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{See Material S1 for data, methods and experimental approach}.
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Here we present methods for the simulated data; methods specific to the empirical data are given below.
Firstly, the results from imposing a multivariate MAR mechanism are reported for all missing data methods.
The likelihood ratio statistic remained highly significant for all missing data methods and levels of missingness.
This paper presents missing data methods for repeated measures data in small samples.
For data integration methods, there are a number of methods emerging and more being developed all the time.
Table 1 Common methods for mining data Method Goal/description Example tasks Reference Classification To define a set of classes, which are usually mutually exclusive.
To assess differential gene expression, LIMMA (linear models for microarray data) method [ 72] was used.
Spatial DFA gave best results for data reduction method.
The corresponding methods for data processing, identification and methods for characterization description are also proposed.
Of the 20 articles examined, half (10) used electronic methods for data collection, although various methods were used, ranging from pen and paper to smartphone applications.
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