ABCT_102618
Import and merge data
healthyU <- read.csv("HealthyUT1itemlevel.csv")
head(healthyU)#colnames(healthyU)
hU <- healthyU[c(1,99:105,134:154,209:242,292:296)]
head(hU)#colnames(hU)
#Import data from HU time 3 to get drug
huT3 <- read.csv("healthyUT3_clean.csv")
huT3drug <- huT3[c(1,202:205)]
#Import data from HU time 2 to get alcohol
huT2 <- read.csv("healthyUT2_clean.csv")
huT2alc <- huT2[c(1,189:192)]
hU <- full_join(hU,huT2alc)Joining, by = "ID"hU <- full_join(hU, huT3drug)Joining, by = "ID"#ADD ALC/DRUGS FOR PRETESTING
ptfall2018 <- read.csv("PSYPretestingFall2018September4reducedover1percent.csv", header = T, na.strings = "")
#colnames(ptfall2018)
preT <- ptfall2018[c(1,86:92,181:192,94:102,104:123,158,160,162,163,165,167,169,170,172,173,174,175,177,179,201:205,193:200)]
head(preT)#colnames(preT)
#colnames(hU)
colnames(hU)[c(64:68)] <- c("ASRM1","ASRM2","ASRM3","ASRM4","ASRM5")Recode variables
#SIR
hUPreT$SIR2r <- 4 - hUPreT$SIR2
hUPreT$SIR4r <- 4 - hUPreT$SIR4
#summary(hUPreT)
#SIAS
hUPreT$SIAS5r <- 4 - hUPreT$SIAS5
hUPreT$SIAS9r <- 4 - hUPreT$SIAS9
hUPreT$SIAS11r <- 4 - hUPreT$SIAS11
hUPreT$SIR2 <- hUPreT$SIR2r
hUPreT$SIR4 <- hUPreT$SIR4r
hUPreT$SIAS5 <- hUPreT$SIAS5r
hUPreT$SIAS9 <- hUPreT$SIAS9r
hUPreT$SIAS11 <- hUPreT$SIAS11rSelect only items to be put into models
colnames(hUPreT) [1] "ID" "GAD_1" "GAD_2" "GAD_3" "GAD_4" "GAD_5" "GAD_6" "GAD_7"
[9] "OCIR2" "OCIR3" "OCIR5" "OCIR6" "OCIR8" "OCIR9" "OCIR11" "OCIR12"
[17] "OCIR14" "OCIR15" "OCIR17" "OCIR18" "DEP1" "DEP2" "DEP3" "DEP4"
[25] "DEP5" "DEP6" "DEP7" "DEP8" "DEP9" "SIAS1" "SIAS2" "SIAS3"
[33] "SIAS4" "SIAS5" "SIAS6" "SIAS7" "SIAS8" "SIAS9" "SIAS10" "SIAS11"
[41] "SIAS12" "SIAS13" "SIAS14" "SIAS15" "SIAS16" "SIAS17" "SIAS18" "SIAS19"
[49] "SIAS20" "SIR2" "SIR4" "SIR6" "SIR7" "SIR9" "SIR11" "SIR13"
[57] "SIR14" "SIR16" "SIR17" "SIR18" "SIR19" "SIR21" "SIR23" "ASRM1"
[65] "ASRM2" "ASRM3" "ASRM4" "ASRM5" "NIDA_ALC" "NIDA_ALC_1" "NIDA_ALC_2" "NIDA_ALC_3"
[73] "NIDA_DRUGS" "NIDA_DRUGS_1" "NIDA_DRUGS_2" "NIDA_DRUGS_3" "SIR2r" "SIR4r" "SIAS5r" "SIAS9r"
[81] "SIAS11r" hP <- hUPreT[c(2:76)]
#summary(hP)Rescale items to be on 0 to 4 scale
colnames(hP) [1] "GAD_1" "GAD_2" "GAD_3" "GAD_4" "GAD_5" "GAD_6" "GAD_7" "OCIR2"
[9] "OCIR3" "OCIR5" "OCIR6" "OCIR8" "OCIR9" "OCIR11" "OCIR12" "OCIR14"
[17] "OCIR15" "OCIR17" "OCIR18" "DEP1" "DEP2" "DEP3" "DEP4" "DEP5"
[25] "DEP6" "DEP7" "DEP8" "DEP9" "SIAS1" "SIAS2" "SIAS3" "SIAS4"
[33] "SIAS5" "SIAS6" "SIAS7" "SIAS8" "SIAS9" "SIAS10" "SIAS11" "SIAS12"
[41] "SIAS13" "SIAS14" "SIAS15" "SIAS16" "SIAS17" "SIAS18" "SIAS19" "SIAS20"
[49] "SIR2" "SIR4" "SIR6" "SIR7" "SIR9" "SIR11" "SIR13" "SIR14"
[57] "SIR16" "SIR17" "SIR18" "SIR19" "SIR21" "SIR23" "ASRM1" "ASRM2"
[65] "ASRM3" "ASRM4" "ASRM5" "NIDA_ALC" "NIDA_ALC_1" "NIDA_ALC_2" "NIDA_ALC_3" "NIDA_DRUGS"
[73] "NIDA_DRUGS_1" "NIDA_DRUGS_2" "NIDA_DRUGS_3"library(scales)
Attaching package: ‘scales’
The following objects are masked from ‘package:psych’:
alpha, rescale#colnames(hP)[c(1:7,20:28)]
cols <- c(1:7,20:28)
for (i in cols) {
hP[i] <- rescale(hP[,i], to = c(0,4))
}
#summary(hP)Run modified parallel analysis
Compare to the empirical data eigenvalue
There appear to be 7 factors
omega.pearson.hP7 <-
omega(R.pearson.hP,
fm="ml",
nfac=7,
nobs=704)
print(omega.pearson.hP7)Omega
Call: omega(m = R.pearson.hP, nfactors = 7, fm = "ml", nobs = 704)
Alpha: 0.95
G.6: 0.97
Omega Hierarchical: 0.66
Omega H asymptotic: 0.69
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* F4* F5* F6* F7* h2 u2 p2
GAD_1 0.68 0.48 0.70 0.30 0.66
GAD_2 0.72 0.55 0.83 0.17 0.63
GAD_3 0.71 0.52 0.78 0.22 0.65
GAD_4 0.69 0.46 0.69 0.31 0.68
GAD_5 0.58 0.29 0.46 0.54 0.72
GAD_6 0.61 0.31 0.50 0.50 0.75
GAD_7 0.59 0.36 0.50 0.50 0.69
OCIR2 0.37 0.42 0.37 0.63 0.37
OCIR3 0.31 0.70 0.59 0.41 0.16
OCIR5 0.25 0.63 0.47 0.53 0.14
OCIR6 0.61 0.28 0.23 0.53 0.47 0.70
OCIR8 0.34 0.54 0.43 0.57 0.27
OCIR9 0.37 0.65 0.57 0.43 0.25
OCIR11 0.33 0.62 0.50 0.50 0.22
OCIR12 0.58 0.36 0.53 0.47 0.65
OCIR14 0.25 0.47 0.32 0.68 0.19
OCIR15 0.27 0.71 0.59 0.41 0.12
OCIR17 0.24 0.61 0.44 0.56 0.13
OCIR18 0.50 0.34 0.42 0.58 0.60
DEP1 0.55 0.41 0.49 0.51 0.62
DEP2 0.63 0.37 0.60 0.40 0.66
DEP3 0.53 0.37 0.46 0.54 0.62
DEP4 0.56 0.40 0.52 0.48 0.61
DEP5 0.52 0.34 0.43 0.57 0.63
DEP6 0.58 0.38 0.51 0.49 0.67
DEP7 0.50 0.30 0.37 0.63 0.68
DEP8 0.45 0.28 0.29 0.71 0.69
DEP9 0.36 0.28 0.24 0.76 0.55
SIAS1 0.32 0.49 0.37 0.63 0.27
SIAS2 0.32 0.51 0.38 0.62 0.26
SIAS3 0.36 0.46 0.34 0.66 0.38
SIAS4 0.44 0.50 0.50 0.50 0.40
SIAS5 0.21 0.46 0.29 0.71 0.15
SIAS6 0.42 0.52 0.46 0.54 0.38
SIAS7 0.45 0.62 0.64 0.36 0.32
SIAS8 0.38 0.47 0.38 0.62 0.37
SIAS9 0.20 0.48 0.34 0.66 0.12
SIAS10 0.43 0.62 0.62 0.38 0.30
SIAS11 0.41 0.22 0.78 0.07
SIAS12 0.42 0.64 0.58 0.42 0.31
SIAS13 0.25 0.35 0.22 0.78 0.28
SIAS14 0.33 0.50 0.39 0.61 0.28
SIAS15 0.48 0.69 0.71 0.29 0.33
SIAS16 0.49 0.67 0.69 0.31 0.35
SIAS17 0.52 0.62 0.65 0.35 0.42
SIAS18 0.50 0.54 0.55 0.45 0.46
SIAS19 0.50 0.65 0.70 0.30 0.36
SIAS20 0.40 0.57 0.48 0.52 0.33
SIR2 0.08 0.92 0.28
SIR4 0.21 0.08 0.92 0.18
SIR6 0.30 0.71 0.58 0.42 0.16
SIR7 0.28 0.65 0.50 0.50 0.16
SIR9 0.36 0.24 0.34 0.34 0.66 0.37
SIR11 0.27 0.55 0.41 0.59 0.17
SIR13 0.29 0.70 0.59 0.41 0.15
SIR14 0.37 0.20 0.47 0.44 0.56 0.32
SIR16 0.35 0.25 0.35 0.33 0.67 0.36
SIR17 0.36 0.58 0.48 0.52 0.27
SIR18 0.27 0.53 -0.22 0.44 0.56 0.17
SIR19 0.33 0.71 0.62 0.38 0.18
SIR21 0.32 0.56 0.42 0.58 0.24
SIR23 0.30 0.41 0.29 0.71 0.31
ASRM1- 0.30 0.41 0.30 0.70 0.31
ASRM2- 0.28 0.26 0.21 0.79 0.37
ASRM3- 0.03 0.97 0.05
ASRM4- 0.36 0.18 0.82 0.05
ASRM5- 0.47 0.26 0.74 0.05
NIDA_ALC 0.44 -0.26 0.33 0.67 0.00
NIDA_ALC_1 0.48 0.32 0.68 0.07
NIDA_ALC_2 0.22 0.08 0.92 0.30
NIDA_ALC_3 0.27 0.13 0.87 0.25
NIDA_DRUGS 0.85 0.73 0.27 0.01
NIDA_DRUGS_1 0.86 0.77 0.23 0.04
NIDA_DRUGS_2 0.50 0.31 0.69 0.07
NIDA_DRUGS_3 0.56 0.37 0.63 0.09
With eigenvalues of:
g F1* F2* F3* F4* F5* F6* F7*
12.0 5.6 1.4 3.9 4.0 2.7 1.6 1.8
general/max 2.16 max/min = 3.97
mean percent general = 0.34 with sd = 0.21 and cv of 0.63
Explained Common Variance of the general factor = 0.36
The degrees of freedom are 2271 and the fit is 9.6
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.04
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 2700 and the fit is 26.7
The root mean square of the residuals is 0.12
The df corrected root mean square of the residuals is 0.13
Measures of factor score adequacy
g F1* F2* F3* F4* F5* F6* F7*
Correlation of scores with factors 0.87 0.92 0.75 0.93 0.94 0.95 0.73 0.87
Multiple R square of scores with factors 0.76 0.85 0.57 0.87 0.88 0.89 0.53 0.75
Minimum correlation of factor score estimates 0.53 0.70 0.14 0.74 0.76 0.79 0.07 0.50
Total, General and Subset omega for each subset
g F1* F2* F3* F4* F5* F6* F7*
Omega total for total scores and subscales 0.96 0.93 0.82 0.88 0.86 0.71 0.90 0.54
Omega general for total scores and subscales 0.66 0.33 0.57 0.29 0.22 0.05 0.64 0.10
Omega group for total scores and subscales 0.20 0.60 0.25 0.59 0.65 0.66 0.27 0.43efa.pearson.hP7 <- fa(r = R.pearson.hP,nfac=7,rotate = "varimax", n.obs=704)
print(efa.pearson.hP7$loadings, cutoff = .3)
Loadings:
MR2 MR1 MR4 MR3 MR5 MR6 MR7
GAD_1 0.719
GAD_2 0.779
GAD_3 0.745
GAD_4 0.760
GAD_5 0.596
GAD_6 0.590
GAD_7 0.641
OCIR2 0.497
OCIR3 0.731
OCIR5 0.661
OCIR6 0.548 0.385
OCIR8 0.615
OCIR9 0.705
OCIR11 0.683
OCIR12 0.454 0.452
OCIR14 0.521
OCIR15 0.741
OCIR17 0.656
OCIR18 0.385 0.379
DEP1 0.551
DEP2 0.622
DEP3 0.598
DEP4 0.588
DEP5 0.544
DEP6 0.558
DEP7 0.539
DEP8 0.475
DEP9 0.336
SIAS1 0.574
SIAS2 0.616
SIAS3 0.557
SIAS4 0.635
SIAS5 0.426
SIAS6 0.643
SIAS7 0.744
SIAS8 0.582
SIAS9 0.473
SIAS10 0.739
SIAS11 0.391
SIAS12 0.716
SIAS13 0.406
SIAS14 0.581
SIAS15 0.791
SIAS16 0.771
SIAS17 0.718
SIAS18 0.637
SIAS19 0.771
SIAS20 0.644
SIR2 0.479
SIR4 0.481
SIR6 0.702
SIR7 0.643
SIR9 0.433
SIR11 0.615
SIR13 0.727
SIR14 0.528
SIR16 0.415
SIR17 0.636
SIR18 0.625
SIR19 0.761
SIR21 0.632
SIR23 0.467
ASRM1 0.688
ASRM2 0.561
ASRM3 0.301
ASRM4 0.544
ASRM5 0.545
NIDA_ALC 0.490 -0.309
NIDA_ALC_1 0.541
NIDA_ALC_2 0.306
NIDA_ALC_3 0.397
NIDA_DRUGS 0.750
NIDA_DRUGS_1 0.795
NIDA_DRUGS_2 0.571
NIDA_DRUGS_3 0.634
MR2 MR1 MR4 MR3 MR5 MR6 MR7
SS loadings 8.747 7.947 5.191 5.055 3.028 1.951 1.737
Proportion Var 0.117 0.106 0.069 0.067 0.040 0.026 0.023
Cumulative Var 0.117 0.223 0.292 0.359 0.400 0.426 0.449efa.pearson.hP7Factor Analysis using method = minres
Call: fa(r = R.pearson.hP, nfactors = 7, n.obs = 704, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
MR2 MR1 MR4 MR3 MR5 MR6 MR7 h2 u2 com
GAD_1 0.22 0.72 0.08 0.17 0.03 -0.14 -0.05 0.63 0.37 1.4
GAD_2 0.18 0.78 0.09 0.19 0.01 -0.11 0.03 0.70 0.30 1.3
GAD_3 0.19 0.74 0.17 0.19 0.00 -0.09 0.02 0.66 0.34 1.4
GAD_4 0.16 0.76 0.09 0.18 0.05 -0.06 0.00 0.65 0.35 1.3
GAD_5 0.13 0.60 0.16 0.24 0.11 -0.02 -0.04 0.47 0.53 1.7
GAD_6 0.20 0.59 0.18 0.21 0.12 -0.06 0.05 0.49 0.51 1.9
GAD_7 0.11 0.64 0.10 0.18 0.02 -0.05 0.13 0.49 0.51 1.4
OCIR2 0.11 0.24 0.20 0.50 -0.05 0.02 0.01 0.36 0.64 2.0
OCIR3 0.09 0.13 0.07 0.73 0.01 0.02 -0.03 0.57 0.43 1.1
OCIR5 0.07 0.08 0.06 0.66 -0.03 -0.06 0.09 0.46 0.54 1.1
OCIR6 0.19 0.55 0.19 0.38 0.11 0.02 0.08 0.54 0.46 2.5
OCIR8 0.14 0.16 0.15 0.61 0.02 0.03 0.04 0.45 0.55 1.4
OCIR9 0.13 0.18 0.12 0.71 0.09 -0.04 -0.02 0.57 0.43 1.3
OCIR11 0.06 0.15 0.12 0.68 0.01 -0.06 0.08 0.51 0.49 1.2
OCIR12 0.27 0.45 0.20 0.45 0.08 0.02 0.10 0.54 0.46 3.2
OCIR14 0.10 0.08 0.17 0.52 -0.06 0.05 0.11 0.33 0.67 1.5
OCIR15 0.08 0.07 0.07 0.74 0.02 0.04 -0.05 0.57 0.43 1.1
OCIR17 0.03 0.07 0.08 0.66 -0.06 -0.03 0.11 0.46 0.54 1.1
OCIR18 0.25 0.39 0.09 0.38 0.17 0.03 0.20 0.43 0.57 3.8
DEP1 0.20 0.55 0.11 0.05 0.12 -0.15 0.09 0.40 0.60 1.7
DEP2 0.29 0.62 0.11 0.03 0.05 -0.22 0.11 0.55 0.45 1.9
DEP3 0.15 0.60 0.08 0.04 0.14 -0.05 -0.04 0.41 0.59 1.3
DEP4 0.23 0.59 0.17 0.01 0.10 -0.05 -0.06 0.44 0.56 1.6
DEP5 0.20 0.54 0.13 0.02 0.19 0.01 -0.01 0.39 0.61 1.7
DEP6 0.30 0.56 0.13 0.04 0.11 -0.05 0.13 0.45 0.55 1.9
DEP7 0.20 0.54 0.13 0.00 0.10 0.00 0.04 0.36 0.64 1.5
DEP8 0.15 0.48 0.09 0.08 0.07 -0.01 0.12 0.28 0.72 1.5
DEP9 0.15 0.34 0.03 0.10 0.16 -0.07 0.13 0.19 0.81 2.6
SIAS1 0.57 0.11 0.14 0.06 -0.07 -0.02 -0.06 0.37 0.63 1.3
SIAS2 0.62 0.09 0.08 0.03 0.01 -0.02 0.11 0.41 0.59 1.1
SIAS3 0.56 0.21 0.06 -0.02 0.02 -0.04 -0.03 0.36 0.64 1.3
SIAS4 0.64 0.23 0.12 0.04 -0.01 -0.10 0.16 0.51 0.49 1.5
SIAS5 0.23 0.09 -0.01 0.05 0.01 -0.26 0.43 0.31 0.69 2.4
SIAS6 0.64 0.18 0.11 0.11 0.04 0.00 0.06 0.48 0.52 1.3
SIAS7 0.74 0.18 0.04 0.11 0.02 -0.10 0.17 0.64 0.36 1.3
SIAS8 0.58 0.17 0.09 0.08 0.03 -0.02 0.08 0.39 0.61 1.3
SIAS9 0.28 0.05 -0.01 0.07 -0.04 -0.22 0.47 0.36 0.64 2.2
SIAS10 0.74 0.17 0.06 0.04 0.00 -0.08 0.19 0.62 0.38 1.3
SIAS11 0.20 0.00 -0.02 0.02 0.02 -0.20 0.39 0.23 0.77 2.1
SIAS12 0.72 0.18 0.10 0.07 0.01 0.00 -0.01 0.56 0.44 1.2
SIAS13 0.41 0.09 0.15 0.10 -0.03 0.14 0.01 0.22 0.78 1.8
SIAS14 0.58 0.07 0.13 0.15 0.02 -0.08 0.04 0.39 0.61 1.3
SIAS15 0.79 0.19 0.12 0.07 0.03 -0.10 0.06 0.70 0.30 1.2
SIAS16 0.77 0.20 0.10 0.11 0.03 -0.15 0.02 0.68 0.32 1.3
SIAS17 0.72 0.26 0.15 0.12 0.02 -0.05 0.04 0.63 0.37 1.4
SIAS18 0.64 0.29 0.12 0.14 0.00 -0.03 0.00 0.53 0.47 1.6
SIAS19 0.77 0.23 0.09 0.11 -0.04 -0.07 0.15 0.69 0.31 1.4
SIAS20 0.64 0.18 0.09 0.05 0.10 -0.05 -0.09 0.48 0.52 1.3
SIR2 0.01 0.12 0.07 0.09 -0.01 0.11 0.48 0.27 0.73 1.3
SIR4 -0.01 0.09 0.11 0.07 -0.01 0.10 0.48 0.26 0.74 1.3
SIR6 0.10 0.10 0.70 0.09 0.01 -0.02 0.05 0.53 0.47 1.1
SIR7 0.14 0.07 0.64 0.11 0.03 0.01 0.05 0.46 0.54 1.2
SIR9 0.13 0.19 0.43 0.29 0.12 0.05 -0.03 0.34 0.66 2.7
SIR11 0.12 0.07 0.62 0.15 0.05 0.05 0.00 0.42 0.58 1.2
SIR13 0.13 0.07 0.73 0.12 -0.02 0.00 0.04 0.57 0.43 1.2
SIR14 0.14 0.19 0.53 0.26 0.04 0.08 0.18 0.44 0.56 2.3
SIR16 0.06 0.24 0.42 0.15 0.15 0.12 0.15 0.32 0.68 2.9
SIR17 0.16 0.19 0.64 0.06 0.05 0.00 0.02 0.47 0.53 1.3
SIR18 0.09 0.11 0.63 0.08 0.10 0.04 -0.19 0.46 0.54 1.4
SIR19 0.13 0.13 0.76 0.08 0.03 0.04 -0.02 0.62 0.38 1.2
SIR21 0.10 0.18 0.63 0.00 0.04 -0.03 0.00 0.45 0.55 1.2
SIR23 0.12 0.16 0.47 0.10 0.05 0.08 0.13 0.29 0.71 1.8
ASRM1 -0.09 -0.27 0.01 -0.03 0.00 0.69 -0.06 0.56 0.44 1.4
ASRM2 -0.21 -0.21 -0.03 0.01 0.07 0.56 0.02 0.41 0.59 1.6
ASRM3 0.04 -0.05 0.03 0.03 0.00 0.30 0.09 0.10 0.90 1.3
ASRM4 -0.11 -0.02 0.11 -0.02 0.01 0.54 -0.06 0.33 0.67 1.2
ASRM5 -0.07 -0.05 0.09 -0.01 0.03 0.54 -0.18 0.35 0.65 1.3
NIDA_ALC -0.09 0.07 -0.01 -0.13 0.49 0.07 -0.31 0.37 0.63 2.0
NIDA_ALC_1 0.03 0.14 0.07 -0.06 0.54 0.08 -0.17 0.36 0.64 1.5
NIDA_ALC_2 0.02 0.12 0.08 0.05 0.31 0.04 0.05 0.12 0.88 1.6
NIDA_ALC_3 0.04 0.12 0.16 0.00 0.40 0.04 0.06 0.21 0.79 1.6
NIDA_DRUGS 0.00 0.06 -0.05 -0.03 0.75 -0.01 -0.14 0.59 0.41 1.1
NIDA_DRUGS_1 0.01 0.11 -0.02 0.03 0.79 0.00 -0.05 0.65 0.35 1.1
NIDA_DRUGS_2 -0.02 0.07 0.10 -0.01 0.57 -0.05 0.14 0.36 0.64 1.2
NIDA_DRUGS_3 0.02 0.09 0.01 0.08 0.63 -0.08 0.15 0.45 0.55 1.2
MR2 MR1 MR4 MR3 MR5 MR6 MR7
SS loadings 8.75 7.95 5.19 5.05 3.03 1.95 1.74
Proportion Var 0.12 0.11 0.07 0.07 0.04 0.03 0.02
Cumulative Var 0.12 0.22 0.29 0.36 0.40 0.43 0.45
Proportion Explained 0.26 0.24 0.15 0.15 0.09 0.06 0.05
Cumulative Proportion 0.26 0.50 0.65 0.80 0.89 0.95 1.00
Mean item complexity = 1.6
Test of the hypothesis that 7 factors are sufficient.
The degrees of freedom for the null model are 2775 and the objective function was 42.74 with Chi Square of 28942.19
The degrees of freedom for the model are 2271 and the objective function was 9.93
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 704 with the empirical chi square 4608.58 with prob < 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000029
The total number of observations was 704 with Likelihood Chi Square = 6676.96 with prob < 0
Tucker Lewis Index of factoring reliability = 0.793
RMSEA index = 0.054 and the 90 % confidence intervals are 0.051 NA
BIC = -8213.48
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR2 MR1 MR4 MR3 MR5 MR6 MR7
Correlation of (regression) scores with factors 0.96 0.95 0.94 0.94 0.92 0.86 0.83
Multiple R square of scores with factors 0.93 0.90 0.88 0.88 0.85 0.74 0.69
Minimum correlation of possible factor scores 0.85 0.80 0.76 0.76 0.69 0.48 0.39remove items that crossload and reverse-coded items (all load onto 1 factor)
SIR2, SIR4, SIAS5, SIAS9, SIAS11, OCIR6, OCIR12, OCIR18
[1] "GAD_1" "GAD_2" "GAD_3" "GAD_4" "GAD_5" "GAD_6" "GAD_7" "OCIR2"
[9] "OCIR3" "OCIR5" "OCIR8" "OCIR9" "OCIR11" "OCIR14" "OCIR15" "OCIR17"
[17] "DEP1" "DEP2" "DEP3" "DEP4" "DEP5" "DEP6" "DEP7" "DEP8"
[25] "DEP9" "SIAS1" "SIAS2" "SIAS3" "SIAS4" "SIAS6" "SIAS7" "SIAS8"
[33] "SIAS10" "SIAS12" "SIAS13" "SIAS14" "SIAS15" "SIAS16" "SIAS17" "SIAS18"
[41] "SIAS19" "SIAS20" "SIR6" "SIR7" "SIR9" "SIR11" "SIR13" "SIR14"
[49] "SIR16" "SIR17" "SIR18" "SIR19" "SIR21" "SIR23" "ASRM1" "ASRM2"
[57] "ASRM3" "ASRM4" "ASRM5" "NIDA_ALC" "NIDA_ALC_1" "NIDA_ALC_2" "NIDA_ALC_3" "NIDA_DRUGS"
[65] "NIDA_DRUGS_1" "NIDA_DRUGS_2" "NIDA_DRUGS_3"R.pearson.hPr <- hetcor(hPr, use="pairwise.complete.obs")$correlations
#R.pearson.hPr
eigens.pearson.hPr <- eigen(R.pearson.hPr)$values
eigens.pearson.hPr [1] 15.7146125 5.0308274 4.2157633 3.7077777 2.7953687 1.9890173 1.5959327 1.3681237 1.2994276 1.2032291 1.1237357
[12] 1.0641888 1.0049324 0.9639962 0.9272568 0.8930907 0.8721258 0.8122590 0.7961456 0.7745626 0.7086454 0.6880797
[23] 0.6670648 0.6582057 0.6469750 0.6285757 0.6213053 0.6102711 0.5916605 0.5795300 0.5749531 0.5641600 0.5315302
[34] 0.5036780 0.5014120 0.4774275 0.4675170 0.4544031 0.4327167 0.4161140 0.4047457 0.3959330 0.3906160 0.3846323
[45] 0.3709423 0.3547233 0.3393680 0.3319122 0.3192917 0.3094467 0.2997742 0.2943438 0.2913407 0.2853267 0.2795400
[56] 0.2706721 0.2552593 0.2461469 0.2352455 0.2144841 0.2125263 0.2010595 0.1978867 0.1754048 0.1651896 0.1577544
[67] 0.1398358options(scipen=999)
plot(eigens.pearson.hPr,pch=16,xlab="",ylab="Eigenvalue", main = "Eigenvalues for Item Level Data")
Attaching package: ‘MASS’
The following object is masked from ‘package:dplyr’:
selectCompare to the empirical data eigenvalue
eigens <- eigen(R.pearson.hPr)$values
eigens
plot(eigens,pch=15,ylab="Eigenvalue",xlab="",ylim=c(0,18),cex=.5, main="Comparison of Simulated Eigenvalues \n(95th Percentile) and Empirical Eigenvalues")
points(15,quant95_15,pch=5,cex=.5)
points(14,quant95_14,pch=5,cex=.5)
points(13,quant95_13,pch=5,cex=.5)
points(12,quant95_12,pch=5,cex=.5)
points(11,quant95_11,pch=5,cex=.5)
points(10,quant95_10,pch=5,cex=.5)
points(9,quant95_9,pch=5,cex=.5)
points(8,quant95_8,pch=5,cex=.5)
points(7,quant95_7,pch=5,cex=.5)
points(6,quant95_6,pch=5,cex=.5)
points(5,quant95_5,pch=5,cex=.5)
points(4,quant95_4,pch=5,cex=.5)There appear to be 6 factors
omega.pearson.hPr6 <-
omega(R.pearson.hPr,
fm="ml",
nfac=6,
nobs=704)Loading required namespace: GPArotation
print(omega.pearson.hPr6)Omega
Call: omega(m = R.pearson.hPr, nfactors = 6, fm = "ml", nobs = 704)
Alpha: 0.94
G.6: 0.97
Omega Hierarchical: 0.66
Omega H asymptotic: 0.68
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* F4* F5* F6* h2 u2 p2
GAD_1 0.68 0.48 0.70 0.30 0.66
GAD_2 0.72 0.55 0.82 0.18 0.63
GAD_3 0.71 0.52 0.78 0.22 0.65
GAD_4 0.68 0.47 0.69 0.31 0.66
GAD_5 0.57 0.30 0.46 0.54 0.70
GAD_6 0.61 0.31 0.51 0.49 0.74
GAD_7 0.58 0.37 0.49 0.51 0.69
OCIR2 0.37 0.41 0.37 0.63 0.37
OCIR3 0.31 0.71 0.60 0.40 0.16
OCIR5 0.26 0.63 0.46 0.54 0.14
OCIR8 0.34 0.53 0.42 0.58 0.27
OCIR9 0.38 0.67 0.59 0.41 0.25
OCIR11 0.33 0.61 0.49 0.51 0.22
OCIR14 0.25 0.47 0.31 0.69 0.20
OCIR15 0.28 0.72 0.61 0.39 0.12
OCIR17 0.24 0.60 0.43 0.57 0.14
DEP1 0.54 0.45 0.51 0.49 0.57
DEP2 0.62 0.43 0.62 0.38 0.62
DEP3 0.51 0.32 0.41 0.59 0.64
DEP4 0.54 0.33 0.45 0.55 0.66
DEP5 0.50 0.28 0.39 0.61 0.65
DEP6 0.57 0.39 0.51 0.49 0.64
DEP7 0.48 0.26 0.34 0.66 0.68
DEP8 0.44 0.29 0.29 0.71 0.67
DEP9 0.35 0.32 0.24 0.76 0.52
SIAS1 0.33 0.48 0.36 0.64 0.29
SIAS2 0.32 0.52 0.38 0.62 0.27
SIAS3 0.36 0.45 0.34 0.66 0.38
SIAS4 0.45 0.52 0.49 0.51 0.41
SIAS6 0.43 0.52 0.46 0.54 0.40
SIAS7 0.46 0.64 0.63 0.37 0.33
SIAS8 0.39 0.48 0.38 0.62 0.39
SIAS10 0.44 0.64 0.61 0.39 0.32
SIAS12 0.42 0.62 0.56 0.44 0.32
SIAS13 0.25 0.33 0.21 0.79 0.30
SIAS14 0.34 0.51 0.39 0.61 0.29
SIAS15 0.49 0.69 0.71 0.29 0.33
SIAS16 0.50 0.67 0.69 0.31 0.36
SIAS17 0.52 0.60 0.64 0.36 0.42
SIAS18 0.50 0.52 0.53 0.47 0.47
SIAS19 0.51 0.66 0.70 0.30 0.37
SIAS20 0.40 0.55 0.47 0.53 0.34
SIR6 0.31 0.66 0.53 0.47 0.18
SIR7 0.29 0.60 0.46 0.54 0.18
SIR9 0.36 0.36 0.25 0.35 0.65 0.37
SIR11 0.27 0.57 0.42 0.58 0.17
SIR13 0.30 0.68 0.57 0.43 0.16
SIR14 0.37 0.47 0.42 0.58 0.32
SIR16 0.33 0.38 0.22 0.32 0.68 0.35
SIR17 0.36 0.59 0.48 0.52 0.27
SIR18 0.27 0.58 0.42 0.58 0.17
SIR19 0.34 0.71 0.63 0.37 0.18
SIR21 0.32 0.58 0.43 0.57 0.23
SIR23 0.29 0.43 0.28 0.72 0.30
ASRM1- 0.31 0.26 0.21 0.79 0.46
ASRM2- 0.29 0.17 0.83 0.48
ASRM3- 0.03 0.97 0.06
ASRM4- -0.20 0.09 0.91 0.11
ASRM5- 0.10 0.90 0.15
NIDA_ALC 0.48 0.28 0.72 0.00
NIDA_ALC_1 0.51 0.31 0.69 0.07
NIDA_ALC_2 0.23 0.08 0.92 0.25
NIDA_ALC_3 0.28 0.13 0.87 0.22
NIDA_DRUGS 0.85 0.72 0.28 0.01
NIDA_DRUGS_1 0.86 0.77 0.23 0.03
NIDA_DRUGS_2 0.47 0.28 0.72 0.07
NIDA_DRUGS_3 0.53 0.33 0.67 0.10
With eigenvalues of:
g F1* F2* F3* F4* F5* F6*
10.8 5.5 4.1 1.5 3.5 2.8 1.6
general/max 1.99 max/min = 3.7
mean percent general = 0.35 with sd = 0.2 and cv of 0.59
Explained Common Variance of the general factor = 0.36
The degrees of freedom are 1824 and the fit is 8.14
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.04
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 2144 and the fit is 23.67
The root mean square of the residuals is 0.13
The df corrected root mean square of the residuals is 0.13
Measures of factor score adequacy
g F1* F2* F3* F4* F5* F6*
Correlation of scores with factors 0.86 0.91 0.93 0.78 0.93 0.94 0.72
Multiple R square of scores with factors 0.75 0.82 0.86 0.61 0.86 0.89 0.51
Minimum correlation of factor score estimates 0.49 0.64 0.73 0.22 0.72 0.77 0.03
Total, General and Subset omega for each subset
g F1* F2* F3* F4* F5* F6*
Omega total for total scores and subscales 0.96 0.93 0.86 0.79 0.86 0.71 0.90
Omega general for total scores and subscales 0.66 0.35 0.23 0.52 0.18 0.04 0.63
Omega group for total scores and subscales 0.22 0.59 0.63 0.27 0.68 0.67 0.28
efa.pearson.hPr6 <- fa(hPr,nfac=6,rotate = "varimax", scores="regression")
print(efa.pearson.hPr6$loadings, cutoff = .3)
Loadings:
MR1 MR3 MR2 MR4 MR5 MR6
GAD_1 0.727
GAD_2 0.783
GAD_3 0.750
GAD_4 0.763
GAD_5 0.599
GAD_6 0.598
GAD_7 0.624
OCIR2 0.495
OCIR3 0.730
OCIR5 0.664
OCIR8 0.613
OCIR9 0.702
OCIR11 0.687
OCIR14 0.528
OCIR15 0.739
OCIR17 0.659
DEP1 0.542
DEP2 0.303 0.616
DEP3 0.600
DEP4 0.585
DEP5 0.544
DEP6 0.312 0.540
DEP7 0.530
DEP8 0.463
DEP9 0.314
SIAS1 0.572
SIAS2 0.622
SIAS3 0.558
SIAS4 0.646
SIAS6 0.648
SIAS7 0.754
SIAS8 0.590
SIAS10 0.750
SIAS12 0.715
SIAS13 0.408
SIAS14 0.584
SIAS15 0.796
SIAS16 0.770
SIAS17 0.725
SIAS18 0.638
SIAS19 0.780
SIAS20 0.638
SIR6 0.703
SIR7 0.647
SIR9 0.437
SIR11 0.617
SIR13 0.728
SIR14 0.546
SIR16 0.433
SIR17 0.638
SIR18 0.609
SIR19 0.757
SIR21 0.632
SIR23 0.475
ASRM1 0.679
ASRM2 0.514
ASRM3
ASRM4 0.559
ASRM5 0.582
NIDA_ALC 0.505
NIDA_ALC_1 0.557
NIDA_ALC_2 0.302
NIDA_ALC_3 0.393
NIDA_DRUGS 0.758
NIDA_DRUGS_1 0.802
NIDA_DRUGS_2 0.560
NIDA_DRUGS_3 0.617
MR1 MR3 MR2 MR4 MR5 MR6
SS loadings 8.577 7.135 5.181 4.545 3.012 1.857
Proportion Var 0.128 0.106 0.077 0.068 0.045 0.028
Cumulative Var 0.128 0.235 0.312 0.380 0.425 0.452efa.pearson.hPr6Factor Analysis using method = minres
Call: fa(r = hPr, nfactors = 6, rotate = "varimax", scores = "regression")
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR3 MR2 MR4 MR5 MR6 h2 u2 com
GAD_1 0.23 0.73 0.08 0.16 0.04 -0.09 0.621 0.38 1.4
GAD_2 0.19 0.78 0.10 0.19 0.02 -0.09 0.702 0.30 1.3
GAD_3 0.20 0.75 0.17 0.18 0.00 -0.06 0.668 0.33 1.4
GAD_4 0.17 0.76 0.10 0.18 0.06 -0.03 0.655 0.34 1.3
GAD_5 0.14 0.60 0.16 0.24 0.12 0.01 0.473 0.53 1.7
GAD_6 0.21 0.60 0.19 0.22 0.12 -0.04 0.501 0.50 1.9
GAD_7 0.13 0.62 0.12 0.18 0.01 -0.10 0.464 0.54 1.4
OCIR2 0.12 0.25 0.20 0.50 -0.05 0.06 0.366 0.63 2.0
OCIR3 0.10 0.13 0.07 0.73 0.02 0.05 0.568 0.43 1.1
OCIR5 0.08 0.08 0.07 0.66 -0.03 -0.07 0.464 0.54 1.1
OCIR8 0.14 0.16 0.15 0.61 0.02 0.04 0.446 0.55 1.4
OCIR9 0.13 0.18 0.12 0.70 0.10 -0.02 0.567 0.43 1.3
OCIR11 0.07 0.14 0.13 0.69 0.01 -0.09 0.520 0.48 1.2
OCIR14 0.11 0.08 0.18 0.53 -0.07 0.03 0.332 0.67 1.4
OCIR15 0.08 0.08 0.08 0.74 0.02 0.07 0.570 0.43 1.1
OCIR17 0.04 0.07 0.09 0.66 -0.07 -0.05 0.457 0.54 1.1
DEP1 0.21 0.54 0.13 0.06 0.12 -0.19 0.408 0.59 1.8
DEP2 0.30 0.62 0.12 0.04 0.05 -0.25 0.554 0.45 2.0
DEP3 0.15 0.60 0.08 0.04 0.15 -0.02 0.415 0.58 1.3
DEP4 0.23 0.58 0.18 0.01 0.12 -0.04 0.440 0.56 1.6
DEP5 0.20 0.54 0.14 0.02 0.19 0.01 0.393 0.61 1.7
DEP6 0.31 0.54 0.15 0.05 0.11 -0.12 0.439 0.56 2.0
DEP7 0.20 0.53 0.14 0.00 0.10 -0.01 0.354 0.65 1.5
DEP8 0.16 0.46 0.11 0.09 0.06 -0.05 0.266 0.73 1.5
DEP9 0.16 0.31 0.05 0.10 0.15 -0.16 0.186 0.81 3.0
SIAS1 0.57 0.11 0.13 0.05 -0.06 0.00 0.363 0.64 1.2
SIAS2 0.62 0.08 0.09 0.03 0.01 -0.04 0.403 0.60 1.1
SIAS3 0.56 0.20 0.06 -0.03 0.02 -0.04 0.357 0.64 1.3
SIAS4 0.65 0.22 0.13 0.05 -0.01 -0.13 0.500 0.50 1.4
SIAS6 0.65 0.17 0.11 0.12 0.04 -0.02 0.479 0.52 1.3
SIAS7 0.75 0.17 0.05 0.12 0.01 -0.12 0.629 0.37 1.2
SIAS8 0.59 0.16 0.10 0.08 0.03 -0.05 0.393 0.61 1.3
SIAS10 0.75 0.16 0.07 0.05 -0.01 -0.11 0.609 0.39 1.2
SIAS12 0.71 0.17 0.09 0.06 0.02 0.03 0.555 0.45 1.2
SIAS13 0.41 0.08 0.15 0.09 -0.02 0.13 0.221 0.78 1.7
SIAS14 0.58 0.06 0.13 0.14 0.02 -0.08 0.388 0.61 1.3
SIAS15 0.80 0.19 0.12 0.07 0.02 -0.09 0.694 0.31 1.2
SIAS16 0.77 0.21 0.09 0.11 0.03 -0.11 0.668 0.33 1.3
SIAS17 0.72 0.25 0.15 0.11 0.02 -0.05 0.627 0.37 1.4
SIAS18 0.64 0.29 0.12 0.13 0.00 0.00 0.522 0.48 1.6
SIAS19 0.78 0.22 0.10 0.12 -0.04 -0.08 0.689 0.31 1.3
SIAS20 0.64 0.18 0.08 0.03 0.11 -0.01 0.458 0.54 1.3
SIR6 0.10 0.10 0.70 0.09 0.01 0.00 0.524 0.48 1.1
SIR7 0.14 0.07 0.65 0.11 0.03 0.02 0.457 0.54 1.2
SIR9 0.12 0.19 0.44 0.30 0.12 0.06 0.349 0.65 2.7
SIR11 0.12 0.06 0.62 0.14 0.05 0.06 0.422 0.58 1.2
SIR13 0.14 0.06 0.73 0.12 -0.02 0.01 0.568 0.43 1.1
SIR14 0.15 0.17 0.55 0.26 0.03 0.02 0.417 0.58 1.8
SIR16 0.07 0.22 0.43 0.16 0.14 0.05 0.289 0.71 2.2
SIR17 0.16 0.19 0.64 0.05 0.05 0.01 0.472 0.53 1.3
SIR18 0.08 0.11 0.61 0.06 0.11 0.10 0.416 0.58 1.2
SIR19 0.13 0.12 0.76 0.08 0.03 0.05 0.615 0.38 1.2
SIR21 0.10 0.17 0.63 -0.01 0.05 -0.03 0.442 0.56 1.2
SIR23 0.13 0.14 0.47 0.11 0.05 0.03 0.278 0.72 1.5
ASRM1 -0.10 -0.27 0.01 -0.03 0.00 0.68 0.546 0.45 1.4
ASRM2 -0.22 -0.22 -0.02 0.01 0.06 0.51 0.364 0.64 1.8
ASRM3 0.04 -0.05 0.03 0.04 -0.01 0.28 0.086 0.91 1.2
ASRM4 -0.12 -0.01 0.11 -0.02 0.02 0.56 0.340 0.66 1.2
ASRM5 -0.09 -0.05 0.08 -0.02 0.04 0.58 0.358 0.64 1.1
NIDA_ALC -0.10 0.07 -0.02 -0.15 0.50 0.13 0.311 0.69 1.5
NIDA_ALC_1 0.02 0.15 0.06 -0.07 0.56 0.13 0.358 0.64 1.3
NIDA_ALC_2 0.03 0.11 0.08 0.04 0.30 0.04 0.114 0.89 1.5
NIDA_ALC_3 0.05 0.11 0.16 0.00 0.39 0.03 0.197 0.80 1.6
NIDA_DRUGS -0.01 0.05 -0.06 -0.04 0.76 0.02 0.583 0.42 1.0
NIDA_DRUGS_1 0.01 0.10 -0.02 0.03 0.80 0.01 0.655 0.35 1.0
NIDA_DRUGS_2 -0.01 0.05 0.12 0.01 0.56 -0.12 0.346 0.65 1.2
NIDA_DRUGS_3 0.04 0.07 0.03 0.10 0.62 -0.15 0.419 0.58 1.2
MR1 MR3 MR2 MR4 MR5 MR6
SS loadings 8.58 7.14 5.18 4.54 3.01 1.86
Proportion Var 0.13 0.11 0.08 0.07 0.04 0.03
Cumulative Var 0.13 0.23 0.31 0.38 0.42 0.45
Proportion Explained 0.28 0.24 0.17 0.15 0.10 0.06
Cumulative Proportion 0.28 0.52 0.69 0.84 0.94 1.00
Mean item complexity = 1.4
Test of the hypothesis that 6 factors are sufficient.
The degrees of freedom for the null model are 2211 and the objective function was 38.06 with Chi Square of 25872.08
The degrees of freedom for the model are 1824 and the objective function was 8.37
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 698 with the empirical chi square 3834.74 with prob < 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000058
The total number of observations was 704 with Likelihood Chi Square = 5657.13 with prob < 0
Tucker Lewis Index of factoring reliability = 0.802
RMSEA index = 0.056 and the 90 % confidence intervals are 0.053 0.056
BIC = -6302.43
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR1 MR3 MR2 MR4 MR5 MR6
Correlation of (regression) scores with factors 0.96 0.95 0.94 0.94 0.92 0.86
Multiple R square of scores with factors 0.93 0.90 0.88 0.88 0.85 0.73
Minimum correlation of possible factor scores 0.86 0.79 0.76 0.75 0.69 0.47
---
title: "ABCT_102618"
output: html_notebook
---

```{r, include=F}
require(psych)
require(dplyr)
require(boot)
require(polycor)
require(car)
require(MBESS)
library(ITEMAN)
library(lavaan)
```

##Import and merge data
```{r}
healthyU <- read.csv("HealthyUT1itemlevel.csv")
head(healthyU)
#colnames(healthyU)

hU <- healthyU[c(1,99:105,134:154,209:242,292:296)]
head(hU)
#colnames(hU)

#Import data from HU time 3 to get drug
huT3 <- read.csv("healthyUT3_clean.csv")
huT3drug <- huT3[c(1,202:205)]

#Import data from HU time 2 to get alcohol
huT2 <- read.csv("healthyUT2_clean.csv")
huT2alc <- huT2[c(1,189:192)]

hU <- full_join(hU,huT2alc)
hU <- full_join(hU, huT3drug)

#ADD ALC/DRUGS FOR PRETESTING
ptfall2018 <- read.csv("PSYPretestingFall2018September4reducedover1percent.csv", header = T, na.strings = "")
#colnames(ptfall2018)
preT <- ptfall2018[c(1,86:92,181:192,94:102,104:123,158,160,162,163,165,167,169,170,172,173,174,175,177,179,201:205,193:200)]
head(preT)

#colnames(preT)
#colnames(hU)
colnames(hU)[c(64:68)] <- c("ASRM1","ASRM2","ASRM3","ASRM4","ASRM5")

colnames(preT) <- colnames(hU)

hU$ID <- as.factor(hU$ID)
preT$ID <- as.factor(preT$ID)

hUPreT <- full_join(hU,preT)
str(hUPreT)
hUPreT$ID <- as.factor(hUPreT$ID)
```

##Recode variables
```{r}
#SIR
hUPreT$SIR2r <- 4 - hUPreT$SIR2
hUPreT$SIR4r <- 4 - hUPreT$SIR4
#summary(hUPreT)
#SIAS
hUPreT$SIAS5r <- 4 - hUPreT$SIAS5
hUPreT$SIAS9r <- 4 - hUPreT$SIAS9
hUPreT$SIAS11r <- 4 - hUPreT$SIAS11

hUPreT$SIR2 <- hUPreT$SIR2r
hUPreT$SIR4 <- hUPreT$SIR4r
hUPreT$SIAS5 <- hUPreT$SIAS5r
hUPreT$SIAS9 <- hUPreT$SIAS9r
hUPreT$SIAS11 <- hUPreT$SIAS11r
```

##Select only items to be put into models
```{r}
colnames(hUPreT)
hP <- hUPreT[c(2:76)]
#summary(hP)
```

##Rescale items to be on 0 to 4 scale
```{r}
colnames(hP)
library(scales)
#colnames(hP)[c(1:7,20:28)]

cols <- c(1:7,20:28)

for (i in cols) {
  hP[i] <- rescale(hP[,i], to = c(0,4))
}

#summary(hP)
```

##Run modified parallel analysis
```{r, warning=F, echo=F}
R.pearson.hP <- hetcor(hP, use="pairwise.complete.obs")$correlations
#R.pearson.hP

eigens.pearson.hP <- eigen(R.pearson.hP)$values
eigens.pearson.hP

options(scipen=999)

plot(eigens.pearson.hP,pch=16,xlab="",ylab="Eigenvalue", main = "Eigenvalues for Item Level Data")
```

```{r, echo=F, warning=F, include=F}
# Eigenvalue distribution from random data to determine if meaningful
library(MASS)
twelve.eigen <- c()
thirteen.eigen <- c()
fourteen.eigen <- c()
fifteen.eigen <- c()
for(i in 1:10000) {
  N = nrow(hP)
  k = ncol(hP)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  twelve.eigen[i]    = eigen(corr)$values[12]
  thirteen.eigen[i]    = eigen(corr)$values[13]
  fourteen.eigen[i]    = eigen(corr)$values[14]
  fifteen.eigen[i]    = eigen(corr)$values[15]
}

hist(twelve.eigen,main="Sampling Distribution of \n12th Eigenvalue for Item Level Data",xlab="")
hist(thirteen.eigen,main="Sampling Distribution of \n13th Eigenvalue for Item Level Data",xlab="")
hist(fourteen.eigen,main="Sampling Distribution of \n14th Eigenvalue for Item Level Data",xlab="")
hist(fifteen.eigen,main="Sampling Distribution of \n15th Eigenvalue for Item Level Data",xlab="")

# Find the 95th percentile of the sampling distribution for the 2nd eigenvalue

quant95_12 = quantile(twelve.eigen,.95)

quant95_12
#abline(v=quant95_2,lty=2)

quant95_13 = quantile(thirteen.eigen,.95)

quant95_13

quant95_14 = quantile(fourteen.eigen,.95)

quant95_14

quant95_15 = quantile(fifteen.eigen,.95)

quant95_15

eight.eigen <- c()
nine.eigen <- c()
ten.eigen <- c()
eleven.eigen <- c()

for(i in 1:10000) {
  N = nrow(hP)
  k = ncol(hP)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  eight.eigen[i]    = eigen(corr)$values[8]
  nine.eigen[i]    = eigen(corr)$values[9]
  ten.eigen[i]    = eigen(corr)$values[10]
  eleven.eigen[i]    = eigen(corr)$values[11]
}


quant95_8 = quantile(eight.eigen,.95)
quant95_8

quant95_9 = quantile(nine.eigen,.95)
quant95_9

quant95_10 = quantile(ten.eigen,.95)
quant95_10

quant95_11 = quantile(eleven.eigen,.95)
quant95_11

four.eigen <- c()
five.eigen <- c()
six.eigen <- c()
seven.eigen <- c()
for(i in 1:10000) {
  N = nrow(hP)
  k = ncol(hP)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  four.eigen[i]    = eigen(corr)$values[4]
  five.eigen[i]    = eigen(corr)$values[5]
  six.eigen[i]    = eigen(corr)$values[6]
  seven.eigen[i]    = eigen(corr)$values[7]
}

quant95_4 = quantile(four.eigen,.95)
quant95_4

quant95_5 = quantile(five.eigen,.95)
quant95_5

quant95_6 = quantile(six.eigen,.95)
quant95_6

quant95_7 = quantile(seven.eigen,.95)
quant95_7
```

Compare to the empirical data eigenvalue
```{r, include=F}
eigens <- eigen(R.pearson.hP)$values
eigens

plot(eigens,pch=15,ylab="Eigenvalue",xlab="",ylim=c(0,18),cex=.5, main="Comparison of Simulated Eigenvalues \n(95th Percentile) and Empirical Eigenvalues")

points(15,quant95_15,pch=5,cex=.5)
points(14,quant95_14,pch=5,cex=.5)
points(13,quant95_13,pch=5,cex=.5)
points(12,quant95_12,pch=5,cex=.5)
points(11,quant95_11,pch=5,cex=.5)
points(10,quant95_10,pch=5,cex=.5)
points(9,quant95_9,pch=5,cex=.5)
points(8,quant95_8,pch=5,cex=.5)
points(7,quant95_7,pch=5,cex=.5)
points(6,quant95_6,pch=5,cex=.5)
points(5,quant95_5,pch=5,cex=.5)
points(4,quant95_4,pch=5,cex=.5)
```

#There appear to be 7 factors
```{r}
omega.pearson.hP7 <- 
  omega(R.pearson.hP,
        fm="ml",
        nfac=7,
        nobs=704)
print(omega.pearson.hP7)
```


```{r}
efa.pearson.hP7 <- fa(r = R.pearson.hP,nfac=7,rotate = "varimax", n.obs=704)
print(efa.pearson.hP7$loadings, cutoff = .3)
efa.pearson.hP7
```

##remove items that crossload and reverse-coded items (all load onto 1 factor)
###SIR2, SIR4, SIAS5, SIAS9, SIAS11, OCIR6, OCIR12, OCIR18
```{r, echo=F}
colnames(hP)
hPr <- hP[-c(11,15,19,33,37,39,49,50)]
colnames(hPr)
```


```{r}
R.pearson.hPr <- hetcor(hPr, use="pairwise.complete.obs")$correlations
#R.pearson.hPr

eigens.pearson.hPr <- eigen(R.pearson.hPr)$values
eigens.pearson.hPr

options(scipen=999)

plot(eigens.pearson.hPr,pch=16,xlab="",ylab="Eigenvalue", main = "Eigenvalues for Item Level Data")
```

```{r, echo=F, warning=F}
# Eigenvalue distribution from random data to determine if meaningful
library(MASS)
twelve.eigen <- c()
thirteen.eigen <- c()
fourteen.eigen <- c()
fifteen.eigen <- c()
for(i in 1:10000) {
  N = nrow(hPr)
  k = ncol(hPr)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  twelve.eigen[i]    = eigen(corr)$values[12]
  thirteen.eigen[i]    = eigen(corr)$values[13]
  fourteen.eigen[i]    = eigen(corr)$values[14]
  fifteen.eigen[i]    = eigen(corr)$values[15]
}

hist(twelve.eigen,main="Sampling Distribution of \n12th Eigenvalue for Item Level Data",xlab="")
hist(thirteen.eigen,main="Sampling Distribution of \n13th Eigenvalue for Item Level Data",xlab="")
hist(fourteen.eigen,main="Sampling Distribution of \n14th Eigenvalue for Item Level Data",xlab="")
hist(fifteen.eigen,main="Sampling Distribution of \n15th Eigenvalue for Item Level Data",xlab="")

# Find the 95th percentile of the sampling distribution for the 2nd eigenvalue

quant95_12 = quantile(twelve.eigen,.95)

quant95_12
#abline(v=quant95_2,lty=2)

quant95_13 = quantile(thirteen.eigen,.95)

quant95_13

quant95_14 = quantile(fourteen.eigen,.95)

quant95_14

quant95_15 = quantile(fifteen.eigen,.95)

quant95_15

eight.eigen <- c()
nine.eigen <- c()
ten.eigen <- c()
eleven.eigen <- c()

for(i in 1:10000) {
  N = nrow(hPr)
  k = ncol(hPr)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  eight.eigen[i]    = eigen(corr)$values[8]
  nine.eigen[i]    = eigen(corr)$values[9]
  ten.eigen[i]    = eigen(corr)$values[10]
  eleven.eigen[i]    = eigen(corr)$values[11]
}


quant95_8 = quantile(eight.eigen,.95)
quant95_8

quant95_9 = quantile(nine.eigen,.95)
quant95_9

quant95_10 = quantile(ten.eigen,.95)
quant95_10

quant95_11 = quantile(eleven.eigen,.95)
quant95_11

four.eigen <- c()
five.eigen <- c()
six.eigen <- c()
seven.eigen <- c()
for(i in 1:10000) {
  N = nrow(hPr)
  k = ncol(hPr)
  sim.data = mvrnorm(N,rep(0,k),diag(k))
  corr   = cor(sim.data)
  four.eigen[i]    = eigen(corr)$values[4]
  five.eigen[i]    = eigen(corr)$values[5]
  six.eigen[i]    = eigen(corr)$values[6]
  seven.eigen[i]    = eigen(corr)$values[7]
}

quant95_4 = quantile(four.eigen,.95)
quant95_4

quant95_5 = quantile(five.eigen,.95)
quant95_5

quant95_6 = quantile(six.eigen,.95)
quant95_6

quant95_7 = quantile(seven.eigen,.95)
quant95_7
```

Compare to the empirical data eigenvalue
```{r}
eigens <- eigen(R.pearson.hPr)$values
eigens

plot(eigens,pch=15,ylab="Eigenvalue",xlab="",ylim=c(0,18),cex=.5, main="Comparison of Simulated Eigenvalues \n(95th Percentile) and Empirical Eigenvalues")

points(15,quant95_15,pch=5,cex=.5)
points(14,quant95_14,pch=5,cex=.5)
points(13,quant95_13,pch=5,cex=.5)
points(12,quant95_12,pch=5,cex=.5)
points(11,quant95_11,pch=5,cex=.5)
points(10,quant95_10,pch=5,cex=.5)
points(9,quant95_9,pch=5,cex=.5)
points(8,quant95_8,pch=5,cex=.5)
points(7,quant95_7,pch=5,cex=.5)
points(6,quant95_6,pch=5,cex=.5)
points(5,quant95_5,pch=5,cex=.5)
points(4,quant95_4,pch=5,cex=.5)
```

#There appear to be 6 factors
```{r}
omega.pearson.hPr6 <- 
  omega(R.pearson.hPr,
        fm="ml",
        nfac=6,
        nobs=704)
print(omega.pearson.hPr6)
```

```{r}
bifactor.pearson.hPr6 <- fa(R.pearson.hPr,nfac=6,rotate = "bifactor", scores="regression")
print(bifactor.pearson.hPr6$loadings, cutoff = .3)
plot(bifactor.pearson.hPr6)
```


```{r}
efa.pearson.hPr6 <- fa(hPr,nfac=6,rotate = "varimax", scores="regression")
print(efa.pearson.hPr6$loadings, cutoff = .3)
efa.pearson.hPr6
```

```{r}
str(hUPreT)
colnames(hUPreT)
hPr2 <- hUPreT[-c(12,16,20,34,38,40,50,51,77:81)]

hPrcomp <- hPr2[complete.cases(hPr2), ]
str(hPrcomp)
f <- factanal(hPrcomp[c(2:68)], factors=7, rotation="varimax", scores="regression")
f
print(f$loadings, cutoff = .3)

g <- fa(hPrcomp[c(2:68)],nfac=6, rotate="varimax",scores="regression")
g
print(g$loadings, cutoff = .3)

gloadings <- print(g$loadings, cutoff = .3)
str(gloadings)

gloadingssum <- as.matrix(gloadings)
#write.csv(gloadingssum, "loadings.csv", row.names=F)

hUpTfactordata <- cbind(hPrcomp, f$scores)
hUpTfactordata2 <- cbind(hPrcomp, g$scores)
colnames(hUpTfactordata)
colnames(hUpTfactordata2)

colnames(hUpTfactordata2)[c(69:74)] <- c("SocAnx","DepAnx","Hoard","OCD","Substance","Mania")

omega.pearson.hPr6 <- 
  omega(hPrcomp[c(2:68)],
        fm="ml",
        nfac=6,
        nobs=704, scores="regression", rotate="Promax")
omega.pearson.hPr6


hUpTfactordata3 <- cbind(hUpTfactordata2, omega.pearson.hPr6$scores)
colnames(hUpTfactordata3)
#general factor, SIAS, SIR, DEP, OCIR,GAD,ALC,
colnames(hUpTfactordata3)[c(75:81)] <- c("GeneralFactor_bifac","SocAnx_bifac","Hoard_bifac","Depression_bifac","OCD_bifac","Anxiety_bifac","Substance_bifac")


#write.csv(hUpTfactordata3, "indfactorscores_withbifactor_rev.csv",row.names=F)
#Factors for g 1=SIAS, 3=GAD+DEP,2=SIR,4=OCIR,5=DRUG/ALC,6=MANIA
```

