The following is a summary of all the missing data supplemental functions provided in the Real Statistics Resource Pack in conjunction with Multiple Imputation (MI) and Full Information Maximum Likelihood (FIML).

**Group 1**

The following are array functions (with the exception of **CountPatterns**, which is not an array function) where R1 is a range where each column represents sample data for a random variable. Blank or non-numeric elements are assumed to represent missing data.

**MissingFreq**(R1, *head*) – generates a summary with the frequency of non-missing data in R1

**MissingPatterns**(R1, *head*, *s*) – generates a summary with missing patterns of data in R1. The argument *s* is used to fill up any extra rows in the output which do not contain data. If *s* is not specified then it defaults to the error value #N/A.

**CountPatterns**(R1) = the number of different missing data patterns there are for the data in R1

**ImputeSimple**(R1, *head*, R2, *iter*) – generates a range with all the missing data in R1 filled in using the simple imputation approach

**ImputeReg**(R1, *j*, *head*, R2, *iter*) – generates a range where all the missing data in column *j* is filled in using one step of the FCS algorithm

**ImputeFCS**(R1, *head*, *iterf*, R2, *iter*) – generates one imputation of the missing data in R1 using FCS; *iterf* is the number of iterations of the FCS algorithm (default = 20)

If *head* is TRUE (the default) then it is assumed that the data range R1 as well as the output contain column headings, while if *head* = FALSE then the R1 should not contain column headings and the output will not contain column headings either.

R2 is a range containing constraints (if R2 is omitted then no constraints are used) and iter is the maximum number of iterations used to obtain a value within the min/max constraints (default = 25). ImputeReg can contain only one constraint, namely for the variable corresponding to column *j*.

**Group 2**

For the following array functions R1 and *head* are as in Group 1, except that the default for *head* is FALSE. If *lab* = TRUE then an extra column is inserted in the output which contains labels (default = FALSE).

**MISummary**(R1, *head*) – generates a compact summary of the regression model for R1 where the last column in R1 is assumed to be the data for the y variable and the other columns are assumed to contain the data for the x variables.

**ImputedData**(R0, R1, *head*)** **– generates a range with all the elements in R1 where there is missing data in range R0.

**DescStats**(R1, *lab, head*) – generates a mini descriptive statistics report for the data in range R1.

**DescStats**(R1, *lab, head*, R0) – generates a mini descriptive statistics report for the data in range R1 which correspond to the missing data in range R0.

**MissingPairwise**(R1, *head*) – generates a summary of the percentage of non-missing data for each pair of variables in R1. If *head* = TRUE then the output includes row headings (as well as column headings).

**Group 3**

The following is an array function:

**MICombine**(R1, *nimp, ncols, head, raw*) – generates a combined compact regression summary derived from the compact summaries of *nimp* imputations if *raw* = FALSE or derived directly from the *nimp* imputations if *raw* = TRUE (default).

If *raw* = FALSE then R1 is the range containing the first of the *nimp* compact regression summaries, while if *raw* = TRUE then R1 is the range containing the first of the *nimp* imputations. The *nimp* imputations or compact regression summaries are separated by *ncols* blank columns (default = 1). *head* is as in Group 2.

**Group 4**

For the following function, R1 is a 1 × *k* row vector, R2 is a *k* ×* k* covariance matrix and R3 is a 1 × *k* mean vector.

**LLReg**(R1, R2, R3) = the value of -2*LL _{i}* for the row represented by R1, possibly containing missing data.

**Group 5**

For the following array functions, R1 is a 2*m* × *n* range where the first *m* rows represent the values of *m* imputed population parameters and the second *m* rows represent the corresponding standard errors for these parameters. The columns represent separate imputations. The argument *size* is the number of elements in the original sample (including missing data) and *head* and *lab* are as in Group 2.

**ImputeVar**(R1, *size, lab, head*) – outputs a a range summarizing the combination rules for variance.

**ImputeParam**(R1, *size, lab, head, alpha*) – outputs a range based on the combination rules and the usual *t* test using the stated value of *alpha* (default = .05).

Hi Charles, my computer does not have survival analysis functions. how can i install them.

If you are using Excel 2007, 2010, 2013 or 2016 (Windows) just install the latest release from http://www.real-statistics.com/free-download/real-statistics-resource-pack/

Charles

Hi Mr Charles Zaiontz

I have a question and am grateful if you respond and guide me:

for discrete data in spss:

when we use transform/replace Missing Values, data transform in continious type

this situation isnot desirable. For example my variable is sex as male(value=1) and female(value=2) Now when we have missing value and use in spss transform/replace Missing Values data transform missing values trasforms 1.4.

I donot know what use I from 1.4 in drawing graphs and report, because value=1=male and value=2=female but value=1.4=?

in continious data we donot have such problem because type of tranformed data is contionious.

I am grateful if you respond and guide me.

David,

I don’t use SPSS and so I am not able to answer your question.

Charles