# Imputing missing values in stata forex

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Mean Imputation of Multiple Columns Often we want to impute all data at once. In R, that is easily possible with a for loop. Imputation of multiple columns i. By doing so, we can impute the whole database with 3 lines of code. Evaluation of Imputed Values As I told you, mean imputation screws your data. Before imputation, X1 is following a normal distribution. After imputing the mean, however, our density has a weird peak at zero in our example the mean of X1. So, how does that affect our data analysis?

Median Mean 3rd Qu. Since our missing data is MCAR , our mean estimation is not biased. The problem is revealed by comparing the 1st and 3rd quartile of X1 pre and post imputation. First quartile before and after imputation: Third quartile before and after imputation: 0. Both quartiles are shifted toward zero, after substituting missing data by the mean. In other words, the quartiles are highly biased. Even bigger problems arise for multivariate measures.

The correlation coefficient between X1 and X2 is shifted toward zero. Observed values are shown in black, imputed values of X1 in red, and imputed values of X2 in green. The observed values are widely spread with a small positive correlation. However, this distribution of X1 and X2 is not reflected by the imputed values.

Instead of imputing the mean of a column as we did before , this method computes the average of each row. Imputing the row mean is mainly used in sociological or psychological research, where data sets often consist of Likert scale items. In research literature, the method is therefore sometimes called person mean or average of the available items. Row mean imputation faces similar statistical problems as the imputation by column means. However, it is also very easy to apply in R: Imputation of one row i.

R imputes NaN Not a Number for these cases. Mean Imputation in SPSS Video As one of the most often used methods for handling missing data, mean substitution is available in all common statistical software packages. He also speaks about the impact of listwise deletion on your data analysis and compares this deletion method with mean imputation see also the first advantage of mean imputation I described above.

Please accept YouTube cookies to play this video. By accepting you will be accessing content from YouTube, a service provided by an external third party. Little, K. Schnabel and J. Baumert eds. Modeling longitudinal and multilevel data: practical issues, applied approaches and specific examples, Mahwah, NJ: Lawrence Erlbaum Associates, pp. Data conversion information From January onwards, almost all data conversions have been performed using software developed by the UKDA.

This enables standardisation of the conversion methods and ensures optimal data quality. Although data conversion is automated, all data files are also subject to visual inspection by a UKDA data processing officer. With some format conversions data, and more especially internal metadata i. Some of this information is specific to the ingest format of the data, that is the format in which the UKDA was supplied the data in. The ingest format for this study was SPSS Please click below to find out information about the format that you have been supplied the data in.

Issues: There is very seldom any loss of data or internal metadata when importing data files into SPSS. Any problems will have been listed above in the Data and Documentation Problems section of this file. User missing values are copied across into STATA as opposed to being collapsed into a single system missing code.

Variables that include both date and time such as dd-mm-yyyy hh:mm:ss e. If the time information is critical, a new variable will have been created in the tab-delimited data file by the UKDA.

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The example data I will use is a data set about air quality. In R, the data is already built in and can be loaded as follows: Load data data airquality Load data data airquality By inspecting the data structure, we can see that there are six variables Ozone, Solar. R, Wind, Temp, Month, and Day and observations included in the data. The variables Ozone and Solar. R have 37 and 7 missing values respectively indicated by NA. Data summaries head airquality Data summaries head airquality If we would base our analysis on listwise deletion, our sample size would be reduced to observations.

Check for number of complete cases sum complete. Fortunately, with missing data imputation we can do better! The mice package includes numerous missing value imputation methods and features for advanced users. At the same time, however, it comes with awesome default specifications and is therefore very easy to apply for beginners. Start by installing and loading the package. Install and load the R package mice install. The imputation process is finished. The reason for that are the predefined default specifications of the mice function.

Usually, it is preferable to impute your data multiple times, but for the sake of simplicity I used a single imputation in the present example. However, if you want to perform multiple imputation, you can either increase m to the number of multiple imputations you like. Mice uses predictive mean matching for numerical variables and multinomial logistic regression imputation for categorical data.

In our case, the variables Solar. Imputation models can be specified with the argument predictorMatrix, but it often makes sense to use as many variables as possible. Organizational variables such as ID columns can also be dropped using the predictorMatrix argument. That is another awesome feature of the R package mice. Missing values are repeatedly replaced and deleted, until the imputation algorithm iteratively converges to an optimal value. The mice function repeats the replacement and deletion steps five times by default.

Unfortunately, unless the mechanism of missing data is MCAR, this method will introduce bias into the parameter estimates. Available Case Analysis: This method involves estimating means, variances and covariances based on all available non-missing cases. Meaning that a covariance or correlation matrix is computed where each element is based on the full set of cases with non-missing values for each pair of variables. This method became popular because the loss of power due to missing information is not as substantial as with complete case analysis.

Depending on the pairwise comparisons examined, the sample size will change based on the amount of missing present in one or both variables. One of the main drawbacks of this method is no consistent sample size and the parameter estimates produced are often much different than the estimates obtained from analysis on the full data or the listwise deletion approach.

Unless the mechanism of missing data is MCAR, this method will introduce bias into the parameter estimates. Therefore, this method is not recommended. Unconditional Mean Imputation: This methods involves replacing the missing values for an individual variable with its overall estimated mean from the available cases. While this is a simple and easily implemented method for dealing with missing values it has some unfortunate consequences.

This also has the unintended consequence of changing the magnitude of correlations between the imputed variable and other variables. We can demonstrate this phenomenon in our data. This will require us to create dummy variables for our categorical predictor prog. You will notice that there is very little change in the mean as you would expect ; however, the standard deviation is noticeably lower after substituting in mean values for the observations with missing information. This is because you reduce the variability in your variables when you impute everyone at the mean.

Moreover, you can see the table of correlation coefficients that the correlation between each of our predictors of interest write , math , female , and prog as well as between predictors and the outcome read have now be attenuated. Therefore, regression models that seek to estimate the associations between these variables will also see their effects weakened.

The strength of this approach is that it uses complete information to impute values. The drawback here is that all your predicted values will fall directly on the regression line once again decreasing variability, just not as much as with unconditional mean imputation. Moreover, statistical models cannot distinguish between observed and imputed values and therefore do not incorporate into the model the error or uncertainly associated with that imputed value.

Additionally, you will see that this method will also inflate the associations between variables because it imputes values that are perfectly correlated with one another. Unfortunately, even under the assumption of MCAR, regression imputation will upwardly bias correlations and R-squared statistics. A residual term, that is randomly drawn from a normal distribution with mean zero and variance equal to the residual variance from the regression model, is added to the predicted scores from the regression imputation thus restoring some of the lost variability.

This method is superior to the previous methods as it will produce unbiased coefficient estimates under MAR. However, the standard errors produced during regression estimation while less biased then the single imputation approach, will still be attenuated.

However, instead of filling in a single value, the distribution of the observed data is used to estimate multiple values that reflect the uncertainty around the true value. These values are then used in the analysis of interest, such as in a OLS model, and the results combined. Each imputed value includes a random component whose magnitude reflects the extent to which other variables in the imputation model cannot predict its true values Johnson and Young, ; White et al, MI has three basic phases: 1.

Imputation or Fill-in Phase: The missing data are filled in with estimated values and a complete data set is created. This process of fill-in is repeated m times. Analysis Phase: Each of the m complete data sets is then analyzed using a statistical method of interest e. Pooling Phase: The parameter estimates e. The imputation method you choose depends on the pattern of missing information as well as the type of variable s with missing information.

Consistency means that your imputation model includes at the very least the same variables that are in your analytic or estimation model. This includes any transformations to variables that will be needed to assess your hypothesis of interest. This can include log transformations, interaction terms, or recodes of a continuous variable into a categorical form, if that is how it will be used in later analysis.

The reason for this relates back to the earlier comments about the purpose of multiple imputation. Otherwise, you are imputing values assuming they have a correlation of zero with the variables you did not include in your imputation model.

This would result in underestimating the association between parameters of interest in your analysis and a loss of power to detect properties of your data that may be of interest such as non-linearities and statistical interactions. For additional reading on this particular topic see: 1. White et al. In general, you want to note the variable s with a high proportion of missing information as they will have the greatest impact on the convergence of your specified imputation model.

Stata has a suite of multiple imputation mi commands to help users not only impute their data but also explore the patterns of missingness present in the data. A dataset that is mi set is given an mi style. This tells Stata how the multiply imputed data is to be stored once the imputation has been completed. For information on these style type help mi styles into the command window. We will use the style mlong. The chosen style can be changed using mi convert. These new variables will be used by Stata to track the imputed datasets and values.

The value is 0 for the original dataset. The mi misstable commands helps users tabulate the amount of missing in their variables of interest summarize as well as examine patterns of missing patterns. Each row represents a set of observations in the data set that share the same pattern of missing information. You can see that there are a total of 12 patterns for the specified variables. You will want to examine this table for any patterns and the appearance of any set of variables that appear to always be missing together.

Moreover, depending on the nature of the data, you may also recognize patterns such as monotone missing which can be observed in longitudinal data when an individual drops out at a particular time point and therefore all data after that is subsequently missing.

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