TESD: Trinomial Effect Size Display (TESD) for correlations
In mottusrene/TESD: Estimate the pairwise proportions of low, medium and high values of two continously distributed variables, known as Trinomial Effect Size Display (TESD)

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/TESD.R

Calculate and plot pairwise probabilities for low, medium and high values for continuously distributed variables x and y. This is procedure is called Trinomial Effect Size Display (TESD). TESD allows heuristically estimating the extents to which correlations apply to individual observations (e.g., the probability that a high value in one variable, x, is matched with a high value in another variable, y). For both variable, low values correspond to the lowest third of the distribution, high values to the highest third of the distribution, and medium values contribute to the middle third of the distribution. You may also plot the expected TESD alongside TESD calculated from the supplied data. Missing values are not permitted!

1	TESD(x, y, plot = TRUE, Xlab = "X", Ylab = "Y", plot.expected.TESD = FALSE)

`x`	First continuously distributed variable
`y`	Second continuously distributed variable
`plot`	Whether a scatterplot with a TESD overlay should be printed
`Xlab`	Label for the x variable
`Ylab`	Label for the y variable
`plot.expected.TESD`	Should expected TESD, given the correlation between x and y and assuming normal distribution for them, be printed (denoted with E)?

When using this function, make sure that both variables can be meaningfully grouped into roughly equal low, medium and high groups (the univariate and multivariate, cross-tabulated, proportions of high, medium and low values for both variables will be printed, so you can see whether this assumption holds). Variables with limited values (e.g., Likert scale scores or those with only certain values possible) will lead to unequal group sizes. If the groups are not of equal size, the TESD calculated from the supplied data will not match the expected TESD (assuming normal distributions of continuously distributed variables). The function will warn you. In this case, it may be better to interpret the expected TESD (also printed), given the correlation between the supplied variables and assuming that they are normally distributed. This is always a safe option.

`univariate.x.proportions`	Proportions for the three groups in variable x
`univariate.y.proportions`	Proportions for the three groups in variable y
`cross.tabulations`	Cross-tabulation of x and y groups
`TESD.table`	Cross-tabulation of the groups of variables x and y
`multivariate.x.proportions`	Sums of the proportions in the groups along variable x. If the sums are very different from 1, be careful! The variable may not be suitable for TESD, and you may want to interpret the expected TESD instead (always a safe option).

René Mõttus

Mõttus (2021).

TESD.expected()

Conscientiousness = rnorm(10000)
Performance = .3 * Conscientiousness + sqrt(1 - .3^2) + rnorm(length(Conscientiousness))
cor(Conscientiousness, Performance)
TESD(Conscientiousness, Performance)