Description Usage Arguments Value Examples
Simulates a personality scale which correlates to an outcome. The function can specify the number of indicators (i.e. indicators) truly relating to the outcome. Also, the function can create a secondary scale, for instance mimicing informant-report
1 2 3 |
n |
Number of participants |
to_n |
Number of indicators in a Trait relating to Outcome |
tn_n |
Number of indicators in a Trait Not relating to outcome. |
indicators2 |
if TRUE, a secondary set of indicators is created, e.g. to mimic informant-report. Defaults to FALSE |
cor_to_tn |
Correlation between to and tn. Defaults to 0.3 |
cor_to_outcome |
correlation between to and outcome. Defaults to 0.4 |
to_min |
minimum factor loading for to_n. Defaults to 0.4 |
to_max |
maximum factor loading for to_n. Defaults to 0.7 |
tn_min |
minimum factor loading for tn_n. Defaults to 0.4 |
tn_max |
maximum factor loading for tn_n. Defaults to 0.7 |
n.cat |
number of response options. when you go larger than 5, update the standard deviation as well. Defaults to 5 |
sdev |
standard deviation. Defaults to 0.8 |
A list object, first object is indicators' matrix and second object is outcome vector. If indicators2=TRUE, then a third object is added, which is the secondary indicators' matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Create a scale-outcome set that violates ION. Only 2 indicators out of 8 relate
## to the outcome, the others just relate to the 2 indicators This setting is similar
## to the N5: Impulsiveness - BMI association in Vainik et al (2015) EJP paper.
set.seed(466)
a<-scale_sim(n=2500, to_n=2, tn_n=6)
# Last 2 indicators have considerably higher correlation with the outcome
cor(a[[1]],a[[2]])
## Create a scale-outcome set that has ION, all 8 indicators relate to the outcome
set.seed(466)
b<-scale_sim(n=2500, to_n=8)
# All indicators correlate largely on the same level with the outcome.
cor(b[[1]],b[[2]])
## Create a scale-outcome set that violates ION - only 1 indicator relates to
##the outcome. Include other-report.
set.seed(466)
c<-scale_sim(n=2500, to_n=1, tn_n=7, indicators2=TRUE)
# Last 2 indicators have considerably higher correlation with the outcome
cor(c[[1]],c[[2]])
cor(c[[3]],c[[2]])
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