rrwRunSmartGridSearch: This function takes the raw data and the rrwModelList as...
In ccpluncw/ccpl_R_RRW: What the Package Does (One Line, Title Case)
View source: R/rrwRunSmartGridSearch.r
rrwRunSmartGridSearch R Documentation
This function takes the raw data and the rrwModelList as input, collapses the data, codes the columns, gets the parameter information, runs the smartGridSearch, and outputs the smartGridSearch results.
Description
Function that the raw data and the rrwModelList as input, collapses the data, codes the columns, gets the parameter information, runs the smartGridSearch, and outputs the smartGridSearch results.
Usage
rrwRunSmartGridSearch(
data,
rrwModelList,
minN = 20,
dataOverlapCol = "overlapRound",
RwSamplesCol = "Q50",
dataRtCol = "rt",
correctCol = "correct",
correctVals = c(TRUE, FALSE),
loopsPerRWstep = 200,
minimizeStat = "BIC",
equalizeRTandPhit = FALSE,
pars.n = NULL,
numLoops = 200,
numIntervals = 25,
optParamListN = 10,
optBoundLoops = 10,
multicore = FALSE,
multicorePackages = NULL,
fileTag = NULL,
numSimsToAverage = 10,
...
)
Arguments
data
This is a dataframe containing the raw trial-by-trial data that must contain the following columns: overlap; RT (often a median); the proportion correct/incorrect; whether or not the row specifies a correct or incorrect trial. The dataset can also contains columns that effect code the influence of different parameters.
rrwModelList
A list that specifies the rrw model. Build the rrwModelList useing rrwAddEffectToRRWModel
minN
A a numeric value that specifies the minimum number of trials necessary for a condition to be included in the analysis. If the condition has less than minN trials, then that condition is assigned and NA. Default=20
dataOverlapCol
A string that identifies the name of the column in data that contains the distributional overlaps for each row. The default is "overlap"
RwSamplesCol
A string that identifies the name of the column in the RRW simulations that contains the summary of the samples that you want to use as a simulation for RT. The possible columns are: "Q25" (the 25th quartile); "Q50" (the median); "mean" (the mean); "Q75" (the 75th quartile). The default is "Q50" because the median is more robust than the mean.
dataRtCol
A string that identifies the name of the column in data that contains the RTs for the specific overlap/correct/condition combination. Default is "rt"
correctCol
a string that specifies the name of the column that specifies if the participant chose the item with the greatest value distribution (correct) or if they did not (incorrect).
correctVals
a vector of two values that specifies the "correct" value (index 1) and the "incorrect" value (index 2). e.g, c("yes", "no"). DEFAULT = c(TRUE, FALSE)
loopsPerRWstep
A number specifying the number of loops that will be run in the RRW simulation when it calculates the summary statistics for each number of samples for each boundary. Higher numbers produce more precise estimates, but also increase the time needed to converge on a solution. Default is 200.
minimizeStat
A string that specifies which statistic to minimize when optimizing the model fit. The options are: "BIC" , "AIC" , or "R_Square". Default is "BIC".
equalizeRTandPhit
A boolean that specifies whether the influence of the pHit should be equal to that of rt. Influence is a function of the number of observations. RT has more observations than pHit because it has both correct RTs and incorrect RTs. If this is set to TRUE, then the influence of the pHit and RT is equalized in the minimization statistic. If it is set to FALSE, then the the minimazation statistic is calculated as usual. DEFAULT = FALSE.
pars.n
The number of free parameters. When NULL, the program will attempt to calculate the number of free parameters from the input. Default = NULL.
numLoops
An integer that specifies the number of loops with different parameter values to be searched in each run of the smartGridSearch before the best fits for that run are identified. DEFAULT = 200.
numIntervals
An integer that specifies the number of intervals that the range between the upper and lower bounds of each parameter space will be segmented into. DEFAULT = 25.
optParamListN
An integer that specifies the number of best fit runs that will be used to extract the new bounds for next set of grid search loops. So, if optParamListN == 10, then the parameter values from the 10 best runs will be used to refine the upper and lower bounds of for each parameter. DEFAULT = 10.
optBoundLoops
An integer that specifies the number of times to re-simulate the best fit run in order to get a mean and SD for the minimized fit statistic. If the mean fit statistic is no longer the best fit, the program will move to the next best fit run, and so on until the it identifies the best mean fit run. DEFAULT = 10.
multicore
An boolean that specifies whether the program should be run using multiple processing cores. DEFAULT = FALSE.
multicorePackages
An vector of strings specifying the package names that are used in "fn." This needs to be used when multicore = TRUE. DEFAULT = NULL.
fileTag
A string that is appended to the name of files to identify the analysis and experiment. The default is NULL, whereby the filetag will just be based on a timestamp.
numSimsToAverage
A number specifying how many simulation runs are in the dataset and should be averaged together to get the final fit. Default is 10
'
Value
Several files are saved, including <fileTag>GridOut.txt (which contains the best 10 parameter fits), <fileTag>rrwData.txt (which contains the inputData), <fileTag>FitResults.txt (which contains the final parameter fits), <fileTag>Fitted.txt (which contains the input data + simulation fit data). The function returns a list containing (1) a list of the parameter values and fit statistics [runStats], and (2) a dataframe containing the input data with the best fit prediction based on the parameter values [df.fitted].
Examples
rrwRunSmartGridSearch(tmp.df, tmpModelList, minN = 40, dataOverlapCol = "overlapRound", RwSamplesCol = "Q50", dataRtCol = "rt", correctCol = "correct01", correctVals = c(1,0), loopsPerRWstep = 200, minimizeStat = 'BIC', equalizeRTandPhit = TRUE, numLoops = 2500, numIntervals = 100, optParamListN = 10, optBoundLoops = 10, multicore = TRUE, multicorePackages = c('RRW'), numSimsToAverage = 40, fileTag = NULL)
ccpluncw/ccpl_R_RRW documentation built on July 4, 2025, 3:24 p.m.
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View source: R/rrwRunSmartGridSearch.r
| rrwRunSmartGridSearch | R Documentation |
This function takes the raw data and the rrwModelList as input, collapses the data, codes the columns, gets the parameter information, runs the smartGridSearch, and outputs the smartGridSearch results.
Description
Function that the raw data and the rrwModelList as input, collapses the data, codes the columns, gets the parameter information, runs the smartGridSearch, and outputs the smartGridSearch results.
Usage
rrwRunSmartGridSearch(
data,
rrwModelList,
minN = 20,
dataOverlapCol = "overlapRound",
RwSamplesCol = "Q50",
dataRtCol = "rt",
correctCol = "correct",
correctVals = c(TRUE, FALSE),
loopsPerRWstep = 200,
minimizeStat = "BIC",
equalizeRTandPhit = FALSE,
pars.n = NULL,
numLoops = 200,
numIntervals = 25,
optParamListN = 10,
optBoundLoops = 10,
multicore = FALSE,
multicorePackages = NULL,
fileTag = NULL,
numSimsToAverage = 10,
...
)
Arguments
data |
This is a dataframe containing the raw trial-by-trial data that must contain the following columns: overlap; RT (often a median); the proportion correct/incorrect; whether or not the row specifies a correct or incorrect trial. The dataset can also contains columns that effect code the influence of different parameters. |
rrwModelList |
A list that specifies the rrw model. Build the rrwModelList useing rrwAddEffectToRRWModel |
minN |
A a numeric value that specifies the minimum number of trials necessary for a condition to be included in the analysis. If the condition has less than minN trials, then that condition is assigned and NA. Default=20 |
dataOverlapCol |
A string that identifies the name of the column in data that contains the distributional overlaps for each row. The default is "overlap" |
RwSamplesCol |
A string that identifies the name of the column in the RRW simulations that contains the summary of the samples that you want to use as a simulation for RT. The possible columns are: "Q25" (the 25th quartile); "Q50" (the median); "mean" (the mean); "Q75" (the 75th quartile). The default is "Q50" because the median is more robust than the mean. |
dataRtCol |
A string that identifies the name of the column in data that contains the RTs for the specific overlap/correct/condition combination. Default is "rt" |
correctCol |
a string that specifies the name of the column that specifies if the participant chose the item with the greatest value distribution (correct) or if they did not (incorrect). |
correctVals |
a vector of two values that specifies the "correct" value (index 1) and the "incorrect" value (index 2). e.g, c("yes", "no"). DEFAULT = c(TRUE, FALSE) |
loopsPerRWstep |
A number specifying the number of loops that will be run in the RRW simulation when it calculates the summary statistics for each number of samples for each boundary. Higher numbers produce more precise estimates, but also increase the time needed to converge on a solution. Default is 200. |
minimizeStat |
A string that specifies which statistic to minimize when optimizing the model fit. The options are: "BIC" , "AIC" , or "R_Square". Default is "BIC". |
equalizeRTandPhit |
A boolean that specifies whether the influence of the pHit should be equal to that of rt. Influence is a function of the number of observations. RT has more observations than pHit because it has both correct RTs and incorrect RTs. If this is set to TRUE, then the influence of the pHit and RT is equalized in the minimization statistic. If it is set to FALSE, then the the minimazation statistic is calculated as usual. DEFAULT = FALSE. |
pars.n |
The number of free parameters. When NULL, the program will attempt to calculate the number of free parameters from the input. Default = NULL. |
numLoops |
An integer that specifies the number of loops with different parameter values to be searched in each run of the smartGridSearch before the best fits for that run are identified. DEFAULT = 200. |
numIntervals |
An integer that specifies the number of intervals that the range between the upper and lower bounds of each parameter space will be segmented into. DEFAULT = 25. |
optParamListN |
An integer that specifies the number of best fit runs that will be used to extract the new bounds for next set of grid search loops. So, if optParamListN == 10, then the parameter values from the 10 best runs will be used to refine the upper and lower bounds of for each parameter. DEFAULT = 10. |
optBoundLoops |
An integer that specifies the number of times to re-simulate the best fit run in order to get a mean and SD for the minimized fit statistic. If the mean fit statistic is no longer the best fit, the program will move to the next best fit run, and so on until the it identifies the best mean fit run. DEFAULT = 10. |
multicore |
An boolean that specifies whether the program should be run using multiple processing cores. DEFAULT = FALSE. |
multicorePackages |
An vector of strings specifying the package names that are used in "fn." This needs to be used when multicore = TRUE. DEFAULT = NULL. |
fileTag |
A string that is appended to the name of files to identify the analysis and experiment. The default is NULL, whereby the filetag will just be based on a timestamp. |
numSimsToAverage |
A number specifying how many simulation runs are in the dataset and should be averaged together to get the final fit. Default is 10 ' |
Value
Several files are saved, including <fileTag>GridOut.txt (which contains the best 10 parameter fits), <fileTag>rrwData.txt (which contains the inputData), <fileTag>FitResults.txt (which contains the final parameter fits), <fileTag>Fitted.txt (which contains the input data + simulation fit data). The function returns a list containing (1) a list of the parameter values and fit statistics [runStats], and (2) a dataframe containing the input data with the best fit prediction based on the parameter values [df.fitted].
Examples
rrwRunSmartGridSearch(tmp.df, tmpModelList, minN = 40, dataOverlapCol = "overlapRound", RwSamplesCol = "Q50", dataRtCol = "rt", correctCol = "correct01", correctVals = c(1,0), loopsPerRWstep = 200, minimizeStat = 'BIC', equalizeRTandPhit = TRUE, numLoops = 2500, numIntervals = 100, optParamListN = 10, optBoundLoops = 10, multicore = TRUE, multicorePackages = c('RRW'), numSimsToAverage = 40, fileTag = NULL)
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