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To convert a string to a numerical value, use the Number function. To convert a numerical value to a string, use the String function. For detailed information on each action, see the online ActionScript Dictionary in the Help menu. Scoping a variable A variable s scope refers to the area in which the variable is known and can be referenced. There are three types of variable scope in ActionScript:

Cooper GM and Hausman RE (2004) The Cell: A Molecular Approach, 3rd edn. Sinauer Associates Inc, Sunderland, MA. Singleton P (2004) Bacteria in Biology, Biotechnology and Medicine, 6th edn. John Wiley & Sons, Chichester.

Figure 1-2

generally suf cient to have good parameter estimation Thus, for data quality analysis, there are two good options that are not available for hypothesis testing These two options are (a) to analyze the EM covariance matrix directly and (b) to analyze a single data set imputed from the EM parameters (with error) EM produces maximum-likelihood estimates of the covariance matrix From this, one may perform exploratory factor analysis and may get excellent estimation of coef cient alpha in the missing data case The main drawback to analyzing the EM covariance matrix has been the logistics of producing a matrix that is readable by existing software We will describe a solution to this logistical problem Producing a single imputed data set from EM parameters is not normally a good solution to hypothesis-testing problems.

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Although the data set itself is a plausible one, the fact that there is just one data set means that it is not possible to estimate standard errors accurately for the parameters estimated from this data set Nevertheless, if one must produce a single imputed data set, using EM parameters is the best option in that EM parameters are, in a sense, in the center of parameter space Another way of thinking about this is to note that the EM parameters are very similar to the average parameters obtained from a large number of imputed data sets The key advantage of using a single data set imputed from EM parameters is that one is dealing with a data set with no missing data Thus, standard statistical software (eg, SAS or SPSS) can be used.

The only caveat is that in analyzing this single data set, one should NOT rely on the t values and p values generated by the analysis Multiple Imputation with NORM In this section, we describe in some detail the use of the NORM software (version 203; Schafer, 1997) NORM will very likely be modi ed and improved in the future In order to maximize the value of the information provided in the present chapter, step-by-step instructions for the operation of the current version of NORM will be maintained at our web site, http://methodologypsuedu Although the step-by-step instructions described here apply speci cally to the NORM program, most of the issues covered will, or should, have counterparts in any multipleimputation program Thus, the instructions provided in the following pages should be seen as being applicable in a quite general way to other multiple-imputation software as well.

extension of the clusters/rules, keeping all the older learned relations as is usually done in all evolving fuzzy system approaches The concept of drift concerns the way the data distribution slides smoothly through the feature space from one region to the other For instance, one may consider a data cluster changing from one position to the other, where the cluster at the older position becomes outdated over time; see Figure 55, where the old data distribution is marked with circles, the new one with rectangles The cluster center (big circle) should move from the left position to the right one When updating the cluster with standard incremental learning algorithms by taking each sample equally weighted, the updated center would end up somewhere in the middle, causing an inaccurate cluster partition and modeling behavior.

The Revisionist or Neo-Freudian Perspective:

In order to perform item analysis (coef cient alpha) or exploratory factor analysis, we must rst perform the rst part of multiple imputation with NORM (Schafer, 1997) For.