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Missing data analysis using mice package in r | data handling in r studio
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#missingdata #missingdataanalysis
Learn the methods to impute missing values in R for data cleaning and exploration
Understand how to use packages like amelia, missForest, hmiscand mice which use bootstrap sampling and predictive modeling.
MICE (Multivariate Imputation via Chained Equations) is one of the commonly used package by R users. Creating multiple imputations as compared to a single imputation (such as mean) takes care of uncertainty in missing values.
MICE assumes that the missing data are Missing at Random (MAR), which means that the probability that a value is missing depends only on observed value and can be predicted using them. It imputes data on a variable by variable basis by specifying an imputation model per variable.
along with that we will also see how to visual the missing data using library mice and VIM.
Learn the methods to impute missing values in R for data cleaning and exploration
Understand how to use packages like amelia, missForest, hmiscand mice which use bootstrap sampling and predictive modeling.
MICE (Multivariate Imputation via Chained Equations) is one of the commonly used package by R users. Creating multiple imputations as compared to a single imputation (such as mean) takes care of uncertainty in missing values.
MICE assumes that the missing data are Missing at Random (MAR), which means that the probability that a value is missing depends only on observed value and can be predicted using them. It imputes data on a variable by variable basis by specifying an imputation model per variable.
along with that we will also see how to visual the missing data using library mice and VIM.
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