Group Iterative Multiple Model Estimation (GIMME)

KM Gates


The Basics

Running GIMME

1. Extract the time series for your variables

3. Installing gimme with R

4. Running gimme The gimme (or equivelently, gimmeSEM) function requires that you input: 1. Data object (if you put your individual data frames in a list) or source directory (if you saved them as seperate files) 2. If you stored them in a folder: How data are separated (e..g, comma-separated values) and if there is a header

All other fields are optional and will go to defaults if no user input is provided. If no output directory is indicated, all information is stored as R objects (see tutorial linked above for details).

gimme(                  # can use "gimme" or "gimmeSEM"
    data = '',          # list object or source directory where your data are 
    out = '',           # output directory where you'd like your output to go
    sep = "",           # how data are separated. "" for space; "," for comma, "/t" for tab-delimited
    header = ,          # TRUE or FALSE, is there a header
    ar = TRUE,          # TRUE (default) or FALSE, start with autoregressive paths open
    plot = TRUE,        # TRUE (default) or FALSE, generate plots
    subgroup = FALSE,   # TRUE or FALSE (default), cluster individuals based on similarities in effects
    paths = NULL,       # option to list paths that will be group-level (semi-confirmatory)
    groupcutoff = .75,  # the proportion that is considered the majority at the group level
    subcutoff = .75,     # the proportion that is considered the majority at the subgroup level
    VAR       = FALSE,  # TRUE or FALSE (default), option to use VAR model instead of uSEM
    hybrid    = FALSE   # TRUE or FALSE (default), option to use hybrid-VAR model instead of uSEM

While gimme is running you will see information iterate in the command window. The algorithm will tell you when it is finished.



How many time points do I need? This is a difficult question since it will be related to the number of variables you are using. Rules of thumb for any analysis can generally be used: the more the better! Having at lest 100 time points is recommended, but adequate results have been obtained in simulation studies with only T = 60.

Do all individuals have to have the same number of observations (T)? No. 

How many people do I need in my sample? For regular gimmme, reliable results are obtained with as few as 10 participants. Remember that in this context, power to detect effects is determined by the number of time points rather than the number of individuals. Still, having at least 10 individuals helps gimme to detect signal from noise by looking for effects that consistently occur.

What do I do if I obtain an error? Do some initial trouble-shooting. 1. Ensure that all of your individuals have the same number of variables (columns) in their data sets. 2. Ensure that all variables have variability (i.e., are not constant). gimme will let you know if this is the case. 3. Ensure your path directories are correct. 4. Ensure that the columns are variables and the rows contain the observations across time. 5. Ensure that variables aren’t perfectly correlated (or nearly perfectly correlated) for all individuals. 6. If all this is correct, please email the error you received, code used to run gimme, and the data (we promise not to use it or share it) to: echo .