multicon-package: Multivariate Constructs
In multicon: Multivariate Constructs
Description
Details
Author(s)
References
See Also
Examples
Description
This package contains functions for examining multivariate constructs (MCs).
Details
Package: multicon
Type: Package
Version: 1.6
Date: 2011-1-29
License: GPL-2
MCs are, as the name implies, constructs that consist of many variables.
For example, personality is not a single variable, but a constellation of
many individual variables. This is problematic for traditional analyses which
only examine the relationships between only 1 variable (or just a few variables)
and some outcome of interest. Within-person analyses are often interested in
MCs as well. This package contains functions for examining such multivariate constructs.
Author(s)
Ryne A. Sherman
Maintainer: Ryne A. Sherman <rsherm13@fau.edu>
Compiler: David G. Serfass <dserfass@fau.edu>
References
Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge.
Funder, D. C., Furr, R. M., Colvin, C. R. (2000). The Riverside Behavioral Q-sort: A tool for the description of social behavior. Journal of Personality, 68, 451-489.
Furr, R. M., Wagerman, S. A., & Funder, D. C. (2010). Personality as manifest in behavior: Direct behavioral observation using the revised Riverside Behavioral Q-sort (RBQ-3.0). In C.R. Agnew, D. E. Carlston, W. G., Graziano, & J. R. Kelly (Eds.), Then a miracle occurs: Focusing on beahvior in social psychological theory and research. (pp. 186-204). Oxford University Press.
Furr, R. M. (2008). A framework for Profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76(5), 1267-1316.
My website:
http://psy2.fau.edu/~shermanr/index.html
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
# Some examples of the core functions in the multicon package:
# Is personality related to behavior? This simple question becomes more
# complex with the recognition that both personality and behavior are multivariate constructs.
# One (of many) ways to quantify personality is with a a 100-item measure,
# the California Adult Q-set (CAQ: Block, 1961). And one (of a few) ways to
# quantify behavior is with a 67-item measure, the Riverside Behavioral Q-sort
# (RBQ: Funder, Furr, & Colvin, Colvin, 2000; Furr, Wagerman, & Funder, 2010).
# How well are these two instruments related? There are 100 * 67 = 6700
# possible correlations that could be examined one at a time. Alternatively,
# we could answer our question more directly by simply seeing what the
# (absolute) average correlation is amongst these two sets of items and testing
# it against a baseline model that assumes zero association. The function
# rand.test() does this.
data(caq)
data(v2rbq)
# Note that in practice more sims (i.e., 1000 or more) might be preffered
rand.test(caq, v2rbq, sims=100, graph=FALSE)
# How is a specific single variable of interest (e.g., Extraversion) related
# to some multivariate construct (e.g., behavior - as measured by the RBQ)?
# Do the relationships differ by sex? The function q.cor() is
# designed to answer this question.
data(RSPdata)
# Note that in practice more sims (i.e., 1000 or more) might be preffered
myobj <- q.cor(RSPdata$sEXT, v2rbq, sex = RSPdata$ssex, fem = 1, male = 2, sims=100)
myobj
# The results can be organized by using q.cor.print() for easier interpretation
data(rbqv3.items)
q.cor.print(myobj, rbqv3.items, "RBQ", short=TRUE)
# How well do two judgments of a target's personality agree with each other?
# Again, assuming personality is measured as a multivariate construct
# (e.g., the 100-item CAQ), this question is not so straightforward. One way
# is to correlate the two judge's ratings across the 100-item pairs (profile correlation).
# This can be done for each target with two judges. The function Profile.r() does this.
data(acq1) # The first friend of a target being judged (N targets = 205)
data(acq2) # The second friend of a target being judged
Profile.r(acq1, acq2) # The agreements (correlations) for each target
# Get summary statistics for the agreements
describe.r(Profile.r(acq1, acq2))
# If we want to control for normativeness (see Furr, 2008) and get
# significance tests (for both overall and distinctive agreement) we
# can simply set distinct=TRUE.
Profile.r(acq1, acq2, distinct=TRUE)
# If we want to know how replicable (reliable) the agreement correaltions are
# we can use Profile.r.rep()
Profile.r.rep(acq1, acq2)
# The package also includes some graphing functions for comparing group means
# based on "The New Statistics" (Cumming, 2012).
y <- c(rnorm(30), rnorm(30, mean=1))
group <- rep(1:2, each=30)
catseye(y, group, las=1, main="A Catseye Plot", xlab="",
grp.names=c("Control", "Experimental"), ylab="DV")
catseye(y, group, las=1, main="A Catseye Plot #2", xlab="",
grp.names=c("Control", "Experimental"), ylab="DV", conf=.80, col="cyan")
df=data.frame(group=group,y=y)
diffPlot(y ~ group,data=df,grp.names=c("Control", "Experimental"), xlab="",
ylab="DV", main="A Difference Plot", sub="Arms are 95 percent CIs")
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
$AbsR
Average Absolute r
N 205.0000
Observed 0.0635
Exp. By Chance 0.0559
Standard Error 0.0010
p 0.0000
99.9% Upperbound p 0.0000
99.9% Lowerbound p 0.0000
95th % 0.0577
$Sig
Number Significant
N 205.000
Observed 575.000
Exp. By Chance 334.230
Standard Error 29.918
p 0.000
99.9% Upperbound p 0.000
99.9% Lowerbound p 0.000
95th % 386.050
Warning message:
In rand.test(caq, v2rbq, sims = 100, graph = FALSE) :
When p=.00, confidence interval for p not valid.
Warning messages:
1: In rand.test(data.frame(x), set, sims = 1000, graph = F, seed = seed) :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
2: In rand.test(data.frame(x), set, sims = 1000, graph = F, seed = seed) :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
3: In rand.test(data.frame(female[, 2]), fem.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
4: In rand.test(data.frame(female[, 2]), fem.cols, sims = sims, graph = F, :
When p=.00, confidence interval for p not valid.
5: In rand.test(data.frame(male[, 2]), male.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
6: In rand.test(data.frame(male[, 2]), male.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
Content Combined p1 Female p2 Male p3
i020 item20 0.26 *** 0.29 ** 0.23 *
i056 item56 0.26 *** 0.19 + 0.32 **
i015 item15 0.19 ** 0.14 0.25 *
i048 item48 0.17 * 0.25 ** 0.09
i002 item2 0.17 * 0.22 * 0.11
i037 item37 0.17 * 0.13 0.23 *
i057 item57 0.14 * 0.29 ** -0.02
i012 item12 0.13 + 0.10 0.17 +
i001 item1 0.12 + 0.06 0.19 +
i007 item7 0.11 -0.02 0.24 *
i062 item62 0.10 0.12 0.07
i053 item53 0.10 0.12 0.07
i005 item5 0.09 0.13 0.04
i058 item58 0.08 0.01 0.14
i030 item30 0.08 0.04 0.12
i063 item63 0.08 0.06 0.09
i033 item33 0.07 0.22 * -0.07
i059 item59 0.06 0.11 0.00
i017 item17 0.06 0.07 0.03
i004 item4 0.05 0.04 0.07
i038 item38 0.05 0.06 0.04
i011 item11 0.04 0.09 0.01
i049 item49 0.04 0.04 0.04
i027 item27 0.03 0.17 + -0.12
i022 item22 0.03 0.09 -0.02
i034 item34 0.03 0.12 -0.06
i010 item10 0.03 -0.09 0.16
i032 item32 0.03 -0.04 0.11
i066 item66 0.01 -0.08 0.10
i019 item19 0.00 -0.09 0.10
i065 item65 0.00 0.04 -0.03
i025 item25 0.00 0.06 -0.07
i046 item46 0.00 0.08 -0.09
i039 item39 -0.01 0.08 -0.10
i061 item61 -0.01 -0.07 0.05
i028 item28 -0.02 -0.11 0.08
i035 item35 -0.02 0.01 -0.05
i067 item67 -0.02 -0.07 0.03
i026 item26 -0.02 0.09 -0.13
i044 item44 -0.02 0.07 -0.12
i029 item29 -0.02 0.01 -0.06
i021 item21 -0.03 0.00 -0.06
i047 item47 -0.03 0.01 -0.08
i009 item9 -0.03 -0.10 0.04
i052 item52 -0.04 -0.16 0.07
i031 item31 -0.05 0.07 -0.17 +
i043 item43 -0.06 -0.17 + 0.07
i042 item42 -0.06 -0.11 -0.01
i036 item36 -0.07 -0.13 0.01
i016 item16 -0.08 -0.19 + 0.02
i064 item64 -0.08 -0.05 -0.12
i023 item23 -0.09 -0.10 -0.08
i041 item41 -0.10 -0.11 -0.09
i045 item45 -0.10 -0.10 -0.11
i006 item6 -0.11 -0.14 -0.07
i055 item55 -0.11 0.00 -0.22 *
i051 item51 -0.11 -0.21 * -0.03
i024 item24 -0.13 + -0.13 -0.12
i054 item54 -0.13 + -0.14 -0.12
i040 item40 -0.13 + 0.03 -0.30 **
i014 item14 -0.14 * -0.11 -0.17 +
i018 item18 -0.15 * -0.27 ** -0.01
i060 item60 -0.15 * -0.17 + -0.14
i003 item3 -0.16 * -0.20 * -0.12
i050 item50 -0.20 ** -0.32 *** -0.07
i008 item8 -0.25 *** -0.17 + -0.33 ***
i013 item13 -0.27 *** -0.29 ** -0.24 *
Note. Item content abbreviated. *** = p < .001, ** = p < .01, * = p < .05, + = p < .10.
Male-Female vector correlation, r = 0.35. Ns are 205, 105, and 100 for Combined, Female, and Male respectively.
Average absolute correlations are 0.086**, 0.114***, and 0.104+ for Combined, Female, and Male respectively.
Content Combined p1 Female p2
RBQ020 Is talkative 0.26 *** 0.29 **
RBQ056 Speaks in a loud voice 0.26 *** 0.19 +
RBQ015 High enthusiasm and energy level 0.19 ** 0.14
RBQ048 Expresses sexual interest 0.17 * 0.25 **
RBQ002 Volunteers Information about Self 0.17 * 0.22 *
RBQ037 Expressive in voice, face, or gesture 0.17 * 0.13
RBQ057 Speaks sarcastically 0.14 * 0.29 **
RBQ012 Seems to like other(s) 0.13 + 0.10
RBQ001 Interviews Other(s) 0.12 + 0.06
RBQ007 Exhibits social skills 0.11 -0.02
RBQ033 Tries to undermine/sabotage 0.07 0.22 *
RBQ013 Exhibits awkward interpersonal style -0.27 *** -0.29 **
RBQ008 Reserved and unexpressive -0.25 *** -0.17 +
RBQ050 Gives up when faced w/obstacles -0.20 ** -0.32 ***
RBQ003 Interested in what Partner(s) say -0.16 * -0.20 *
RBQ060 Seems detached from situation -0.15 * -0.17 +
RBQ018 Expresses agreement frequently -0.15 * -0.27 **
RBQ014 Compares self to other(s) -0.14 * -0.11
RBQ040 Keeps other(s) at a distance -0.13 + 0.03
RBQ054 Emphasizes accomplishments -0.13 + -0.14
RBQ024 Expresses sympathy -0.13 + -0.13
RBQ051 Behaves in stereotypical gender style or manner -0.11 -0.21 *
RBQ055 Behaves in competitive manner -0.11 0.00
Male p3
RBQ020 0.23 *
RBQ056 0.32 **
RBQ015 0.25 *
RBQ048 0.09
RBQ002 0.11
RBQ037 0.23 *
RBQ057 -0.02
RBQ012 0.17 +
RBQ001 0.19 +
RBQ007 0.24 *
RBQ033 -0.07
RBQ013 -0.24 *
RBQ008 -0.33 ***
RBQ050 -0.07
RBQ003 -0.12
RBQ060 -0.14
RBQ018 -0.01
RBQ014 -0.17 +
RBQ040 -0.30 **
RBQ054 -0.12
RBQ024 -0.12
RBQ051 -0.03
RBQ055 -0.22 *
Note. Item content abbreviated. *** = p < .001, ** = p < .01, * = p < .05, + = p < .10.
Male-Female vector correlation, r = 0.35. Ns are 205, 105, and 100 for Combined, Female, and Male respectively.
Average absolute correlations are 0.086**, 0.114***, and 0.104+ for Combined, Female, and Male respectively.
NULL
Warning message:
q.cor.print is deprecated. Please use the print function.
[1] 0.537037037 0.689814815 -0.094907407 0.243055556 0.694444444
[6] 0.425925926 0.458333333 0.576388889 -0.060185185 0.331018519
[11] 0.042687417 0.287037037 0.625000000 0.564814815 0.153928526
[16] 0.648148148 0.437500000 0.236214344 0.347222222 0.310185185
[21] -0.006944444 0.196759259 0.076388889 0.393518519 0.641203704
[26] 0.428240741 0.134259259 0.541666667 0.074074074 0.284722222
[31] 0.194444444 0.187500000 0.439814815 0.474537037 0.152777778
[36] 0.726851852 -0.092592593 0.467592593 0.186218518 0.585648148
[41] 0.412037037 0.608796296 -0.013776428 0.504629630 0.486111111
[46] 0.275462963 0.333333333 0.479166667 0.601851852 0.111111111
[51] 0.307870370 0.252314815 0.058612926 0.287037037 0.458333333
[56] 0.129629630 -0.039351852 0.423611111 0.738425926 0.252314815
[61] 0.298611111 0.238425926 -0.034722222 0.063472505 0.511574074
[66] 0.516203704 0.418981481 0.659722222 0.583333333 0.402777778
[71] 0.506944444 0.451388889 0.273148148 0.328703704 0.253111597
[76] 0.213304331 0.321759259 0.192129630 0.377314815 0.377314815
[81] 0.520833333 0.634259259 0.226851852 -0.143548366 0.006944444
[86] 0.215277778 0.557870370 0.583333333 0.712962963 0.083333333
[91] 0.261963586 0.344907407 0.490740741 0.534722222 0.599537037
[96] 0.254629630 0.520833333 0.627314815 0.270338861 -0.175925926
[101] 0.217592593 0.393518519 0.729166667 0.175925926 0.618055556
[106] 0.395833333 0.069444444 0.409722222 0.000000000 0.185185185
[111] 0.259259259 0.226851852 0.328703704 0.384259259 0.243055556
[116] 0.564814815 0.372685185 0.132837965 0.509259259 0.284722222
[121] 0.597222222 0.398148148 0.280092593 0.185185185 0.375000000
[126] 0.446759259 0.655092593 0.703703704 0.416666667 0.469907407
[131] 0.574074074 0.098071585 0.291666667 0.562500000 0.537037037
[136] 0.814814815 0.506944444 0.016217477 0.194444444 0.361111111
[141] 0.209374072 0.400462963 0.513888889 0.527777778 0.016198402
[146] 0.293981481 0.176251718 0.132481323 0.240740741 0.555555556
[151] 0.161084485 0.666666667 0.247685185 0.173611111 0.395833333
[156] 0.574074074 0.557870370 0.664351852 0.523148148 0.469907407
[161] 0.604166667 0.104166667 0.298611111 0.444444444 0.236140352
[166] 0.368055556 0.493055556 0.114834836 0.685185185 0.513888889
[171] 0.537037037 0.273148148 0.328703704 0.465277778 0.506944444
[176] 0.002314815 -0.069444444 0.474537037 0.203703704 0.597222222
[181] -0.182870370 0.293981481 0.583333333 0.326388889 0.136574074
[186] 0.557870370 0.557870370 0.520833333 0.562500000 0.175925926
[191] 0.136574074 0.252314815 0.291666667 0.057307531 0.409722222
[196] 0.516203704 0.393518519 0.080796744 0.541666667 0.047793651
[201] -0.085857859 0.641203704 0.641203704 0.226851852 0.175925926
var n miss mean sd median trimmed mad min
1 1 205 0 0.3699396 0.2514333 0.3680556 0.3670939 0.2762678 -0.1828704
max range skew kurtosis se
1 0.8148148 0.8683032 0.1060303 -0.4899812 0.0179437
$xNorm
acq1CAQ001 acq1CAQ002 acq1CAQ003 acq1CAQ004 acq1CAQ005 acq1CAQ006 acq1CAQ007
4.673171 7.404878 6.400000 6.960976 6.448780 3.712195 4.717073
acq1CAQ008 acq1CAQ009 acq1CAQ010 acq1CAQ011 acq1CAQ012 acq1CAQ013 acq1CAQ014
6.658537 4.639024 3.829268 5.868293 4.424390 3.400000 3.443902
acq1CAQ015 acq1CAQ016 acq1CAQ017 acq1CAQ018 acq1CAQ019 acq1CAQ020 acq1CAQ021
6.146341 5.434146 6.351220 7.078049 4.624390 4.892683 3.839024
acq1CAQ022 acq1CAQ023 acq1CAQ024 acq1CAQ025 acq1CAQ026 acq1CAQ027 acq1CAQ028
2.887805 3.234146 4.975610 4.078049 6.473171 3.151220 6.282927
acq1CAQ029 acq1CAQ030 acq1CAQ031 acq1CAQ032 acq1CAQ033 acq1CAQ034 acq1CAQ035
5.868293 3.634146 5.278049 5.556098 6.424390 3.814634 6.692683
acq1CAQ036 acq1CAQ037 acq1CAQ038 acq1CAQ039 acq1CAQ040 acq1CAQ041 acq1CAQ042
3.092683 2.521951 3.268293 4.882927 3.575610 5.073171 4.292683
acq1CAQ043 acq1CAQ044 acq1CAQ045 acq1CAQ046 acq1CAQ047 acq1CAQ048 acq1CAQ049
6.360976 5.263415 3.882927 4.878049 4.073171 3.804878 3.531707
acq1CAQ050 acq1CAQ051 acq1CAQ052 acq1CAQ053 acq1CAQ054 acq1CAQ055 acq1CAQ056
4.190244 6.024390 5.980488 3.975610 5.941463 3.414634 7.614634
acq1CAQ057 acq1CAQ058 acq1CAQ059 acq1CAQ060 acq1CAQ061 acq1CAQ062 acq1CAQ063
7.053659 5.946341 5.414634 5.951220 3.053659 4.302439 4.087805
acq1CAQ064 acq1CAQ065 acq1CAQ066 acq1CAQ067 acq1CAQ068 acq1CAQ069 acq1CAQ070
5.273171 4.653659 5.634146 4.892683 4.185366 4.560976 6.112195
acq1CAQ071 acq1CAQ072 acq1CAQ073 acq1CAQ074 acq1CAQ075 acq1CAQ076 acq1CAQ077
6.102439 4.878049 4.512195 5.648780 5.526829 4.692683 6.248780
acq1CAQ078 acq1CAQ079 acq1CAQ080 acq1CAQ081 acq1CAQ082 acq1CAQ083 acq1CAQ084
3.375610 4.668293 6.429268 5.780488 4.575610 5.570732 6.385366
acq1CAQ085 acq1CAQ086 acq1CAQ087 acq1CAQ088 acq1CAQ089 acq1CAQ090 acq1CAQ091
4.790244 4.287805 4.585366 6.063415 4.473171 4.892683 4.692683
acq1CAQ092 acq1CAQ093 acq1CAQ094 acq1CAQ095 acq1CAQ096 acq1CAQ097 acq1CAQ098
5.780488 5.551220 3.829268 5.600000 5.921951 4.112195 5.917073
acq1CAQ099 acq1CAQ100
4.307317 4.795122
$yNorm
acq2CAQ001 acq2CAQ002 acq2CAQ003 acq2CAQ004 acq2CAQ005 acq2CAQ006 acq2CAQ007
4.765854 6.765854 6.429268 6.892683 6.356098 4.078049 4.634146
acq2CAQ008 acq2CAQ009 acq2CAQ010 acq2CAQ011 acq2CAQ012 acq2CAQ013 acq2CAQ014
6.112195 4.585366 4.068293 5.590244 4.946341 3.985366 3.351220
acq2CAQ015 acq2CAQ016 acq2CAQ017 acq2CAQ018 acq2CAQ019 acq2CAQ020 acq2CAQ021
5.975610 5.219512 5.985366 6.658537 4.751220 5.029268 3.990244
acq2CAQ022 acq2CAQ023 acq2CAQ024 acq2CAQ025 acq2CAQ026 acq2CAQ027 acq2CAQ028
3.434146 3.248780 5.034146 4.195122 6.482927 3.526829 5.834146
acq2CAQ029 acq2CAQ030 acq2CAQ031 acq2CAQ032 acq2CAQ033 acq2CAQ034 acq2CAQ035
5.346341 3.580488 5.600000 5.580488 5.770732 3.921951 6.751220
acq2CAQ036 acq2CAQ037 acq2CAQ038 acq2CAQ039 acq2CAQ040 acq2CAQ041 acq2CAQ042
2.868293 2.517073 3.043902 4.873171 3.590244 5.034146 4.439024
acq2CAQ043 acq2CAQ044 acq2CAQ045 acq2CAQ046 acq2CAQ047 acq2CAQ048 acq2CAQ049
5.609756 4.985366 3.848780 4.839024 4.073171 3.907317 3.541463
acq2CAQ050 acq2CAQ051 acq2CAQ052 acq2CAQ053 acq2CAQ054 acq2CAQ055 acq2CAQ056
4.395122 5.897561 5.839024 4.268293 6.034146 3.434146 7.141463
acq2CAQ057 acq2CAQ058 acq2CAQ059 acq2CAQ060 acq2CAQ061 acq2CAQ062 acq2CAQ063
6.917073 6.321951 5.326829 5.951220 2.956098 4.575610 4.765854
acq2CAQ064 acq2CAQ065 acq2CAQ066 acq2CAQ067 acq2CAQ068 acq2CAQ069 acq2CAQ070
5.204878 4.687805 5.531707 5.224390 3.912195 4.556098 5.951220
acq2CAQ071 acq2CAQ072 acq2CAQ073 acq2CAQ074 acq2CAQ075 acq2CAQ076 acq2CAQ077
6.248780 4.819512 4.175610 5.639024 5.200000 4.414634 5.892683
acq2CAQ078 acq2CAQ079 acq2CAQ080 acq2CAQ081 acq2CAQ082 acq2CAQ083 acq2CAQ084
3.536585 4.629268 6.365854 5.526829 4.682927 5.502439 6.541463
acq2CAQ085 acq2CAQ086 acq2CAQ087 acq2CAQ088 acq2CAQ089 acq2CAQ090 acq2CAQ091
4.960976 4.658537 4.707317 6.282927 4.439024 4.921951 4.702439
acq2CAQ092 acq2CAQ093 acq2CAQ094 acq2CAQ095 acq2CAQ096 acq2CAQ097 acq2CAQ098
5.639024 5.321951 4.185366 5.502439 5.809756 4.468293 5.775610
acq2CAQ099 acq2CAQ100
4.282927 5.029268
$Norm.r
[1] 0.9760003
$Agreement
Overall Distinctive
1 0.537037037 -0.065251481
2 0.689814815 0.175933686
3 -0.094907407 -0.123577341
4 0.243055556 0.167643259
5 0.694444444 0.155925416
6 0.425925926 0.155230407
7 0.458333333 0.419693801
8 0.576388889 -0.004623474
9 -0.060185185 -0.098297065
10 0.331018519 0.407606861
11 0.042687417 -0.032207925
12 0.287037037 0.072798978
13 0.625000000 0.204542695
14 0.564814815 0.268350977
15 0.153928526 0.079325435
16 0.648148148 -0.039489298
17 0.437500000 0.146205221
18 0.236214344 0.133043673
19 0.347222222 0.147392764
20 0.310185185 0.111860718
21 -0.006944444 0.044409449
22 0.196759259 -0.066431430
23 0.076388889 -0.038848009
24 0.393518519 0.168026073
25 0.641203704 0.140721994
26 0.428240741 0.190267143
27 0.134259259 0.051669320
28 0.541666667 0.255198906
29 0.074074074 0.072308635
30 0.284722222 0.124303572
31 0.194444444 0.191811303
32 0.187500000 0.068190583
33 0.439814815 -0.011987681
34 0.474537037 0.176586810
35 0.152777778 0.047512754
36 0.726851852 0.310607248
37 -0.092592593 -0.182679614
38 0.467592593 0.043369331
39 0.186218518 0.084647000
40 0.585648148 0.223959827
41 0.412037037 0.108028206
42 0.608796296 0.312586117
43 -0.013776428 -0.061005426
44 0.504629630 -0.035518253
45 0.486111111 -0.032542082
46 0.275462963 0.114913717
47 0.333333333 0.011532804
48 0.479166667 0.216533306
49 0.601851852 0.080446065
50 0.111111111 0.077641396
51 0.307870370 0.231978763
52 0.252314815 0.248315468
53 0.058612926 -0.144802734
54 0.287037037 0.022881884
55 0.458333333 0.199318057
56 0.129629630 -0.170723411
57 -0.039351852 0.188752652
58 0.423611111 -0.074133714
59 0.738425926 0.312741846
60 0.252314815 0.297098254
61 0.298611111 -0.043359204
62 0.238425926 0.136367447
63 -0.034722222 0.200036058
64 0.063472505 -0.184927890
65 0.511574074 0.070526398
66 0.516203704 0.452370483
67 0.418981481 0.010293946
68 0.659722222 0.055198536
69 0.583333333 0.268400284
70 0.402777778 0.115055986
71 0.506944444 0.237225940
72 0.451388889 0.155529895
73 0.273148148 0.062271686
74 0.328703704 0.075160519
75 0.253111597 0.121144371
76 0.213304331 0.007190857
77 0.321759259 -0.010817783
78 0.192129630 0.183318361
79 0.377314815 0.065934117
80 0.377314815 0.012751150
81 0.520833333 0.405405583
82 0.634259259 -0.060184577
83 0.226851852 0.004164546
84 -0.143548366 -0.155158396
85 0.006944444 0.008591496
86 0.215277778 0.165972895
87 0.557870370 0.190212659
88 0.583333333 -0.188273191
89 0.712962963 0.268547323
90 0.083333333 -0.012014196
91 0.261963586 0.018896429
92 0.344907407 0.168111740
93 0.490740741 0.308613855
94 0.534722222 0.022121513
95 0.599537037 0.344537858
96 0.254629630 0.204584047
97 0.520833333 -0.015861550
98 0.627314815 0.222819486
99 0.270338861 0.166565376
100 -0.175925926 0.001325282
101 0.217592593 0.206732284
102 0.393518519 0.245650574
103 0.729166667 0.211707212
104 0.175925926 0.086301585
105 0.618055556 0.125049803
106 0.395833333 -0.170184518
107 0.069444444 -0.076352129
108 0.409722222 0.252475364
109 0.000000000 0.026845766
110 0.185185185 0.100550407
111 0.259259259 0.231812628
112 0.226851852 0.041539461
113 0.328703704 -0.098918432
114 0.384259259 0.149684332
115 0.243055556 -0.155272156
116 0.564814815 0.075484761
117 0.372685185 0.331882727
118 0.132837965 0.135460378
119 0.509259259 0.203589066
120 0.284722222 0.121098217
121 0.597222222 0.137571595
122 0.398148148 0.115417903
123 0.280092593 0.266035059
124 0.185185185 0.227744914
125 0.375000000 0.159511760
126 0.446759259 0.097419898
127 0.655092593 0.089494549
128 0.703703704 0.283377729
129 0.416666667 0.110536260
130 0.469907407 -0.150972744
131 0.574074074 0.151999254
132 0.098071585 0.028987846
133 0.291666667 0.152324029
134 0.562500000 -0.016860317
135 0.537037037 0.226751404
136 0.814814815 0.387519529
137 0.506944444 0.094099525
138 0.016217477 -0.040361620
139 0.194444444 -0.044617375
140 0.361111111 -0.048772697
141 0.209374072 0.014184111
142 0.400462963 0.103520783
143 0.513888889 0.334203405
144 0.527777778 0.006508125
145 0.016198402 -0.035400467
146 0.293981481 0.035865627
147 0.176251718 0.014712193
148 0.132481323 0.156568022
149 0.240740741 0.132128186
150 0.555555556 0.246894535
151 0.161084485 -0.081967996
152 0.666666667 0.234804340
153 0.247685185 0.006491970
154 0.173611111 0.228865024
155 0.395833333 0.065368495
156 0.574074074 0.074397792
157 0.557870370 0.551373490
158 0.664351852 0.177435996
159 0.523148148 0.065714862
160 0.469907407 0.191729508
161 0.604166667 0.509769823
162 0.104166667 0.202605982
163 0.298611111 0.304687280
164 0.444444444 0.359294561
165 0.236140352 0.211271817
166 0.368055556 0.157573285
167 0.493055556 0.352191192
168 0.114834836 0.092842191
169 0.685185185 0.169758048
170 0.513888889 0.303989423
171 0.537037037 0.095838069
172 0.273148148 0.127090475
173 0.328703704 -0.107653956
174 0.465277778 0.294512532
175 0.506944444 0.109004351
176 0.002314815 0.050207042
177 -0.069444444 -0.175258789
178 0.474537037 0.029638024
179 0.203703704 0.113090737
180 0.597222222 0.100296503
181 -0.182870370 -0.143786583
182 0.293981481 0.270947805
183 0.583333333 0.191412908
184 0.326388889 0.182864940
185 0.136574074 0.093703948
186 0.557870370 0.069307138
187 0.557870370 0.069307138
188 0.520833333 0.098816300
189 0.562500000 0.225736121
190 0.175925926 -0.114064591
191 0.136574074 -0.270002117
192 0.252314815 0.015901745
193 0.291666667 -0.002763987
194 0.057307531 0.031467439
195 0.409722222 0.074942723
196 0.516203704 0.037104723
197 0.393518519 0.129364009
198 0.080796744 -0.049269654
199 0.541666667 0.290305742
200 0.047793651 -0.086954136
201 -0.085857859 -0.088522769
202 0.641203704 0.084548404
203 0.641203704 0.139759180
204 0.226851852 0.259463849
205 0.175925926 0.215759684
$Tests
Overall Distinctive
N 2.050000e+02 2.050000e+02
Mean 3.699396e-01 1.096436e-01
baseline 2.689034e-01 0.000000e+00
t 6.278509e+00 1.059806e+01
p-value 1.010598e-09 1.722550e-21
Replicability Lower Limit Upper Limit
Overall 0.7966126 0.7362851 0.8491297
Distinctive 0.4740320 0.3180228 0.6098436
multicon documentation built on May 2, 2019, 3:18 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References See Also Examples
Description
This package contains functions for examining multivariate constructs (MCs).
Details
| Package: | multicon |
| Type: | Package |
| Version: | 1.6 |
| Date: | 2011-1-29 |
| License: | GPL-2 |
MCs are, as the name implies, constructs that consist of many variables. For example, personality is not a single variable, but a constellation of many individual variables. This is problematic for traditional analyses which only examine the relationships between only 1 variable (or just a few variables) and some outcome of interest. Within-person analyses are often interested in MCs as well. This package contains functions for examining such multivariate constructs.
Author(s)
Ryne A. Sherman
Maintainer: Ryne A. Sherman <rsherm13@fau.edu>
Compiler: David G. Serfass <dserfass@fau.edu>
References
Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge.
Funder, D. C., Furr, R. M., Colvin, C. R. (2000). The Riverside Behavioral Q-sort: A tool for the description of social behavior. Journal of Personality, 68, 451-489.
Furr, R. M., Wagerman, S. A., & Funder, D. C. (2010). Personality as manifest in behavior: Direct behavioral observation using the revised Riverside Behavioral Q-sort (RBQ-3.0). In C.R. Agnew, D. E. Carlston, W. G., Graziano, & J. R. Kelly (Eds.), Then a miracle occurs: Focusing on beahvior in social psychological theory and research. (pp. 186-204). Oxford University Press.
Furr, R. M. (2008). A framework for Profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76(5), 1267-1316.
My website: http://psy2.fau.edu/~shermanr/index.html
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # Some examples of the core functions in the multicon package:
# Is personality related to behavior? This simple question becomes more
# complex with the recognition that both personality and behavior are multivariate constructs.
# One (of many) ways to quantify personality is with a a 100-item measure,
# the California Adult Q-set (CAQ: Block, 1961). And one (of a few) ways to
# quantify behavior is with a 67-item measure, the Riverside Behavioral Q-sort
# (RBQ: Funder, Furr, & Colvin, Colvin, 2000; Furr, Wagerman, & Funder, 2010).
# How well are these two instruments related? There are 100 * 67 = 6700
# possible correlations that could be examined one at a time. Alternatively,
# we could answer our question more directly by simply seeing what the
# (absolute) average correlation is amongst these two sets of items and testing
# it against a baseline model that assumes zero association. The function
# rand.test() does this.
data(caq)
data(v2rbq)
# Note that in practice more sims (i.e., 1000 or more) might be preffered
rand.test(caq, v2rbq, sims=100, graph=FALSE)
# How is a specific single variable of interest (e.g., Extraversion) related
# to some multivariate construct (e.g., behavior - as measured by the RBQ)?
# Do the relationships differ by sex? The function q.cor() is
# designed to answer this question.
data(RSPdata)
# Note that in practice more sims (i.e., 1000 or more) might be preffered
myobj <- q.cor(RSPdata$sEXT, v2rbq, sex = RSPdata$ssex, fem = 1, male = 2, sims=100)
myobj
# The results can be organized by using q.cor.print() for easier interpretation
data(rbqv3.items)
q.cor.print(myobj, rbqv3.items, "RBQ", short=TRUE)
# How well do two judgments of a target's personality agree with each other?
# Again, assuming personality is measured as a multivariate construct
# (e.g., the 100-item CAQ), this question is not so straightforward. One way
# is to correlate the two judge's ratings across the 100-item pairs (profile correlation).
# This can be done for each target with two judges. The function Profile.r() does this.
data(acq1) # The first friend of a target being judged (N targets = 205)
data(acq2) # The second friend of a target being judged
Profile.r(acq1, acq2) # The agreements (correlations) for each target
# Get summary statistics for the agreements
describe.r(Profile.r(acq1, acq2))
# If we want to control for normativeness (see Furr, 2008) and get
# significance tests (for both overall and distinctive agreement) we
# can simply set distinct=TRUE.
Profile.r(acq1, acq2, distinct=TRUE)
# If we want to know how replicable (reliable) the agreement correaltions are
# we can use Profile.r.rep()
Profile.r.rep(acq1, acq2)
# The package also includes some graphing functions for comparing group means
# based on "The New Statistics" (Cumming, 2012).
y <- c(rnorm(30), rnorm(30, mean=1))
group <- rep(1:2, each=30)
catseye(y, group, las=1, main="A Catseye Plot", xlab="",
grp.names=c("Control", "Experimental"), ylab="DV")
catseye(y, group, las=1, main="A Catseye Plot #2", xlab="",
grp.names=c("Control", "Experimental"), ylab="DV", conf=.80, col="cyan")
df=data.frame(group=group,y=y)
diffPlot(y ~ group,data=df,grp.names=c("Control", "Experimental"), xlab="",
ylab="DV", main="A Difference Plot", sub="Arms are 95 percent CIs")
|
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
$AbsR
Average Absolute r
N 205.0000
Observed 0.0635
Exp. By Chance 0.0559
Standard Error 0.0010
p 0.0000
99.9% Upperbound p 0.0000
99.9% Lowerbound p 0.0000
95th % 0.0577
$Sig
Number Significant
N 205.000
Observed 575.000
Exp. By Chance 334.230
Standard Error 29.918
p 0.000
99.9% Upperbound p 0.000
99.9% Lowerbound p 0.000
95th % 386.050
Warning message:
In rand.test(caq, v2rbq, sims = 100, graph = FALSE) :
When p=.00, confidence interval for p not valid.
Warning messages:
1: In rand.test(data.frame(x), set, sims = 1000, graph = F, seed = seed) :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
2: In rand.test(data.frame(x), set, sims = 1000, graph = F, seed = seed) :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
3: In rand.test(data.frame(female[, 2]), fem.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
4: In rand.test(data.frame(female[, 2]), fem.cols, sims = sims, graph = F, :
When p=.00, confidence interval for p not valid.
5: In rand.test(data.frame(male[, 2]), male.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
6: In rand.test(data.frame(male[, 2]), male.cols, sims = sims, graph = F, :
Confidence intervals for p-values may be inaccurate. Try a larger number of sims.
Content Combined p1 Female p2 Male p3
i020 item20 0.26 *** 0.29 ** 0.23 *
i056 item56 0.26 *** 0.19 + 0.32 **
i015 item15 0.19 ** 0.14 0.25 *
i048 item48 0.17 * 0.25 ** 0.09
i002 item2 0.17 * 0.22 * 0.11
i037 item37 0.17 * 0.13 0.23 *
i057 item57 0.14 * 0.29 ** -0.02
i012 item12 0.13 + 0.10 0.17 +
i001 item1 0.12 + 0.06 0.19 +
i007 item7 0.11 -0.02 0.24 *
i062 item62 0.10 0.12 0.07
i053 item53 0.10 0.12 0.07
i005 item5 0.09 0.13 0.04
i058 item58 0.08 0.01 0.14
i030 item30 0.08 0.04 0.12
i063 item63 0.08 0.06 0.09
i033 item33 0.07 0.22 * -0.07
i059 item59 0.06 0.11 0.00
i017 item17 0.06 0.07 0.03
i004 item4 0.05 0.04 0.07
i038 item38 0.05 0.06 0.04
i011 item11 0.04 0.09 0.01
i049 item49 0.04 0.04 0.04
i027 item27 0.03 0.17 + -0.12
i022 item22 0.03 0.09 -0.02
i034 item34 0.03 0.12 -0.06
i010 item10 0.03 -0.09 0.16
i032 item32 0.03 -0.04 0.11
i066 item66 0.01 -0.08 0.10
i019 item19 0.00 -0.09 0.10
i065 item65 0.00 0.04 -0.03
i025 item25 0.00 0.06 -0.07
i046 item46 0.00 0.08 -0.09
i039 item39 -0.01 0.08 -0.10
i061 item61 -0.01 -0.07 0.05
i028 item28 -0.02 -0.11 0.08
i035 item35 -0.02 0.01 -0.05
i067 item67 -0.02 -0.07 0.03
i026 item26 -0.02 0.09 -0.13
i044 item44 -0.02 0.07 -0.12
i029 item29 -0.02 0.01 -0.06
i021 item21 -0.03 0.00 -0.06
i047 item47 -0.03 0.01 -0.08
i009 item9 -0.03 -0.10 0.04
i052 item52 -0.04 -0.16 0.07
i031 item31 -0.05 0.07 -0.17 +
i043 item43 -0.06 -0.17 + 0.07
i042 item42 -0.06 -0.11 -0.01
i036 item36 -0.07 -0.13 0.01
i016 item16 -0.08 -0.19 + 0.02
i064 item64 -0.08 -0.05 -0.12
i023 item23 -0.09 -0.10 -0.08
i041 item41 -0.10 -0.11 -0.09
i045 item45 -0.10 -0.10 -0.11
i006 item6 -0.11 -0.14 -0.07
i055 item55 -0.11 0.00 -0.22 *
i051 item51 -0.11 -0.21 * -0.03
i024 item24 -0.13 + -0.13 -0.12
i054 item54 -0.13 + -0.14 -0.12
i040 item40 -0.13 + 0.03 -0.30 **
i014 item14 -0.14 * -0.11 -0.17 +
i018 item18 -0.15 * -0.27 ** -0.01
i060 item60 -0.15 * -0.17 + -0.14
i003 item3 -0.16 * -0.20 * -0.12
i050 item50 -0.20 ** -0.32 *** -0.07
i008 item8 -0.25 *** -0.17 + -0.33 ***
i013 item13 -0.27 *** -0.29 ** -0.24 *
Note. Item content abbreviated. *** = p < .001, ** = p < .01, * = p < .05, + = p < .10.
Male-Female vector correlation, r = 0.35. Ns are 205, 105, and 100 for Combined, Female, and Male respectively.
Average absolute correlations are 0.086**, 0.114***, and 0.104+ for Combined, Female, and Male respectively.
Content Combined p1 Female p2
RBQ020 Is talkative 0.26 *** 0.29 **
RBQ056 Speaks in a loud voice 0.26 *** 0.19 +
RBQ015 High enthusiasm and energy level 0.19 ** 0.14
RBQ048 Expresses sexual interest 0.17 * 0.25 **
RBQ002 Volunteers Information about Self 0.17 * 0.22 *
RBQ037 Expressive in voice, face, or gesture 0.17 * 0.13
RBQ057 Speaks sarcastically 0.14 * 0.29 **
RBQ012 Seems to like other(s) 0.13 + 0.10
RBQ001 Interviews Other(s) 0.12 + 0.06
RBQ007 Exhibits social skills 0.11 -0.02
RBQ033 Tries to undermine/sabotage 0.07 0.22 *
RBQ013 Exhibits awkward interpersonal style -0.27 *** -0.29 **
RBQ008 Reserved and unexpressive -0.25 *** -0.17 +
RBQ050 Gives up when faced w/obstacles -0.20 ** -0.32 ***
RBQ003 Interested in what Partner(s) say -0.16 * -0.20 *
RBQ060 Seems detached from situation -0.15 * -0.17 +
RBQ018 Expresses agreement frequently -0.15 * -0.27 **
RBQ014 Compares self to other(s) -0.14 * -0.11
RBQ040 Keeps other(s) at a distance -0.13 + 0.03
RBQ054 Emphasizes accomplishments -0.13 + -0.14
RBQ024 Expresses sympathy -0.13 + -0.13
RBQ051 Behaves in stereotypical gender style or manner -0.11 -0.21 *
RBQ055 Behaves in competitive manner -0.11 0.00
Male p3
RBQ020 0.23 *
RBQ056 0.32 **
RBQ015 0.25 *
RBQ048 0.09
RBQ002 0.11
RBQ037 0.23 *
RBQ057 -0.02
RBQ012 0.17 +
RBQ001 0.19 +
RBQ007 0.24 *
RBQ033 -0.07
RBQ013 -0.24 *
RBQ008 -0.33 ***
RBQ050 -0.07
RBQ003 -0.12
RBQ060 -0.14
RBQ018 -0.01
RBQ014 -0.17 +
RBQ040 -0.30 **
RBQ054 -0.12
RBQ024 -0.12
RBQ051 -0.03
RBQ055 -0.22 *
Note. Item content abbreviated. *** = p < .001, ** = p < .01, * = p < .05, + = p < .10.
Male-Female vector correlation, r = 0.35. Ns are 205, 105, and 100 for Combined, Female, and Male respectively.
Average absolute correlations are 0.086**, 0.114***, and 0.104+ for Combined, Female, and Male respectively.
NULL
Warning message:
q.cor.print is deprecated. Please use the print function.
[1] 0.537037037 0.689814815 -0.094907407 0.243055556 0.694444444
[6] 0.425925926 0.458333333 0.576388889 -0.060185185 0.331018519
[11] 0.042687417 0.287037037 0.625000000 0.564814815 0.153928526
[16] 0.648148148 0.437500000 0.236214344 0.347222222 0.310185185
[21] -0.006944444 0.196759259 0.076388889 0.393518519 0.641203704
[26] 0.428240741 0.134259259 0.541666667 0.074074074 0.284722222
[31] 0.194444444 0.187500000 0.439814815 0.474537037 0.152777778
[36] 0.726851852 -0.092592593 0.467592593 0.186218518 0.585648148
[41] 0.412037037 0.608796296 -0.013776428 0.504629630 0.486111111
[46] 0.275462963 0.333333333 0.479166667 0.601851852 0.111111111
[51] 0.307870370 0.252314815 0.058612926 0.287037037 0.458333333
[56] 0.129629630 -0.039351852 0.423611111 0.738425926 0.252314815
[61] 0.298611111 0.238425926 -0.034722222 0.063472505 0.511574074
[66] 0.516203704 0.418981481 0.659722222 0.583333333 0.402777778
[71] 0.506944444 0.451388889 0.273148148 0.328703704 0.253111597
[76] 0.213304331 0.321759259 0.192129630 0.377314815 0.377314815
[81] 0.520833333 0.634259259 0.226851852 -0.143548366 0.006944444
[86] 0.215277778 0.557870370 0.583333333 0.712962963 0.083333333
[91] 0.261963586 0.344907407 0.490740741 0.534722222 0.599537037
[96] 0.254629630 0.520833333 0.627314815 0.270338861 -0.175925926
[101] 0.217592593 0.393518519 0.729166667 0.175925926 0.618055556
[106] 0.395833333 0.069444444 0.409722222 0.000000000 0.185185185
[111] 0.259259259 0.226851852 0.328703704 0.384259259 0.243055556
[116] 0.564814815 0.372685185 0.132837965 0.509259259 0.284722222
[121] 0.597222222 0.398148148 0.280092593 0.185185185 0.375000000
[126] 0.446759259 0.655092593 0.703703704 0.416666667 0.469907407
[131] 0.574074074 0.098071585 0.291666667 0.562500000 0.537037037
[136] 0.814814815 0.506944444 0.016217477 0.194444444 0.361111111
[141] 0.209374072 0.400462963 0.513888889 0.527777778 0.016198402
[146] 0.293981481 0.176251718 0.132481323 0.240740741 0.555555556
[151] 0.161084485 0.666666667 0.247685185 0.173611111 0.395833333
[156] 0.574074074 0.557870370 0.664351852 0.523148148 0.469907407
[161] 0.604166667 0.104166667 0.298611111 0.444444444 0.236140352
[166] 0.368055556 0.493055556 0.114834836 0.685185185 0.513888889
[171] 0.537037037 0.273148148 0.328703704 0.465277778 0.506944444
[176] 0.002314815 -0.069444444 0.474537037 0.203703704 0.597222222
[181] -0.182870370 0.293981481 0.583333333 0.326388889 0.136574074
[186] 0.557870370 0.557870370 0.520833333 0.562500000 0.175925926
[191] 0.136574074 0.252314815 0.291666667 0.057307531 0.409722222
[196] 0.516203704 0.393518519 0.080796744 0.541666667 0.047793651
[201] -0.085857859 0.641203704 0.641203704 0.226851852 0.175925926
var n miss mean sd median trimmed mad min
1 1 205 0 0.3699396 0.2514333 0.3680556 0.3670939 0.2762678 -0.1828704
max range skew kurtosis se
1 0.8148148 0.8683032 0.1060303 -0.4899812 0.0179437
$xNorm
acq1CAQ001 acq1CAQ002 acq1CAQ003 acq1CAQ004 acq1CAQ005 acq1CAQ006 acq1CAQ007
4.673171 7.404878 6.400000 6.960976 6.448780 3.712195 4.717073
acq1CAQ008 acq1CAQ009 acq1CAQ010 acq1CAQ011 acq1CAQ012 acq1CAQ013 acq1CAQ014
6.658537 4.639024 3.829268 5.868293 4.424390 3.400000 3.443902
acq1CAQ015 acq1CAQ016 acq1CAQ017 acq1CAQ018 acq1CAQ019 acq1CAQ020 acq1CAQ021
6.146341 5.434146 6.351220 7.078049 4.624390 4.892683 3.839024
acq1CAQ022 acq1CAQ023 acq1CAQ024 acq1CAQ025 acq1CAQ026 acq1CAQ027 acq1CAQ028
2.887805 3.234146 4.975610 4.078049 6.473171 3.151220 6.282927
acq1CAQ029 acq1CAQ030 acq1CAQ031 acq1CAQ032 acq1CAQ033 acq1CAQ034 acq1CAQ035
5.868293 3.634146 5.278049 5.556098 6.424390 3.814634 6.692683
acq1CAQ036 acq1CAQ037 acq1CAQ038 acq1CAQ039 acq1CAQ040 acq1CAQ041 acq1CAQ042
3.092683 2.521951 3.268293 4.882927 3.575610 5.073171 4.292683
acq1CAQ043 acq1CAQ044 acq1CAQ045 acq1CAQ046 acq1CAQ047 acq1CAQ048 acq1CAQ049
6.360976 5.263415 3.882927 4.878049 4.073171 3.804878 3.531707
acq1CAQ050 acq1CAQ051 acq1CAQ052 acq1CAQ053 acq1CAQ054 acq1CAQ055 acq1CAQ056
4.190244 6.024390 5.980488 3.975610 5.941463 3.414634 7.614634
acq1CAQ057 acq1CAQ058 acq1CAQ059 acq1CAQ060 acq1CAQ061 acq1CAQ062 acq1CAQ063
7.053659 5.946341 5.414634 5.951220 3.053659 4.302439 4.087805
acq1CAQ064 acq1CAQ065 acq1CAQ066 acq1CAQ067 acq1CAQ068 acq1CAQ069 acq1CAQ070
5.273171 4.653659 5.634146 4.892683 4.185366 4.560976 6.112195
acq1CAQ071 acq1CAQ072 acq1CAQ073 acq1CAQ074 acq1CAQ075 acq1CAQ076 acq1CAQ077
6.102439 4.878049 4.512195 5.648780 5.526829 4.692683 6.248780
acq1CAQ078 acq1CAQ079 acq1CAQ080 acq1CAQ081 acq1CAQ082 acq1CAQ083 acq1CAQ084
3.375610 4.668293 6.429268 5.780488 4.575610 5.570732 6.385366
acq1CAQ085 acq1CAQ086 acq1CAQ087 acq1CAQ088 acq1CAQ089 acq1CAQ090 acq1CAQ091
4.790244 4.287805 4.585366 6.063415 4.473171 4.892683 4.692683
acq1CAQ092 acq1CAQ093 acq1CAQ094 acq1CAQ095 acq1CAQ096 acq1CAQ097 acq1CAQ098
5.780488 5.551220 3.829268 5.600000 5.921951 4.112195 5.917073
acq1CAQ099 acq1CAQ100
4.307317 4.795122
$yNorm
acq2CAQ001 acq2CAQ002 acq2CAQ003 acq2CAQ004 acq2CAQ005 acq2CAQ006 acq2CAQ007
4.765854 6.765854 6.429268 6.892683 6.356098 4.078049 4.634146
acq2CAQ008 acq2CAQ009 acq2CAQ010 acq2CAQ011 acq2CAQ012 acq2CAQ013 acq2CAQ014
6.112195 4.585366 4.068293 5.590244 4.946341 3.985366 3.351220
acq2CAQ015 acq2CAQ016 acq2CAQ017 acq2CAQ018 acq2CAQ019 acq2CAQ020 acq2CAQ021
5.975610 5.219512 5.985366 6.658537 4.751220 5.029268 3.990244
acq2CAQ022 acq2CAQ023 acq2CAQ024 acq2CAQ025 acq2CAQ026 acq2CAQ027 acq2CAQ028
3.434146 3.248780 5.034146 4.195122 6.482927 3.526829 5.834146
acq2CAQ029 acq2CAQ030 acq2CAQ031 acq2CAQ032 acq2CAQ033 acq2CAQ034 acq2CAQ035
5.346341 3.580488 5.600000 5.580488 5.770732 3.921951 6.751220
acq2CAQ036 acq2CAQ037 acq2CAQ038 acq2CAQ039 acq2CAQ040 acq2CAQ041 acq2CAQ042
2.868293 2.517073 3.043902 4.873171 3.590244 5.034146 4.439024
acq2CAQ043 acq2CAQ044 acq2CAQ045 acq2CAQ046 acq2CAQ047 acq2CAQ048 acq2CAQ049
5.609756 4.985366 3.848780 4.839024 4.073171 3.907317 3.541463
acq2CAQ050 acq2CAQ051 acq2CAQ052 acq2CAQ053 acq2CAQ054 acq2CAQ055 acq2CAQ056
4.395122 5.897561 5.839024 4.268293 6.034146 3.434146 7.141463
acq2CAQ057 acq2CAQ058 acq2CAQ059 acq2CAQ060 acq2CAQ061 acq2CAQ062 acq2CAQ063
6.917073 6.321951 5.326829 5.951220 2.956098 4.575610 4.765854
acq2CAQ064 acq2CAQ065 acq2CAQ066 acq2CAQ067 acq2CAQ068 acq2CAQ069 acq2CAQ070
5.204878 4.687805 5.531707 5.224390 3.912195 4.556098 5.951220
acq2CAQ071 acq2CAQ072 acq2CAQ073 acq2CAQ074 acq2CAQ075 acq2CAQ076 acq2CAQ077
6.248780 4.819512 4.175610 5.639024 5.200000 4.414634 5.892683
acq2CAQ078 acq2CAQ079 acq2CAQ080 acq2CAQ081 acq2CAQ082 acq2CAQ083 acq2CAQ084
3.536585 4.629268 6.365854 5.526829 4.682927 5.502439 6.541463
acq2CAQ085 acq2CAQ086 acq2CAQ087 acq2CAQ088 acq2CAQ089 acq2CAQ090 acq2CAQ091
4.960976 4.658537 4.707317 6.282927 4.439024 4.921951 4.702439
acq2CAQ092 acq2CAQ093 acq2CAQ094 acq2CAQ095 acq2CAQ096 acq2CAQ097 acq2CAQ098
5.639024 5.321951 4.185366 5.502439 5.809756 4.468293 5.775610
acq2CAQ099 acq2CAQ100
4.282927 5.029268
$Norm.r
[1] 0.9760003
$Agreement
Overall Distinctive
1 0.537037037 -0.065251481
2 0.689814815 0.175933686
3 -0.094907407 -0.123577341
4 0.243055556 0.167643259
5 0.694444444 0.155925416
6 0.425925926 0.155230407
7 0.458333333 0.419693801
8 0.576388889 -0.004623474
9 -0.060185185 -0.098297065
10 0.331018519 0.407606861
11 0.042687417 -0.032207925
12 0.287037037 0.072798978
13 0.625000000 0.204542695
14 0.564814815 0.268350977
15 0.153928526 0.079325435
16 0.648148148 -0.039489298
17 0.437500000 0.146205221
18 0.236214344 0.133043673
19 0.347222222 0.147392764
20 0.310185185 0.111860718
21 -0.006944444 0.044409449
22 0.196759259 -0.066431430
23 0.076388889 -0.038848009
24 0.393518519 0.168026073
25 0.641203704 0.140721994
26 0.428240741 0.190267143
27 0.134259259 0.051669320
28 0.541666667 0.255198906
29 0.074074074 0.072308635
30 0.284722222 0.124303572
31 0.194444444 0.191811303
32 0.187500000 0.068190583
33 0.439814815 -0.011987681
34 0.474537037 0.176586810
35 0.152777778 0.047512754
36 0.726851852 0.310607248
37 -0.092592593 -0.182679614
38 0.467592593 0.043369331
39 0.186218518 0.084647000
40 0.585648148 0.223959827
41 0.412037037 0.108028206
42 0.608796296 0.312586117
43 -0.013776428 -0.061005426
44 0.504629630 -0.035518253
45 0.486111111 -0.032542082
46 0.275462963 0.114913717
47 0.333333333 0.011532804
48 0.479166667 0.216533306
49 0.601851852 0.080446065
50 0.111111111 0.077641396
51 0.307870370 0.231978763
52 0.252314815 0.248315468
53 0.058612926 -0.144802734
54 0.287037037 0.022881884
55 0.458333333 0.199318057
56 0.129629630 -0.170723411
57 -0.039351852 0.188752652
58 0.423611111 -0.074133714
59 0.738425926 0.312741846
60 0.252314815 0.297098254
61 0.298611111 -0.043359204
62 0.238425926 0.136367447
63 -0.034722222 0.200036058
64 0.063472505 -0.184927890
65 0.511574074 0.070526398
66 0.516203704 0.452370483
67 0.418981481 0.010293946
68 0.659722222 0.055198536
69 0.583333333 0.268400284
70 0.402777778 0.115055986
71 0.506944444 0.237225940
72 0.451388889 0.155529895
73 0.273148148 0.062271686
74 0.328703704 0.075160519
75 0.253111597 0.121144371
76 0.213304331 0.007190857
77 0.321759259 -0.010817783
78 0.192129630 0.183318361
79 0.377314815 0.065934117
80 0.377314815 0.012751150
81 0.520833333 0.405405583
82 0.634259259 -0.060184577
83 0.226851852 0.004164546
84 -0.143548366 -0.155158396
85 0.006944444 0.008591496
86 0.215277778 0.165972895
87 0.557870370 0.190212659
88 0.583333333 -0.188273191
89 0.712962963 0.268547323
90 0.083333333 -0.012014196
91 0.261963586 0.018896429
92 0.344907407 0.168111740
93 0.490740741 0.308613855
94 0.534722222 0.022121513
95 0.599537037 0.344537858
96 0.254629630 0.204584047
97 0.520833333 -0.015861550
98 0.627314815 0.222819486
99 0.270338861 0.166565376
100 -0.175925926 0.001325282
101 0.217592593 0.206732284
102 0.393518519 0.245650574
103 0.729166667 0.211707212
104 0.175925926 0.086301585
105 0.618055556 0.125049803
106 0.395833333 -0.170184518
107 0.069444444 -0.076352129
108 0.409722222 0.252475364
109 0.000000000 0.026845766
110 0.185185185 0.100550407
111 0.259259259 0.231812628
112 0.226851852 0.041539461
113 0.328703704 -0.098918432
114 0.384259259 0.149684332
115 0.243055556 -0.155272156
116 0.564814815 0.075484761
117 0.372685185 0.331882727
118 0.132837965 0.135460378
119 0.509259259 0.203589066
120 0.284722222 0.121098217
121 0.597222222 0.137571595
122 0.398148148 0.115417903
123 0.280092593 0.266035059
124 0.185185185 0.227744914
125 0.375000000 0.159511760
126 0.446759259 0.097419898
127 0.655092593 0.089494549
128 0.703703704 0.283377729
129 0.416666667 0.110536260
130 0.469907407 -0.150972744
131 0.574074074 0.151999254
132 0.098071585 0.028987846
133 0.291666667 0.152324029
134 0.562500000 -0.016860317
135 0.537037037 0.226751404
136 0.814814815 0.387519529
137 0.506944444 0.094099525
138 0.016217477 -0.040361620
139 0.194444444 -0.044617375
140 0.361111111 -0.048772697
141 0.209374072 0.014184111
142 0.400462963 0.103520783
143 0.513888889 0.334203405
144 0.527777778 0.006508125
145 0.016198402 -0.035400467
146 0.293981481 0.035865627
147 0.176251718 0.014712193
148 0.132481323 0.156568022
149 0.240740741 0.132128186
150 0.555555556 0.246894535
151 0.161084485 -0.081967996
152 0.666666667 0.234804340
153 0.247685185 0.006491970
154 0.173611111 0.228865024
155 0.395833333 0.065368495
156 0.574074074 0.074397792
157 0.557870370 0.551373490
158 0.664351852 0.177435996
159 0.523148148 0.065714862
160 0.469907407 0.191729508
161 0.604166667 0.509769823
162 0.104166667 0.202605982
163 0.298611111 0.304687280
164 0.444444444 0.359294561
165 0.236140352 0.211271817
166 0.368055556 0.157573285
167 0.493055556 0.352191192
168 0.114834836 0.092842191
169 0.685185185 0.169758048
170 0.513888889 0.303989423
171 0.537037037 0.095838069
172 0.273148148 0.127090475
173 0.328703704 -0.107653956
174 0.465277778 0.294512532
175 0.506944444 0.109004351
176 0.002314815 0.050207042
177 -0.069444444 -0.175258789
178 0.474537037 0.029638024
179 0.203703704 0.113090737
180 0.597222222 0.100296503
181 -0.182870370 -0.143786583
182 0.293981481 0.270947805
183 0.583333333 0.191412908
184 0.326388889 0.182864940
185 0.136574074 0.093703948
186 0.557870370 0.069307138
187 0.557870370 0.069307138
188 0.520833333 0.098816300
189 0.562500000 0.225736121
190 0.175925926 -0.114064591
191 0.136574074 -0.270002117
192 0.252314815 0.015901745
193 0.291666667 -0.002763987
194 0.057307531 0.031467439
195 0.409722222 0.074942723
196 0.516203704 0.037104723
197 0.393518519 0.129364009
198 0.080796744 -0.049269654
199 0.541666667 0.290305742
200 0.047793651 -0.086954136
201 -0.085857859 -0.088522769
202 0.641203704 0.084548404
203 0.641203704 0.139759180
204 0.226851852 0.259463849
205 0.175925926 0.215759684
$Tests
Overall Distinctive
N 2.050000e+02 2.050000e+02
Mean 3.699396e-01 1.096436e-01
baseline 2.689034e-01 0.000000e+00
t 6.278509e+00 1.059806e+01
p-value 1.010598e-09 1.722550e-21
Replicability Lower Limit Upper Limit
Overall 0.7966126 0.7362851 0.8491297
Distinctive 0.4740320 0.3180228 0.6098436
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.