| Title: | Normalisation of Multiple Variables in Large-Scale Datasets |
| Version: | 1.0.0 |
| Description: | The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) http://www.jstor.org/stable/2984418 provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package 'normalr' that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality. |
| Depends: | R (≥ 3.3.0) |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | MASS, parallel, purrr, magrittr, rlang, shiny |
| Suggests: | testthat, covr |
| RoxygenNote: | 6.0.1 |
| URL: | https://github.com/kcha193/normalr |
| BugReports: | https://github.com/kcha193/normalr/issues |
| NeedsCompilation: | no |
| Packaged: | 2018-03-30 03:10:03 UTC; kcha193 |
| Author: | Kevin Chang [aut, cre], Matthew Courtney [aut] |
| Maintainer: | Kevin Chang <k.chang@auckland.ac.nz> |
| Repository: | CRAN |
| Date/Publication: | 2018-03-30 03:20:03 UTC |
Get Optimal Lambda value
Description
Computes optimal lambda value using boxcox function from the provided data.
Usage
getLambda(dat, lambda = seq(-10, 10, 1/100), parallel = TRUE)
Arguments
dat |
a data frame containing the variables of numeric or integer vectors. |
lambda |
a vector of values of lambda – default (-10, 10) in steps of 0.01. |
parallel |
perform the computation in parallel, default setting is TRUE. |
Value
a numeric vector
References
Courtney, M. G. R., and Chang, K. C. (2018) Dealing with non-normality: an introduction and step-by-step guide using R. TEST, doi: 10.1111/test.12154.
Examples
## Not run: getLambda(mtcars)
getLambda(mtcars, parallel = FALSE)
Apply normalisation on a numeric vector using a specific Lambda value
Description
Apply normalisation on a numeric vector using a specific Lambda value
Usage
normalise(x, lambda = 3)
Arguments
x |
a numeric vector to be normalised. |
lambda |
a numeric vector from the boxcox function |
Value
a numeric vector
Examples
x <- c(1, 5, 9, 9, 9, 9, 10, 10, 10, 11, 11, 12)
normalise(x, lambda = 3)
Apply normalisation on a data frame using specific Lambda value
Description
Apply normalisation on a data frame using specific Lambda value
Usage
normaliseData(dat, lambdas)
Arguments
dat |
a data frame containing the variables. |
lambdas |
a numeric vector from the boxcox function |
Value
a data frame
Examples
## Not run: normaliseData(mtcars, getLambda(mtcars, parallel = FALSE))
normaliseData(mtcars, getLambda(mtcars, parallel = FALSE))
Shiny application of the normalr
Description
Shiny application of the normalr
Usage
normalrShiny(example = "normalr")
Arguments
example |
name of the shiny apps |
Examples
## Not run: normalrShiny()
Test dataset for the paper
Description
Test dataset for the paper
Usage
testData
Format
An object of class data.frame with 957 rows and 9 columns.