Add MissImpute/ImputeGenMICE.R (finished!!!)
Add "MissImpute/MICEimput.RData"
This file contains the workspace with
the outcomes of "ImputeGenMICE.R"
Add "Dane/FullDatInd231.csv"
This is the dataset that is read for scaling and analysis.
It does not contain any missing values or extreme outliers
and is properly preprocessed, labelled and formatted.
There are few missing values, but only in variables of no analytical
interest, i.e. those which will be used only for the purpose
of descriptive statistics.