drawsample (version 1.0.2)

draw_sample_n: Sample data close to desired characteristics - nearest

Description

A Function to sample data close to desired characteristics - nearest

Usage

draw_sample_n(
  dist,
  n,
  skew,
  kurts,
  location = 0,
  delta_var = 0,
  save.output = FALSE,
  output_name = c("sample", "default")
)

Value

This function returns a list including following:

• a matrix: Descriptive statistics of the given data, the reference vector and the sample.

• a data frame: The id's and scores of the sample

• graph: Histograms for the “data” and the “sample”

Arguments

dist

data frame:consists of id and scores with no missing

n

numeric: desired sample size

skew

numeric: the skewness value

kurts

numeric: the kurtosis value

location

numeric: the value for adjusting mean (default is 0).

delta_var

numeric: the value for adjusting variance (default is 0).

save.output

logical: should the output be saved into a text file? (Default is FALSE).

output_name

character: a vector of two components. The first component is the name of the output file, user can change the second component.

Details

The desired skewness and kurtosis values cannot be met while the function execution is faster. The attributes of kurtosis are in doubt. This is because the range of kurtosis is greater than the skewness. For location values can be entered to position the midpoint or mean of the distribution differently. For delta_var the value can be entered for how much will increase or decrease the variability of reference distribution. In other words, the reference distribution is generated as the standard normal distribution, unless the user changes the default values of the location and delta_var arguments.

References

Fleishman AI (1978). A Method for Simulating Non-normal Distributions. Psychometrika, 43, 521-532. tools:::Rd_expr_doi("10.1007/BF02293811").

Fialkowski, A. C. (2018). SimMultiCorrData: Simulation of Correlated Data with Multiple #' Variable Types. R package version 0.2.2. Retrieved from https://cran.r-project.org/web/packages/SimMultiCorrData/index.html

Examples

# Example data provided with package
data(example_data)
# Draw a sample based on Score_1
output2 <- draw_sample_n(dist=example_data[,c(1,2)],n=200,skew = 0,
kurts = 0, location=0, delta_var=0,save.output=FALSE) # Histogram of the reference data set
# descriptive statistics of the given data,reference data, and drawn sample
output2$desc
# First 6 rows of the drawn sample
head(output2$sample)
# Histogram of the given data set and drawn sample
output2$graph
if (FALSE) {
# Draw a sample based on Score_2 (location par)
# draw_sample_n(dist=example_data[,c(1,3)],n=200,skew = 1,kurts = 1,location=-0.5,delta_var=0,
# save.output=TRUE, output_name = c("sample", "2"))
# Draw a sample based on Score_2 (delta_var par)
# draw_sample_n(dist=example_data[,c(1,3)],n=200,skew = 0.5,kurts = 0.4,location=0,delta_var=0.3,
# save.output=TRUE, output_name = c("sample", "3"))
}