This folder contains packages that are part of the basic installation. When R is installed on your computer also a folder called library is created. There are more possibilities, but most of the times you use the default Input setting. Examples are, Input for independent variable, Target for dependent or outcome variable, Both, independent and dependent variable. Further, you can change variable options as: Type: e.g. numeric or string variable Width: number of digits Decimals: the number of decimal places displayed Label: add some extra information about the type of information in the variable Values: To assign numbers to the categories of a variable Missing: you can define specified data values as user-missing or system missing Columns: To change the number of characters displayed in the Data View window Align: to specify the alignment of the data Measure: to specify the level of each variable, scale (continuous), ordinal or nominal Role: Here you can define the role of the variable during your analysis. In the Variable View window, you can add new variables, by entering the name in the name column. 13.2 Multiple parameter Wald test or D2 method.13.1 The pooled sampling variance or D1 method.13 Pooling Methods for Categorical variables.10.3 Fraction of Missing Information - FMI.10.1 Fraction of Missing Information - Lambda.10 Measures of Missing data information.VII Part VII: Background information to Multiple Imputation Methods.8.2.1 Parcel summary multiple imputation.8.2 Practical issues with missing data in questionnaires.8.1.3 (Stochastic) regression imputation.8.1 Methods for missing questionnaire data.VI Part VI: Missing Data in Questionnaires.7.13 Missing data in Dichotomous variables.
7.12 Missing data in continuous variables.7.10 Multilevel Multiple Imputation models.7.9 Sporadically and systematically missing data.7.7 Restructuring from wide to long in R.7.6 Restructuring from wide to long in SPSS.7.5 Longitudinal Multilevel data - from wide to long.7.4 Multilevel data - Clusters and Levels.7.1 Advanced Multiple Imputation models for Multilevel data.7 Multiple Imputation models for Multilevel data.V Part V: Advanced Multiple Imputation methods.6.4.2 Variable Selection with Cox Regression models in R.6.4.1 Variable Selection with Logistic Regression models in R.6.3 Cox Regression with a categorical variable in R.6.2 Logistic regression with a categorical variable in R.6.1 Regression modeling with categorical covariates.6 More topics on Multiple Imputation and Regression Modelling.5.2.6 Analysis of Variance (ANOVA) pooling.5.2.2 Pooling Means and Standard Deviations in R.5.2.1 Pooling Means and Standard deviations in SPSS.5 Data analysis after Multiple Imputation.IV Part IV: Data Analysis After Multiple Imputation.4.14 Number of Imputed datasets and iterations.4.13 Imputation of categorical variables.
2.7.2 Compare and test group comparisons.II Part II: Basic Missing Data Handling.1.15 Useful Missing data Packages and links.1.6.4 Indexing Vectors, Matrices, Lists and Data frames.1.6.3 Vectors, matrices, lists and data frames.