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\T1/ptm/m/n/10 for per-son-al-ity and psy-cho-log-i-cal re-search. Some of the 
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\T1/zi4/m/n/10 psych / intro . pdf$[][] [][]$https : / / personality-[]project 
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[]\T1/ptm/m/n/10 An ad-di-tional set of func-tions gen-er-ate sim-u-lated data 
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\T1/zi4/m/n/10 / / personality-[]project . org/$[][]\T1/ptm/m/n/10 . See also t
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[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An In-tro-duc-tion to Psy-cho-m
e-t-ric The-ory with ap-pli-ca-tions in R. Springer.
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 []\T1/zi4/m/n/10 AUC(t=NULL,BR=NULL,SR=NULL,Phi=NULL,VP=NULL,labels=NULL,plot=
"b",zero=TRUE,correct=.5, 
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[22]
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[]\T1/ptm/m/n/10 Wiggins, Jerry S. (1973) Per-son-al-ity and Pre-dic-tion: Prin
-ci-ples of Per-son-al-ity As-sess-ment. Addison-
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 []\T1/zi4/m/n/10 bassAckward(r, nfactors = 1, fm = "minres", rotate = "oblimin
", scores = "tenBerge", 
 []

[23] [24] [25]
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\T1/ptm/m/n/10 berg's Bass-Ackwards method, Jour-nal of Re-search in Per-son-al
-ity, 41, 4, 745-752, DOI: 10.1016/j.jrp.2006.08.005 
 []

[26
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.1}) has been already used, duplicate ignored
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                        ] [27]
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[]\T1/ptm/m/n/10 Tucker (1958) uses 9 vari-ables from Thur-stone and Thur-stone
 (1941) for his ex-am-ple of [][][]\T1/zi4/m/n/10 interbattery[][][]
 []

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\T1/ptm/m/n/10 Or-thog-o-nal-iza-tion and Jennrich-Bentler An-a-lytic Ro-ta-tio
ns, Mul-ti-vari-ate Be-hav-ioral Re-search, 51:5,
 []

[29]
Overfull \hbox (8.23738pt too wide) in paragraph at lines 1160--1161
[]\T1/ptm/m/n/10 Which vari-ables (by name or lo-ca-tion) should be the em-pir-
i-cal tar-get for bestScales
 []

[30]
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[]\T1/ptm/m/n/10 The de-rived model can be fur-ther val-i-dated against yet an-
other hold out sam-ple us-ing the [][][]\T1/zi4/m/n/10 predict.psych[][][]
 []

[31] [32]
Overfull \hbox (10.77748pt too wide) in paragraph at lines 1285--1286
[]\T1/ptm/m/n/10 The re-sult-ing best scales can be cross val-i-dated on a dif-
fer-ent hold out sam-ple us-ing [][][]\T1/zi4/m/n/10 crossValidation[][][][]\T1
/ptm/m/n/10 .
 []


Overfull \hbox (1.01723pt too wide) in paragraph at lines 1315--1316
[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An in-tro-duc-tion to psy-cho-m
e-t-ric the-ory with ap-pli-ca-tions in R. Springer.
 []

[33] [34] [35]
Overfull \hbox (9.30832pt too wide) in paragraph at lines 1433--1434
\T1/zi4/m/n/10 org/$[][]\T1/ptm/m/n/10 ) project. To see an ex-am-ple of the da
ta col-lec-tion tech-nique, visit [][]$\T1/zi4/m/n/10 https : / / www . SAPA-[]
project .
 []

[36]
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 [] \T1/zi4/m/n/9 scores <- psych::scoreItems(bfi.keys,bfi,min=1,max=6) #specif
y the minimum and maximum values[] 
 []

[37] [38]
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[39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50]
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 []    \T1/zi4/m/n/10 jiggle=FALSE,amount=.02,pch=18,pos,show.points=TRUE,choos
e=NULL,main=NULL,...)[] 
 []

[51]
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[]\T1/ptm/m/n/10 The out-put of the kmeans clus-ter-ing func-tion pro-duces a v
ec-tor of clus-ter mem-ber-ship. The [][][]\T1/zi4/m/n/10 score.items[][][]
 []

[52]
Overfull \hbox (3.11081pt too wide) in paragraph at lines 2345--2345
 []\T1/zi4/m/n/10 cohen.d(x, group,alpha=.05,std=TRUE,sort=NULL,dictionary=NULL
,MD=TRUE,data=NULL) 
 []

[53] [54] [55]
Overfull \hbox (2.77719pt too wide) in paragraph at lines 2464--2465
[]\T1/ptm/m/n/10 Cohen, Jackob (1988) Sta-tis-ti-cal Power Anal-y-sis for the B
e-hav-ioral Sci-ences. 2nd Edi-tion, Lawrence
 []

[56] [57] [58] [59]
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 [60]
[61] [62] [63] [64]
Overfull \hbox (11.27475pt too wide) in paragraph at lines 2925--2925
 []\T1/zi4/m/n/9 plot(burt[lower.tri(burt)],bs[lower.tri(bs)],ylab="smoothed va
lues",xlab="original values")[] 
 []

[65] [66] [67] [68]
Overfull \hbox (11.74858pt too wide) in paragraph at lines 3092--3093
\T1/ptm/m/n/10 show the ma-trix in terms of the con-fi-dence in-ter-vals, use [
][][]\T1/zi4/m/n/10 cor.plot.upperLowerCi[][][][]\T1/ptm/m/n/10 . 
 []

[69]
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 []  \T1/zi4/m/n/9 alpha=c("-A1","A2","A3","A4","A5","C1","C2","C3","-C4","-C5"
,"N1","N2","N3","N4","N5"),[] 
 []

[70]
Overfull \hbox (21.74715pt too wide) in paragraph at lines 3214--3215
[]\T1/ptm/m/n/10 To use the re-sult-ing cor-re-la-tions, see [][][]\T1/zi4/m/n/
10 fa[][][][]\T1/ptm/m/n/10 . To see the pair-wise pat-tern of miss-ing-ness, s
ee [][][]\T1/zi4/m/n/10 count.pairwise[][][][]\T1/ptm/m/n/10 . 
 []

[71]
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 []  \T1/zi4/m/n/10 show.legend=TRUE, labels=NULL,n.legend=10,keep.par=TRUE,sel
ect=NULL, pval=NULL,[] 
 []


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 []  \T1/zi4/m/n/10 digits=2, trailing=TRUE, cuts=c(.001,.01),scale=TRUE,cex,MA
R,upper=TRUE,diag=TRUE,[] 
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 []  \T1/zi4/m/n/10 symmetric=TRUE,stars=FALSE, adjust="holm",xaxis=1, xlas=0, 
ylas=2,ysrt=0,xsrt=0,[] 
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 []  \T1/zi4/m/n/10 digits=2, trailing=TRUE, cuts=c(.001,.01),scale=TRUE,cex,MA
R,upper=TRUE,diag=TRUE,[] 
 []


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 []  \T1/zi4/m/n/10 symmetric=TRUE,stars=FALSE, adjust="holm",xaxis=1, xlas=0, 
ylas=2,ysrt=0,xsrt=0,[] 
 []


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 []     \T1/zi4/m/n/10 gr=NULL, alpha=.75, min.length=NULL, sort=FALSE, n.obs=N
ULL,...)     #deprecated[] 
 []

[72]
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[]\T1/ptm/m/n/10 A color gra-di-ent: e.g., gr <- col-or-Ramp-Palette(c("#B52127
", "white", "#2171B5"))
 []

[73] [74]
Overfull \hbox (15.76555pt too wide) in paragraph at lines 3361--3361
 []\T1/zi4/m/n/9 corPlot(Thurstone, zlim=c(0,1),main="9 cognitive variables fro
m Thurstone",select=c(1:3,7:9))[] 
 []


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 []\T1/zi4/m/n/9 corPlot(r=rs$r,numbers=TRUE,pval=rs$p,main="Correlations scale
d by probability values",gr=gr)[] 
 []

[75]
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[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An In-tro-duc-tion to Psy-cho-m
e-t-ric The-ory with ap-pli-ca-tions in R. Springer.
 []

[76] [77]
Overfull \hbox (36.64662pt too wide) in paragraph at lines 3517--3518
[]\T1/ptm/m/n/10 use="pairwise" is the de-fault value and will do pair-wise del
e-tion of cases. use="complete"
 []


Overfull \hbox (6.10776pt too wide) in paragraph at lines 3525--3526
[]\T1/ptm/m/n/10 By de-fault, prob-a-bil-i-ties for method="spearman" and metho
d="kendall" are found
 []


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\T1/ptm/m/n/10 by nor-mal the-ory. If nor-mal=="FALSE", then repet-i-tive calls
 are made to cor.test.
 []

[78] [79]
Overfull \hbox (14.25328pt too wide) in paragraph at lines 3587--3587
 []\T1/zi4/m/n/9 cts <- corr.test(attitude[1:3],attitude[4:6]) #reports all val
ues corrected for multiple tests[] 
 []


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 []\T1/zi4/m/n/10 cortest.normal(R1, R2 = NULL, n1 = NULL, n2 = NULL, fisher = 
TRUE)      #the steiger test[] 
 []

[80] [81] [82]
Overfull \hbox (12.32683pt too wide) in paragraph at lines 3753--3754
[]\T1/ptm/m/n/10 Note that if ap-plied to resid-u-als from fac-tor anal-y-sis (
[][][]\T1/zi4/m/n/10 fa[][][][]\T1/ptm/m/n/10 ) or prin-ci-pal com-po-nents ana
l-y-sis ([][][]\T1/zi4/m/n/10 principal[][][][]\T1/ptm/m/n/10 )
 []

[83]
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 []\T1/zi4/m/n/10 cosinor.plot(angle,x=NULL,data = NULL, IDloc=NULL, ID=NULL,ho
urs=TRUE, period=24,[] 
 []


Overfull \hbox (3.11081pt too wide) in paragraph at lines 3825--3825
 [] \T1/zi4/m/n/10 cosinor.period(angle,x=NULL,code=NULL,data=NULL,hours=TRUE,p
eriod=seq(23,26,1),[] 
 []

[84] [85] [86] [87]
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 []\T1/zi4/m/n/10 cta (n=3,t=5000, cues = NULL, act=NULL, inhibit=NULL,expect =
 NULL, consume = NULL, 
 []


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 []\T1/zi4/m/n/10 tendency = NULL,tstrength=NULL, type="both", fast=2,compare=F
ALSE,learn=TRUE,reward=NULL)[] 
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 []\T1/zi4/m/n/10 cta.15(n = 3, t = 5000, cues = NULL,stim=NULL, act = NULL, in
hibit = NULL, consume = NULL,[] 
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[88] [89] [90]
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 []\T1/zi4/m/n/10 densityBy(x, var=NULL, grp=NULL,data=NULL, freq=FALSE, col=c(
"blue","red","black"),[] 
 []

[91] [92]
Overfull \hbox (1.39984pt too wide) in paragraph at lines 4242--4242
 []              \T1/zi4/m/n/10 type=3,check=TRUE,fast=NULL,quant=NULL,IQR=FALS
E,omit=FALSE,data=NULL,[] 
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[95]
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 []\T1/zi4/m/n/9 print(describeFast(sat.act),short=FALSE)  #even faster is just
 counts  (just less information)[] 
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[97]
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[][][]\T1/zi4/m/n/10 bassAckward[][][] []\T1/ptm/m/n/10 or con-fir-ma-tory fac-
tor anal-y-sis, cfa, or struc-tural equa-tion model,sem,
 []

[98] [99]
Overfull \hbox (10.7289pt too wide) in paragraph at lines 4589--4590
[]\T1/ptm/m/n/10 The di-a-gram func-tions that use the dia func-tions: [][][]\T
1/zi4/m/n/10 fa.diagram[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 structure.
diagram[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 omega.diagram[][][][]\T1/p
tm/m/n/10 ,
 []


Overfull \hbox (36.77168pt too wide) in paragraph at lines 4597--4597
 []\T1/zi4/m/n/9 plot(NA,xlim=xlim,ylim=ylim,main="Demonstration of diagram fun
ctions",axes=FALSE,xlab="",ylab="")[] 
 []

[100] [101] [102] [103] [104] [105] [106] [107]
Overfull \hbox (17.5776pt too wide) in paragraph at lines 4968--4969
[]\T1/ptm/m/n/10 other pa-ram-e-ters to pass to the plot func-tion, e.g., typ="
b" to draw lines, lty="dashed"
 []

[108]
Overfull \hbox (88.0062pt too wide) in paragraph at lines 5018--5019
[]\T1/ptm/m/n/10 For ad-vice why not to draw bar graphs with er-ror bars, see t
he page at bio-stat.mc.vanderbilt.edu/wiki/Main/DynamitePlots. 
 []

[109]
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 []\T1/zi4/m/n/10 error.bars.by(x,group,data=NULL, by.var=FALSE,x.cat=TRUE,ylab
 =NULL,xlab=NULL, main=NULL, 
 []

[110] [111] [112]
Overfull \hbox (14.23488pt too wide) in paragraph at lines 5228--5228
 []\T1/zi4/m/n/9 #this shows how to convert many levels of a grouping variable 
(age) into more manageable levels.[] 
 []


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 []\T1/zi4/m/n/10 xlab=NULL,ylab=NULL,pos=NULL,offset=1,arrow.len=.2,alpha=.05,
sd=FALSE,add=FALSE,[] 
 []

[113]
Overfull \hbox (59.50018pt too wide) in paragraph at lines 5294--5295
\T1/ptm/m/n/10 and neg-a-tive af-fect, see [][]$\T1/zi4/m/n/10 https : / / pers
onality-[]project . org / revelle / publications / happy-[]sad-[]appendix /
 []

[114]
Overfull \hbox (6.33861pt too wide) in paragraph at lines 5357--5360
\T1/ptm/m/n/10 with er-ror bars for dif-fer-ent groups or vari-ables is found u
s-ing from [][][]\T1/zi4/m/n/10 describe[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/
m/n/10 describeBy[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 statsBy[][][][]\
T1/ptm/m/n/10 ,
 []


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 [] \T1/zi4/m/n/10 alpha = 0.05, eyes = FALSE,items=FALSE, min.n = NULL, max.la
bels = 40, labels = NULL,[] 
 []

[115] [116]
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[][][]\T1/zi4/m/n/10 describe[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 desc
ribeBy[][][][]\T1/ptm/m/n/10 , or [][][]\T1/zi4/m/n/10 statsBy[][][] []\T1/ptm/
m/n/10 as well as [][][]\T1/zi4/m/n/10 error.bars[][][][]\T1/ptm/m/n/10 , [][][
]\T1/zi4/m/n/10 error.bars.by[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 stat
sBy[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 bestScales[][][]
 []

[117]
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 []\T1/zi4/m/n/10 main = NULL, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NU
LL,add=FALSE, pos = NULL,[] 
 []

[118]
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 []\T1/zi4/m/n/9 errorCircles("EA1","TA1",data=affect.stats,labels=films,pch=16
,cex=2,colors=colors,add=TRUE)[] 
 []

[119]
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 []\T1/zi4/m/n/10 cancorDiagram(fit, cut=.1,digits=2, nfX = NULL, nfY = NULL,si
mple=FALSE,e.size=.1,[] 
 []

[120]
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[]\T1/ptm/m/n/10 Alternatively con-sider the df1 for the X model and df2 for th
e Y model. esem.dof
 []

[121] [122] [123]
Overfull \hbox (21.74101pt too wide) in paragraph at lines 5759--5759
 []\T1/zi4/m/n/9 interbattery(Thurstone.9,1:4,5:9,2,2)  #compare to the solutio
n of Tucker.  We are not there yet.[] 
 []


Overfull \hbox (6.43051pt too wide) in paragraph at lines 5782--5782
 []\T1/zi4/m/n/10 fac(r,nfactors=1,n.obs = NA, rotate="oblimin", scores="tenBer
ge", residuals=FALSE,[] 
 []

[124] [125] [126] [127] [128] [129] [130]
Overfull \hbox (12.19873pt too wide) in paragraph at lines 5958--5961
\T1/ptm/m/n/10 upon the ob-jec-tive func-tion, f (see above). Us-ing the for-mu
la from [][][]\T1/zi4/m/n/10 factanal[][][][]\T1/ptm/m/n/10 (which
 []

[131] [132] [133] [134]
Overfull \hbox (24.73795pt too wide) in paragraph at lines 6109--6109
 []\T1/zi4/m/n/9 #using the Harman 24 mental tests, compare a principal factor 
with a principal components solution 
 []

[135]
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 []\T1/zi4/m/n/9 #   bfi[1001:1500,1:25],bfi[1501:2000,1:25],bfi[2001:2500,1:25
])  #five different data sets[] 
 []

[136]
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 []\T1/zi4/m/n/10 extension.diagram(fa.results,Phi=NULL,fe.results=NULL,sort=TR
UE,labels=NULL,cut=.3,[] 
 []

[137]
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[][][]\T1/zi4/m/n/10 extension.diagram[][][] []\T1/ptm/m/n/10 will draw a [][][
]\T1/zi4/m/n/10 fa.extend[][][] []\T1/ptm/m/n/10 re-sult with slightly more con
-trol than us-ing [][][]\T1/zi4/m/n/10 fa.diagram[][][]
 []

[138]
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[]\T1/ptm/m/n/10 To spec-ify the model for a struc-tural equa-tion con-fir-ma-t
ory anal-y-sis of the re-sults, use [][][]\T1/zi4/m/n/10 structure.diagram[][][
]
 []


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 []\T1/zi4/m/n/9 fa.diagram(ICLUST(Thurstone,2,title="Two cluster solution of T
hurstone"),main="Input from ICLUST")[] 
 []

[139]
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 [] \T1/zi4/m/n/10 , np.obs=NULL,correct=TRUE,rotate="oblimin",SMC=TRUE,warning
s=TRUE, fm="minres",alpha=.1,[] 
 []


Overfull \hbox (11.44073pt too wide) in paragraph at lines 6332--6332
 [] \T1/zi4/m/n/10 omega=FALSE,cor="cor",use="pairwise",cor.correct=.5,weight=N
ULL,smooth=TRUE, ...)[] 
 []


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 [] \T1/zi4/m/n/10 , np.obs=NULL,correct=TRUE,rotate="oblimin",SMC=TRUE,warning
s=TRUE, fm="minres",alpha=.1,[] 
 []


Overfull \hbox (11.44073pt too wide) in paragraph at lines 6336--6336
 [] \T1/zi4/m/n/10 omega=FALSE,cor="cor",use="pairwise",cor.correct=.5,weight=N
ULL,smooth=TRUE, ...)[] 
 []

[140] [141] [142]
Overfull \hbox (8.22261pt too wide) in paragraph at lines 6459--6459
 [] \T1/zi4/m/n/9 #note that we have to make sure that the variables are in the
 order to do the factor congruence[] 
 []


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 []\T1/zi4/m/n/9 fload <- matrix(c(c(c(.9,.8,.7,.6),rep(0,20)),c(c(.9,.8,.7,.6)
,rep(0,20)),c(c(.9,.8,.7,.6),[] 
 []


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 [143]
Overfull \hbox (14.78088pt too wide) in paragraph at lines 6515--6515
 []\T1/zi4/m/n/10 lookupFromKeys(keys.list,dictionary,n=20,cors=NULL,sort=TRUE,
suppress.names=FALSE,[] 
 []


Overfull \hbox (8.23738pt too wide) in paragraph at lines 6525--6526
[]\T1/ptm/m/n/10 Which vari-ables (by name or lo-ca-tion) should be the em-pir-
i-cal tar-get for bestScales
 []

[144] [145]
Overfull \hbox (1.01723pt too wide) in paragraph at lines 6585--6586
[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An in-tro-duc-tion to psy-cho-m
e-t-ric the-ory with ap-pli-ca-tions in R. Springer.
 []


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 []\T1/zi4/m/n/9 list(agree=c("-A1","A2","A3","A4","A5"),conscientiousness=c("C
1","C2","C3","-C4","-C5"),[] 
 []

[146]
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 [] \T1/zi4/m/n/10 FALSE,impute = "median", min.err = 0.001, max.iter = 50, sym
metric = TRUE, warnings[] 
 []


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 []\T1/zi4/m/n/10 fa.multi.diagram(multi.results,sort=TRUE,labels=NULL,flabels=
NULL,f2labels=NULL,cut=.2,[] 
 []


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\T1/ptm/m/n/10 "bentlerQ, "ge-om-inQ" and "bi-quar-timin" and "clus-ter" are po
s-si-ble oblique trans-
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[147] [148] [149]
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 []\T1/zi4/m/n/10 n.iter=20,error.bars=FALSE,se.bars=FALSE,SMC=FALSE,ylabel=NUL
L,show.legend=TRUE,[] 
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\T1/ptm/m/n/10 fac-tor anal-y-sis (fa="fa") or both prin-ci-pal com-po-nents an
d prin-ci-pal fac-tors (fa="both") 
 []

[150] [151] [152] [153]
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 []\T1/zi4/m/n/9 #fpt <- fa.parallel(psychTools::ability,cor="tet",quant=.95)  
#do a tetrachoric correlation and[] 
 []


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 []\T1/zi4/m/n/10 fa.poly(x,nfactors=1,n.obs = NA, n.iter=1, rotate="oblimin", 
SMC=TRUE,  missing=FALSE,[] 
 []


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 []\T1/zi4/m/n/10 scores = FALSE,SMC=TRUE, missing=FALSE,impute="median",min.er
r = 0.001, digits = 2,[] 
 []

[154]
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 []\T1/zi4/m/n/9 #using the Harman 24 mental tests, compare a principal factor 
with a principal components solution[] 
 []

[155]
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 []\T1/zi4/m/n/10 fa.random(data, nfactors = 1, fix = TRUE, n.obs = NA, n.iter 
= 1, rotate = "oblimin", 
 []


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 [] \T1/zi4/m/n/10 scores = "regression", residuals = FALSE, SMC = TRUE, covar 
= FALSE, missing = FALSE,[] 
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 [] \T1/zi4/m/n/10 impute = "median", min.err = 0.001, max.iter = 50, symmetric
 = TRUE, warnings = TRUE,[] 
 []

[156] [157]
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\T1/ptm/m/n/10 upon the ob-jec-tive func-tion, f (see above). Us-ing the for-mu
la from [][][]\T1/zi4/m/n/10 factanal[][][][]\T1/ptm/m/n/10 (which
 []

[158] [159] [160] [161]
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 [] \T1/zi4/m/n/10 c("oblimin", "oblimin"), scores = c("tenBerge", "tenBerge"),
 adjust=c(TRUE,TRUE),[] 
 []


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 []     \T1/zi4/m/n/10 use = "pairwise", cor = "cor", weight = NULL, correct = 
0.5,Target=list(NULL,NULL))[] 
 []

[162]
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[][][]\T1/zi4/m/n/10 fa[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 pca[][][][
]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 omega[][][] []\T1/ptm/m/n/10 and [][][]\
T1/zi4/m/n/10 iclust[][][][]\T1/ptm/m/n/10 , and []\T1/zi4/m/n/10 {link{bassAck
ward} []\T1/ptm/m/n/10 for al-ter-na-tive hi-er-ar-chi-cal so-lu-tions. [][][]\
T1/zi4/m/n/10 fa.extend[][][]
 []

[163]
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[]\T1/ptm/m/n/10 If NULL, then no load-ing ma-tri-ces may con-tain miss-ing val
-ues. If use="complete"
 []

[164]
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[]\T1/ptm/m/n/10 Revelle, W. (In prepa-ra-tion) An In-tro-duc-tion to Psy-cho-m
e-t-ric The-ory with ap-pli-ca-tions in R ([][]$\T1/zi4/m/n/10 https :
 []

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[170]
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 []\T1/zi4/m/n/9 print(factor.congruence(f4,f4r90),digits=3) #factor 2 and 3 ha
ve been exchanged and 3 flipped[] 
 []


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 []\T1/zi4/m/n/9 f2r <- factor.rotate(f2,-33,plot=TRUE,xlim=c(-1,1),ylim=c(-1,1
),title="rotated -33 degrees")[] 
 []

[171]
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[]\T1/ptm/m/n/10 method="Harman" finds weights based upon so-called "ide-al-ize
d" vari-ables: $\OML/cmm/m/it/10 W \OT1/cmr/m/n/10 = \OML/cmm/m/it/10 F\OT1/cmr
/m/n/10 (\OML/cmm/m/it/10 t\OT1/cmr/m/n/10 (\OML/cmm/m/it/10 F\OT1/cmr/m/n/10 )
\OML/cmm/m/it/10 F\OT1/cmr/m/n/10 )[]$\T1/ptm/m/n/10 . 
 []

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[176]
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 []\T1/zi4/m/n/10 faRotations(loadings, r = NULL, rotate = "oblimin", hyper = 0
.15, n.rotations = 10,...) 
 []

[177]
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\T1/ptm/m/n/10 ploratory Fac-tor Anal-y-sis. Psy-cho-log-i-cal Meth-ods. Ad-van
ce on-line pub-li-ca-tion. doi 10.1037/met0000467 
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[178] [179] [180]
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[]\T1/ptm/m/n/10 The data were down-loaded from the web-pages of An-drew Hayes 
(https://www.afhayes.com/public/hayes2018data.zip)
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[]\T1/ptm/m/n/10 The data are used by kind per-mis-sion of Donna M. Gar-cia, Mi
chael T. Schmitt, Nyla R. Branscombe,
 []

[181]
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 []\T1/zi4/m/n/9 lmCor(respappr ~ prot2 * sexism,std=FALSE,data=Garcia,main="Mo
derated (don\TS1/zi4/m/n/9 '\T1/zi4/m/n/9 t center)", zero=FALSE)[] 
 []

[182] [183]
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[]\T1/ptm/m/n/10 Changing Up-Bounds can be used to re-lax con-straints $\OML/cm
m/m/it/10 x[] \OMS/cmsy/m/n/10 ^^T \OML/cmm/m/it/10 v[]$ \T1/ptm/m/n/10 or to f
ix $\OML/cmm/m/it/10 x[]$\T1/ptm/m/n/10 -values by set-ting LoBounds[i]
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[184]
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[185] [186] [187]
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[]\T1/ptm/m/n/10 See also the Holzinger-Swineford data set of 301 sub-jects wit
h 26 vari-ables in [][][]\T1/zi4/m/n/10 holzinger.swineford[][][][]\T1/ptm/m/n/
10 .
 []

[188] [189]
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\T1/ptm/m/n/10 Lon-don, John Mur-ray, 1916, p. 694-696 (re-trieved from the web
 at https://www.biodiversitylibrary.org/item/95822#790 
 []

[190] [191] [192]
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\T1/ptm/m/n/10 (This is a one-way ANOVA fixed ef-fects model and is found by (M
SB- MSW)/(MSB+ (nr-1)*MSW)) 
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[193] [194] [195] [196] [197]
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\T1/ptm/m/n/10 item com-mu-nal-ity (SMC=TRUE) or use the max-i-mum cor-re-la-ti
on (SMC=FALSE). 
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[198] [199] [200] [201] [202] [203] [204] [205] [206] [207]
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 [208]
Overfull \hbox (9.78088pt too wide) in paragraph at lines 9530--9530
 []\T1/zi4/m/n/9 #V1;V2;V3;V4;V5;V6;V7;V8;V9;V10;V11;V12;V13;V14;V15;V16;V17;V1
8;V19;V20;V21;V22;V23;V24;}}[] 
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\T1/ptm/m/n/10 dot code is also gen-er-ated and saved in a file. To use the dot
 code, use ei-ther https://www.graphviz.org/
 []

[210] [211] [212] [213] [214]
Overfull \hbox (19.78088pt too wide) in paragraph at lines 9848--9848
 []\T1/zi4/m/n/10 irt.fa(x,nfactors=1,correct=TRUE,plot=TRUE,n.obs=NULL,rotate=
"oblimin",fm="minres", 
 []


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[]\T1/ptm/m/n/10 If true, then cor-rect the tetra-choric cor-re-la-tions for co
n-ti-nu-ity. (See [][][]\T1/zi4/m/n/10 tetrachoric[][][][]\T1/ptm/m/n/10 ). 
 []

[215] [216] [217]
Overfull \hbox (15.75635pt too wide) in paragraph at lines 9967--9967
 []\T1/zi4/m/n/9 items <- sim.congeneric(N=500,short=FALSE,categorical=TRUE) #5
00 responses to 4 discrete items[] 
 []


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 []\T1/zi4/m/n/9 iq.tf <- score.multiple.choice(iq.keys,psychTools::iqitems,sco
re=FALSE)  #just the responses[] 
 []

[218] [219] [220] [221] [222] [223] [224]
Overfull \hbox (21.41006pt too wide) in paragraph at lines 10315--10315
 []       \T1/zi4/m/n/10 main="Regression Models",plot=TRUE,show=FALSE,zero=TRU
E, alpha = .05,part=FALSE)[] 
 []


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 []       \T1/zi4/m/n/10 main="Regression Models",plot=TRUE,show=FALSE,zero=TRU
E, alpha = .05,part=FALSE)[] 
 []


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 []\T1/zi4/m/n/10 mat.regress(y, x,data, z=NULL,n.obs=NULL,use="pairwise",squar
e=FALSE) #the old form[] 
 []


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\T1/ptm/m/n/10 the y set (e.g., c(2,4,6) or the col-umn names of the y set (e.g
., c("Flags","Addition").
 []

[225]
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[]\T1/ptm/m/n/10 If us-ing a bestScales ob-ject, which set of keys to use ("bes
t.key","weights","optimal.key","optimal.weights") 
 []

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[229]
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\T1/ptm/m/n/10 given re-gres-sion weights. [][][]\T1/zi4/m/n/10 partial.r[][][]
 []\T1/ptm/m/n/10 for par-tial and part cor-re-la-tions. [][][]\T1/zi4/m/n/10 c
luster.cor[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 factor2cluster[][][][]\
T1/ptm/m/n/10 ,[][][]\T1/zi4/m/n/10 principal[][][][]\T1/ptm/m/n/10 ,[][][]\T1/
zi4/m/n/10 ICLUST[][][][]\T1/ptm/m/n/10 ,
 []

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 [231]
[232] [233]
Overfull \hbox (15.74715pt too wide) in paragraph at lines 10752--10752
 [] \T1/zi4/m/n/9 corPlot(both,numbers=TRUE,main="Differences between r and par
tial r for the sat.act data set")[] 
 []

[234]
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[]\T1/ptm/m/n/10 There are three ways to cre-ate keys for the [][][]\T1/zi4/m/n
/10 scoreItems[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 scoreOverlap[][][][
]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 scoreIrt.1pl[][][][]\T1/ptm/m/n/10 , or 
[][][]\T1/zi4/m/n/10 scoreIrt.2pl[][][]
 []

[235] [236]
Overfull \hbox (14.75021pt too wide) in paragraph at lines 10884--10884
 [] \T1/zi4/m/n/10 main = "Manhattan Plot of", adjust="holm",ylim = NULL,digits
=2,dictionary=NULL, ...)[] 
 []

[237] [238] [239] [240] [241] [242]
Overfull \hbox (31.44073pt too wide) in paragraph at lines 11187--11187
 []\T1/zi4/m/n/10 moderate.diagram(medi,digits=2,ylim=c(2,8),main="Moderation m
odel", cex=1,l.cex=1,...)[] 
 []

[243] [244] [245]
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[]\T1/ptm/m/n/10 See the ``how to do me-di-a-tion and mod-er-a-tion" at persona
lity-project.org/r/psych/HowTo/mediation.pdf
 []


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 []\T1/zi4/m/n/9 ), row.names = c(NA, -30L), class = "data.frame", variable.lab
els = structure(c("Satisfaction",[] 
 []

[246] [247]
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 []\T1/zi4/m/n/10 mixedCor(data=NULL,c=NULL,p=NULL,d=NULL,smooth=TRUE,correct=.
5,global=TRUE,ncat=8, 
 []

[248] [249]
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\T1/ptm/m/n/10 in un-der-stand-ing per-son-al-ity struc-ture. Per-son-al-ity an
d In-di-vid-ual Dif-fer-ences, []\T1/zi4/m/n/10 /10.1016/j.paid.2020.109905 
 []

[250] [251]
Overfull \hbox (2.13803pt too wide) in paragraph at lines 11577--11578
[]\T1/ptm/m/n/10 See Also [][][]\T1/zi4/m/n/10 rmssd[][][] []\T1/ptm/m/n/10 for
 the stan-dard de-vi-a-tion or [][][]\T1/zi4/m/n/10 describe[][][] []\T1/ptm/m/
n/10 for more con-ven-tional statis-tics. [][][]\T1/zi4/m/n/10 describeBy[][][]

 []


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[]\T1/ptm/m/n/10 and [][][]\T1/zi4/m/n/10 statsBy[][][] []\T1/ptm/m/n/10 give g
roup level statis-tics. See also []\T1/zi4/m/n/10 link{mlr}[]\T1/ptm/m/n/10 , [
]\T1/zi4/m/n/10 link{mlreliability}[]\T1/ptm/m/n/10 , []\T1/zi4/m/n/10 link{mlP
lot}
 []

[252] [253]
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 []\T1/zi4/m/n/10 multilevel.reliability(x, grp = "id", Time = "time", items = 
c(3:5),alpha=TRUE,icc=FALSE,[] 
 []

[254] [255] [256]
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\T1/ptm/m/n/10 As-sess-ment, 31, 12, 1395-1411. https://doi.org/10.1037/pas0000
754. [][]$\T1/zi4/m/n/10 https : / / personality-[]project .
 []

[257] [258]
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 []    \T1/zi4/m/n/10 plot=TRUE,n.obs=NA,rotate="oblimin",Phi=NULL,option="equa
l",covar=FALSE, ...)[] 
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 []   \T1/zi4/m/n/10 n.obs=NA,rotate="oblimin",Phi = NULL,option="equal",covar=
FALSE,two.ok=FALSE,...)[] 
 []

[259]
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\T1/zi4/m/n/10 revelle / publications / zinbarg . revelle . pmet . 05 . pdf$[][
] \T1/ptm/m/n/10 com-pare Mc-Don-ald's $\OML/cmm/m/it/10 ![]$ \T1/ptm/m/n/10 to
 Cron-bach's
 []

[260] [261]
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[][][]\T1/zi4/m/n/10 omega[][][] []\T1/ptm/m/n/10 is an ex-ploratory fac-tor an
al-y-sis func-tion that uses a Schmid-Leiman trans-for-ma-tion. [][][]\T1/zi4/m
/n/10 omegaSem[][][]
 []

[262]
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[]\T1/ptm/m/n/10 Moral: Find-ing omega_h is tricky and one should prob-a-bly co
m-pare [][][]\T1/zi4/m/n/10 omega[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 
omegaSem[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 omegaDirect[][][]
 []

[263]
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[]\T1/ptm/m/n/10 f3 <- fa(data,3,scores="tenBerge", oblique.rotation=TRUE f1 <-
 fa(f3$scores) hier.scores <- data.frame(f1$scores,f3$scores) 
 []


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[]\T1/ptm/m/n/10 Omega can also be found from an anal-y-sis done us-ing lavaan 
or sem di-rectly by call-ing [][][]\T1/zi4/m/n/10 omegaFromSem[][][]
 []

[264]
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[265]
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 []\T1/zi4/m/n/9 #round(om$omega.group[2]/om$omega.group[1],2)  #fraction of re
liable that is general variance[] 
 []


Overfull \hbox (5.25328pt too wide) in paragraph at lines 12154--12154
 []\T1/zi4/m/n/9 # round(om$omega.group[3]/om$omega.group[1],2)  #fraction of r
eliable that is group variance[] 
 []


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 []\T1/zi4/m/n/9 #om.jen <- omegaSem(jensen,lavaan=TRUE)  #do the exploratory o
mega with confirmatory as well[] 
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 []\T1/zi4/m/n/9 #lav.jen.1<- cfa(lav.mod.1,sample.cov=jensen,sample.nobs=500,o
rthogonal=TRUE,std.lv=TRUE)[] 
 []


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[]\T1/ptm/m/n/10 Graphic pre-sen-ta-tions of these two al-ter-na-tives are help
-ful in un-der-stand-ing the struc-ture. omega.graph
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[267]
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 []     \T1/zi4/m/n/10 digits = 2,method="pearson", pch = 20, lm=FALSE,cor=TRUE
,jiggle=FALSE,factor=2,[] 
 []


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 []     \T1/zi4/m/n/10 hist.col="cyan",show.points=TRUE,rug=TRUE, breaks = "Stu
rges",cex.cor=1,wt=NULL,[] 
 []

[273]
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[]\T1/ptm/m/n/10 Sometimes it use-ful to draw the cor-re-la-tion el-lipses and 
best fit-ting loess with-out the points. (points.false=TRUE). 
 []

[274]
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 []  \T1/zi4/m/n/9 pch=21+as.numeric(iris$Species),main="Fisher Iris data by Sp
ecies",hist.col="red",stars=TRUE)[] 
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[275]
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 []\T1/zi4/m/n/10 pairwisePlot(x,y=NULL,upper=TRUE,diagonal=TRUE,labels=TRUE,sh
ow.legend=TRUE,n.legend=10,[] 
 []

[276]
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[]\T1/ptm/m/n/10 A color gra-di-ent: e.g., gr <- col-or-Ramp-Palette(c("#B52127
", "white", "#2171B5"))
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[]\T1/ptm/m/n/10 To rem-edy the prob-lem of miss-ing cor-re-la-tions, we im-put
e the miss-ing cor-re-la-tions us-ing [][][]\T1/zi4/m/n/10 pairwiseImpute[][][]
[]\T1/ptm/m/n/10 .
 []

[277] [278] [279] [280] [281]
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\T1/ptm/m/n/10 See [][]$\T1/zi4/m/n/10 https : / / personality-[]project . org 
/ r / simulating-[]personality . html$[][] \T1/ptm/m/n/10 and [][]$\T1/zi4/m/n/
10 https : / / personality-[]project .
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[][][]$\T1/zi4/m/n/10 https : / / personality-[]project . org / r / simulating-
[]personality . html$[][] \T1/ptm/m/n/10 and [][]$\T1/zi4/m/n/10 https : / / pe
rsonality-[]project .
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[][][]\T1/zi4/m/n/10 VSS.plot[][][] []\T1/ptm/m/n/10 and [][][]\T1/zi4/m/n/10 f
a.plot[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 cluster.plot[][][][]\T1/ptm
/m/n/10 , [][][]\T1/zi4/m/n/10 fa[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 
irt.fa[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 VSS[][][][]\T1/ptm/m/n/10 ,
 [][][]\T1/zi4/m/n/10 ICLUST[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 omega
[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/10 principal[][][] []\T1/ptm/m/n/10 
or [][][]\T1/zi4/m/n/10 plot.reliability[][][] 
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[]\T1/ptm/m/n/10 scoring op-tions for bestScales ob-jects ("best.keys","weights
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[]\T1/ptm/m/n/10 If not spec-i-fied, these will be found. Oth-er-wise, this is 
the out-put from [][][]\T1/zi4/m/n/10 reliability[][][][]\T1/ptm/m/n/10 . 
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 []\T1/zi4/m/n/9 round(p$Vaccounted,2)   # the amount of variance accounted for
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it=1000,Target=NULL)[] 
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it=1000,Target=NULL)[] 
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[303] [304] [305] [306] [307] [308]
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[]\T1/ptm/m/n/10 Cohen, J. and Co-hen, P. and West, S.G. and Aiken, L.S. (2003)
 Ap-plied mul-ti-ple re-gres-sion/correlation
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[]\T1/ptm/m/n/10 Several other cases of re-stric-tion were also con-sid-ered by
 Thorndike and are im-ple-mented in [][][]\T1/zi4/m/n/10 rangeCorrection[][][][
]\T1/ptm/m/n/10 . 
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[]\T1/ptm/m/n/10 RMSEA is just a chisquare ad-justed by its de-grees of free-do
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[]\T1/ptm/m/n/10 Revelle, William, Wilt, Joshua, and Rosen-thal, Allen (2009) P
er-son-al-ity and Cog-ni-tion: The Personality-
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,"red")[sat.act$gender],grid=TRUE)[] 
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[]\T1/ptm/m/n/10 An in-tro-duc-tion to psy-cho-me-t-ric the-ory with ap-pli-ca-
tions in R (in prepa-ra-tion). [][]$\T1/zi4/m/n/10 https : / / personality-[]pr
oject .
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[]\T1/ptm/m/n/10 impute="median" re-places miss-ing val-ues with the item me-di
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[]\T1/ptm/m/n/10 See SAPA Rev-elle, W., Wilt, J., and Rosen-thal, A. (2010) Per
-son-al-ity and Cog-ni-tion: The Personality-
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[]\T1/ptm/m/n/10 General fac-tor cor-re-la-tions in sim.general and gen-eral fa
c-tor load-ings in sim.omega
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[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An In-tro-duc-tion to Psy-cho-m
e-t-ric The-ory with ap-pli-ca-tions in R. Springer.
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[]\T1/ptm/m/n/10 the cor-re-la-tion be-tween the re-peated mea-sures (if they e
x-ist). This can be thought
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[]\T1/ptm/m/n/10 MacCallum, R. C., & Tucker, L. R. (1991). Rep-re-sent-ing sour
ces of er-ror in the common-factormodel:
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[]\T1/ptm/m/n/10 Loadings of items on the group fac-tors. De-faults to ma-trix(
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\T1/ptm/m/n/10 ture. Meth-ods of Psy-cho-log-i-cal Re-search On-line, Vol. 9, N
o. 1 (for-merly (https://www.dgps.de/fachgruppen/methoden/mpr-
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[]\T1/ptm/m/n/10 See also the im-ple-men-ta-tion in this se-ries of func-tions 
to gen-er-ate nu-mer-ous data struc-tures. [][][]\T1/zi4/m/n/10 simCor[][][][]\
T1/ptm/m/n/10 ,
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[][][]\T1/zi4/m/n/10 simulation.circ[][][][]\T1/ptm/m/n/10 , [][][]\T1/zi4/m/n/
10 circ.tests[][][] []\T1/ptm/m/n/10 as well as other sim-u-la-tions ( [][][]\T
1/zi4/m/n/10 sim.structural[][][] [][]sim.hierarchical[][][][]\T1/ptm/m/n/10 ) 

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[]\T1/ptm/m/n/10 dichot=FALSE give con-tin-u-ous vari-ables, di-chot=TRUE gives
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\T1/ptm/m/n/10 ture. Meth-ods of Psy-cho-log-i-cal Re-search On-line, Vol. 9, N
o. 1 (for-merly at https://www.dgps.de/fachgruppen/methoden/mpr-
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\T1/ptm/m/n/10 port to the Army Re-search In-sti-tute on con-tract MDA 903-93-K
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[]\T1/ptm/m/n/10 The smc is found from all the items. A mod-i-fi-ca-tion to Gut
tman $\OML/cmm/m/it/10 ^^U[]$\T1/ptm/m/n/10 , $\OML/cmm/m/it/10 ^^U[]\OMS/cmsy/
m/n/10 ^^C$ \T1/ptm/m/n/10 re-ported by the [][][]\T1/zi4/m/n/10 score.items[][
][]
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\T1/ptm/m/n/10 cfa, sem and mimic type out-put. It takes the out-put ob-ject fr
om \T1/ptm/m/it/10 lavaan \T1/ptm/m/n/10 and then calls [][][]\T1/zi4/m/n/10 st
ructure.diagram[][][][]\T1/ptm/m/n/10 . 
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[]\T1/ptm/m/n/10 A n x m ma-trix or a list of such ma-tri-ces, or the out-put o
b-ject from []\T1/zi4/m/n/10 link{scoreOverlap} 
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 []\T1/zi4/m/n/9 responses <- table2df(bock.table[,2:6],count=bock.table[,7],la
bs= paste("lsat6.",1:5,sep=""))[] 
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[]\T1/ptm/m/n/10 The data were down-loaded from the web-pages of An-drew Hayes 
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[]\T1/ptm/m/n/10 Revelle, W. (in prepa-ra-tion) An in-tro-duc-tion to psy-cho-m
e-t-ric the-ory with ap-pli-ca-tions in R. Springer.
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[408]
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colnames(psychTools::msqR))][] 
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[]\T1/ptm/m/n/10 If us-ing data with a vari-able num-ber of re-sponse al-ter-na
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[]\T1/ptm/m/n/10 Revelle, W. An in-tro-duc-tion to psy-cho-me-t-ric the-ory wit
h ap-pli-ca-tions in R. (in prepa-ra-tion), Springer.
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[]\T1/ptm/m/n/10 By de-fault, find the Pear-son cor-re-la-tion, other op-tions 
are "spear-man","kendall","tet"(for
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\T1/ptm/m/n/10 the-ory with ap-pli-ca-tions in R (in prep) Springer. Draft chap
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