Different types of correlations can be requested: PMC, Kendall tau rank correlation, Spearman rank correlation.
Usage
constructCor(
x,
method = c("pearson", "kendall", "spearman"),
trim = 20,
index = FALSE
)Arguments
- x
repgridobject.- method
A character string indicating which correlation coefficient is to be computed. One of
"pearson"(default),"kendall"or"spearman", can be abbreviated. The default is"pearson".- trim
The number of characters a construct is trimmed to (default is
20). IfNAno trimming occurs. Trimming simply saves space when displaying correlation of constructs with long names.- index
Whether to print the number of the construct.
Examples
# three different types of correlations
constructCor(mackay1992)
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: pearson
#>
#> 1 2 3 4 5 6
#> Quick - *Slow 1 0.38 0.77 0.13 0.52 0.29
#> *Satisfied - Bitter 2 0.18 0.82 0.56 0.29
#> Talkative - *Quiet 3 0.14 0.72 0.58
#> *Succesful - Loser 4 0.64 0.47
#> Emotional - *Calm 5 0.92
#> *Caring - Selfish 6
constructCor(mackay1992, method = "kendall")
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: kendall
#>
#> 1 2 3 4 5 6
#> Quick - *Slow 1 0.38 0.77 0.00 0.46 0.15
#> *Satisfied - Bitter 2 0.08 0.40 0.54 0.15
#> Talkative - *Quiet 3 0.00 0.38 0.15
#> *Succesful - Loser 4 0.64 0.39
#> Emotional - *Calm 5 0.74
#> *Caring - Selfish 6
constructCor(mackay1992, method = "spearman")
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: spearman
#>
#> 1 2 3 4 5 6
#> Quick - *Slow 1 0.50 0.83 0.00 0.56 0.19
#> *Satisfied - Bitter 2 0.09 0.56 0.64 0.13
#> Talkative - *Quiet 3 0.00 0.39 0.21
#> *Succesful - Loser 4 0.69 0.49
#> Emotional - *Calm 5 0.81
#> *Caring - Selfish 6
# format output
constructCor(mackay1992, trim = 6)
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: pearson
#>
#> 1 2 3 4 5 6
#> Qui - *Sl 1 0.38 0.77 0.13 0.52 0.29
#> *Sa - Bit 2 0.18 0.82 0.56 0.29
#> Tal - *Qu 3 0.14 0.72 0.58
#> *Su - Los 4 0.64 0.47
#> Emo - *Ca 5 0.92
#> *Ca - Sel 6
constructCor(mackay1992, index = TRUE, trim = 6)
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: pearson
#>
#> 1 2 3 4 5 6
#> (1) Qui - *Sl 1 0.38 0.77 0.13 0.52 0.29
#> (2) *Sa - Bit 2 0.18 0.82 0.56 0.29
#> (3) Tal - *Qu 3 0.14 0.72 0.58
#> (4) *Su - Los 4 0.64 0.47
#> (5) Emo - *Ca 5 0.92
#> (6) *Ca - Sel 6
# save correlation matrix for further processing
r <- constructCor(mackay1992)
r
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: pearson
#>
#> 1 2 3 4 5 6
#> Quick - *Slow 1 0.38 0.77 0.13 0.52 0.29
#> *Satisfied - Bitter 2 0.18 0.82 0.56 0.29
#> Talkative - *Quiet 3 0.14 0.72 0.58
#> *Succesful - Loser 4 0.64 0.47
#> Emotional - *Calm 5 0.92
#> *Caring - Selfish 6
print(r, digits = 5)
#>
#> ##############################
#> Correlation between constructs
#> ##############################
#>
#> Type of correlation: pearson
#>
#> 1 2 3 4 5 6
#> Quick - *Slow 1 0.37709 0.77226 0.12913 0.51682 0.28911
#> *Satisfied - Bitter 2 0.18383 0.81969 0.56239 0.28917
#> Talkative - *Quiet 3 0.13771 0.71984 0.57656
#> *Succesful - Loser 4 0.63623 0.47133
#> Emotional - *Calm 5 0.92394
#> *Caring - Selfish 6
# accessing the correlation matrix
r[1, 3]
#> [1] 0.7722623