simDataSet can be used to conveniently and quickly simulate a dataset that satisfies certain constraints, such as a specific correlation structure, means, ranges of the items, and measurement levels of the variables. Note that the results are approximate; mvrnorm is used to generate the correlation matrix, but the factor are only created after that, so cutting the variable into factors may change the correlations a bit.
simDataSet(n, varNames, correlations = c(0.1, 0.4), specifiedCorrelations = NULL, means = 0, sds = 1, ranges = c(1, 7), factors = NULL, cuts = NULL, labels = NULL, seed = 20160503, empirical = TRUE, silent = FALSE)
| n | Number of requires cases (records, entries, participants, rows) in the final dataset. |
|---|---|
| varNames | Names of the variables in a vector; note that the length of this vector will determine the number of variables simulated. |
| correlations | The correlations between the variables are randomly sampled from this range using the uniform distribution; this way, it's easy to have a relatively 'messy' correlation matrix without the need to specify every correlation manually. |
| specifiedCorrelations | The correlations that have to have a specific value can be specified here, as a list of vectors, where each vector's first two elements specify variables names, and the last one the correlation between those two variables. Note that tweaking the correlations may take some time; the |
| means, sds | The means and standard deviations of the variables. Note that is you set |
| ranges | The desired ranges of the variables, supplied as a named list where the name of each element corresponds to a variable. The |
| factors | A vector of variable names that should be converted into factors (using |
| cuts | A list of vectors that specify, for each factor, where to 'cut' the numeric vector into factor levels. |
| labels | A list of vectors that specify, for each factor, and for each level, the labels that should be assigned to the factor levels. Each vector in this list has to have one more element than each vector in the |
| seed | The seed to use when generating the dataset (to make sure the exact same dataset can be generated repeatedly). |
| empirical | Whether to generate the data using the exact ( |
| silent | Whether to show intermediate and final descriptive information (correlation and covariance matrices as well as summaries). |
This function was intended to allow relatively quick generation of datasets that satisfy specific constraints, e.g. including a number of factors, variables with a specified minimum and maximum value or specified means and standard deviations, and of course specific correlations. Because all correlations except those specified are randomly generated from a uniform distribution, it's quite convenient to generate messy kind of real looking datasets quickly. Note that it's mostly a convenience function, and datasets will still require tweaking; for example, factors are simply numeric vectors that are cut *after* mvrnorm generated the data, so the associations will change slightly.
The generated dataframe is returned invisibly.
mvrnorm
dat <- simDataSet(500, varNames=c('age', 'sex', 'educationLevel', 'negativeLifeEventsInPast10Years', 'problemCoping', 'emotionCoping', 'resilience', 'depression'), means = c(40, 0, 0, 5, 3.5, 3.5, 3.5, 3.5), sds = c(10, 1, 1, 1.5, 1.5, 1.5, 1.5, 1.5), specifiedCorrelations = list(c('problemCoping', 'emotionCoping', -.5), c('problemCoping', 'resilience', .5), c('problemCoping', 'depression', -.4), c('depression', 'emotionCoping', .6), c('depression', 'resilience', -.3)), ranges = list(age = c(18, 54), negativeLifeEventsInPast10Years = c(0,8), problemCoping = c(1, 7), emotionCoping = c(1, 7)), factors=c("sex", "educationLevel"), cuts=list(c(0), c(-.5, .5)), labels=list(c('female', 'male'), c('lower', 'middle', 'higher')), silent=FALSE);#> Correlation matrix that will be used for the simulation: #> 1. 2. 3. 4. 5. 6. 7. 8. #> 1. age 1.00 0.22 0.11 0.23 0.17 0.10 0.35 0.29 #> 2. sex 0.38 1.00 0.32 0.21 0.10 0.13 0.21 0.14 #> 3. educationLevel 0.36 0.15 1.00 0.22 0.39 0.39 0.10 0.27 #> 4. negativeLifeEventsInPast10Years 0.12 0.32 0.20 1.00 0.14 0.37 0.20 0.23 #> 5. problemCoping 0.30 0.25 0.28 0.20 1.00 -0.50 0.50 -0.40 #> 6. emotionCoping 0.37 0.30 0.29 0.18 -0.50 1.00 0.21 0.60 #> 7. resilience 0.16 0.17 0.25 0.14 0.50 0.17 1.00 -0.30 #> 8. depression 0.27 0.31 0.15 0.18 -0.40 0.60 -0.30 1.00 #> #> Covariance matrix that will be used for the simulation: #> 1. 2. 3. 4. 5. 6. 7. 8. #> 1. age 100.0 2.16 1.15 3.40 2.58 1.57 5.23 4.36 #> 2. sex 3.8 1.00 0.32 0.32 0.15 0.19 0.32 0.21 #> 3. educationLevel 3.6 0.15 1.00 0.33 0.59 0.59 0.15 0.40 #> 4. negativeLifeEventsInPast10Years 1.8 0.48 0.31 2.25 0.32 0.82 0.45 0.52 #> 5. problemCoping 4.5 0.38 0.42 0.45 2.25 -1.12 1.12 -0.90 #> 6. emotionCoping 5.5 0.45 0.44 0.40 -1.12 2.25 0.46 1.35 #> 7. resilience 2.4 0.26 0.38 0.31 1.12 0.38 2.25 -0.67 #> 8. depression 4.1 0.47 0.23 0.40 -0.90 1.35 -0.67 2.25 #> #> Correlation matrix that was simulated based on this covariance matrix: #> 1. 2. 3. 4. 5. 6. 7. 8. #> 1. age 1.00 0.30 0.35 0.12 0.30 0.37 0.16 0.27 #> 2. sex 0.30 1.00 0.17 0.22 0.26 0.21 0.19 0.21 #> 3. educationLevel 0.35 0.17 1.00 0.18 0.26 0.27 0.22 0.14 #> 4. negativeLifeEventsInPast10Years 0.12 0.22 0.18 1.00 0.20 0.18 0.14 0.18 #> 5. problemCoping 0.30 0.26 0.26 0.20 1.00 -0.50 0.50 -0.40 #> 6. emotionCoping 0.37 0.21 0.27 0.18 -0.50 1.00 0.17 0.60 #> 7. resilience 0.16 0.19 0.22 0.14 0.50 0.17 1.00 -0.30 #> 8. depression 0.27 0.21 0.14 0.18 -0.40 0.60 -0.30 1.00 #> #> Summaries: #> age sex educationLevel negativeLifeEventsInPast10Years #> Min. :18.00 female:244 lower :138 Min. :0.000 #> 1st Qu.:33.10 male :256 middle:204 1st Qu.:3.170 #> Median :36.99 higher:158 Median :4.165 #> Mean :37.09 Mean :4.093 #> 3rd Qu.:41.30 3rd Qu.:5.013 #> Max. :54.00 Max. :8.000 #> problemCoping emotionCoping resilience depression #> Min. :1.000 Min. :1.000 Min. :-0.7345 Min. :-0.5076 #> 1st Qu.:3.461 1st Qu.:3.286 1st Qu.: 2.4609 1st Qu.: 2.4984 #> Median :4.018 Median :3.891 Median : 3.3760 Median : 3.5385 #> Mean :4.073 Mean :3.898 Mean : 3.5000 Mean : 3.5000 #> 3rd Qu.:4.742 3rd Qu.:4.518 3rd Qu.: 4.4909 3rd Qu.: 4.5594 #> Max. :7.000 Max. :7.000 Max. : 8.6622 Max. : 7.8757