Profile.r: Profile Correlations
In multicon: Multivariate Constructs
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
Computes overall and distinctive profile correlations for each observation (row) with item pairs making up the columns in x.set and y.set.
Usage
1
Arguments
x.set
A data.frame or matrix of the first set of variables with columns corresponding to y.set
y.set
A data.frame or matrix of the second set of variables with columns corresponding to x.set
nomiss
A numeric between .00 and 1.00 specifying the proportion of x-y pairs required to be complete before NA is returned instead of the profile correlation. The default of 1.00 means that if any values are missing an NA will be returned
distinct
A logical indicating whether distinctive profile correlations (agreement) between x.set and y.set should be computed.
alt
A character string specifying the alternative hypothesis for tests of overall and distinctive agreement against baseline values. Must be one of "greater" (default), "less" or "two-sided".
Details
When distinct is set to its default FALSE: For each observational unit a correlation between its x.set and y.set of variables is returned. If the observational unit has less than "nomiss" pairs with missing data the function returns NA as the unit's result
When distinct is set to TRUE: The function does the same analysis a when distinct is set to false, but it provides a number of additional results. Following Furr's (2008) discusison of distinctiveness and normativeness, when distinct is set to TRUE the normative (average) Profile of x.set and y.set is computed. These normative Profiles are then used to predict each Profile in their respective set (i.e., the average Profile of x.set is used to predict each Profile (row) in x.set) using linear regression and the residuals for each set are retained. The correlation between the two normative Profiles is computed and returned. Finally, for each observational unit the correlation between the residualized x.set and the residualized y.set are computed and returned. If the unit has less than "nomiss" pairs with missing data the functions returns NA as the unit's result.
Value
xNorm
The average (with missing values removed) Profile for x.set.
yNorm
The average (with missing values removed) Profile for y.set.
Norm.r
The correlation between the average x.set and average y.set Profiles.
Agreement
A data.frame containing the overall and distinctive Profile correlations.
Overall
The column containing the overall Profile agreements. These are the same values as returned by the function when distinct==FALSE.
Distinctive
The column containing the distinctive Profile agreements.
Test
A data.frame containing the sample sizes, average Profile agreements, baseline Profile agreements, t-tests against the baseline, and p-values for both Overall and Distinctive Profile correlations.
Note
Furr's (2008) discussion of distinctiveness uses difference scores rather than the regression approach used by this function, but the conceptual idea surrounding "distinctiveness" is similar.
Author(s)
Ryne A. Sherman
References
Furr, R. M. (2008). A framework for Profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76(5), 1267-1316.
See Also
Profile.reg
temp.match
temp.resid
t.test
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
data(acq1)
data(caq)
#Lets look at Profile correlations between self-report California Adult Q-Sort
#ratings of personality and Aquaintance ratings of the same person.
head(acq1)
head(caq)
Profile.r(caq, acq1) # The basic Profile agreements
describe.r(Profile.r(caq, acq1)) # Descriptive Statistics for the Agreements
# Now let's look at both overall and distinctive agreement
myres <- Profile.r(caq, acq1, distinct = TRUE)
myres
describe.r(myres$Agreement) # The average overall and distinctive agreements
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
acq1CAQ001 acq1CAQ002 acq1CAQ003 acq1CAQ004 acq1CAQ005 acq1CAQ006 acq1CAQ007
1 4 6 7 6 6 6 6
2 4 7 7 8 6 8 6
3 2 9 5 8 9 2 5
4 5 3 9 9 6 7 5
5 1 9 8 6 6 5 2
6 4 6 7 9 7 4 8
acq1CAQ008 acq1CAQ009 acq1CAQ010 acq1CAQ011 acq1CAQ012 acq1CAQ013 acq1CAQ014
1 9 3 5 9 5 2 1
2 9 5 4 6 2 2 4
3 5 5 1 9 7 1 3
4 8 3 5 3 5 4 5
5 6 2 1 5 4 2 1
6 8 4 4 7 4 2 1
acq1CAQ015 acq1CAQ016 acq1CAQ017 acq1CAQ018 acq1CAQ019 acq1CAQ020 acq1CAQ021
1 8 6 8 8 5 7 5
2 8 3 5 9 4 4 3
3 6 6 4 3 8 5 3
4 7 5 3 6 2 7 3
5 8 6 9 8 5 3 3
6 8 5 7 9 5 4 6
acq1CAQ022 acq1CAQ023 acq1CAQ024 acq1CAQ025 acq1CAQ026 acq1CAQ027 acq1CAQ028
1 1 1 5 3 8 1 9
2 2 1 6 4 7 1 5
3 1 1 5 6 5 2 8
4 5 4 5 5 7 4 6
5 1 2 2 3 7 1 9
6 3 3 6 3 6 2 6
acq1CAQ029 acq1CAQ030 acq1CAQ031 acq1CAQ032 acq1CAQ033 acq1CAQ034 acq1CAQ035
1 8 4 6 8 7 3 8
2 5 1 6 4 5 1 7
3 9 2 5 8 7 2 8
4 4 5 6 5 4 4 1
5 5 5 5 7 9 2 8
6 6 1 5 8 6 3 9
acq1CAQ036 acq1CAQ037 acq1CAQ038 acq1CAQ039 acq1CAQ040 acq1CAQ041 acq1CAQ042
1 3 3 3 2 3 5 3
2 2 1 2 6 2 5 3
3 3 2 2 6 5 5 3
4 5 8 5 9 5 3 9
5 3 2 2 4 4 5 6
6 3 1 2 5 4 8 5
acq1CAQ043 acq1CAQ044 acq1CAQ045 acq1CAQ046 acq1CAQ047 acq1CAQ048 acq1CAQ049
1 5 5 1 5 2 2 2
2 9 6 4 7 4 2 2
3 5 7 5 6 8 2 4
4 7 5 6 5 4 7 5
5 4 6 4 6 5 3 3
6 9 5 3 6 3 3 2
acq1CAQ050 acq1CAQ051 acq1CAQ052 acq1CAQ053 acq1CAQ054 acq1CAQ055 acq1CAQ056
1 2 6 6 4 2 2 7
2 4 9 5 4 6 3 7
3 4 7 6 3 6 3 7
4 8 6 6 5 4 4 6
5 3 6 5 3 6 3 8
6 7 7 8 5 7 2 9
acq1CAQ057 acq1CAQ058 acq1CAQ059 acq1CAQ060 acq1CAQ061 acq1CAQ062 acq1CAQ063
1 7 5 5 7 3 4 3
2 8 7 5 6 3 3 3
3 7 7 6 6 1 3 4
4 7 6 4 6 1 8 4
5 6 6 5 7 3 3 4
6 8 5 4 7 1 4 5
acq1CAQ064 acq1CAQ065 acq1CAQ066 acq1CAQ067 acq1CAQ068 acq1CAQ069 acq1CAQ070
1 6 4 7 3 4 5 9
2 5 3 9 8 5 5 6
3 7 5 6 6 6 5 7
4 6 7 8 7 1 3 2
5 7 4 4 8 3 4 7
6 6 2 7 5 3 4 6
acq1CAQ071 acq1CAQ072 acq1CAQ073 acq1CAQ074 acq1CAQ075 acq1CAQ076 acq1CAQ077
1 7 4 4 9 7 5 7
2 6 7 3 6 7 5 6
3 5 4 3 6 7 4 8
4 6 4 3 8 3 2 4
5 5 6 6 8 4 5 7
6 7 5 6 6 6 2 5
acq1CAQ078 acq1CAQ079 acq1CAQ080 acq1CAQ081 acq1CAQ082 acq1CAQ083 acq1CAQ084
1 4 4 7 5 3 6 8
2 3 6 7 5 3 7 8
3 4 6 4 5 4 7 7
4 3 7 8 8 9 4 6
5 4 4 7 6 4 7 9
6 4 3 8 5 2 5 6
acq1CAQ085 acq1CAQ086 acq1CAQ087 acq1CAQ088 acq1CAQ089 acq1CAQ090 acq1CAQ091
1 4 5 4 6 4 5 5
2 5 4 5 6 3 7 4
3 9 3 4 6 4 4 3
4 1 2 3 6 4 2 2
5 6 4 5 6 5 4 4
6 5 4 4 7 4 4 5
acq1CAQ092 acq1CAQ093 acq1CAQ094 acq1CAQ095 acq1CAQ096 acq1CAQ097 acq1CAQ098
1 6 7 4 6 6 4 5
2 5 5 4 4 6 4 8
3 5 4 4 8 5 4 6
4 4 6 1 6 7 2 7
5 8 7 5 5 7 5 7
6 6 3 3 6 5 1 7
acq1CAQ099 acq1CAQ100
1 4 6
2 8 5
3 3 4
4 3 2
5 5 7
6 4 5
sCAQ001 sCAQ002 sCAQ003 sCAQ004 sCAQ005 sCAQ006 sCAQ007 sCAQ008 sCAQ009
1 5 7 5 9 8 5 3 8 3
2 2 9 7 7 7 3 8 9 6
3 3 9 8 5 9 8 4 4 7
4 6 7 7 6 6 7 6 8 4
5 2 9 8 7 7 2 6 5 3
6 4 8 7 9 6 5 5 7 3
sCAQ010 sCAQ011 sCAQ012 sCAQ013 sCAQ014 sCAQ015 sCAQ016 sCAQ017 sCAQ018
1 5 6 3 1 1 8 5 6 8
2 1 6 2 3 1 4 6 8 8
3 1 9 6 3 4 3 4 7 9
4 8 5 5 7 5 6 9 6 5
5 3 5 5 1 6 1 4 8 5
6 3 5 3 2 3 8 4 6 9
sCAQ019 sCAQ020 sCAQ021 sCAQ022 sCAQ023 sCAQ024 sCAQ025 sCAQ026 sCAQ027
1 2 6 5 5 5 4 4 5 2
2 2 5 5 5 3 6 3 7 1
3 5 3 8 1 2 2 2 7 3
4 6 2 6 4 2 2 5 5 1
5 8 6 4 2 2 1 3 8 2
6 4 4 5 4 1 5 1 9 1
sCAQ028 sCAQ029 sCAQ030 sCAQ031 sCAQ032 sCAQ033 sCAQ034 sCAQ035 sCAQ036
1 7 7 4 5 9 6 4 6 3
2 7 7 2 8 8 9 2 8 3
3 6 9 4 2 7 9 2 8 2
4 5 4 6 6 6 6 6 7 3
5 4 7 6 3 7 9 2 9 1
6 5 6 2 7 4 6 6 8 2
sCAQ037 sCAQ038 sCAQ039 sCAQ040 sCAQ041 sCAQ042 sCAQ043 sCAQ044 sCAQ045
1 1 4 9 2 1 3 7 5 2
2 1 2 7 5 6 4 6 5 2
3 1 2 6 8 8 3 5 6 4
4 2 6 4 3 6 4 5 9 8
5 1 8 4 3 3 4 5 7 3
6 1 2 5 4 4 4 9 6 2
sCAQ046 sCAQ047 sCAQ048 sCAQ049 sCAQ050 sCAQ051 sCAQ052 sCAQ053 sCAQ054
1 9 5 6 4 3 7 7 4 7
2 5 4 3 3 3 9 7 3 4
3 6 7 1 3 5 4 3 7 6
4 7 8 4 3 3 8 3 3 4
5 7 8 4 3 4 7 4 4 6
6 7 4 1 4 6 7 7 3 5
sCAQ055 sCAQ056 sCAQ057 sCAQ058 sCAQ059 sCAQ060 sCAQ061 sCAQ062 sCAQ063
1 2 8 6 5 2 6 3 9 3
2 4 6 6 5 6 8 2 5 1
3 2 8 7 7 7 5 3 6 4
4 5 8 6 7 3 9 4 7 4
5 2 7 6 7 3 6 4 3 5
6 2 8 7 8 6 7 2 6 1
sCAQ064 sCAQ065 sCAQ066 sCAQ067 sCAQ068 sCAQ069 sCAQ070 sCAQ071 sCAQ072
1 2 5 4 6 6 4 6 8 4
2 7 4 5 3 3 4 9 7 5
3 7 6 7 6 5 3 5 4 5
4 9 1 8 3 5 4 5 5 7
5 6 4 6 4 3 4 8 9 5
6 5 4 8 3 3 2 6 6 5
sCAQ073 sCAQ074 sCAQ075 sCAQ076 sCAQ077 sCAQ078 sCAQ079 sCAQ080 sCAQ081
1 1 3 6 7 3 3 4 6 5
2 4 6 6 4 7 3 4 6 6
3 3 7 6 3 5 6 6 6 5
4 1 2 4 3 4 5 7 5 8
5 3 8 6 4 5 2 6 7 5
6 7 6 5 4 5 3 5 5 5
sCAQ082 sCAQ083 sCAQ084 sCAQ085 sCAQ086 sCAQ087 sCAQ088 sCAQ089 sCAQ090
1 6 7 8 5 4 4 6 3 4
2 4 7 8 5 4 4 5 4 6
3 6 4 5 5 8 5 6 4 5
4 9 4 6 5 2 4 5 4 7
5 3 9 7 6 5 5 6 6 4
6 5 6 5 6 3 3 8 4 7
sCAQ091 sCAQ092 sCAQ093 sCAQ094 sCAQ095 sCAQ096 sCAQ097 sCAQ098 sCAQ099
1 7 7 2 3 4 6 4 6 5
2 4 6 6 5 3 6 5 5 4
3 4 8 4 1 5 4 4 5 1
4 1 4 5 6 3 5 1 7 4
5 5 6 5 5 5 8 4 5 5
6 6 8 4 3 4 9 3 6 7
sCAQ100
1 8
2 5
3 5
4 3
5 5
6 5
[1] 0.450914218 0.607611946 0.534325884 0.203801723 0.591794924
[6] 0.705831860 0.283556172 0.654403711 0.211385861 0.407857141
[11] 0.606481481 0.407852524 0.543368661 0.552425065 0.300133900
[16] 0.404311454 0.467426488 0.479845747 0.429693209 0.577356815
[21] 0.057606941 0.394506019 0.138888889 0.555208803 0.449074074
[26] 0.184760278 0.328764592 0.257219737 -0.029887133 0.387242465
[31] 0.318240338 0.222222222 0.181208523 0.425925926 0.130889759
[36] 0.660831868 0.080835350 0.202776432 0.673710070 0.203658923
[41] 0.624285083 0.656110214 0.624247760 0.474537037 0.287711188
[46] 0.448941409 0.336830404 0.209062939 0.577960857 0.108421734
[51] 0.527641081 0.105037418 0.228904530 0.345825808 0.404741407
[56] 0.584283110 -0.113431177 0.437639506 0.746679036 0.121982701
[61] 0.661071738 0.482109170 -0.139051533 0.375000000 0.587340624
[66] 0.233796296 0.438417048 0.576343952 0.383082765 0.564814815
[71] 0.676132781 0.368055556 0.460648148 0.349537037 0.170690501
[76] 0.377314815 0.125000000 0.476949972 0.381944444 0.289606043
[81] 0.325111793 0.688166561 0.483998004 0.146273126 -0.142865213
[86] 0.250297961 0.561818018 0.650857715 0.567855526 0.463343313
[91] 0.539381984 0.353508715 0.250000000 0.523266221 0.717428920
[96] 0.097336073 0.487783201 0.548611111 0.585648148 0.488425926
[101] -0.027097584 0.325209016 0.634259259 0.251446149 0.565228052
[106] 0.525462963 0.334613286 0.321313047 0.038952207 0.417467737
[111] 0.381962128 0.381331495 0.338774309 0.465277778 0.619109448
[116] 0.401003291 0.482225428 0.041567887 0.143849114 0.543933731
[121] 0.401964418 0.408419829 0.238412538 0.414351852 0.355928292
[126] 0.355834430 0.546936029 0.483167682 0.378609136 0.554792120
[131] 0.459470066 0.229166667 0.354166667 0.499244382 0.472496949
[136] 0.367658762 0.495370370 0.641867408 0.459214458 0.509180197
[141] 0.659757785 0.514373790 0.272177624 0.383518978 0.708333333
[146] 0.581123072 0.504700164 0.520978914 0.448080924 0.333348767
[151] 0.505344916 0.591727071 0.388963708 0.006912833 0.356481481
[156] 0.658262627 0.296224192 0.779766102 0.267160389 0.442131498
[161] 0.351647008 0.448365667 0.346957461 0.445053781 0.132583402
[166] 0.000000000 0.712147428 0.208762341 0.652837248 0.612319550
[171] 0.463301129 0.253594585 0.550295740 0.535289852 0.399579765
[176] 0.368249665 0.376202399 0.580433773 0.118468999 0.575046216
[181] -0.202075418 0.333333333 0.606481481 0.524578923 0.235678694
[186] 0.574752557 0.567829596 0.438778446 0.603106438 0.145169491
[191] 0.361111111 0.317677950 0.452606528 0.451180692 0.534722222
[196] 0.481481481 0.462927878 0.780092593 0.389995582 0.521551613
[201] 0.046296296 0.558575500 0.604685960 0.077616250 0.023148148
var n miss mean sd median trimmed mad min
1 1 205 0 0.4196886 0.2315886 0.4296932 0.4275148 0.2318617 -0.2020754
max range skew kurtosis se
1 0.7800926 0.8484245 -0.1805952 -0.1262255 0.01647221
$xNorm
sCAQ001 sCAQ002 sCAQ003 sCAQ004 sCAQ005 sCAQ006 sCAQ007 sCAQ008
4.707317 7.565854 6.995122 6.341463 6.653659 4.278049 4.756098 6.600000
sCAQ009 sCAQ010 sCAQ011 sCAQ012 sCAQ013 sCAQ014 sCAQ015 sCAQ016
4.575610 3.765854 6.546341 4.702439 3.770732 3.336585 6.014634 5.878049
sCAQ017 sCAQ018 sCAQ019 sCAQ020 sCAQ021 sCAQ022 sCAQ023 sCAQ024
6.819512 6.560976 5.073171 5.160976 4.380488 2.775610 2.673171 5.351220
sCAQ025 sCAQ026 sCAQ027 sCAQ028 sCAQ029 sCAQ030 sCAQ031 sCAQ032
4.419512 6.502439 3.121951 6.356098 6.439024 3.448780 5.468293 6.024390
sCAQ033 sCAQ034 sCAQ035 sCAQ036 sCAQ037 sCAQ038 sCAQ039 sCAQ040
6.312195 3.673171 7.043902 2.478049 2.736585 3.370732 5.117073 3.424390
sCAQ041 sCAQ042 sCAQ043 sCAQ044 sCAQ045 sCAQ046 sCAQ047 sCAQ048
5.229268 4.131707 6.024390 5.853659 3.531707 5.800000 4.082927 3.609756
sCAQ049 sCAQ050 sCAQ051 sCAQ052 sCAQ053 sCAQ054 sCAQ055 sCAQ056
3.751220 4.180488 6.307317 5.434146 3.697561 5.839024 3.092683 7.326829
sCAQ057 sCAQ058 sCAQ059 sCAQ060 sCAQ061 sCAQ062 sCAQ063 sCAQ064
6.482927 6.692683 5.497561 6.078049 3.004878 4.360976 4.048780 5.624390
sCAQ065 sCAQ066 sCAQ067 sCAQ068 sCAQ069 sCAQ070 sCAQ071 sCAQ072
4.800000 5.868293 4.741463 4.385366 4.570732 5.853659 6.209756 5.087805
sCAQ073 sCAQ074 sCAQ075 sCAQ076 sCAQ077 sCAQ078 sCAQ079 sCAQ080
4.239024 5.126829 4.946341 4.600000 5.521951 2.921951 4.707317 6.790244
sCAQ081 sCAQ082 sCAQ083 sCAQ084 sCAQ085 sCAQ086 sCAQ087 sCAQ088
5.458537 4.551220 5.712195 6.121951 5.102439 4.039024 4.541463 6.053659
sCAQ089 sCAQ090 sCAQ091 sCAQ092 sCAQ093 sCAQ094 sCAQ095 sCAQ096
4.902439 5.151220 4.258537 5.507317 5.380488 3.668293 5.253659 5.902439
sCAQ097 sCAQ098 sCAQ099 sCAQ100
3.858537 5.219512 3.956098 4.365854
$yNorm
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
$Norm.r
[1] 0.9570786
$Agreement
Overall Distinctive
1 0.450914218 -0.0374790248
2 0.607611946 0.0439287853
3 0.534325884 0.2358528215
4 0.203801723 0.1512042587
5 0.591794924 0.2549769512
6 0.705831860 0.2153937389
7 0.283556172 0.1123474930
8 0.654403711 0.1001669745
9 0.211385861 0.1902683384
10 0.407857141 0.5166442180
11 0.606481481 0.2094973212
12 0.407852524 0.1887763098
13 0.543368661 0.2374377735
14 0.552425065 0.2955297017
15 0.300133900 0.2154134930
16 0.404311454 -0.0013410510
17 0.467426488 0.1504732045
18 0.479845747 0.0630388222
19 0.429693209 0.0798881184
20 0.577356815 0.3135368702
21 0.057606941 0.1318862557
22 0.394506019 0.2822357211
23 0.138888889 0.1058364580
24 0.555208803 0.2613591623
25 0.449074074 0.1549252409
26 0.184760278 -0.1221475242
27 0.328764592 0.2827848198
28 0.257219737 -0.1921553715
29 -0.029887133 -0.1608581332
30 0.387242465 0.0047642742
31 0.318240338 0.2948976108
32 0.222222222 0.1834463726
33 0.181208523 -0.0053623827
34 0.425925926 0.1947938907
35 0.130889759 0.0754502766
36 0.660831868 0.2513688999
37 0.080835350 0.0524297420
38 0.202776432 0.1233729255
39 0.673710070 0.1121068568
40 0.203658923 -0.0386532608
41 0.624285083 0.1596448722
42 0.656110214 0.4814688744
43 0.624247760 0.1981567609
44 0.474537037 0.2136284288
45 0.287711188 0.1243802030
46 0.448941409 0.4022923235
47 0.336830404 0.1355095749
48 0.209062939 0.3307598515
49 0.577960857 0.1667567254
50 0.108421734 -0.0444068923
51 0.527641081 0.4439906473
52 0.105037418 0.1366553365
53 0.228904530 0.0811530570
54 0.345825808 0.0989090915
55 0.404741407 0.0820027684
56 0.584283110 0.1417983468
57 -0.113431177 0.0004295209
58 0.437639506 0.1067999199
59 0.746679036 0.3718112182
60 0.121982701 0.1419232850
61 0.661071738 0.2548999153
62 0.482109170 0.1589547620
63 -0.139051533 0.0608845065
64 0.375000000 -0.0599565734
65 0.587340624 0.1764053318
66 0.233796296 0.1523437836
67 0.438417048 0.0114728398
68 0.576343952 0.2445447862
69 0.383082765 -0.0603144190
70 0.564814815 0.3473536560
71 0.676132781 0.3301867911
72 0.368055556 0.0719981551
73 0.460648148 0.2767452992
74 0.349537037 0.0122150179
75 0.170690501 -0.0623476741
76 0.377314815 -0.0148311473
77 0.125000000 -0.0360322981
78 0.476949972 0.2362181903
79 0.381944444 0.0166720172
80 0.289606043 0.0979432400
81 0.325111793 -0.1665760217
82 0.688166561 0.0761439410
83 0.483998004 0.4229183861
84 0.146273126 0.1569960449
85 -0.142865213 -0.1832471612
86 0.250297961 0.1658047824
87 0.561818018 0.1025918708
88 0.650857715 0.0738133371
89 0.567855526 0.2583668012
90 0.463343313 0.2836494869
91 0.539381984 0.0692442115
92 0.353508715 0.2543242211
93 0.250000000 0.2326649489
94 0.523266221 0.0559181802
95 0.717428920 0.4885448271
96 0.097336073 0.0098172953
97 0.487783201 0.2064053065
98 0.548611111 -0.0446142569
99 0.585648148 0.2477944437
100 0.488425926 0.1104880148
101 -0.027097584 0.1084226019
102 0.325209016 -0.1145535337
103 0.634259259 0.2192252406
104 0.251446149 0.2261417599
105 0.565228052 0.0537182228
106 0.525462963 0.0839985003
107 0.334613286 0.0339975645
108 0.321313047 0.0522532244
109 0.038952207 0.0369557729
110 0.417467737 0.0426387652
111 0.381962128 0.0940160425
112 0.381331495 0.0549967860
113 0.338774309 0.0639452875
114 0.465277778 0.1673972610
115 0.619109448 0.2498178201
116 0.401003291 0.1256361651
117 0.482225428 0.2662125840
118 0.041567887 0.0417470641
119 0.143849114 -0.0758034229
120 0.543933731 0.2245599538
121 0.401964418 0.2518074980
122 0.408419829 0.0643988213
123 0.238412538 0.1657555748
124 0.414351852 0.4153752161
125 0.355928292 0.1208658622
126 0.355834430 -0.0394305530
127 0.546936029 0.1249218972
128 0.483167682 -0.0497974501
129 0.378609136 0.0905372958
130 0.554792120 -0.0292742815
131 0.459470066 0.1058557413
132 0.229166667 0.0267909715
133 0.354166667 0.1925870182
134 0.499244382 0.1726757644
135 0.472496949 0.1436257165
136 0.367658762 -0.1646967436
137 0.495370370 -0.0649664160
138 0.641867408 0.1653129895
139 0.459214458 -0.0126084388
140 0.509180197 0.2283992628
141 0.659757785 0.0444872626
142 0.514373790 0.2109370800
143 0.272177624 0.0084010555
144 0.383518978 0.0424204890
145 0.708333333 0.3171969054
146 0.581123072 0.2993182487
147 0.504700164 0.0788770221
148 0.520978914 0.0192303404
149 0.448080924 0.1913163957
150 0.333348767 -0.0729679808
151 0.505344916 -0.0322301158
152 0.591727071 0.1158893214
153 0.388963708 0.0347932260
154 0.006912833 0.1471533965
155 0.356481481 -0.0510923188
156 0.658262627 0.3468457086
157 0.296224192 0.2811412870
158 0.779766102 0.3163588770
159 0.267160389 -0.0304903333
160 0.442131498 0.2213958786
161 0.351647008 0.1480855638
162 0.448365667 0.2650667106
163 0.346957461 0.3518106499
164 0.445053781 0.4832693204
165 0.132583402 0.1249978363
166 0.000000000 -0.0482328930
167 0.712147428 0.5138512850
168 0.208762341 0.2105278240
169 0.652837248 0.0687529355
170 0.612319550 0.2753305276
171 0.463301129 0.1446808313
172 0.253594585 0.1465911113
173 0.550295740 0.3538662133
174 0.535289852 0.2035546915
175 0.399579765 0.0830781223
176 0.368249665 0.0804147114
177 0.376202399 0.1915981011
178 0.580433773 0.2579667033
179 0.118468999 0.0813353826
180 0.575046216 0.0275800601
181 -0.202075418 -0.1297447023
182 0.333333333 0.2421344113
183 0.606481481 0.1433326806
184 0.524578923 0.0663179408
185 0.235678694 0.0375255209
186 0.574752557 0.3175350741
187 0.567829596 0.2995005837
188 0.438778446 0.0209010494
189 0.603106438 0.3421061251
190 0.145169491 -0.1307610437
191 0.361111111 0.0299366916
192 0.317677950 0.1334039831
193 0.452606528 0.2962729264
194 0.451180692 -0.0927058184
195 0.534722222 0.2301832038
196 0.481481481 0.0533455674
197 0.462927878 0.0608492122
198 0.780092593 0.3699517466
199 0.389995582 0.1574270003
200 0.521551613 0.3552935241
201 0.046296296 -0.0649111531
202 0.558575500 -0.0042524577
203 0.604685960 0.1605325413
204 0.077616250 -0.0309578079
205 0.023148148 0.0621787037
$Tests
Overall Distinctive
N 2.050000e+02 2.050000e+02
Mean 4.196886e-01 1.377897e-01
baseline 3.073769e-01 0.000000e+00
t 7.871208e+00 1.323607e+01
p-value 1.016087e-13 1.410192e-29
var n miss mean sd median trimmed mad
Overall 1 205 0 0.4196886 0.2315886 0.4296932 0.4275148 0.2318617
Distinctive 2 205 0 0.1377897 0.1488901 0.1256362 0.1313845 0.1453209
min max range skew kurtosis se
Overall -0.2020754 0.7800926 0.8484245 -0.1805952 -0.1262255 0.01647221
Distinctive -0.1921554 0.5166442 0.6447877 0.3518071 0.1191943 0.01047643
multicon documentation built on May 2, 2019, 3:18 a.m.
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Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
Computes overall and distinctive profile correlations for each observation (row) with item pairs making up the columns in x.set and y.set.
Usage
1 |
Arguments
x.set |
A data.frame or matrix of the first set of variables with columns corresponding to y.set |
y.set |
A data.frame or matrix of the second set of variables with columns corresponding to x.set |
nomiss |
A numeric between .00 and 1.00 specifying the proportion of x-y pairs required to be complete before NA is returned instead of the profile correlation. The default of 1.00 means that if any values are missing an NA will be returned |
distinct |
A logical indicating whether distinctive profile correlations (agreement) between x.set and y.set should be computed. |
alt |
A character string specifying the alternative hypothesis for tests of overall and distinctive agreement against baseline values. Must be one of "greater" (default), "less" or "two-sided". |
Details
When distinct is set to its default FALSE: For each observational unit a correlation between its x.set and y.set of variables is returned. If the observational unit has less than "nomiss" pairs with missing data the function returns NA as the unit's result When distinct is set to TRUE: The function does the same analysis a when distinct is set to false, but it provides a number of additional results. Following Furr's (2008) discusison of distinctiveness and normativeness, when distinct is set to TRUE the normative (average) Profile of x.set and y.set is computed. These normative Profiles are then used to predict each Profile in their respective set (i.e., the average Profile of x.set is used to predict each Profile (row) in x.set) using linear regression and the residuals for each set are retained. The correlation between the two normative Profiles is computed and returned. Finally, for each observational unit the correlation between the residualized x.set and the residualized y.set are computed and returned. If the unit has less than "nomiss" pairs with missing data the functions returns NA as the unit's result.
Value
xNorm |
The average (with missing values removed) Profile for x.set. |
yNorm |
The average (with missing values removed) Profile for y.set. |
Norm.r |
The correlation between the average x.set and average y.set Profiles. |
Agreement |
A data.frame containing the overall and distinctive Profile correlations. |
Overall |
The column containing the overall Profile agreements. These are the same values as returned by the function when distinct==FALSE. |
Distinctive |
The column containing the distinctive Profile agreements. |
Test |
A data.frame containing the sample sizes, average Profile agreements, baseline Profile agreements, t-tests against the baseline, and p-values for both Overall and Distinctive Profile correlations. |
Note
Furr's (2008) discussion of distinctiveness uses difference scores rather than the regression approach used by this function, but the conceptual idea surrounding "distinctiveness" is similar.
Author(s)
Ryne A. Sherman
References
Furr, R. M. (2008). A framework for Profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76(5), 1267-1316.
See Also
Profile.reg
temp.match
temp.resid
t.test
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(acq1)
data(caq)
#Lets look at Profile correlations between self-report California Adult Q-Sort
#ratings of personality and Aquaintance ratings of the same person.
head(acq1)
head(caq)
Profile.r(caq, acq1) # The basic Profile agreements
describe.r(Profile.r(caq, acq1)) # Descriptive Statistics for the Agreements
# Now let's look at both overall and distinctive agreement
myres <- Profile.r(caq, acq1, distinct = TRUE)
myres
describe.r(myres$Agreement) # The average overall and distinctive agreements
|
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
acq1CAQ001 acq1CAQ002 acq1CAQ003 acq1CAQ004 acq1CAQ005 acq1CAQ006 acq1CAQ007
1 4 6 7 6 6 6 6
2 4 7 7 8 6 8 6
3 2 9 5 8 9 2 5
4 5 3 9 9 6 7 5
5 1 9 8 6 6 5 2
6 4 6 7 9 7 4 8
acq1CAQ008 acq1CAQ009 acq1CAQ010 acq1CAQ011 acq1CAQ012 acq1CAQ013 acq1CAQ014
1 9 3 5 9 5 2 1
2 9 5 4 6 2 2 4
3 5 5 1 9 7 1 3
4 8 3 5 3 5 4 5
5 6 2 1 5 4 2 1
6 8 4 4 7 4 2 1
acq1CAQ015 acq1CAQ016 acq1CAQ017 acq1CAQ018 acq1CAQ019 acq1CAQ020 acq1CAQ021
1 8 6 8 8 5 7 5
2 8 3 5 9 4 4 3
3 6 6 4 3 8 5 3
4 7 5 3 6 2 7 3
5 8 6 9 8 5 3 3
6 8 5 7 9 5 4 6
acq1CAQ022 acq1CAQ023 acq1CAQ024 acq1CAQ025 acq1CAQ026 acq1CAQ027 acq1CAQ028
1 1 1 5 3 8 1 9
2 2 1 6 4 7 1 5
3 1 1 5 6 5 2 8
4 5 4 5 5 7 4 6
5 1 2 2 3 7 1 9
6 3 3 6 3 6 2 6
acq1CAQ029 acq1CAQ030 acq1CAQ031 acq1CAQ032 acq1CAQ033 acq1CAQ034 acq1CAQ035
1 8 4 6 8 7 3 8
2 5 1 6 4 5 1 7
3 9 2 5 8 7 2 8
4 4 5 6 5 4 4 1
5 5 5 5 7 9 2 8
6 6 1 5 8 6 3 9
acq1CAQ036 acq1CAQ037 acq1CAQ038 acq1CAQ039 acq1CAQ040 acq1CAQ041 acq1CAQ042
1 3 3 3 2 3 5 3
2 2 1 2 6 2 5 3
3 3 2 2 6 5 5 3
4 5 8 5 9 5 3 9
5 3 2 2 4 4 5 6
6 3 1 2 5 4 8 5
acq1CAQ043 acq1CAQ044 acq1CAQ045 acq1CAQ046 acq1CAQ047 acq1CAQ048 acq1CAQ049
1 5 5 1 5 2 2 2
2 9 6 4 7 4 2 2
3 5 7 5 6 8 2 4
4 7 5 6 5 4 7 5
5 4 6 4 6 5 3 3
6 9 5 3 6 3 3 2
acq1CAQ050 acq1CAQ051 acq1CAQ052 acq1CAQ053 acq1CAQ054 acq1CAQ055 acq1CAQ056
1 2 6 6 4 2 2 7
2 4 9 5 4 6 3 7
3 4 7 6 3 6 3 7
4 8 6 6 5 4 4 6
5 3 6 5 3 6 3 8
6 7 7 8 5 7 2 9
acq1CAQ057 acq1CAQ058 acq1CAQ059 acq1CAQ060 acq1CAQ061 acq1CAQ062 acq1CAQ063
1 7 5 5 7 3 4 3
2 8 7 5 6 3 3 3
3 7 7 6 6 1 3 4
4 7 6 4 6 1 8 4
5 6 6 5 7 3 3 4
6 8 5 4 7 1 4 5
acq1CAQ064 acq1CAQ065 acq1CAQ066 acq1CAQ067 acq1CAQ068 acq1CAQ069 acq1CAQ070
1 6 4 7 3 4 5 9
2 5 3 9 8 5 5 6
3 7 5 6 6 6 5 7
4 6 7 8 7 1 3 2
5 7 4 4 8 3 4 7
6 6 2 7 5 3 4 6
acq1CAQ071 acq1CAQ072 acq1CAQ073 acq1CAQ074 acq1CAQ075 acq1CAQ076 acq1CAQ077
1 7 4 4 9 7 5 7
2 6 7 3 6 7 5 6
3 5 4 3 6 7 4 8
4 6 4 3 8 3 2 4
5 5 6 6 8 4 5 7
6 7 5 6 6 6 2 5
acq1CAQ078 acq1CAQ079 acq1CAQ080 acq1CAQ081 acq1CAQ082 acq1CAQ083 acq1CAQ084
1 4 4 7 5 3 6 8
2 3 6 7 5 3 7 8
3 4 6 4 5 4 7 7
4 3 7 8 8 9 4 6
5 4 4 7 6 4 7 9
6 4 3 8 5 2 5 6
acq1CAQ085 acq1CAQ086 acq1CAQ087 acq1CAQ088 acq1CAQ089 acq1CAQ090 acq1CAQ091
1 4 5 4 6 4 5 5
2 5 4 5 6 3 7 4
3 9 3 4 6 4 4 3
4 1 2 3 6 4 2 2
5 6 4 5 6 5 4 4
6 5 4 4 7 4 4 5
acq1CAQ092 acq1CAQ093 acq1CAQ094 acq1CAQ095 acq1CAQ096 acq1CAQ097 acq1CAQ098
1 6 7 4 6 6 4 5
2 5 5 4 4 6 4 8
3 5 4 4 8 5 4 6
4 4 6 1 6 7 2 7
5 8 7 5 5 7 5 7
6 6 3 3 6 5 1 7
acq1CAQ099 acq1CAQ100
1 4 6
2 8 5
3 3 4
4 3 2
5 5 7
6 4 5
sCAQ001 sCAQ002 sCAQ003 sCAQ004 sCAQ005 sCAQ006 sCAQ007 sCAQ008 sCAQ009
1 5 7 5 9 8 5 3 8 3
2 2 9 7 7 7 3 8 9 6
3 3 9 8 5 9 8 4 4 7
4 6 7 7 6 6 7 6 8 4
5 2 9 8 7 7 2 6 5 3
6 4 8 7 9 6 5 5 7 3
sCAQ010 sCAQ011 sCAQ012 sCAQ013 sCAQ014 sCAQ015 sCAQ016 sCAQ017 sCAQ018
1 5 6 3 1 1 8 5 6 8
2 1 6 2 3 1 4 6 8 8
3 1 9 6 3 4 3 4 7 9
4 8 5 5 7 5 6 9 6 5
5 3 5 5 1 6 1 4 8 5
6 3 5 3 2 3 8 4 6 9
sCAQ019 sCAQ020 sCAQ021 sCAQ022 sCAQ023 sCAQ024 sCAQ025 sCAQ026 sCAQ027
1 2 6 5 5 5 4 4 5 2
2 2 5 5 5 3 6 3 7 1
3 5 3 8 1 2 2 2 7 3
4 6 2 6 4 2 2 5 5 1
5 8 6 4 2 2 1 3 8 2
6 4 4 5 4 1 5 1 9 1
sCAQ028 sCAQ029 sCAQ030 sCAQ031 sCAQ032 sCAQ033 sCAQ034 sCAQ035 sCAQ036
1 7 7 4 5 9 6 4 6 3
2 7 7 2 8 8 9 2 8 3
3 6 9 4 2 7 9 2 8 2
4 5 4 6 6 6 6 6 7 3
5 4 7 6 3 7 9 2 9 1
6 5 6 2 7 4 6 6 8 2
sCAQ037 sCAQ038 sCAQ039 sCAQ040 sCAQ041 sCAQ042 sCAQ043 sCAQ044 sCAQ045
1 1 4 9 2 1 3 7 5 2
2 1 2 7 5 6 4 6 5 2
3 1 2 6 8 8 3 5 6 4
4 2 6 4 3 6 4 5 9 8
5 1 8 4 3 3 4 5 7 3
6 1 2 5 4 4 4 9 6 2
sCAQ046 sCAQ047 sCAQ048 sCAQ049 sCAQ050 sCAQ051 sCAQ052 sCAQ053 sCAQ054
1 9 5 6 4 3 7 7 4 7
2 5 4 3 3 3 9 7 3 4
3 6 7 1 3 5 4 3 7 6
4 7 8 4 3 3 8 3 3 4
5 7 8 4 3 4 7 4 4 6
6 7 4 1 4 6 7 7 3 5
sCAQ055 sCAQ056 sCAQ057 sCAQ058 sCAQ059 sCAQ060 sCAQ061 sCAQ062 sCAQ063
1 2 8 6 5 2 6 3 9 3
2 4 6 6 5 6 8 2 5 1
3 2 8 7 7 7 5 3 6 4
4 5 8 6 7 3 9 4 7 4
5 2 7 6 7 3 6 4 3 5
6 2 8 7 8 6 7 2 6 1
sCAQ064 sCAQ065 sCAQ066 sCAQ067 sCAQ068 sCAQ069 sCAQ070 sCAQ071 sCAQ072
1 2 5 4 6 6 4 6 8 4
2 7 4 5 3 3 4 9 7 5
3 7 6 7 6 5 3 5 4 5
4 9 1 8 3 5 4 5 5 7
5 6 4 6 4 3 4 8 9 5
6 5 4 8 3 3 2 6 6 5
sCAQ073 sCAQ074 sCAQ075 sCAQ076 sCAQ077 sCAQ078 sCAQ079 sCAQ080 sCAQ081
1 1 3 6 7 3 3 4 6 5
2 4 6 6 4 7 3 4 6 6
3 3 7 6 3 5 6 6 6 5
4 1 2 4 3 4 5 7 5 8
5 3 8 6 4 5 2 6 7 5
6 7 6 5 4 5 3 5 5 5
sCAQ082 sCAQ083 sCAQ084 sCAQ085 sCAQ086 sCAQ087 sCAQ088 sCAQ089 sCAQ090
1 6 7 8 5 4 4 6 3 4
2 4 7 8 5 4 4 5 4 6
3 6 4 5 5 8 5 6 4 5
4 9 4 6 5 2 4 5 4 7
5 3 9 7 6 5 5 6 6 4
6 5 6 5 6 3 3 8 4 7
sCAQ091 sCAQ092 sCAQ093 sCAQ094 sCAQ095 sCAQ096 sCAQ097 sCAQ098 sCAQ099
1 7 7 2 3 4 6 4 6 5
2 4 6 6 5 3 6 5 5 4
3 4 8 4 1 5 4 4 5 1
4 1 4 5 6 3 5 1 7 4
5 5 6 5 5 5 8 4 5 5
6 6 8 4 3 4 9 3 6 7
sCAQ100
1 8
2 5
3 5
4 3
5 5
6 5
[1] 0.450914218 0.607611946 0.534325884 0.203801723 0.591794924
[6] 0.705831860 0.283556172 0.654403711 0.211385861 0.407857141
[11] 0.606481481 0.407852524 0.543368661 0.552425065 0.300133900
[16] 0.404311454 0.467426488 0.479845747 0.429693209 0.577356815
[21] 0.057606941 0.394506019 0.138888889 0.555208803 0.449074074
[26] 0.184760278 0.328764592 0.257219737 -0.029887133 0.387242465
[31] 0.318240338 0.222222222 0.181208523 0.425925926 0.130889759
[36] 0.660831868 0.080835350 0.202776432 0.673710070 0.203658923
[41] 0.624285083 0.656110214 0.624247760 0.474537037 0.287711188
[46] 0.448941409 0.336830404 0.209062939 0.577960857 0.108421734
[51] 0.527641081 0.105037418 0.228904530 0.345825808 0.404741407
[56] 0.584283110 -0.113431177 0.437639506 0.746679036 0.121982701
[61] 0.661071738 0.482109170 -0.139051533 0.375000000 0.587340624
[66] 0.233796296 0.438417048 0.576343952 0.383082765 0.564814815
[71] 0.676132781 0.368055556 0.460648148 0.349537037 0.170690501
[76] 0.377314815 0.125000000 0.476949972 0.381944444 0.289606043
[81] 0.325111793 0.688166561 0.483998004 0.146273126 -0.142865213
[86] 0.250297961 0.561818018 0.650857715 0.567855526 0.463343313
[91] 0.539381984 0.353508715 0.250000000 0.523266221 0.717428920
[96] 0.097336073 0.487783201 0.548611111 0.585648148 0.488425926
[101] -0.027097584 0.325209016 0.634259259 0.251446149 0.565228052
[106] 0.525462963 0.334613286 0.321313047 0.038952207 0.417467737
[111] 0.381962128 0.381331495 0.338774309 0.465277778 0.619109448
[116] 0.401003291 0.482225428 0.041567887 0.143849114 0.543933731
[121] 0.401964418 0.408419829 0.238412538 0.414351852 0.355928292
[126] 0.355834430 0.546936029 0.483167682 0.378609136 0.554792120
[131] 0.459470066 0.229166667 0.354166667 0.499244382 0.472496949
[136] 0.367658762 0.495370370 0.641867408 0.459214458 0.509180197
[141] 0.659757785 0.514373790 0.272177624 0.383518978 0.708333333
[146] 0.581123072 0.504700164 0.520978914 0.448080924 0.333348767
[151] 0.505344916 0.591727071 0.388963708 0.006912833 0.356481481
[156] 0.658262627 0.296224192 0.779766102 0.267160389 0.442131498
[161] 0.351647008 0.448365667 0.346957461 0.445053781 0.132583402
[166] 0.000000000 0.712147428 0.208762341 0.652837248 0.612319550
[171] 0.463301129 0.253594585 0.550295740 0.535289852 0.399579765
[176] 0.368249665 0.376202399 0.580433773 0.118468999 0.575046216
[181] -0.202075418 0.333333333 0.606481481 0.524578923 0.235678694
[186] 0.574752557 0.567829596 0.438778446 0.603106438 0.145169491
[191] 0.361111111 0.317677950 0.452606528 0.451180692 0.534722222
[196] 0.481481481 0.462927878 0.780092593 0.389995582 0.521551613
[201] 0.046296296 0.558575500 0.604685960 0.077616250 0.023148148
var n miss mean sd median trimmed mad min
1 1 205 0 0.4196886 0.2315886 0.4296932 0.4275148 0.2318617 -0.2020754
max range skew kurtosis se
1 0.7800926 0.8484245 -0.1805952 -0.1262255 0.01647221
$xNorm
sCAQ001 sCAQ002 sCAQ003 sCAQ004 sCAQ005 sCAQ006 sCAQ007 sCAQ008
4.707317 7.565854 6.995122 6.341463 6.653659 4.278049 4.756098 6.600000
sCAQ009 sCAQ010 sCAQ011 sCAQ012 sCAQ013 sCAQ014 sCAQ015 sCAQ016
4.575610 3.765854 6.546341 4.702439 3.770732 3.336585 6.014634 5.878049
sCAQ017 sCAQ018 sCAQ019 sCAQ020 sCAQ021 sCAQ022 sCAQ023 sCAQ024
6.819512 6.560976 5.073171 5.160976 4.380488 2.775610 2.673171 5.351220
sCAQ025 sCAQ026 sCAQ027 sCAQ028 sCAQ029 sCAQ030 sCAQ031 sCAQ032
4.419512 6.502439 3.121951 6.356098 6.439024 3.448780 5.468293 6.024390
sCAQ033 sCAQ034 sCAQ035 sCAQ036 sCAQ037 sCAQ038 sCAQ039 sCAQ040
6.312195 3.673171 7.043902 2.478049 2.736585 3.370732 5.117073 3.424390
sCAQ041 sCAQ042 sCAQ043 sCAQ044 sCAQ045 sCAQ046 sCAQ047 sCAQ048
5.229268 4.131707 6.024390 5.853659 3.531707 5.800000 4.082927 3.609756
sCAQ049 sCAQ050 sCAQ051 sCAQ052 sCAQ053 sCAQ054 sCAQ055 sCAQ056
3.751220 4.180488 6.307317 5.434146 3.697561 5.839024 3.092683 7.326829
sCAQ057 sCAQ058 sCAQ059 sCAQ060 sCAQ061 sCAQ062 sCAQ063 sCAQ064
6.482927 6.692683 5.497561 6.078049 3.004878 4.360976 4.048780 5.624390
sCAQ065 sCAQ066 sCAQ067 sCAQ068 sCAQ069 sCAQ070 sCAQ071 sCAQ072
4.800000 5.868293 4.741463 4.385366 4.570732 5.853659 6.209756 5.087805
sCAQ073 sCAQ074 sCAQ075 sCAQ076 sCAQ077 sCAQ078 sCAQ079 sCAQ080
4.239024 5.126829 4.946341 4.600000 5.521951 2.921951 4.707317 6.790244
sCAQ081 sCAQ082 sCAQ083 sCAQ084 sCAQ085 sCAQ086 sCAQ087 sCAQ088
5.458537 4.551220 5.712195 6.121951 5.102439 4.039024 4.541463 6.053659
sCAQ089 sCAQ090 sCAQ091 sCAQ092 sCAQ093 sCAQ094 sCAQ095 sCAQ096
4.902439 5.151220 4.258537 5.507317 5.380488 3.668293 5.253659 5.902439
sCAQ097 sCAQ098 sCAQ099 sCAQ100
3.858537 5.219512 3.956098 4.365854
$yNorm
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
$Norm.r
[1] 0.9570786
$Agreement
Overall Distinctive
1 0.450914218 -0.0374790248
2 0.607611946 0.0439287853
3 0.534325884 0.2358528215
4 0.203801723 0.1512042587
5 0.591794924 0.2549769512
6 0.705831860 0.2153937389
7 0.283556172 0.1123474930
8 0.654403711 0.1001669745
9 0.211385861 0.1902683384
10 0.407857141 0.5166442180
11 0.606481481 0.2094973212
12 0.407852524 0.1887763098
13 0.543368661 0.2374377735
14 0.552425065 0.2955297017
15 0.300133900 0.2154134930
16 0.404311454 -0.0013410510
17 0.467426488 0.1504732045
18 0.479845747 0.0630388222
19 0.429693209 0.0798881184
20 0.577356815 0.3135368702
21 0.057606941 0.1318862557
22 0.394506019 0.2822357211
23 0.138888889 0.1058364580
24 0.555208803 0.2613591623
25 0.449074074 0.1549252409
26 0.184760278 -0.1221475242
27 0.328764592 0.2827848198
28 0.257219737 -0.1921553715
29 -0.029887133 -0.1608581332
30 0.387242465 0.0047642742
31 0.318240338 0.2948976108
32 0.222222222 0.1834463726
33 0.181208523 -0.0053623827
34 0.425925926 0.1947938907
35 0.130889759 0.0754502766
36 0.660831868 0.2513688999
37 0.080835350 0.0524297420
38 0.202776432 0.1233729255
39 0.673710070 0.1121068568
40 0.203658923 -0.0386532608
41 0.624285083 0.1596448722
42 0.656110214 0.4814688744
43 0.624247760 0.1981567609
44 0.474537037 0.2136284288
45 0.287711188 0.1243802030
46 0.448941409 0.4022923235
47 0.336830404 0.1355095749
48 0.209062939 0.3307598515
49 0.577960857 0.1667567254
50 0.108421734 -0.0444068923
51 0.527641081 0.4439906473
52 0.105037418 0.1366553365
53 0.228904530 0.0811530570
54 0.345825808 0.0989090915
55 0.404741407 0.0820027684
56 0.584283110 0.1417983468
57 -0.113431177 0.0004295209
58 0.437639506 0.1067999199
59 0.746679036 0.3718112182
60 0.121982701 0.1419232850
61 0.661071738 0.2548999153
62 0.482109170 0.1589547620
63 -0.139051533 0.0608845065
64 0.375000000 -0.0599565734
65 0.587340624 0.1764053318
66 0.233796296 0.1523437836
67 0.438417048 0.0114728398
68 0.576343952 0.2445447862
69 0.383082765 -0.0603144190
70 0.564814815 0.3473536560
71 0.676132781 0.3301867911
72 0.368055556 0.0719981551
73 0.460648148 0.2767452992
74 0.349537037 0.0122150179
75 0.170690501 -0.0623476741
76 0.377314815 -0.0148311473
77 0.125000000 -0.0360322981
78 0.476949972 0.2362181903
79 0.381944444 0.0166720172
80 0.289606043 0.0979432400
81 0.325111793 -0.1665760217
82 0.688166561 0.0761439410
83 0.483998004 0.4229183861
84 0.146273126 0.1569960449
85 -0.142865213 -0.1832471612
86 0.250297961 0.1658047824
87 0.561818018 0.1025918708
88 0.650857715 0.0738133371
89 0.567855526 0.2583668012
90 0.463343313 0.2836494869
91 0.539381984 0.0692442115
92 0.353508715 0.2543242211
93 0.250000000 0.2326649489
94 0.523266221 0.0559181802
95 0.717428920 0.4885448271
96 0.097336073 0.0098172953
97 0.487783201 0.2064053065
98 0.548611111 -0.0446142569
99 0.585648148 0.2477944437
100 0.488425926 0.1104880148
101 -0.027097584 0.1084226019
102 0.325209016 -0.1145535337
103 0.634259259 0.2192252406
104 0.251446149 0.2261417599
105 0.565228052 0.0537182228
106 0.525462963 0.0839985003
107 0.334613286 0.0339975645
108 0.321313047 0.0522532244
109 0.038952207 0.0369557729
110 0.417467737 0.0426387652
111 0.381962128 0.0940160425
112 0.381331495 0.0549967860
113 0.338774309 0.0639452875
114 0.465277778 0.1673972610
115 0.619109448 0.2498178201
116 0.401003291 0.1256361651
117 0.482225428 0.2662125840
118 0.041567887 0.0417470641
119 0.143849114 -0.0758034229
120 0.543933731 0.2245599538
121 0.401964418 0.2518074980
122 0.408419829 0.0643988213
123 0.238412538 0.1657555748
124 0.414351852 0.4153752161
125 0.355928292 0.1208658622
126 0.355834430 -0.0394305530
127 0.546936029 0.1249218972
128 0.483167682 -0.0497974501
129 0.378609136 0.0905372958
130 0.554792120 -0.0292742815
131 0.459470066 0.1058557413
132 0.229166667 0.0267909715
133 0.354166667 0.1925870182
134 0.499244382 0.1726757644
135 0.472496949 0.1436257165
136 0.367658762 -0.1646967436
137 0.495370370 -0.0649664160
138 0.641867408 0.1653129895
139 0.459214458 -0.0126084388
140 0.509180197 0.2283992628
141 0.659757785 0.0444872626
142 0.514373790 0.2109370800
143 0.272177624 0.0084010555
144 0.383518978 0.0424204890
145 0.708333333 0.3171969054
146 0.581123072 0.2993182487
147 0.504700164 0.0788770221
148 0.520978914 0.0192303404
149 0.448080924 0.1913163957
150 0.333348767 -0.0729679808
151 0.505344916 -0.0322301158
152 0.591727071 0.1158893214
153 0.388963708 0.0347932260
154 0.006912833 0.1471533965
155 0.356481481 -0.0510923188
156 0.658262627 0.3468457086
157 0.296224192 0.2811412870
158 0.779766102 0.3163588770
159 0.267160389 -0.0304903333
160 0.442131498 0.2213958786
161 0.351647008 0.1480855638
162 0.448365667 0.2650667106
163 0.346957461 0.3518106499
164 0.445053781 0.4832693204
165 0.132583402 0.1249978363
166 0.000000000 -0.0482328930
167 0.712147428 0.5138512850
168 0.208762341 0.2105278240
169 0.652837248 0.0687529355
170 0.612319550 0.2753305276
171 0.463301129 0.1446808313
172 0.253594585 0.1465911113
173 0.550295740 0.3538662133
174 0.535289852 0.2035546915
175 0.399579765 0.0830781223
176 0.368249665 0.0804147114
177 0.376202399 0.1915981011
178 0.580433773 0.2579667033
179 0.118468999 0.0813353826
180 0.575046216 0.0275800601
181 -0.202075418 -0.1297447023
182 0.333333333 0.2421344113
183 0.606481481 0.1433326806
184 0.524578923 0.0663179408
185 0.235678694 0.0375255209
186 0.574752557 0.3175350741
187 0.567829596 0.2995005837
188 0.438778446 0.0209010494
189 0.603106438 0.3421061251
190 0.145169491 -0.1307610437
191 0.361111111 0.0299366916
192 0.317677950 0.1334039831
193 0.452606528 0.2962729264
194 0.451180692 -0.0927058184
195 0.534722222 0.2301832038
196 0.481481481 0.0533455674
197 0.462927878 0.0608492122
198 0.780092593 0.3699517466
199 0.389995582 0.1574270003
200 0.521551613 0.3552935241
201 0.046296296 -0.0649111531
202 0.558575500 -0.0042524577
203 0.604685960 0.1605325413
204 0.077616250 -0.0309578079
205 0.023148148 0.0621787037
$Tests
Overall Distinctive
N 2.050000e+02 2.050000e+02
Mean 4.196886e-01 1.377897e-01
baseline 3.073769e-01 0.000000e+00
t 7.871208e+00 1.323607e+01
p-value 1.016087e-13 1.410192e-29
var n miss mean sd median trimmed mad
Overall 1 205 0 0.4196886 0.2315886 0.4296932 0.4275148 0.2318617
Distinctive 2 205 0 0.1377897 0.1488901 0.1256362 0.1313845 0.1453209
min max range skew kurtosis se
Overall -0.2020754 0.7800926 0.8484245 -0.1805952 -0.1262255 0.01647221
Distinctive -0.1921554 0.5166442 0.6447877 0.3518071 0.1191943 0.01047643
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