P50 CESUD Results and Figures
Wesley J.B. Vaught & Makiah P. Torress
September 04, 2025
Analysis prepared by Wesley J.B. Vaught & Makiah P. Torres | secondary data analysis of AI participants’ EEG data from T1000 and NCAIR phase 1.
1 Preparation
1.1 Packages
library(psych) # for descriptive statistics and internacy consistency calcs
library(gmodels) # for frequency calculation and tables for categorical variables
library(dplyr) # for creating tibbles and matrices for correlation matrices
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(corrr) # for calculating (Spearman's) correlations
library(corrplot) # for visualizing correlation plotscorrplot 0.95 loadedlibrary(interactions) # for creating a Johnson-Neyman plot
library(ggplot2) # for data visualization with scatterplots and histograms
Attaching package: 'ggplot2'The following objects are masked from 'package:psych':
%+%, alphalibrary(ggpmisc) # an extension of ggplot2Loading required package: ggppRegistered S3 methods overwritten by 'ggpp':
method from
heightDetails.titleGrob ggplot2
widthDetails.titleGrob ggplot2
Attaching package: 'ggpp'The following object is masked from 'package:ggplot2':
annotatelibrary(ggpubr) # for data visualization simplified
Attaching package: 'ggpubr'The following objects are masked from 'package:ggpp':
as_npc, as_npcx, as_npcylibrary(qqplotr) # for QQ plots
Attaching package: 'qqplotr'The following objects are masked from 'package:ggplot2':
stat_qq_line, StatQqLinelibrary(car) # for calculating VIF (variance inflation factor)Loading required package: carData
Attaching package: 'car'The following object is masked from 'package:dplyr':
recodeThe following object is masked from 'package:psych':
logitlibrary(boot.pval) # for bootstrapping the multiple linear regression and moderation
library(stringr) # for replacing the written scoring for the HLS to numbers1.2 Setting Working Directory & Reading in Dataframes
setwd("~/01. EEG CORE/05. Papers/Culture & Cognition/finalAnalysis")dfSST<-read.csv("df_FinalK99Dataset(n59)_051723.csv",header=TRUE)
dfMID<-read.csv("dfMID.csv",header=TRUE)
dfLPP<-read.csv("df_aim2_lpp.csv",header=TRUE)
dfSSTaies<-read.csv("dfSSTaies.csv",header=TRUE)
dfSSTnass<-read.csv("dfSSTnass.csv",header=TRUE)
dfSSTnaas<-read.csv("dfSSTnaas.csv",header=TRUE)
dfSSThls<-read.csv("dfSSThls.csv",header=TRUE)
dfMIDaies<-read.csv("dfMIDaies.csv",header=TRUE)
dfMIDnass<-read.csv("dfMIDnass.csv",header=TRUE)
dfMIDnaas<-read.csv("dfMIDnaas.csv",header=TRUE)
dfMIDhls<-read.csv("dfMIDhls.csv",header=TRUE)
dfLPPhls<-read.csv("dfLPPhls.csv",header=TRUE)
dfLPPaies<-read.csv("dfLPPaies.csv",header=TRUE)
dfLPPnass<-read.csv("dfLPPnass.csv",header=TRUE)
dfLPPnaas<-read.csv("dfLPPnaas.csv",header=TRUE)Every participant in this sample came from NCAIR phase one; however, they did their scans at different times. For example, participants who did the SST have EEG data from the K99 award period and their self-report comes from the K99 award period too; participants who did the MID have EEG data from the T1000 but their self-report comes the K99 award period; and participants who did the CPV have EEG data and self-report data from the K99. Therefore, we cannot reasonably compare ERP correlates of cognitive processes because they were administered at different times.
2 Descriptive statistics
2.1 Descriptive statistics for variables in SST
tibble4SST<-tibble(
#Subject=dfSST$Subject,
sex=dfSST$Gender,
age=dfSST$Age,
goIncorrERN_FCz=dfSST$ERNFCz_GoIncorr, # We have 52 ERN and Pe entries
goCorrERN_FCz=dfSST$ERNFCz_GoCorr,
allIncorrN2_FCz=dfSST$N2FCz_AllIncorrn59,
allCorrN2_FCz=dfSST$N2FCz_AllCorrn59,
allIncorrP3_Pz=dfSST$P3Pz_AllIncorrn59,
allCorrP3_Pz=dfSST$P3Pz_AllCorrn59,
goIncorrPe_Pz=dfSST$PePz_GoIncorr,
goCorrPe_Pz=dfSST$PePz_GoCorr,
#CDDRposRein=dfSST$CDDR_PosReinforcement,
#CDDRnegRein=dfSST$CDDR_NegReinforcement,
PROMISanx=dfSST$PROMIS_AnxietyTscore,
PROMISalcUse=dfSST$PROMIS_AlcoUseTscore,
PROMISdepress=dfSST$PROMIS_DepressTscore,
#PROMISappealUse30=dfSST$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfSST$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfSST$PHQ9_Suicide,
#Age=dfSST$Age,
#Sex=dfSST$Gender,
#Income=dfSST$Income,
#Education=dfSST$Education_4levelCategory,
#MRT=dfSST$ss_mrt_calibration,
#goErrorEasyRT=dfSST$ss_meanrt_erroreasy,
#goErrorHardRT=dfSST$ss_meanrt_errorhard,
AIES=dfSST$AIES_scale,
NAAS=dfSST$NAAS_scale,
NASS=dfSST$NASS_scale,
HLS=dfSST$HLS_scale
)
cleanedTibble4SST<-na.omit(tibble4SST) # retains 52 participants, will exclude sex when correlation matrixDescriptive statistics of Age in SST sample
describeAgeSST<-describe(cleanedTibble4SST$age)
describeAgeSST vars n mean sd median trimmed mad min max range skew kurtosis
X1 1 51 35.36 10.43 33.02 35.08 11.17 18.37 54.88 36.51 0.2 -1.13
se
X1 1.46Frequencies of Sex in the SST sample
freqSexSST<-CrossTable(cleanedTibble4SST$sex, format = 'SPSS')
Cell Contents
|-------------------------|
| Count |
| Row Percent |
|-------------------------|
Total Observations in Table: 51
| Female | Male |
|-----------|-----------|
| 41 | 10 |
| 80.392% | 19.608% |
|-----------|-----------|
freqSexSSTNULLDescriptive statistics of AIES in the SST sample
describeAIESsst<-describe(cleanedTibble4SST$AIES)
describeAIESsst vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 37.43 14.02 35 36 13.34 18 75 57 0.81 -0.01 1.96Descriptive statistics of NASS in the SST sample
describeNASSsst<-describe(cleanedTibble4SST$NASS)
describeNASSsst vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 34.96 8.37 37 35.59 5.93 11 50 39 -0.75 0.1 1.17Descriptive statistics of NAAS in the SST sample
describeNAASsst<-describe(cleanedTibble4SST$NAAS)
describeNAASsst vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 3.86 0.34 3.9 3.86 0.37 3.05 4.45 1.4 -0.12 -0.77 0.05Descriptive statistics of HLS in the SST sample
describeHLSsst<-describe(cleanedTibble4SST$HLS)
describeHLSsst vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 42.1 4.68 41 42.32 5.93 30 50 20 -0.33 -0.4 0.66Descriptive statistics of PROMIS Depression in the SST sample
describeDepSST<-describe(cleanedTibble4SST$PROMISdepress)
describeDepSST vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 55.33 7.61 56.3 55.85 6.38 34.2 69.5 35.3 -0.6 0.11 1.07Descriptive statistics of PROMIS Anxiety in the SST sample
describeAnxSST<-describe(cleanedTibble4SST$PROMISanx)
describeAnxSST vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 57.45 9.09 58 58.23 7.86 32.9 75.3 42.4 -0.75 0.57 1.27Descriptive statistics of PROMIS Alcohol Use in the SST sample
describeAlcUseSST<-describe(cleanedTibble4SST$PROMISalcUse)
describeAlcUseSST vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 51 49.17 5.07 50 49.26 0 37.5 62.4 24.9 -0.1 1 0.712.2 Descriptive statistics for variables in MID
tibble4MID<-tibble(
#Subject=dfMID$Subject,
Age=dfMID$Age,
#Sex=dfMID$Gender,
"P300 Pz Loss"=dfMID$P3pzMIDcueLoss,
#cueNogainPzP3=dfMID$P3pzMIDcueNogain,
"P300 Pz Gain"=dfMID$P3pzMIDcueGain,
"SPN Fz Success"=dfMID$SPNfzMIDfeedbackSuccess,
#failFzSPN=dfMID$SPNfzMIDfeedbackFail,
'RewP FCz Gain'=dfMID$RewPfczMIDfeedbackGain,
#CDDRposRein=dfMID$CDDR_PosReinforcement,
#CDDRnegRein=dfMID$CDDR_NegReinforcement,
"PROMIS Anxiety"=dfMID$PROMIS_AnxietyTscore,
"PROMIS Alcohol Use"=dfMID$PROMIS_AlcoUseTscore,
"PROMIS Depression"=dfMID$PROMIS_DepressTscore,
#PROMISappealUse30=dfMID$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfMID$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfMID$PHQ9_Suicide,
#Age=dfMID$Age,
#Sex=dfMID$Gender,
#Income=dfMID$Income,
#Education=dfMID$Education_4levelCategory,
#MRT=dfMID$ss_mrt_calibration,
#goErrorEasyRT=dfMID$ss_meanrt_erroreasy,
#goErrorHardRT=dfMID$ss_meanrt_errorhard,
"AIES"=dfMID$AIES_scale,
"NAAS"=dfMID$NAAS_scale,
"NASS"=dfMID$NASS_scale,
"HLS"=dfMID$HLS_scale
)
cleanedData4Hyp1b<-na.omit(tibble4MID) # retains 44participants
tibble4MIDcalc<-tibble(
Age=dfMID$Age,
Sex=dfMID$Gender,
P3PzLoss=tibble4MID$`P300 Pz Loss`,
P3PzGain=tibble4MID$`P300 Pz Gain`,
RewPFCzGain=tibble4MID$`RewP FCz Gain`,
successSPN=tibble4MID$`SPN Fz Success`,
promisDep=tibble4MID$`PROMIS Depression`,
promisAnx=tibble4MID$`PROMIS Anxiety`,
promisAlcUse=tibble4MID$`PROMIS Alcohol Use`,
AIES=tibble4MID$AIES,
NAAS=tibble4MID$NAAS,
NASS=tibble4MID$NASS,
HLS=tibble4MID$HLS
)
cleanedTibble4MIDcalc<-na.omit(tibble4MIDcalc)Descriptive statistics of Age in the MID sample
describeAgeMID<-describe(cleanedTibble4MIDcalc$Age)
describeAgeMID vars n mean sd median trimmed mad min max range skew kurtosis
X1 1 44 34.32 10.19 32.83 33.88 12.15 18.37 54.88 36.51 0.24 -1.02
se
X1 1.54Frequencies of Sex in the MID sample
freqSexMID<-CrossTable(cleanedTibble4MIDcalc$Sex, format = 'SPSS')
Cell Contents
|-------------------------|
| Count |
| Row Percent |
|-------------------------|
Total Observations in Table: 44
| Female | Male |
|-----------|-----------|
| 34 | 10 |
| 77.273% | 22.727% |
|-----------|-----------|
freqSexMIDNULLDescriptive statistics of AIES in the MID sample
describeAIESmid<-describe(cleanedTibble4MIDcalc$AIES)
describeAIESmid vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 34.32 11.71 32 33.08 11.86 19 66 47 0.82 0.08 1.76Descriptive statistics of NASS in the MID sample
describeNASSmid<-describe(cleanedTibble4MIDcalc$NASS)
describeNASSmid vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 33.36 9.31 36 34.33 6.67 9 49 40 -0.95 0.2 1.4Descriptive statistics of NAAS in the MID sample
describeNAASmid<-describe(cleanedTibble4MIDcalc$NAAS)
describeNAASmid vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 3.95 0.33 4 3.95 0.37 3.3 4.5 1.2 -0.18 -1.04 0.05Descriptive statistics of HLS in the MID sample
describeHLSmid<-describe(cleanedTibble4MIDcalc$HLS)
describeHLSmid vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 42.07 4.79 41.5 42.36 5.19 30 50 20 -0.44 -0.45 0.72Descriptive statistics of PROMIS Depression in the MID sample
describeDepMID<-describe(cleanedTibble4MIDcalc$promisDep)
describeDepMID vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 56.31 7.79 57.4 56.74 7.19 39 69.5 30.5 -0.46 -0.51 1.17Descriptive statistics of PROMIS Anxiety in the MID sample
describeAnxMID<-describe(cleanedTibble4MIDcalc$promisAnx)
describeAnxMID vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 57.57 9.41 57.85 58.05 8.08 32.9 75.3 42.4 -0.42 -0.22 1.42Descriptive statistics of PROMIS Alcohol Use in the MID sample
describeAlcUseMID<-describe(cleanedTibble4MIDcalc$promisAlcUse)
describeAlcUseMID vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 44 49.5 4.69 50 49.52 0 38.2 61.2 23 -0.09 1.04 0.712.3 Descriptive statistics for variables in CPV
Frequencies of Sex in the CPV sample
freqSexCPV<-CrossTable(dfLPP$Sex, format = 'SPSS')
Cell Contents
|-------------------------|
| Count |
| Row Percent |
|-------------------------|
Total Observations in Table: 40
| 1 | 2 |
|-----------|-----------|
| 32 | 8 |
| 80.000% | 20.000% |
|-----------|-----------|
freqSexCPVNULLDescriptive statistics of Age
describeAgeCPV<-describe(dfLPP$Age)
describeAgeCPV vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 35.6 10.12 36 35.38 12.6 21 53 32 0.13 -1.28 1.6Descriptive statistics of AIES
describeAIEScpv<-describe(dfLPP$AIES_scale)
describeAIEScpv vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 41.02 20.12 36.5 37.69 14.08 20 108 88 1.73 3.04 3.18Descriptive statistics of NASS
describeNASScpv<-describe(dfLPP$NASS_scale)
describeNASScpv vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 33.25 10.87 33.5 33.12 12.6 14 54 40 0.05 -1.11 1.72Descriptive statistics of NAAS
describeNAAScpv<-describe(dfLPP$NAAS_scale)
describeNAAScpv vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 3.85 0.46 3.95 3.88 0.41 2.4 4.5 2.1 -0.85 0.63 0.07Descriptive statistics of HLS
describeHLScpv<-describe(dfLPP$HLS_scale)
describeHLScpv vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 44.52 4.69 44 44.31 3.71 30 55 25 -0.01 1.2 0.74Descriptive statistics of PROMIS Anxiety
describeAnxCPV<-describe(dfLPP$PROMIS_AnxietyTscore)
describeAnxCPV vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 53.72 8.55 53.65 53.73 7.12 36.6 71.3 34.7 -0.04 -0.5 1.35Descriptive statistics of PROMIS Depression
describeDepCPV<-describe(dfLPP$PROMIS_DepressTscore)
describeDepCPV vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 51.61 9.33 50.9 50.89 7.12 34.2 81.6 47.4 0.81 1.33 1.47Descriptive statistics of Alcohol Use
describeAlcUseCPV<-describe(dfLPP$PROMIS_AlcoUseTscore)
describeAlcUseCPV vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 40 47.71 5.43 50 48.27 3.26 33.4 58.3 24.9 -0.88 0.32 0.863 Tests of Normality
3.1 Shapiro-Wilk tests for variables in SST
shapiro.test(dfSST$AIES_scale)
Shapiro-Wilk normality test
data: dfSST$AIES_scale
W = 0.92412, p-value = 0.001255shapiro.test(dfSST$NAAS_scale)
Shapiro-Wilk normality test
data: dfSST$NAAS_scale
W = 0.97612, p-value = 0.297shapiro.test(dfSST$NASS_scale)
Shapiro-Wilk normality test
data: dfSST$NASS_scale
W = 0.93388, p-value = 0.003208shapiro.test(dfSST$HLS_scale)
Shapiro-Wilk normality test
data: dfSST$HLS_scale
W = 0.96593, p-value = 0.09718shapiro.test(dfSST$PROMIS_AlcoUseTscore)
Shapiro-Wilk normality test
data: dfSST$PROMIS_AlcoUseTscore
W = 0.87187, p-value = 1.66e-05shapiro.test(dfSST$PROMIS_AnxietyTscore)
Shapiro-Wilk normality test
data: dfSST$PROMIS_AnxietyTscore
W = 0.95281, p-value = 0.02277shapiro.test(dfSST$PROMIS_DepressTscore)
Shapiro-Wilk normality test
data: dfSST$PROMIS_DepressTscore
W = 0.96793, p-value = 0.12143.2 Shapiro-Wilk tests for variables in MID
shapiro.test(dfMID$AIES_scale)
Shapiro-Wilk normality test
data: dfMID$AIES_scale
W = 0.92412, p-value = 0.001255shapiro.test(dfMID$NAAS_scale)
Shapiro-Wilk normality test
data: dfMID$NAAS_scale
W = 0.97612, p-value = 0.297shapiro.test(dfMID$NASS_scale)
Shapiro-Wilk normality test
data: dfMID$NASS_scale
W = 0.93388, p-value = 0.003208shapiro.test(dfMID$HLS_scale)
Shapiro-Wilk normality test
data: dfMID$HLS_scale
W = 0.96593, p-value = 0.09718shapiro.test(dfMID$PROMIS_AlcoUseTscore)
Shapiro-Wilk normality test
data: dfMID$PROMIS_AlcoUseTscore
W = 0.87187, p-value = 1.66e-05shapiro.test(dfMID$PROMIS_AnxietyTscore)
Shapiro-Wilk normality test
data: dfMID$PROMIS_AnxietyTscore
W = 0.95281, p-value = 0.02277shapiro.test(dfMID$PROMIS_DepressTscore)
Shapiro-Wilk normality test
data: dfMID$PROMIS_DepressTscore
W = 0.96793, p-value = 0.12143.3 Shapiro-Wilk tests for variables in CPV
shapiro.test(dfLPP$AIES_scale)
Shapiro-Wilk normality test
data: dfLPP$AIES_scale
W = 0.81075, p-value = 1.144e-05shapiro.test(dfLPP$NAAS_scale)
Shapiro-Wilk normality test
data: dfLPP$NAAS_scale
W = 0.93969, p-value = 0.03375shapiro.test(dfLPP$NASS_scale)
Shapiro-Wilk normality test
data: dfLPP$NASS_scale
W = 0.96939, p-value = 0.3441shapiro.test(dfLPP$HLS_scale)
Shapiro-Wilk normality test
data: dfLPP$HLS_scale
W = 0.93393, p-value = 0.02168shapiro.test(dfLPP$PROMIS_AlcoUseTscore)
Shapiro-Wilk normality test
data: dfLPP$PROMIS_AlcoUseTscore
W = 0.88176, p-value = 0.0005875shapiro.test(dfLPP$PROMIS_AnxietyTscore)
Shapiro-Wilk normality test
data: dfLPP$PROMIS_AnxietyTscore
W = 0.97951, p-value = 0.6713shapiro.test(dfLPP$PROMIS_DepressTscore)
Shapiro-Wilk normality test
data: dfLPP$PROMIS_DepressTscore
W = 0.94602, p-value = 0.055374 Internal consistency
4.1 Cronbach’s alpha and MacDonald’s omega for SST cultural self-reports
omega(dfSSTaies)Loading required namespace: GPArotation
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.89
G.6: 0.94
Omega Hierarchical: 0.56
Omega H asymptotic: 0.6
Omega Total 0.93
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1 0.31 0.40 0.27 0.27 0.73 0.36 1.99
aies_2 0.60 0.67 0.82 0.82 0.18 0.43 2.03
aies_3 0.59 0.55 0.67 0.67 0.33 0.53 2.07
aies_4 0.42 0.54 0.49 0.49 0.51 0.36 2.01
aies_5 0.46 0.22 0.27 0.37 0.37 0.63 0.58 2.52
aies_6 0.37 0.39 0.31 0.31 0.69 0.44 2.22
aies_7 0.57 0.60 0.69 0.69 0.31 0.47 2.01
aies_8 0.52 0.31 0.21 0.45 0.45 0.55 0.60 2.34
aies_9 0.43 0.45 0.41 0.41 0.59 0.46 2.18
aies_10 0.44 0.67 0.65 0.65 0.35 0.30 1.78
aies_11 0.55 0.37 0.35 0.56 0.56 0.44 0.53 2.54
aies_12 0.37 0.83 0.83 0.83 0.17 0.16 1.40
aies_13 0.47 0.46 0.46 0.46 0.54 0.48 2.30
aies_14 0.60 0.65 0.79 0.79 0.21 0.45 2.02
aies_15 0.28 0.44 0.27 0.27 0.73 0.28 1.73
aies_16 0.49 0.56 0.20 0.60 0.60 0.40 0.40 2.25
aies_17 0.24 0.68 0.54 0.54 0.46 0.11 1.38
With Sums of squares of:
g F1* F2* F3* h2
3.7 2.3 1.7 1.5 5.5
general/max 0.67 max/min = 3.68
mean percent general = 0.41 with sd = 0.14 and cv of 0.33
Explained Common Variance of the general factor = 0.4
The degrees of freedom are 88 and the fit is 3.23
The number of observations was 52 with Chi Square = 137.38 with prob < 6e-04
The root mean square of the residuals is 0.07
The df corrected root mean square of the residuals is 0.09
RMSEA index = 0.102 and the 90 % confidence intervals are 0.069 0.138
BIC = -210.33
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 119 and the fit is 6.62
The number of observations was 52 with Chi Square = 289.97 with prob < 2.7e-16
The root mean square of the residuals is 0.19
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.165 and the 90 % confidence intervals are 0.143 0.192
BIC = -180.23
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.76 0.77 0.82 0.88
Multiple R square of scores with factors 0.57 0.59 0.68 0.78
Minimum correlation of factor score estimates 0.14 0.19 0.35 0.56
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.93 0.90 0.81 0.79
Omega general for total scores and subscales 0.56 0.47 0.29 0.27
Omega group for total scores and subscales 0.27 0.43 0.52 0.52omega(dfSSThls)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.93
G.6: 0.95
Omega Hierarchical: 0.77
Omega H asymptotic: 0.81
Omega Total 0.95
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
hls_1 0.57 0.57 0.67 0.67 0.33 0.49 2.08
hls_2 0.60 0.55 0.67 0.67 0.33 0.54 2.00
hls_3 0.60 0.60 0.73 0.73 0.27 0.49 2.03
hls_4 0.67 0.51 0.72 0.72 0.28 0.61 1.97
hls_5 0.71 0.28 0.23 0.63 0.63 0.37 0.79 1.55
hls_6 0.60 0.36 0.50 0.50 0.50 0.72 1.70
hls_7 0.66 0.57 0.76 0.76 0.24 0.57 1.97
hls_8 0.67 0.40 0.62 0.62 0.38 0.72 1.71
hls_9 0.70 0.45 0.70 0.70 0.30 0.71 1.70
hls_10 0.79 0.56 0.94 0.94 0.06 0.66 1.81
hls_11 0.67 0.40 0.62 0.62 0.38 0.73 1.66
hls_12 0.68 0.29 0.58 0.58 0.42 0.79 1.56
With Sums of squares of:
g F1* F2* F3* h2
5.26 1.31 0.81 0.76 5.65
general/max 0.93 max/min = 7.43
mean percent general = 0.65 with sd = 0.11 and cv of 0.17
Explained Common Variance of the general factor = 0.65
The degrees of freedom are 33 and the fit is 1.28
The number of observations was 52 with Chi Square = 56.36 with prob < 0.0069
The root mean square of the residuals is 0.05
The df corrected root mean square of the residuals is 0.07
RMSEA index = 0.115 and the 90 % confidence intervals are 0.062 0.169
BIC = -74.03
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 3.29
The number of observations was 52 with Chi Square = 149.54 with prob < 6.6e-11
The root mean square of the residuals is 0.14
The df corrected root mean square of the residuals is 0.15
RMSEA index = 0.183 and the 90 % confidence intervals are 0.151 0.222
BIC = -63.83
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.89 0.81 0.74 0.73
Multiple R square of scores with factors 0.79 0.66 0.55 0.54
Minimum correlation of factor score estimates 0.59 0.32 0.09 0.07
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.95 0.90 0.86 0.89
Omega general for total scores and subscales 0.77 0.49 0.65 0.63
Omega group for total scores and subscales 0.13 0.41 0.21 0.27omega(dfSSTnass)Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a
different factor score estimation method.Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
ultra-Heywood case was detected. Examine the results carefully
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.83
G.6: 0.88
Omega Hierarchical: 0.53
Omega H asymptotic: 0.6
Omega Total 0.89
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
nass_1 0.63 0.48 0.66 0.66 0.34 0.59 2.10
nass_2 0.72 0.62 0.92 0.92 0.08 0.57 2.00
nass_3 0.29 -0.21 0.40 0.29 0.29 0.71 0.29 2.41
nass_4 0.35 0.56 0.44 0.44 0.56 0.28 1.74
nass_5 0.47 0.61 0.61 0.61 0.39 0.37 1.96
nass_6 0.49 0.67 0.72 0.72 0.28 0.33 1.99
nass_7 0.21 0.98 1.00 1.00 0.00 0.04 1.09
nass_8 0.34 0.38 0.28 0.28 0.72 0.41 2.22
nass_9 0.47 0.64 0.64 0.64 0.36 0.35 1.89
nass_10 0.28 0.31 0.21 0.21 0.79 0.37 2.85
nass_11 0.59 0.51 0.65 0.65 0.35 0.54 2.20
nass_12 0.30 0.22 0.21 0.18 0.82 0.49 2.69
With Sums of squares of:
g F1* F2* F3* h2
2.48 2.02 0.96 1.14 4.43
general/max 0.56 max/min = 4.63
mean percent general = 0.39 with sd = 0.15 and cv of 0.4
Explained Common Variance of the general factor = 0.38
The degrees of freedom are 33 and the fit is 0.6
The number of observations was 52 with Chi Square = 26.38 with prob < 0.79
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.06
RMSEA index = 0 and the 90 % confidence intervals are 0 0.07
BIC = -104.01
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 2.7
The number of observations was 52 with Chi Square = 123.03 with prob < 2.6e-07
The root mean square of the residuals is 0.19
The df corrected root mean square of the residuals is 0.21
RMSEA index = 0.156 and the 90 % confidence intervals are 0.121 0.195
BIC = -90.34
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.79 0.83 0.71 1.00
Multiple R square of scores with factors 0.62 0.68 0.50 1.00
Minimum correlation of factor score estimates 0.24 0.36 0.01 0.99
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.89 0.87 0.75 1.00
Omega general for total scores and subscales 0.53 0.34 0.41 0.04
Omega group for total scores and subscales 0.33 0.53 0.34 0.96omega(dfSSTnaas)In smc, smcs < 0 were set to .0In smc, smcs < 0 were set to .0
In smc, smcs < 0 were set to .0Warning in cor.smooth(r): Matrix was not positive definite, smoothing was doneIn factor.scores, the correlation matrix is singular, the pseudo inverse is usedWarning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a
different factor score estimation method.
Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
An ultra-Heywood case was detected. Examine the results carefullyIn smc, smcs < 0 were set to .0Warning in cor.smooth(r): Matrix was not positive definite, smoothing was doneWarning in cov2cor(t(w) %*% r %*% w): diag(V) had non-positive or NA entries;
the non-finite result may be dubiousWarning in cor.smooth(r): Matrix was not positive definite, smoothing was done
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.79
G.6: 0.88
Omega Hierarchical: 0.61
Omega H asymptotic: 0.71
Omega Total 0.86
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
naas_1 0.33 0.49 0.36 0.36 0.64 0.30 1.77
naas_2 0.52 0.37 0.41 0.41 0.59 0.66 1.80
naas_3 0.88 0.78 0.78 0.22 0.00 1.01
naas_4 0.62 0.40 0.40 0.60 0.00 1.02
naas_5- -0.20 0.06 0.94 0.33 1.81
naas_6 0.39 -0.32 0.25 0.25 0.75 0.62 1.94
naas_7 0.51 -0.29 0.35 0.35 0.65 0.74 1.65
naas_8 0.45 0.26 0.27 0.27 0.73 0.74 1.60
naas_9 0.39 0.17 0.83 0.87 1.40
naas_10 0.40 0.23 0.22 0.22 0.78 0.71 1.67
naas_11 0.51 0.27 0.27 0.73 0.03 1.12
naas_12 0.54 0.34 0.34 0.66 0.87 1.27
naas_13 0.73 0.25 0.60 0.60 0.40 0.89 1.24
naas_14 0.62 0.40 0.40 0.60 0.97 1.07
naas_15 0.87 0.77 0.77 0.23 0.02 1.05
naas_16 0.29 0.78 0.70 0.70 0.30 0.12 1.29
naas_17 0.87 0.77 0.77 0.23 0.02 1.05
naas_18 0.30 0.42 0.27 0.27 0.73 0.32 2.02
naas_19 0.24 0.75 0.62 0.62 0.38 0.10 1.22
naas_20 0.74 0.58 0.58 0.42 0.93 1.13
With Sums of squares of:
g F1* F2* F3* h2
3.4 2.8 0.0 2.4 4.6
general/max 0.73 max/min = Inf
mean percent general = 0.46 with sd = 0.37 and cv of 0.8
Explained Common Variance of the general factor = 0.39
The degrees of freedom are 133 and the fit is 24.3
The number of observations was 52 with Chi Square = 1008.36 with prob < 2e-134
The root mean square of the residuals is 0.09
The df corrected root mean square of the residuals is 0.1
RMSEA index = 0.355 and the 90 % confidence intervals are 0.339 0.38
BIC = 482.84
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 170 and the fit is 28.77
The number of observations was 52 with Chi Square = 1232.5 with prob < 1.8e-160
The root mean square of the residuals is 0.19
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.346 and the 90 % confidence intervals are 0.332 0.369
BIC = 560.79
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.92 0.95 0 0.94
Multiple R square of scores with factors 0.85 0.91 0 0.89
Minimum correlation of factor score estimates 0.70 0.81 -1 0.79
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.86 0.83 NA 0.76
Omega general for total scores and subscales 0.61 0.60 NA 0.21
Omega group for total scores and subscales 0.27 0.23 NA 0.554.2 Cronbach’s alpha and MacDonald’s omega for MID cultural self-reports
omega(dfMIDaies)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.87
G.6: 0.94
Omega Hierarchical: 0.53
Omega H asymptotic: 0.59
Omega Total 0.91
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1 0.29 0.36 -0.29 0.30 0.30 0.70 0.29 2.91
aies_2 0.62 0.66 0.83 0.83 0.17 0.47 2.02
aies_3 0.59 0.56 0.66 0.66 0.34 0.52 2.04
aies_4 0.46 0.43 0.30 0.49 0.49 0.51 0.44 2.72
aies_5 0.45 0.49 0.24 0.50 0.50 0.50 0.40 2.46
aies_6 0.29 0.39 0.34 0.36 0.36 0.64 0.24 2.96
aies_7 0.54 0.61 0.67 0.67 0.33 0.43 1.99
aies_8 0.41 0.39 0.28 0.40 0.40 0.60 0.42 2.81
aies_9 0.47 0.55 0.55 0.55 0.45 0.40 2.22
aies_10 0.39 0.54 0.46 0.46 0.54 0.32 1.95
aies_11 0.55 0.47 0.25 0.60 0.60 0.40 0.50 2.51
aies_12 0.39 0.65 0.61 0.61 0.39 0.25 1.78
aies_13 0.31 0.42 0.31 0.31 0.69 0.32 2.22
aies_14 0.55 0.20 0.46 0.57 0.57 0.43 0.54 2.30
aies_15 0.34 0.19 0.81 0.11 2.36
aies_16 0.46 0.33 0.47 0.55 0.55 0.45 0.38 2.90
aies_17 0.64 0.43 0.43 0.57 0.03 1.09
With Sums of squares of:
g F1* F2* F3* h2
3.2 2.0 1.8 1.5 4.6
general/max 0.7 max/min = 3.18
mean percent general = 0.36 with sd = 0.14 and cv of 0.39
Explained Common Variance of the general factor = 0.38
The degrees of freedom are 88 and the fit is 3.76
The number of observations was 44 with Chi Square = 129.85 with prob < 0.0025
The root mean square of the residuals is 0.08
The df corrected root mean square of the residuals is 0.1
RMSEA index = 0.101 and the 90 % confidence intervals are 0.064 0.142
BIC = -203.16
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 119 and the fit is 6.65
The number of observations was 44 with Chi Square = 238.3 with prob < 5.4e-10
The root mean square of the residuals is 0.19
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.149 and the 90 % confidence intervals are 0.124 0.181
BIC = -212.01
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.75 0.81 0.75 0.84
Multiple R square of scores with factors 0.57 0.66 0.57 0.70
Minimum correlation of factor score estimates 0.13 0.32 0.13 0.40
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.91 0.84 0.87 0.58
Omega general for total scores and subscales 0.53 0.35 0.44 0.13
Omega group for total scores and subscales 0.27 0.49 0.44 0.44omega(dfMIDhls)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.94
G.6: 0.96
Omega Hierarchical: 0.8
Omega H asymptotic: 0.83
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
hls_1 0.59 0.54 0.65 0.65 0.35 0.54 2.02
hls_2 0.58 0.59 0.69 0.69 0.31 0.49 2.02
hls_3 0.59 0.62 0.75 0.75 0.25 0.47 2.03
hls_4 0.65 0.48 0.66 0.66 0.34 0.64 1.90
hls_5 0.73 0.20 0.22 0.63 0.63 0.37 0.85 1.37
hls_6 0.59 0.31 0.45 0.45 0.55 0.78 1.53
hls_7 0.75 0.52 0.83 0.83 0.17 0.67 1.79
hls_8 0.75 0.38 0.72 0.72 0.28 0.79 1.51
hls_9 0.75 0.46 0.77 0.77 0.23 0.73 1.66
hls_10 0.81 0.55 0.96 0.96 0.04 0.68 1.77
hls_11 0.71 0.30 0.62 0.62 0.38 0.81 1.47
hls_12 0.72 0.36 0.66 0.66 0.34 0.79 1.52
With Sums of squares of:
g F1* F2* F3* h2
5.71 0.70 1.30 0.67 6.02
general/max 0.95 max/min = 8.98
mean percent general = 0.69 with sd = 0.13 and cv of 0.19
Explained Common Variance of the general factor = 0.68
The degrees of freedom are 33 and the fit is 1.67
The number of observations was 44 with Chi Square = 60.5 with prob < 0.0024
The root mean square of the residuals is 0.05
The df corrected root mean square of the residuals is 0.07
RMSEA index = 0.136 and the 90 % confidence intervals are 0.082 0.194
BIC = -64.37
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 3.77
The number of observations was 44 with Chi Square = 141.36 with prob < 9.4e-10
The root mean square of the residuals is 0.13
The df corrected root mean square of the residuals is 0.15
RMSEA index = 0.19 and the 90 % confidence intervals are 0.155 0.233
BIC = -62.98
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.91 0.71 0.83 0.76
Multiple R square of scores with factors 0.83 0.50 0.69 0.58
Minimum correlation of factor score estimates 0.66 0.00 0.38 0.16
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.96 0.89 0.89 0.91
Omega general for total scores and subscales 0.80 0.68 0.48 0.72
Omega group for total scores and subscales 0.12 0.21 0.41 0.19omega(dfMIDnass)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.85
G.6: 0.91
Omega Hierarchical: 0.64
Omega H asymptotic: 0.71
Omega Total 0.91
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
nass_1 0.53 0.62 0.69 0.69 0.31 0.40 2.16
nass_2 0.58 0.70 0.85 0.85 0.15 0.40 2.05
nass_3 0.30 -0.23 0.16 0.84 0.00 2.31
nass_4 0.50 0.33 0.37 0.37 0.63 0.67 1.84
nass_5 0.63 0.44 0.59 0.59 0.41 0.67 1.79
nass_6 0.74 0.49 0.81 0.81 0.19 0.68 1.81
nass_7 0.20 0.69 0.52 0.52 0.48 0.08 1.17
nass_8 0.47 0.40 0.40 0.40 0.60 0.55 2.16
nass_9 0.68 0.45 0.69 0.69 0.31 0.68 1.81
nass_10 0.60 0.59 0.73 0.73 0.27 0.49 2.10
nass_11 0.70 0.30 0.33 0.68 0.68 0.32 0.71 1.83
nass_12 0.31 0.29 0.38 0.32 0.32 0.68 0.30 2.86
With Sums of squares of:
g F1* F2* F3* h2
3.5 1.0 1.5 0.8 4.4
general/max 0.8 max/min = 5.48
mean percent general = 0.47 with sd = 0.24 and cv of 0.52
Explained Common Variance of the general factor = 0.51
The degrees of freedom are 33 and the fit is 1.22
The number of observations was 44 with Chi Square = 43.96 with prob < 0.096
The root mean square of the residuals is 0.05
The df corrected root mean square of the residuals is 0.07
RMSEA index = 0.084 and the 90 % confidence intervals are 0 0.151
BIC = -80.91
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 3.17
The number of observations was 44 with Chi Square = 118.72 with prob < 9.3e-07
The root mean square of the residuals is 0.16
The df corrected root mean square of the residuals is 0.17
RMSEA index = 0.163 and the 90 % confidence intervals are 0.126 0.208
BIC = -85.63
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.83 0.61 0.83 0.82
Multiple R square of scores with factors 0.69 0.37 0.70 0.66
Minimum correlation of factor score estimates 0.38 -0.26 0.39 0.33
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.91 0.86 0.87 0.53
Omega general for total scores and subscales 0.64 0.58 0.41 0.10
Omega group for total scores and subscales 0.22 0.28 0.46 0.43omega(dfMIDnaas)Warning in cor.smooth(r): Matrix was not positive definite, smoothing was doneIn factor.scores, the correlation matrix is singular, the pseudo inverse is usedWarning in cor.smooth(r): Matrix was not positive definite, smoothing was done
Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.79
G.6: 0.89
Omega Hierarchical: 0.22
Omega H asymptotic: 0.25
Omega Total 0.86
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
naas_1 0.43 0.20 0.20 0.80 0.03 1.13
naas_2 0.93 0.90 0.90 0.10 0.04 1.09
naas_3 0.31 0.84 0.81 0.81 0.19 0.12 1.27
naas_4 0.44 0.21 0.21 0.79 0.10 1.22
naas_5 0.37 0.18 0.82 0.07 1.71
naas_6 0.23 0.63 0.48 0.48 0.52 0.11 1.40
naas_7 0.25 0.64 0.48 0.48 0.52 0.13 1.36
naas_8 0.30 0.32 0.40 0.36 0.36 0.64 0.26 2.82
naas_9 0.34 0.13 0.87 0.14 1.36
naas_10 0.28 0.26 0.34 0.28 0.28 0.72 0.28 3.24
naas_11 0.58 0.39 0.39 0.61 0.06 1.32
naas_12 0.21 0.37 0.19 0.81 0.23 1.82
naas_13 0.20 0.45 0.26 0.32 0.32 0.68 0.13 2.08
naas_14 0.33 0.62 0.52 0.52 0.48 0.21 1.62
naas_15 0.66 0.51 0.51 0.49 0.05 1.32
naas_16 0.73 0.56 0.56 0.44 0.03 1.12
naas_17 0.93 0.90 0.90 0.10 0.04 1.09
naas_18 0.23 0.44 0.28 0.28 0.72 0.19 1.95
naas_19 0.27 0.78 0.68 0.68 0.32 0.11 1.25
naas_20 0.30 0.41 0.39 0.41 0.41 0.59 0.21 2.91
With Sums of squares of:
g F1* F2* F3* h2
0.98 3.40 2.42 1.99 4.93
general/max 0.2 max/min = 2.47
mean percent general = 0.13 with sd = 0.08 and cv of 0.62
Explained Common Variance of the general factor = 0.11
The degrees of freedom are 133 and the fit is 23.86
The number of observations was 44 with Chi Square = 799.32 with prob < 1.2e-95
The root mean square of the residuals is 0.09
The df corrected root mean square of the residuals is 0.11
RMSEA index = 0.337 and the 90 % confidence intervals are 0.319 0.364
BIC = 296.03
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 170 and the fit is 30.47
The number of observations was 44 with Chi Square = 1061.38 with prob < 9.2e-129
The root mean square of the residuals is 0.23
The df corrected root mean square of the residuals is 0.25
RMSEA index = 0.344 and the 90 % confidence intervals are 0.329 0.369
BIC = 418.07
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.49 0.96 0.91 0.83
Multiple R square of scores with factors 0.24 0.92 0.83 0.69
Minimum correlation of factor score estimates -0.51 0.84 0.66 0.38
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.86 0.86 0.81 0.74
Omega general for total scores and subscales 0.22 0.06 0.09 0.16
Omega group for total scores and subscales 0.54 0.80 0.72 0.584.3 Cronbach’s alpha and MacDonald’s omega for CPV cultural self-reports
omega(dfLPPaies)Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a
different factor score estimation method.Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
ultra-Heywood case was detected. Examine the results carefullyWarning in cov2cor(t(w) %*% r %*% w): diag(V) had non-positive or NA entries;
the non-finite result may be dubious
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.94
G.6: 0.97
Omega Hierarchical: 0.78
Omega H asymptotic: 0.82
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1 0.72 0.53 0.53 0.47 0.98 1.05
aies_2 0.47 0.58 -0.30 0.65 0.65 0.35 0.34 2.49
aies_3 0.62 0.67 0.84 0.84 0.16 0.46 2.04
aies_4 0.41 0.24 -0.24 0.29 0.29 0.71 0.59 2.30
aies_5 0.37 0.67 0.60 0.60 0.40 0.23 1.63
aies_6 0.63 0.20 0.43 0.43 0.57 0.91 1.20
aies_7 0.47 0.77 0.81 0.81 0.19 0.27 1.66
aies_8 0.87 0.79 0.79 0.21 0.97 1.05
aies_9 0.49 0.57 0.57 0.57 0.43 0.42 2.01
aies_10 0.96 0.94 0.94 0.06 0.98 1.05
aies_11 0.54 0.64 0.71 0.71 0.29 0.42 1.97
aies_12 0.75 0.59 0.59 0.41 0.96 1.08
aies_13 0.76 0.61 0.61 0.39 0.94 1.11
aies_14 0.79 0.27 -0.22 0.74 0.74 0.26 0.83 1.40
aies_15 0.59 0.41 0.31 0.61 0.61 0.39 0.57 2.38
aies_16 0.55 0.28 0.41 0.41 0.59 0.75 1.65
aies_17 0.72 0.53 0.53 0.47 0.99 1.03
With Sums of squares of:
g F1* F2* F3* h2
7.20 0.00 2.60 0.85 7.12
general/max 1.01 max/min = Inf
mean percent general = 0.68 with sd = 0.28 and cv of 0.41
Explained Common Variance of the general factor = 0.68
The degrees of freedom are 88 and the fit is 3.46
The number of observations was 40 with Chi Square = 105.51 with prob < 0.098
The root mean square of the residuals is 0.06
The df corrected root mean square of the residuals is 0.07
RMSEA index = 0.066 and the 90 % confidence intervals are 0 0.118
BIC = -219.11
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 119 and the fit is 7.11
The number of observations was 40 with Chi Square = 226.47 with prob < 1.1e-08
The root mean square of the residuals is 0.16
The df corrected root mean square of the residuals is 0.17
RMSEA index = 0.148 and the 90 % confidence intervals are 0.122 0.182
BIC = -212.51
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.99 0 0.97 0.83
Multiple R square of scores with factors 0.98 0 0.95 0.69
Minimum correlation of factor score estimates 0.96 -1 0.89 0.38
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.96 NA 0.95 0.82
Omega general for total scores and subscales 0.78 NA 0.71 0.77
Omega group for total scores and subscales 0.16 NA 0.24 0.05omega(dfLPPhls)Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a
different factor score estimation method.Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
ultra-Heywood case was detected. Examine the results carefullyWarning in cov2cor(t(w) %*% r %*% w): diag(V) had non-positive or NA entries;
the non-finite result may be dubious
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.96
G.6: 0.98
Omega Hierarchical: 0.86
Omega H asymptotic: 0.88
Omega Total 0.97
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
hls_1 0.89 0.23 0.86 0.86 0.14 0.91 1.21
hls_2 0.83 0.24 -0.37 0.88 0.88 0.12 0.78 1.57
hls_3 0.86 0.76 0.76 0.24 0.98 1.04
hls_4 0.87 0.76 0.76 0.24 0.99 1.03
hls_5 0.71 0.54 0.54 0.46 0.93 1.15
hls_6 0.88 0.78 0.78 0.22 0.99 1.02
hls_7 0.59 0.24 0.65 0.82 0.82 0.18 0.42 2.25
hls_8 0.86 0.32 0.26 0.91 0.91 0.09 0.81 1.46
hls_9 0.67 0.56 0.77 0.77 0.23 0.58 1.96
hls_10 0.72 0.58 0.85 0.85 0.15 0.60 1.92
hls_11 0.59 0.74 0.89 0.89 0.11 0.39 1.92
hls_12 0.70 0.45 0.69 0.69 0.31 0.71 1.72
With Sums of squares of:
g F1* F2* F3* h2
7.12 1.68 0.00 0.72 7.66
general/max 0.93 max/min = Inf
mean percent general = 0.76 with sd = 0.22 and cv of 0.29
Explained Common Variance of the general factor = 0.75
The degrees of freedom are 33 and the fit is 1.76
The number of observations was 40 with Chi Square = 56.7 with prob < 0.0063
The root mean square of the residuals is 0.03
The df corrected root mean square of the residuals is 0.05
RMSEA index = 0.132 and the 90 % confidence intervals are 0.072 0.194
BIC = -65.03
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 4.92
The number of observations was 40 with Chi Square = 164.71 with prob < 4e-13
The root mean square of the residuals is 0.14
The df corrected root mean square of the residuals is 0.16
RMSEA index = 0.225 and the 90 % confidence intervals are 0.19 0.27
BIC = -34.49
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.98 0.94 0 0.92
Multiple R square of scores with factors 0.96 0.89 0 0.85
Minimum correlation of factor score estimates 0.91 0.78 -1 0.69
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.97 0.96 NA 0.92
Omega general for total scores and subscales 0.86 0.73 NA 0.89
Omega group for total scores and subscales 0.09 0.23 NA 0.03omega(dfLPPnaas)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.9
G.6: 0.96
Omega Hierarchical: 0.63
Omega H asymptotic: 0.68
Omega Total 0.93
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
naas_1 0.45 0.33 0.34 0.34 0.66 0.59 2.21
naas_2 0.43 0.59 -0.20 0.58 0.58 0.42 0.32 2.16
naas_3 0.75 0.25 0.67 0.67 0.33 0.85 1.35
naas_4 0.47 -0.30 0.37 0.37 0.63 0.60 2.30
naas_5 0.24 0.67 0.51 0.51 0.49 0.12 1.29
naas_6 0.64 0.52 0.70 0.70 0.30 0.59 2.00
naas_7 0.58 0.23 0.41 0.41 0.59 0.82 1.45
naas_8 0.38 0.43 0.35 0.35 0.65 0.41 2.18
naas_9 0.25 0.24 0.38 0.27 0.27 0.73 0.24 2.52
naas_10 0.55 0.32 0.32 0.68 0.93 1.16
naas_11 0.64 0.31 0.53 0.53 0.47 0.76 1.62
naas_12 0.30 0.59 0.44 0.44 0.56 0.21 1.50
naas_13 0.20 0.35 0.49 0.40 0.40 0.60 0.10 2.20
naas_14 0.60 0.31 0.48 0.48 0.52 0.74 1.68
naas_15 0.33 0.63 0.54 0.54 0.46 0.20 1.68
naas_16 0.75 0.58 0.58 0.42 0.04 1.09
naas_17 0.21 0.88 0.83 0.83 0.17 0.06 1.12
naas_18 0.71 0.22 0.58 0.58 0.42 0.88 1.29
naas_19 0.79 0.21 0.68 0.68 0.32 0.93 1.16
naas_20 0.61 0.39 0.26 0.60 0.60 0.40 0.62 2.18
With Sums of squares of:
g F1* F2* F3* h2
5.08 0.27 2.87 1.96 5.59
general/max 0.91 max/min = 21.01
mean percent general = 0.5 with sd = 0.32 and cv of 0.63
Explained Common Variance of the general factor = 0.5
The degrees of freedom are 133 and the fit is 6.39
The number of observations was 40 with Chi Square = 188.38 with prob < 0.0011
The root mean square of the residuals is 0.08
The df corrected root mean square of the residuals is 0.1
RMSEA index = 0.099 and the 90 % confidence intervals are 0.067 0.136
BIC = -302.24
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 170 and the fit is 10.03
The number of observations was 40 with Chi Square = 309.13 with prob < 3.7e-10
The root mean square of the residuals is 0.18
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.141 and the 90 % confidence intervals are 0.119 0.17
BIC = -317.98
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.92 0.27 0.95 0.88
Multiple R square of scores with factors 0.85 0.07 0.90 0.78
Minimum correlation of factor score estimates 0.70 -0.85 0.80 0.55
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.93 0.78 0.88 0.82
Omega general for total scores and subscales 0.63 0.72 0.31 0.52
Omega group for total scores and subscales 0.21 0.06 0.57 0.30omega(dfLPPnass)
Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.8
G.6: 0.89
Omega Hierarchical: 0.25
Omega H asymptotic: 0.28
Omega Total 0.89
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
nass_1 0.40 0.44 0.32 0.46 0.46 0.54 0.34 2.82
nass_2 0.59 0.71 0.86 0.86 0.14 0.40 2.02
nass_3 -0.21 0.54 0.38 0.38 0.62 0.08 1.59
nass_4 0.66 -0.22 0.49 0.49 0.51 0.01 1.24
nass_5 0.29 0.57 0.43 0.43 0.57 0.20 1.63
nass_6 0.34 0.88 0.89 0.89 0.11 0.13 1.31
nass_7- 0.28 0.47 -0.26 0.37 0.37 0.63 0.21 2.26
nass_8 0.66 -0.20 0.48 0.48 0.52 0.01 1.21
nass_9 0.29 0.82 0.79 0.79 0.21 0.11 1.32
nass_10 0.20 0.69 0.52 0.52 0.48 0.08 1.22
nass_11 0.35 0.80 0.77 0.77 0.23 0.16 1.41
nass_12 0.29 0.67 0.54 0.54 0.46 0.16 1.41
With Sums of squares of:
g F1* F2* F3* h2
1.16 3.53 0.95 1.34 4.46
general/max 0.26 max/min = 4.67
mean percent general = 0.16 with sd = 0.12 and cv of 0.75
Explained Common Variance of the general factor = 0.17
The degrees of freedom are 33 and the fit is 1.04
The number of observations was 40 with Chi Square = 33.61 with prob < 0.44
The root mean square of the residuals is 0.05
The df corrected root mean square of the residuals is 0.07
RMSEA index = 0 and the 90 % confidence intervals are 0 0.12
BIC = -88.12
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 54 and the fit is 5.49
The number of observations was 40 with Chi Square = 183.8 with prob < 4.7e-16
The root mean square of the residuals is 0.31
The df corrected root mean square of the residuals is 0.34
RMSEA index = 0.244 and the 90 % confidence intervals are 0.209 0.288
BIC = -15.4
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.64 0.94 0.77 0.83
Multiple R square of scores with factors 0.41 0.88 0.59 0.69
Minimum correlation of factor score estimates -0.18 0.75 0.18 0.39
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.89 0.91 0.73 0.72
Omega general for total scores and subscales 0.25 0.11 0.25 0.08
Omega group for total scores and subscales 0.63 0.79 0.47 0.645 Bivariate correlations
5.1 Creating matrix for SST participants
tibble4SST<-tibble(
#Subject=dfSST$Subject,
#sex=dfSST$Gender,
#age=dfSST$Age,
goIncorrERN_FCz=dfSST$ERNFCz_GoIncorr, # We have 52 ERN and Pe entries
goCorrERN_FCz=dfSST$ERNFCz_GoCorr,
allIncorrN2_FCz=dfSST$N2FCz_AllIncorrn59,
allCorrN2_FCz=dfSST$N2FCz_AllCorrn59,
allIncorrP3_Pz=dfSST$P3Pz_AllIncorrn59,
allCorrP3_Pz=dfSST$P3Pz_AllCorrn59,
goIncorrPe_Pz=dfSST$PePz_GoIncorr,
goCorrPe_Pz=dfSST$PePz_GoCorr,
#CDDRposRein=dfSST$CDDR_PosReinforcement,
#CDDRnegRein=dfSST$CDDR_NegReinforcement,
PROMISanx=dfSST$PROMIS_AnxietyTscore,
PROMISalcUse=dfSST$PROMIS_AlcoUseTscore,
PROMISdepress=dfSST$PROMIS_DepressTscore,
#PROMISappealUse30=dfSST$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfSST$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfSST$PHQ9_Suicide,
#Age=dfSST$Age,
#Sex=dfSST$Gender,
#Income=dfSST$Income,
#Education=dfSST$Education_4levelCategory,
#MRT=dfSST$ss_mrt_calibration,
#goErrorEasyRT=dfSST$ss_meanrt_erroreasy,
#goErrorHardRT=dfSST$ss_meanrt_errorhard,
AIES=dfSST$AIES_scale,
NAAS=dfSST$NAAS_scale,
NASS=dfSST$NASS_scale,
HLS=dfSST$HLS_scale
)
cleanedTibble4SST<-na.omit(tibble4SST) # retains 52 participants
# residualized ERN
residual_incorrectFCzERN<-lm(goIncorrERN_FCz~goCorrERN_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$ERNresid<-resid(residual_incorrectFCzERN)
# residualized correct N2
residual_correctFCzN2<-lm(allCorrN2_FCz~allIncorrN2_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$N2corrResid<-resid(residual_correctFCzN2)
# residualized incorrect N2
residual_incorrectFCzN2<-lm(allIncorrN2_FCz~allCorrN2_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$N2resid<-resid(residual_incorrectFCzN2)
# residualized correct P3
residual_correctPzP3<-lm(allCorrP3_Pz~allIncorrP3_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$P3corrResid<-resid(residual_correctPzP3)
# residualized incorrect P3
residual_incorrectPzP3<-lm(allIncorrP3_Pz~allCorrP3_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$P3resid<-resid(residual_incorrectPzP3)
# residualized Pe
residual_incorrectPzPe<-lm(goIncorrPe_Pz~goCorrPe_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$PeResid<-resid(residual_incorrectPzPe)
updatedTibbleSST<-tibble(
"Resid. ERN"=cleanedTibble4SST$ERNresid,
"Resid Incorr P3"=cleanedTibble4SST$P3resid,
"Resid. Pe"=cleanedTibble4SST$PeResid,
#CDDRposRein=cleanedTibble4SST$CDDR_PosReinforcement,
#CDDRnegRein=cleanedTibble4SST$CDDR_NegReinforcement,
"PROMIS Anxiety"=cleanedTibble4SST$PROMISanx,
"PROMIS Alcohol Use"=cleanedTibble4SST$PROMISalcUse,
"PROMIS Depression"=cleanedTibble4SST$PROMISdepress,
#PROMISappealUse30=dfSST$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfSST$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfSST$PHQ9_Suicide,
#Age=dfSST$Age,
#Sex=dfSST$Gender,
#Income=dfSST$Income,
#Education=dfSST$Education_4levelCategory,
#MRT=dfSST$ss_mrt_calibration,
#goErrorEasyRT=dfSST$ss_meanrt_erroreasy,
#goErrorHardRT=dfSST$ss_meanrt_errorhard,
"AIES"=cleanedTibble4SST$AIES,
"NAAS"=cleanedTibble4SST$NAAS,
"NASS"=cleanedTibble4SST$NASS,
"HLS"=cleanedTibble4SST$HLS
)5.2 Correlation matrix for SST ERPs, cultural self-reports, and clinical self-reports
p1<-corr.test(updatedTibbleSST,method='spearman',adjust='none')$p
MH1<-cor(updatedTibbleSST)
# create correlation matrix for all variables including cultural variables
corrplot(MH1,p.mat=p1,method='color',diag=FALSE,type='lower',
sig.level=c(.001,.01,.05),pch.cex = 0.75,insig='label_sig',
pch.col = 'black')
5.3 Creating matrix for MID participants
tibble4MID<-tibble(
#Subject=dfMID$Subject,
Age=dfMID$Age,
#Sex=dfMID$Gender,
"P300 Pz Loss"=dfMID$P3pzMIDcueLoss,
#cueNogainPzP3=dfMID$P3pzMIDcueNogain,
"P300 Pz Gain"=dfMID$P3pzMIDcueGain,
"SPN Fz Success"=dfMID$SPNfzMIDfeedbackSuccess,
#failFzSPN=dfMID$SPNfzMIDfeedbackFail,
'RewP FCz Gain'=dfMID$RewPfczMIDfeedbackGain,
#CDDRposRein=dfMID$CDDR_PosReinforcement,
#CDDRnegRein=dfMID$CDDR_NegReinforcement,
"PROMIS Anxiety"=dfMID$PROMIS_AnxietyTscore,
"PROMIS Alcohol Use"=dfMID$PROMIS_AlcoUseTscore,
"PROMIS Depression"=dfMID$PROMIS_DepressTscore,
#PROMISappealUse30=dfMID$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfMID$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfMID$PHQ9_Suicide,
#Age=dfMID$Age,
#Sex=dfMID$Gender,
#Income=dfMID$Income,
#Education=dfMID$Education_4levelCategory,
#MRT=dfMID$ss_mrt_calibration,
#goErrorEasyRT=dfMID$ss_meanrt_erroreasy,
#goErrorHardRT=dfMID$ss_meanrt_errorhard,
"AIES"=dfMID$AIES_scale,
"NAAS"=dfMID$NAAS_scale,
"NASS"=dfMID$NASS_scale,
"HLS"=dfMID$HLS_scale
)
cleanedData4Hyp1b<-na.omit(tibble4MID) # retains 44participants
tibble4MIDcalc<-tibble(
Age=dfMID$Age,
#Sex=dfMID$Gender,
P3PzLoss=tibble4MID$`P300 Pz Loss`,
P3PzGain=tibble4MID$`P300 Pz Gain`,
RewPFCzGain=tibble4MID$`RewP FCz Gain`,
successSPN=tibble4MID$`SPN Fz Success`,
promisDep=tibble4MID$`PROMIS Depression`,
promisAnx=tibble4MID$`PROMIS Anxiety`,
promisAlcUse=tibble4MID$`PROMIS Alcohol Use`,
AIES=tibble4MID$AIES,
NAAS=tibble4MID$NAAS,
NASS=tibble4MID$NASS,
HLS=tibble4MID$HLS
)
cleanedTibble4MIDcalc<-na.omit(tibble4MIDcalc)5.4 Correlation matrix for MID ERPs, cultural self-reports, and clinical self-reports
# assign the p-values and matrix to objects in R
p1<-corr.test(cleanedData4Hyp1b,method='spearman',adjust='none')$p
MH1<-cor(cleanedData4Hyp1b)
# create correlation matrix for all variables including cultural variables
corrplot(MH1,p.mat=p1,method='color',diag=FALSE,type='lower',
sig.level=c(.001,.01,.05),pch.cex = 0.75,insig='label_sig',
pch.col = 'black')
5.5 Reverse scoring the HLS for CPV
5.6 Creating matrix for CPV participants
data4matrixH2<-tibble(
#Age=dfLPP$Age,
#Sex=dfLPP$Sex,
#CDDRposRein=dfLPP$CDDR_PosReinforcement,
#CDDRnegRein=dfLPP$CDDR_NegReinforcement,
promisDep=dfLPP$PROMIS_DepressTscore,
promisAnx=dfLPP$PROMIS_AnxietyTscore,
promisAlcUse=dfLPP$PROMIS_AlcoUseTscore,
#promisAppeal30=dfLPP$PROMIS_AppealSubUse30DaysTscore,
#promisAppeal3=dfLPP$PROMIS_AppealSubUse3MonthTscore,
eCulturalLPP=dfLPP$Early_LPPPz.Average_MastRef_Cultural,
eNeutralLPP=dfLPP$Early_LPPPz.Average_MastRef_Neutral,
CulturalLPP=dfLPP$LPP_Pz_Cultural,
NeutralLPP=dfLPP$LPP_Pz_Neutral,
mCulturalLPP=dfLPP$Mid_LPP_Pz.Average_MastRef_Cultural,
mNeutralLPP=dfLPP$Mid_LPP_Pz.Average_MastRef_Neutral,
HLS=dfLPP$hlsTot,
AIES=dfLPP$AIES_scale,
NAAS=dfLPP$NAAS_scale,
NASS=dfLPP$NASS_scale
)
cleanedData4matrixH2<-na.omit(data4matrixH2) # retains 40 participants (did not drop any of them)
# create residualized (to culture) LPP, early LPP, and mid LPP amplitudes according to method proposed by Meyer et al., 2017
residualCulturalPzEarlyLPP<-lm(eCulturalLPP~eNeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residEarlyLPP<-resid(residualCulturalPzEarlyLPP)
residualCulturalPzLPP<-lm(CulturalLPP~NeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residLPP<-resid(residualCulturalPzLPP)
residualCulturalPzMidLPP<-lm(mCulturalLPP~mNeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residMidLPP<-resid(residualCulturalPzMidLPP)
updatedTibble<-tibble(
#CDDRposRein=cleanedData4matrixH2$CDDR_PosReinforcement,
#CDDRnegRein=cleanedData4matrixH2$CDDR_NegReinforcement,
"PROMIS Anxiety"=cleanedData4matrixH2$promisAnx,
"PROMIS Alcohol Use"=cleanedData4matrixH2$promisAlcUse,
"PROMIS Depression"=cleanedData4matrixH2$promisDep,
#promisAppeal30=cleanedData4matrixH2$PROMIS_AppealSubUse30DaysTscore,
#promisAppeal3=cleanedData4matrixH2$PROMIS_AppealSubUse3MonthTscore,
#eCulturalLPP=cleanedData4matrixH2$Early_LPPPz.Average_MastRef_Cultural,
#eNeutralLPP=cleanedData4matrixH2$Early_LPPPz.Average_MastRef_Neutral,
#CulturalLPP=cleanedData4matrixH2$LPP_Pz_Cultural,
#NeutralLPP=cleanedData4matrixH2$LPP_Pz_Neutral,
#mCulturalLPP=cleanedData4matrixH2$Mid_LPP_Pz.Average_MastRef_Cultural,
#mNeutralLPP=cleanedData4matrixH2$Mid_LPP_Pz.Average_MastRef_Neutral,
"Resid. Early LPP Pz"=cleanedData4matrixH2$residEarlyLPP,
"Resid. Mid LPP Pz"=cleanedData4matrixH2$residMidLPP,
"Resid. LPP Pz"=cleanedData4matrixH2$residLPP,
"HLS"=cleanedData4matrixH2$HLS,
"AIES"=cleanedData4matrixH2$AIES,
"NAAS"=cleanedData4matrixH2$NAAS,
"NASS"=cleanedData4matrixH2$NASS,
)5.7 Correlation matrix for CPV participants
# assign the p-values and matrix to objects in R
pLPP<-corr.test(updatedTibble,method='spearman',adjust='none')$p
mLPP<-cor(updatedTibble)
# create correlation matrix for all variables including cultural variables
corrplot(mLPP,p.mat=pLPP,method='color',diag=FALSE,type='lower',
sig.level=c(.001,.01,.05),pch.cex = 0.75,insig='label_sig',
pch.col = 'black')
6 Linear regressions of significant correlations in SST
6.1 QQ plots for ERN amplitudes
ERNresidQQ<-ggplot2::ggplot(data = cleanedTibble4SST, mapping = aes(sample = ERNresid)) +
stat_qq_band(alpha = 0.5, conf = 0.95, bandType = "pointwise", fill = "green") +
stat_qq_line(identity = TRUE) +
stat_qq_point(col = "black") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw()
ERNresidQQ
6.2 Residualized ERN amplitudes predicting PROMIS Depression scores
ggplot(cleanedTibble4SST, aes(x = ERNresid, y = PROMISdepress)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "blue") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "ERNresid",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "Residualized ERN Amplitudes", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
6.3 Spirituality predicting PROMIS Depression scores
ggplot(cleanedTibble4SST, aes(x = NASS, y = PROMISdepress)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "red") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "NASS",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "NASS", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
7 Linear regressions of significant correlations in MID
7.1 QQ plots for Cue Loss P300 amplitudes
cueLossP3qq<-ggplot2::ggplot(data = tibble4MIDcalc, mapping = aes(sample = P3PzLoss)) +
stat_qq_band(alpha = 0.5, conf = 0.95, bandType = "pointwise", fill = "green") +
stat_qq_line(identity = TRUE) +
stat_qq_point(col = "black") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw()
cueLossP3qq
7.2 Cue loss P300 amplitudes predicting PROMIS Alcohol Use Scores
ggplot(cleanedData4Hyp1b, aes(x = `P300 Pz Loss`, y = `PROMIS Alcohol Use`)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "blue") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisAlcUse~~`=`~~",
eq.x.rhs = "cueLossP3",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "P300 Amplitudes @ Pz to Loss Cues", y = "PROMIS Alcohol Use Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
7.3 Spirituality predicitng PROMIS Depression scores
ggplot(cleanedData4Hyp1b, aes(x = NASS, y = `PROMIS Depression`)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "red") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "NASS",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "NASS", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
8 Linear regressions of significant correlations in CPV
8.1 QQ plots for Residualized Mid-LPP amplitudes
residMidLPPqq<-ggplot2::ggplot(data = cleanedData4matrixH2, mapping = aes(sample = residMidLPP)) +
stat_qq_band(alpha = 0.5, conf = 0.95, bandType = "pointwise", fill = "green") +
stat_qq_line(identity = TRUE) +
stat_qq_point(col = "black") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw()
residMidLPPqq
8.2 Residualized Mid-LPP amplitudes predicting PROMIS Depression scores
ggplot(updatedTibble, aes(x = `Resid. Mid LPP Pz`, y = `PROMIS Depression`)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "red") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "residMidLPP",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "Residualized Mid-LPP amplitudes @ Pz to Cultural Cues", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
8.3 Residualized Mid-LPP amplitudes predicting PROMIS Anxiety Scores
ggplot(updatedTibble, aes(x = `Resid. Mid LPP Pz`, y = `PROMIS Anxiety`)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, level=0.95, color = "red") +
stat_poly_eq(
formula = y ~ x,
mapping = use_label("eq", "adj.R2", "p"), # <--- tell it which stats to show
eq.with.lhs = "promisAnx~~`=`~~",
eq.x.rhs = "residMidLPP",
parse = TRUE,
label.x.npc = "left",
label.y.npc = "top"
) +
labs(x = "Residualized Mid-LPP amplitudes @ Pz to Cultural Cues", y = "PROMIS Anxiety Score") +
theme_classic(base_size = 15)Warning in stat_poly_eq(formula = y ~ x, mapping = use_label("eq", "adj.R2", :
Ignoring unknown parameters: `label.x.npc` and `label.y.npc``geom_smooth()` using formula = 'y ~ x'
9 Multiple linear regression and moderation analyses in SST
tibble4SST<-tibble(
#Subject=dfSST$Subject,
sex=dfSST$Gender,
age=dfSST$Age,
goIncorrERN_FCz=dfSST$ERNFCz_GoIncorr, # We have 52 ERN and Pe entries
goCorrERN_FCz=dfSST$ERNFCz_GoCorr,
allIncorrN2_FCz=dfSST$N2FCz_AllIncorrn59,
allCorrN2_FCz=dfSST$N2FCz_AllCorrn59,
allIncorrP3_Pz=dfSST$P3Pz_AllIncorrn59,
allCorrP3_Pz=dfSST$P3Pz_AllCorrn59,
goIncorrPe_Pz=dfSST$PePz_GoIncorr,
goCorrPe_Pz=dfSST$PePz_GoCorr,
#CDDRposRein=dfSST$CDDR_PosReinforcement,
#CDDRnegRein=dfSST$CDDR_NegReinforcement,
PROMISanx=dfSST$PROMIS_AnxietyTscore,
PROMISalcUse=dfSST$PROMIS_AlcoUseTscore,
PROMISdepress=dfSST$PROMIS_DepressTscore,
#PROMISappealUse30=dfSST$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfSST$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfSST$PHQ9_Suicide,
#Age=dfSST$Age,
#Sex=dfSST$Gender,
#Income=dfSST$Income,
#Education=dfSST$Education_4levelCategory,
#MRT=dfSST$ss_mrt_calibration,
#goErrorEasyRT=dfSST$ss_meanrt_erroreasy,
#goErrorHardRT=dfSST$ss_meanrt_errorhard,
AIES=dfSST$AIES_scale,
NAAS=dfSST$NAAS_scale,
NASS=dfSST$NASS_scale,
HLS=dfSST$HLS_scale
)
cleanedTibble4SST<-na.omit(tibble4SST)
# residualized ERN
residual_incorrectFCzERN<-lm(goIncorrERN_FCz~goCorrERN_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$ERNresid<-resid(residual_incorrectFCzERN)
# residualized correct N2
residual_correctFCzN2<-lm(allCorrN2_FCz~allIncorrN2_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$N2corrResid<-resid(residual_correctFCzN2)
# residualized incorrect N2
residual_incorrectFCzN2<-lm(allIncorrN2_FCz~allCorrN2_FCz,data=cleanedTibble4SST)
cleanedTibble4SST$N2resid<-resid(residual_incorrectFCzN2)
# residualized correct P3
residual_correctPzP3<-lm(allCorrP3_Pz~allIncorrP3_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$P3corrResid<-resid(residual_correctPzP3)
# residualized incorrect P3
residual_incorrectPzP3<-lm(allIncorrP3_Pz~allCorrP3_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$P3resid<-resid(residual_incorrectPzP3)
# residualized Pe
residual_incorrectPzPe<-lm(goIncorrPe_Pz~goCorrPe_Pz,data=cleanedTibble4SST)
cleanedTibble4SST$PeResid<-resid(residual_incorrectPzPe)9.1 Residualized ERN amplitudes and acculturation predict PROMIS Depression
model1<-lm(PROMISdepress~ERNresid+NAAS+age+sex, data=cleanedTibble4SST)
summary(model1)
Call:
lm(formula = PROMISdepress ~ ERNresid + NAAS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-19.7256 -4.4523 -0.6464 5.0780 14.8117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 41.92412 12.49389 3.356 0.0016 **
ERNresid 0.48054 0.18933 2.538 0.0146 *
NAAS 3.07120 2.97380 1.033 0.3071
age 0.07219 0.09964 0.724 0.4724
sexMale -5.15865 2.56259 -2.013 0.0500 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.163 on 46 degrees of freedom
Multiple R-squared: 0.1856, Adjusted R-squared: 0.1148
F-statistic: 2.622 on 4 and 46 DF, p-value: 0.04683boot_model1<-boot_summary(model1, type='perc', method='residual',R = 10000)Loading required namespace: bootboot_model1 Estimate Lower.bound Upper.bound p.value
(Intercept) 41.92411792 18.0247792 66.5009669 8e-04
ERNresid 0.48054142 0.1101003 0.8460146 0.0099
NAAS 3.07120002 -2.7601030 8.8221921 0.3113
age 0.07218854 -0.1235351 0.2648916 0.4773
sexMale -5.15864774 -10.3413053 -0.1846511 0.0425vif(model1)ERNresid NAAS age sex
1.023638 1.023091 1.051987 1.028995 9.2 Residualized ERN amplitudes and enculturation predict PROMIS Depression
model2<-lm(PROMISdepress~ERNresid+AIES+age+sex, data=cleanedTibble4SST)
summary(model2)
Call:
lm(formula = PROMISdepress ~ ERNresid + AIES + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-20.5536 -4.4204 -0.3033 5.3396 15.4321
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.68501 4.30569 12.701 <2e-16 ***
ERNresid 0.48371 0.19148 2.526 0.0150 *
AIES -0.01299 0.07380 -0.176 0.8611
age 0.05997 0.10068 0.596 0.5544
sexMale -5.03850 2.59090 -1.945 0.0579 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.243 on 46 degrees of freedom
Multiple R-squared: 0.1673, Adjusted R-squared: 0.09492
F-statistic: 2.311 on 4 and 46 DF, p-value: 0.07187boot_model2<-boot_summary(model2, type='perc', method='residual',R = 10000)
boot_model2 Estimate Lower.bound Upper.bound p.value
(Intercept) 54.68501410 46.3611022 62.86617104 <1e-04
ERNresid 0.48371453 0.1093464 0.86141907 0.0096
AIES -0.01298551 -0.1543702 0.13019240 0.874
age 0.05996759 -0.1354888 0.25282254 0.5309
sexMale -5.03850339 -10.2419730 -0.06829282 0.0481vif(model2)ERNresid AIES age sex
1.023972 1.020297 1.050407 1.028716 9.3 Residualized ERN amplitudes and historical loss predict PROMIS Depression
model3<-lm(PROMISdepress~ERNresid+HLS+age+sex, data=cleanedTibble4SST)
summary(model3)
Call:
lm(formula = PROMISdepress ~ ERNresid + HLS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-18.5278 -4.7389 -0.2029 4.3611 15.7359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 39.04479 10.11151 3.861 0.000351 ***
ERNresid 0.47938 0.18634 2.573 0.013386 *
HLS 0.34578 0.21494 1.609 0.114525
age 0.07536 0.09770 0.771 0.444468
sexMale -4.76666 2.52315 -1.889 0.065182 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.05 on 46 degrees of freedom
Multiple R-squared: 0.2111, Adjusted R-squared: 0.1425
F-statistic: 3.078 on 4 and 46 DF, p-value: 0.02504boot_model3<-boot_summary(model3, type='perc', method='residual',R = 10000)
boot_model3 Estimate Lower.bound Upper.bound p.value
(Intercept) 39.04478928 19.17562541 58.8991644 5e-04
ERNresid 0.47938082 0.11607487 0.8498836 0.0108
HLS 0.34578005 -0.07714463 0.7743315 0.1063
age 0.07535835 -0.11773773 0.2678234 0.4489
sexMale -4.76665974 -9.85753589 0.1802791 0.0608vif(model3)ERNresid HLS age sex
1.023627 1.019346 1.044088 1.029812 9.4 Residualized ERN amplitudes and spirituality predict PROMIS Depression
model4<-lm(PROMISdepress~ERNresid+NASS+age+sex, data=cleanedTibble4SST)
summary(model4)
Call:
lm(formula = PROMISdepress ~ ERNresid + NASS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-18.9380 -3.7116 0.1487 3.7880 13.2539
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.2615 4.4674 13.713 <2e-16 ***
ERNresid 0.4088 0.1828 2.237 0.0302 *
NASS -0.3287 0.1350 -2.436 0.0188 *
age 0.1857 0.1076 1.725 0.0912 .
sexMale -5.1106 2.4363 -2.098 0.0415 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.819 on 46 degrees of freedom
Multiple R-squared: 0.2619, Adjusted R-squared: 0.1978
F-statistic: 4.081 on 4 and 46 DF, p-value: 0.006497boot_model4<-boot_summary(model4, type='perc', method='residual',R = 10000)
boot_model4 Estimate Lower.bound Upper.bound p.value
(Intercept) 61.2614900 52.46328286 69.83579412 <1e-04
ERNresid 0.4088358 0.04593785 0.76126548 0.024
NASS -0.3287419 -0.59541459 -0.06426708 0.0122
age 0.1856702 -0.02209107 0.39105009 0.0805
sexMale -5.1105505 -9.91610226 -0.33430094 0.0362vif(model4)ERNresid NASS age sex
1.052677 1.372881 1.354225 1.026226 9.5 Residualized ERN amplitudes predict PROMIS Depression moderated by acculturation
model5<-lm(PROMISdepress~ERNresid*NAAS+age+sex,data= cleanedTibble4SST)
summary(model5)
Call:
lm(formula = PROMISdepress ~ ERNresid * NAAS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-19.7962 -4.9408 -0.4977 4.9607 14.5935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 41.97946 12.60314 3.331 0.00174 **
ERNresid -0.46408 2.06989 -0.224 0.82361
NAAS 3.13349 3.00274 1.044 0.30227
age 0.06194 0.10297 0.602 0.55046
sexMale -4.89843 2.64650 -1.851 0.07075 .
ERNresid:NAAS 0.24980 0.54504 0.458 0.64893
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.225 on 45 degrees of freedom
Multiple R-squared: 0.1894, Adjusted R-squared: 0.09937
F-statistic: 2.103 on 5 and 45 DF, p-value: 0.08244boot_model5<-boot_summary(model5, type='perc', method='residual',R = 10000)
boot_model5 Estimate Lower.bound Upper.bound p.value
(Intercept) 41.97945624 17.5736052 66.9429301 0.0012
ERNresid -0.46407960 -4.5239344 3.7494396 0.8203
NAAS 3.13348935 -2.7114063 8.9000131 0.2942
age 0.06194338 -0.1407346 0.2617626 0.5361
sexMale -4.89843472 -10.1889733 0.2197940 0.0583
ERNresid:NAAS 0.24980059 -0.8682516 1.3118030 0.6533johnson_neyman(
model5,
pred=ERNresid,
modx=NAAS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of resid. ERN amplitudes x PROMIS Depression",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When NAAS is INSIDE the interval [3.60, 4.18], the slope of ERNresid is p <
.05.
Note: The range of observed values of NAAS is [3.05, 4.45]
vif(model5)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid NAAS age sex ERNresid:NAAS
120.251097 1.025191 1.104022 1.078638 119.304341 9.6 Residualized ERN amplitudes predict PROMIS Depression moderated by enculturation
model6<-lm(PROMISdepress~ERNresid*AIES+age+sex, data=cleanedTibble4SST)
summary(model6)
Call:
lm(formula = PROMISdepress ~ ERNresid * AIES + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-20.2550 -4.7074 -0.1787 5.1604 14.8290
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.90917 4.36389 12.583 2.44e-16 ***
ERNresid 0.78671 0.63250 1.244 0.2200
AIES -0.02037 0.07584 -0.269 0.7895
age 0.05926 0.10152 0.584 0.5623
sexMale -4.64000 2.72967 -1.700 0.0961 .
ERNresid:AIES -0.00725 0.01441 -0.503 0.6174
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.302 on 45 degrees of freedom
Multiple R-squared: 0.172, Adjusted R-squared: 0.07998
F-statistic: 1.869 on 5 and 45 DF, p-value: 0.1187boot_model6<-boot_summary(model6, type = 'perc',method = 'residual',R = 10000)
boot_model6 Estimate Lower.bound Upper.bound p.value
(Intercept) 54.909168154 46.34796781 63.62641277 <1e-04
ERNresid 0.786706408 -0.44276856 2.01612704 0.2104
AIES -0.020368350 -0.17000892 0.12478752 0.7832
age 0.059262826 -0.13982387 0.25872594 0.5603
sexMale -4.639999186 -9.98789234 0.59007859 0.0836
ERNresid:AIES -0.007249777 -0.03589079 0.02060679 0.6181johnson_neyman(
model6,
pred=ERNresid,
modx=AIES,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of resid. ERN amplitudes x PROMIS Depression",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When AIES is INSIDE the interval [26.20, 48.51], the slope of ERNresid is p
< .05.
Note: The range of observed values of AIES is [18.00, 75.00]
vif(model6)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid AIES age sex ERNresid:AIES
10.991573 1.059997 1.050607 1.123325 11.285358 9.7 Residualized ERN amplitudes predict PROMIS Depression moderated by historical loss
model7<-lm(PROMISdepress~ERNresid*HLS+age+sex,data=cleanedTibble4SST)
summary(model7)
Call:
lm(formula = PROMISdepress ~ ERNresid * HLS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-19.0571 -4.5595 0.1548 4.4206 15.4637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.41370 10.47627 3.285 0.00198 **
ERNresid -2.68377 2.16716 -1.238 0.22200
HLS 0.41868 0.21807 1.920 0.06122 .
age 0.11589 0.10040 1.154 0.25446
sexMale -4.28827 2.51360 -1.706 0.09490 .
ERNresid:HLS 0.07344 0.05013 1.465 0.14991
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.964 on 45 degrees of freedom
Multiple R-squared: 0.2471, Adjusted R-squared: 0.1634
F-statistic: 2.953 on 5 and 45 DF, p-value: 0.02176boot_model7<-boot_summary(model7, type='perc', method='residual',R = 10000)
boot_model7 Estimate Lower.bound Upper.bound p.value
(Intercept) 34.4137005 13.37190722 54.8179223 0.001
ERNresid -2.6837739 -6.90518073 1.5568738 0.2182
HLS 0.4186787 0.00736690 0.8569256 0.0461
age 0.1158890 -0.08070560 0.3132751 0.2498
sexMale -4.2882683 -9.38451653 0.6069322 0.091
ERNresid:HLS 0.0734372 -0.02483867 0.1709820 0.1392johnson_neyman(
model7,
pred=ERNresid,
modx=HLS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of resid. ERN amplitudes x PROMIS Depression",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
When HLS is INSIDE the interval [41.86, 58.95], the slope of ERNresid is p
< .05.
Note: The range of observed values of HLS is [30.00, 50.00]
vif(model7)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid HLS age sex ERNresid:HLS
141.906411 1.075347 1.129906 1.047493 142.415303 9.8 Residualized ERN amplitudes predict PROMIS Depression moderated by spirituality
model8<-lm(PROMISdepress~ERNresid*NASS+age+sex,data= cleanedTibble4SST)
summary(model8)
Call:
lm(formula = PROMISdepress ~ ERNresid * NASS + age + sex, data = cleanedTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-19.0103 -3.8103 0.1009 4.0828 13.1655
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60.49521 4.87882 12.400 4.09e-16 ***
ERNresid 0.78761 0.94012 0.838 0.4066
NASS -0.30186 0.15110 -1.998 0.0518 .
age 0.17864 0.10996 1.625 0.1112
sexMale -5.19141 2.46649 -2.105 0.0409 *
ERNresid:NASS -0.01021 0.02486 -0.411 0.6831
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.881 on 45 degrees of freedom
Multiple R-squared: 0.2647, Adjusted R-squared: 0.183
F-statistic: 3.24 on 5 and 45 DF, p-value: 0.01392boot_model8<-boot_summary(model8, type='perc', method='residual',R = 10000)
boot_model8 Estimate Lower.bound Upper.bound p.value
(Intercept) 60.49520567 51.07553469 69.739032889 <1e-04
ERNresid 0.78761069 -1.05787290 2.609886195 0.3825
NASS -0.30186492 -0.59154722 -0.009333231 0.042
age 0.17863547 -0.03622401 0.393432306 0.1011
sexMale -5.19140961 -10.02705147 -0.390891496 0.0342
ERNresid:NASS -0.01021405 -0.05830201 0.038306876 0.6582johnson_neyman(
model8,
pred=ERNresid,
modx=NASS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of resid. ERN amplitudes x PROMIS Depression",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When NASS is INSIDE the interval [31.45, 39.24], the slope of ERNresid is p
< .05.
Note: The range of observed values of NASS is [11.00, 50.00]
vif(model8)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid NASS age sex ERNresid:NASS
27.345413 1.689483 1.387874 1.032801 26.522430 10 Multiple linear regression and moderation analyses in MID
tibble4MID<-tibble(
#Subject=dfMID$Subject,
Age=dfMID$Age,
Sex=dfMID$Gender,
"P300 Pz Loss"=dfMID$P3pzMIDcueLoss,
#cueNogainPzP3=dfMID$P3pzMIDcueNogain,
"P300 Pz Gain"=dfMID$P3pzMIDcueGain,
"SPN Fz Success"=dfMID$SPNfzMIDfeedbackSuccess,
#failFzSPN=dfMID$SPNfzMIDfeedbackFail,
'RewP FCz Gain'=dfMID$RewPfczMIDfeedbackGain,
#CDDRposRein=dfMID$CDDR_PosReinforcement,
#CDDRnegRein=dfMID$CDDR_NegReinforcement,
"PROMIS Anxiety"=dfMID$PROMIS_AnxietyTscore,
"PROMIS Alcohol Use"=dfMID$PROMIS_AlcoUseTscore,
"PROMIS Depression"=dfMID$PROMIS_DepressTscore,
#PROMISappealUse30=dfMID$PROMIS_AppealSubUse30DaysTscore,
#PROMISappealUse3=dfMID$PROMIS_AppealSubUse3MonthTscore,
#phqSuicide=dfMID$PHQ9_Suicide,
#Age=dfMID$Age,
#Sex=dfMID$Gender,
#Income=dfMID$Income,
#Education=dfMID$Education_4levelCategory,
#MRT=dfMID$ss_mrt_calibration,
#goErrorEasyRT=dfMID$ss_meanrt_erroreasy,
#goErrorHardRT=dfMID$ss_meanrt_errorhard,
"AIES"=dfMID$AIES_scale,
"NAAS"=dfMID$NAAS_scale,
"NASS"=dfMID$NASS_scale,
"HLS"=dfMID$HLS_scale
)
cleanedData4Hyp1b<-na.omit(tibble4MID) # retains 44participants
tibble4MIDcalc<-tibble(
Age=tibble4MID$Age,
Sex=tibble4MID$Sex,
P3PzLoss=tibble4MID$`P300 Pz Loss`,
P3PzGain=tibble4MID$`P300 Pz Gain`,
RewPFCzGain=tibble4MID$`RewP FCz Gain`,
successSPN=tibble4MID$`SPN Fz Success`,
promisDep=tibble4MID$`PROMIS Depression`,
promisAnx=tibble4MID$`PROMIS Anxiety`,
promisAlcUse=tibble4MID$`PROMIS Alcohol Use`,
AIES=tibble4MID$AIES,
NAAS=tibble4MID$NAAS,
NASS=tibble4MID$NASS,
HLS=tibble4MID$HLS
)
cleanedTibble4MIDcalc<-na.omit(tibble4MIDcalc)10.1 Cue Loss P3 amplitudes and acculturation predict PROMIS Alcohol Use
model9<-lm(`PROMIS Alcohol Use`~`P300 Pz Loss`+NAAS+Age+Sex, data=cleanedData4Hyp1b)
summary(model9)
Call:
lm(formula = `PROMIS Alcohol Use` ~ `P300 Pz Loss` + NAAS + Age +
Sex, data = cleanedData4Hyp1b)
Residuals:
Min 1Q Median 3Q Max
-9.4883 -1.2260 -0.2065 1.5432 12.0419
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.05858 9.15087 4.050 0.000236 ***
`P300 Pz Loss` -0.50979 0.27607 -1.847 0.072408 .
NAAS 3.67439 2.09913 1.750 0.087908 .
Age -0.02814 0.07403 -0.380 0.705881
SexMale 0.43327 1.66143 0.261 0.795634
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.503 on 39 degrees of freedom
Multiple R-squared: 0.1638, Adjusted R-squared: 0.07799
F-statistic: 1.909 on 4 and 39 DF, p-value: 0.1282boot_model9<-boot_summary(model9, type='perc', method='residual',R = 10000)
boot_model9 Estimate Lower.bound Upper.bound p.value
(Intercept) 37.05858093 19.1864552 54.93630683 <1e-04
`P300 Pz Loss` -0.50978669 -1.0660389 0.05594956 0.0731
NAAS 3.67438933 -0.4437887 7.81144566 0.076
Age -0.02814448 -0.1747962 0.11816925 0.6938
SexMale 0.43326914 -2.8729624 3.82950373 0.7909vif(model9)`P300 Pz Loss` NAAS Age Sex
1.242421 1.024610 1.206926 1.051933 10.2 Cue Loss P3 amplitudes and enculturation predit PROMIS Alcohol Use
model10<-lm(`PROMIS Alcohol Use`~`P300 Pz Loss`+AIES+Age+Sex, data=cleanedData4Hyp1b)
summary(model10)
Call:
lm(formula = `PROMIS Alcohol Use` ~ `P300 Pz Loss` + AIES + Age +
Sex, data = cleanedData4Hyp1b)
Residuals:
Min 1Q Median 3Q Max
-11.4164 -0.9719 0.0337 1.2461 11.0693
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.38500 3.58158 14.905 <2e-16 ***
`P300 Pz Loss` -0.55437 0.28511 -1.944 0.0591 .
AIES -0.03910 0.06146 -0.636 0.5283
Age -0.03993 0.07632 -0.523 0.6038
SexMale 0.60339 1.71322 0.352 0.7266
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.652 on 39 degrees of freedom
Multiple R-squared: 0.1073, Adjusted R-squared: 0.01577
F-statistic: 1.172 on 4 and 39 DF, p-value: 0.338boot_model10<-boot_summary(model10, type='perc', method='residual',R = 10000)
boot_model10 Estimate Lower.bound Upper.bound p.value
(Intercept) 53.38499775 46.2241688 60.52097952 <1e-04
`P300 Pz Loss` -0.55436999 -1.1446219 0.02330244 0.061
AIES -0.03910370 -0.1614053 0.08276589 0.5271
Age -0.03992563 -0.1971436 0.11138381 0.6178
SexMale 0.60339428 -2.9667186 4.02662306 0.7325vif(model10)`P300 Pz Loss` AIES Age Sex
1.241356 1.028327 1.201669 1.047821 10.3 Cue Loss P3 amplitudes and historical loss predict PROMIS Alcohol Use
model11<-lm(`PROMIS Alcohol Use`~`P300 Pz Loss`+HLS+Sex+Age, data=cleanedData4Hyp1b)
summary(model11)
Call:
lm(formula = `PROMIS Alcohol Use` ~ `P300 Pz Loss` + HLS + Sex +
Age, data = cleanedData4Hyp1b)
Residuals:
Min 1Q Median 3Q Max
-11.8522 -1.1971 0.1136 1.2974 9.8846
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.323750 7.930435 6.598 7.68e-08 ***
`P300 Pz Loss` -0.542295 0.290182 -1.869 0.0692 .
HLS -0.002512 0.160725 -0.016 0.9876
SexMale 0.618344 1.730905 0.357 0.7228
Age -0.045954 0.078289 -0.587 0.5606
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.677 on 39 degrees of freedom
Multiple R-squared: 0.09806, Adjusted R-squared: 0.005558
F-statistic: 1.06 on 4 and 39 DF, p-value: 0.3892boot_model11<-boot_summary(model11, type='perc', method='residual',R = 10000)
boot_model11 Estimate Lower.bound Upper.bound p.value
(Intercept) 52.323749634 36.9904574 67.68246703 <1e-04
`P300 Pz Loss` -0.542294871 -1.1151274 0.03395812 0.0646
HLS -0.002512433 -0.3050851 0.31700840 0.9771
SexMale 0.618344363 -2.8499949 3.98301793 0.7112
Age -0.045953717 -0.1980508 0.10523374 0.5578vif(model11)`P300 Pz Loss` HLS Sex Age
1.272684 1.163234 1.058589 1.251430 10.4 Cue Loss P3 amplitudes and spirituality predict Alcohol Use
model12<-lm(`PROMIS Alcohol Use`~`P300 Pz Loss`+NASS+Age+Sex, data=cleanedData4Hyp1b)
summary(model12)
Call:
lm(formula = `PROMIS Alcohol Use` ~ `P300 Pz Loss` + NASS + Age +
Sex, data = cleanedData4Hyp1b)
Residuals:
Min 1Q Median 3Q Max
-11.8061 -1.2507 0.1061 1.2581 9.9403
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.319012 3.572685 14.644 <2e-16 ***
`P300 Pz Loss` -0.543003 0.286020 -1.898 0.0651 .
NASS -0.005353 0.088189 -0.061 0.9519
Age -0.043477 0.084317 -0.516 0.6090
SexMale 0.593373 1.781367 0.333 0.7408
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.676 on 39 degrees of freedom
Multiple R-squared: 0.09814, Adjusted R-squared: 0.005646
F-statistic: 1.061 on 4 and 39 DF, p-value: 0.3887boot_model12<-boot_summary(model12, type='perc', method='residual',R = 10000)
boot_model12 Estimate Lower.bound Upper.bound p.value
(Intercept) 52.319012215 45.2731810 59.21095869 <1e-04
`P300 Pz Loss` -0.543002560 -1.0910482 0.01716475 0.0592
NASS -0.005353336 -0.1791385 0.16920206 0.9561
Age -0.043476966 -0.2111959 0.12317847 0.6056
SexMale 0.593373250 -2.9890737 4.03524547 0.7299vif(model12)`P300 Pz Loss` NASS Age Sex
1.236544 1.326642 1.451710 1.121310 10.5 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by acculturation
model13<-lm(promisAlcUse~P3PzLoss*NAAS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model13)
Call:
lm(formula = promisAlcUse ~ P3PzLoss * NAAS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-9.7526 -1.5083 -0.1721 1.6587 11.1550
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.37916 11.67775 3.715 0.000652 ***
P3PzLoss -2.98274 2.83835 -1.051 0.299955
NAAS 2.18570 2.70641 0.808 0.424347
Age -0.03979 0.07544 -0.527 0.600913
SexMale 0.32167 1.67130 0.192 0.848403
P3PzLoss:NAAS 0.62636 0.71547 0.875 0.386835
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.517 on 38 degrees of freedom
Multiple R-squared: 0.1803, Adjusted R-squared: 0.07243
F-statistic: 1.672 on 5 and 38 DF, p-value: 0.1651boot_model13<-boot_summary(model13, type='perc', method='residual',R = 10000)
boot_model13 Estimate Lower.bound Upper.bound p.value
(Intercept) 43.3791645 20.0545160 66.8771364 <1e-04
P3PzLoss -2.9827417 -8.7331490 2.8079237 0.2791
NAAS 2.1856968 -3.2846136 7.6066815 0.4355
Age -0.0397927 -0.1908026 0.1116093 0.6094
SexMale 0.3216651 -2.9552625 3.6941857 0.8302
P3PzLoss:NAAS 0.6263555 -0.8092312 2.0960219 0.3675johnson_neyman(
model13,
pred=P3PzLoss,
modx=NAAS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of P300 Amplitudes to Loss x PROMIS Alcohol Use",
y.label="Slope of P300 Amplitudes to Loss",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When NAAS is INSIDE the interval [3.70, 3.82], the slope of P3PzLoss is p <
.05.
Note: The range of observed values of NAAS is [3.30, 4.50]
vif(model13)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3PzLoss NAAS Age Sex P3PzLoss:NAAS
130.540613 1.692996 1.245679 1.058089 128.260553 10.6 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by enculturation
model14<-lm(promisAlcUse~P3PzLoss*AIES+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model14)
Call:
lm(formula = promisAlcUse ~ P3PzLoss * AIES + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-11.8943 -0.9810 0.1594 1.4629 8.2664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.87844 3.83525 13.005 1.44e-15 ***
P3PzLoss 0.89188 0.75109 1.187 0.2424
AIES 0.06222 0.07671 0.811 0.4224
Age -0.03846 0.07331 -0.525 0.6029
SexMale 0.51000 1.64612 0.310 0.7584
P3PzLoss:AIES -0.04414 0.02134 -2.068 0.0455 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.469 on 38 degrees of freedom
Multiple R-squared: 0.1976, Adjusted R-squared: 0.09204
F-statistic: 1.872 on 5 and 38 DF, p-value: 0.1223boot_model14<-boot_summary(model14, type='perc', method='residual',R = 10000)
boot_model14 Estimate Lower.bound Upper.bound p.value
(Intercept) 49.87844434 42.00767298 5.761399e+01 <1e-04
P3PzLoss 0.89188046 -0.67683162 2.428748e+00 0.2514
AIES 0.06221504 -0.09794887 2.196063e-01 0.4165
Age -0.03845545 -0.18359562 1.096207e-01 0.6118
SexMale 0.51000412 -2.92289768 3.614940e+00 0.7583
P3PzLoss:AIES -0.04413779 -0.08808263 1.525169e-04 0.0511johnson_neyman(
model14,
pred=P3PzLoss,
modx=AIES,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of P300 Amplitudes to Loss x PROMIS Alcohol Use",
y.label="Slope of P300 Amplitudes to Loss",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When AIES is OUTSIDE the interval [-570.93, 32.77], the slope of P3PzLoss
is p < .05.
Note: The range of observed values of AIES is [19.00, 66.00]
vif(model14)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3PzLoss AIES Age Sex P3PzLoss:AIES
9.338481 1.736885 1.201782 1.048610 9.266174 10.7 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by spirituality
model15<-lm(promisAlcUse~P3PzLoss*NASS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model15)
Call:
lm(formula = promisAlcUse ~ P3PzLoss * NASS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-11.8965 -1.0556 0.1937 1.3716 9.2809
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.07048 4.01710 12.464 5.36e-15 ***
P3PzLoss 0.39325 0.83084 0.473 0.639
NASS 0.06502 0.10552 0.616 0.541
Age -0.04579 0.08387 -0.546 0.588
SexMale 0.46297 1.77477 0.261 0.796
P3PzLoss:NASS -0.02910 0.02426 -1.199 0.238
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.65 on 38 degrees of freedom
Multiple R-squared: 0.131, Adjusted R-squared: 0.0167
F-statistic: 1.146 on 5 and 38 DF, p-value: 0.3533boot_model15<-boot_summary(model15, type='perc', method='residual',R = 10000)
boot_model15 Estimate Lower.bound Upper.bound p.value
(Intercept) 50.07047649 42.01540752 57.66735521 <1e-04
P3PzLoss 0.39324927 -1.24591796 2.11959352 0.6005
NASS 0.06502059 -0.13838138 0.27578873 0.5151
Age -0.04578676 -0.21201498 0.11746648 0.5621
SexMale 0.46297404 -3.05532617 3.85509368 0.7835
P3PzLoss:NASS -0.02909850 -0.07811971 0.01805369 0.2122johnson_neyman(
model15,
pred=P3PzLoss,
modx=NASS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of P300 Amplitudes to Loss x PROMIS Alcohol Use",
y.label="Slope of P300 Amplitudes to Loss",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When NASS is INSIDE the interval [33.41, 51.12], the slope of P3PzLoss is p
< .05.
Note: The range of observed values of NASS is [9.00, 49.00]
vif(model15)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3PzLoss NASS Age Sex P3PzLoss:NASS
10.551230 1.920540 1.452476 1.125534 10.483681 10.8 Cue Loss P3 amplitudes predict PROMIS Alcohol Use mdoerated by historical loss
model16<-lm(promisAlcUse~P3PzLoss*HLS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model16)
Call:
lm(formula = promisAlcUse ~ P3PzLoss * HLS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-11.6846 -1.3158 0.1452 1.2265 10.2018
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.04552 9.77385 4.916 1.73e-05 ***
P3PzLoss 1.61185 2.86056 0.563 0.576
HLS 0.10551 0.21560 0.489 0.627
Age -0.05418 0.07947 -0.682 0.499
SexMale 0.83434 1.76369 0.473 0.639
P3PzLoss:HLS -0.04965 0.06559 -0.757 0.454
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.702 on 38 degrees of freedom
Multiple R-squared: 0.1115, Adjusted R-squared: -0.005449
F-statistic: 0.9534 on 5 and 38 DF, p-value: 0.4583boot_model16<-boot_summary(model16, type='perc', method='residual',R = 10000)
boot_model16 Estimate Lower.bound Upper.bound p.value
(Intercept) 48.04552417 29.3135303 66.84973018 <1e-04
P3PzLoss 1.61184984 -3.8776741 7.18062914 0.5573
HLS 0.10551041 -0.3036491 0.52012389 0.6225
Age -0.05418127 -0.2076216 0.09750254 0.4655
SexMale 0.83433985 -2.6815151 4.18503269 0.6203
P3PzLoss:HLS -0.04964810 -0.1775188 0.07570930 0.4375johnson_neyman(
model16,
pred=P3PzLoss,
modx=HLS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of P300 Amplitudes to Loss x PROMIS Alcohol Use",
y.label="Slope of P300 Amplitudes to Loss",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
The Johnson-Neyman interval could not be found. Is the p value for your
interaction term below the specified alpha?
vif(model16)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3PzLoss HLS Age Sex P3PzLoss:HLS
122.320697 2.070136 1.275285 1.087040 129.702832 10.9 QQ plots for SPN amplitudes
successSPNqq<-ggplot2::ggplot(data = tibble4MIDcalc, mapping = aes(sample = successSPN)) +
stat_qq_band(alpha = 0.5, conf = 0.95, bandType = "pointwise", fill = "green") +
stat_qq_line(identity = TRUE) +
stat_qq_point(col = "black") +
labs(x = "Theoretical Quantiles", y = "Sample Quantiles") + theme_bw()
successSPNqq
10.10 Success SPN amplitudes and acculturation predict PROMIS Depression
model17<-lm(promisDep~successSPN+NAAS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model17)
Call:
lm(formula = promisDep ~ successSPN + NAAS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-18.562 -4.175 -0.125 4.896 17.491
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 57.78286 15.41746 3.748 0.000577 ***
successSPN 0.00477 0.40707 0.012 0.990710
NAAS 0.01637 3.64479 0.004 0.996439
Age -0.01016 0.11817 -0.086 0.931934
SexMale -5.30539 2.83097 -1.874 0.068429 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.833 on 39 degrees of freedom
Multiple R-squared: 0.08376, Adjusted R-squared: -0.01021
F-statistic: 0.8913 on 4 and 39 DF, p-value: 0.4783boot_model17<-boot_summary(model17, type='perc',method='residual',R = 10000)
boot_model17 Estimate Lower.bound Upper.bound p.value
(Intercept) 57.782863598 26.8299336 88.3256239 6e-04
successSPN 0.004770211 -0.8086792 0.8113201 0.9765
NAAS 0.016370409 -7.2462976 7.2195097 0.9903
Age -0.010158064 -0.2393146 0.2194329 0.9202
SexMale -5.305391460 -10.8061559 0.2312428 0.0621vif(model17)successSPN NAAS Age Sex
1.002073 1.020930 1.016245 1.009402 10.11 Success SPN amplitudes and enculturation predict PROMIS Depression
model18<-lm(promisDep~successSPN+AIES+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model18)
Call:
lm(formula = promisDep ~ successSPN + AIES + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-18.378 -4.777 -0.189 5.488 17.951
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.972582 5.230103 10.702 3.62e-13 ***
successSPN 0.004629 0.404672 0.011 0.9909
AIES 0.066432 0.102714 0.647 0.5216
Age -0.021992 0.118129 -0.186 0.8533
SexMale -5.298331 2.807030 -1.888 0.0665 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.791 on 39 degrees of freedom
Multiple R-squared: 0.09348, Adjusted R-squared: 0.0005078
F-statistic: 1.005 on 4 and 39 DF, p-value: 0.4164boot_model18<-boot_summary(model18, type='perc',method='residual',R=10000)
boot_model18 Estimate Lower.bound Upper.bound p.value
(Intercept) 55.972581991 45.5650469 66.1376420 <1e-04
successSPN 0.004628775 -0.7919063 0.7906365 0.9942
AIES 0.066432309 -0.1344970 0.2712292 0.5082
Age -0.021991900 -0.2527028 0.2161183 0.85
SexMale -5.298330655 -10.7611238 0.3342002 0.0632vif(model18)successSPN AIES Age Sex
1.000918 1.024330 1.026521 1.003047 10.12 Success SPN amplitudes and historical loss predict PROMIS Depression
model19<-lm(promisDep~successSPN+HLS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model19)
Call:
lm(formula = promisDep ~ successSPN + HLS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-19.4058 -4.2492 -0.1113 4.9590 16.9418
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.22890 13.06156 3.386 0.00163 **
successSPN -0.01756 0.40113 -0.044 0.96530
HLS 0.28899 0.26163 1.105 0.27611
Age 0.03141 0.12154 0.258 0.79741
SexMale -4.87159 2.80638 -1.736 0.09048 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.713 on 39 degrees of freedom
Multiple R-squared: 0.1116, Adjusted R-squared: 0.02043
F-statistic: 1.224 on 4 and 39 DF, p-value: 0.3163boot_model19<-boot_summary(model19, type='perc',method='residual',R=10000)
boot_model19 Estimate Lower.bound Upper.bound p.value
(Intercept) 44.22890333 17.6929460 70.4527090 4e-04
successSPN -0.01756433 -0.7934015 0.7627172 0.9644
HLS 0.28899091 -0.2286709 0.8148420 0.2803
Age 0.03141452 -0.2106780 0.2723925 0.795
SexMale -4.87159233 -10.4293214 0.6240753 0.0858vif(model19)successSPN HLS Age Sex
1.003482 1.133085 1.108794 1.022976 10.13 Success SPN amplitudes and spirituality predict PROMIS Depression
model20<-lm(promisDep~successSPN+NASS+Age+Sex,data=cleanedTibble4MIDcalc)
summary(model20)
Call:
lm(formula = promisDep ~ successSPN + NASS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-18.0766 -3.6994 -0.2879 4.2371 14.3074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.6091 5.2588 12.476 3.43e-15 ***
successSPN -0.2696 0.3982 -0.677 0.5024
NASS -0.3400 0.1446 -2.351 0.0239 *
Age 0.1284 0.1246 1.030 0.3094
SexMale -7.0129 2.7392 -2.560 0.0144 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.331 on 39 degrees of freedom
Multiple R-squared: 0.1975, Adjusted R-squared: 0.1152
F-statistic: 2.4 on 4 and 39 DF, p-value: 0.06645boot_model20<-boot_summary(model20, type='perc',method='residual',R = 10000)
boot_model20 Estimate Lower.bound Upper.bound p.value
(Intercept) 65.6090567 55.0203404 75.9352579 <1e-04
successSPN -0.2696248 -1.0700563 0.5278483 0.4935
NASS -0.3399550 -0.6307701 -0.0581637 0.0189
Age 0.1283694 -0.1191762 0.3748331 0.3089
SexMale -7.0129049 -12.5056591 -1.5727316 0.0093vif(model20)successSPN NASS Age Sex
1.095006 1.451333 1.290897 1.078975 10.14 Success SPN amplitudes predict PROMIS Depression moderated by acculturation
model21<-lm(promisDep~successSPN*NAAS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model21)
Call:
lm(formula = promisDep ~ successSPN * NAAS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-18.7178 -4.2465 0.0595 4.8030 17.4905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60.43306 20.83489 2.901 0.00616 **
successSPN -1.00122 5.25366 -0.191 0.84987
NAAS -0.65350 5.07778 -0.129 0.89828
Age -0.01091 0.11971 -0.091 0.92789
SexMale -5.36472 2.88318 -1.861 0.07054 .
successSPN:NAAS 0.25699 1.33795 0.192 0.84871
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.931 on 38 degrees of freedom
Multiple R-squared: 0.08465, Adjusted R-squared: -0.03579
F-statistic: 0.7028 on 5 and 38 DF, p-value: 0.6248boot_model21<-boot_summary(model21, type='perc',method='residual',R=10000)
boot_model21 Estimate Lower.bound Upper.bound p.value
(Intercept) 60.43306313 18.3034407 101.7178474 0.0037
successSPN -1.00122069 -11.4918139 9.6817575 0.8426
NAAS -0.65350259 -10.5856219 9.4215485 0.9055
Age -0.01090659 -0.2513004 0.2339537 0.9487
SexMale -5.36471889 -10.9617549 0.4683073 0.0702
successSPN:NAAS 0.25698835 -2.4626774 2.9189628 0.8405johnson_neyman(
model21,
pred=successSPN,
modx=NAAS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of SPN amplitudes to Success x PROMIS Depression",
y.label="Slope of SPN amplitudes to Success",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
The Johnson-Neyman interval could not be found. Is the p value for your
interaction term below the specified alpha?
vif(model21)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif successSPN NAAS Age Sex successSPN:NAAS
162.788582 1.932583 1.017323 1.021121 164.683434 10.15 Success SPN amplitudes predict PROMIS Depression moderated by enculturation
model22<-lm(promisDep~successSPN*AIES+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model22)
Call:
lm(formula = promisDep ~ successSPN * AIES + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-18.2903 -4.5943 -0.2469 5.4401 17.7687
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.77015 6.01619 9.436 1.67e-11 ***
successSPN -0.38873 1.46867 -0.265 0.7927
AIES 0.03344 0.15748 0.212 0.8330
Age -0.01457 0.12247 -0.119 0.9059
SexMale -5.20643 2.85987 -1.821 0.0766 .
successSPN:AIES 0.01206 0.04326 0.279 0.7818
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.885 on 38 degrees of freedom
Multiple R-squared: 0.09534, Adjusted R-squared: -0.0237
F-statistic: 0.8009 on 5 and 38 DF, p-value: 0.556boot_model22<-boot_summary(model22, type='perc',method='residual',R=10000)
johnson_neyman(
model22,
pred=successSPN,
modx=AIES,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of SPN amplitudes to Success x PROMIS Depression",
y.label="Slope of SPN amplitudes to Success",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
The Johnson-Neyman interval could not be found. Is the p value for your
interaction term below the specified alpha?
boot_model22 Estimate Lower.bound Upper.bound p.value
(Intercept) 56.77014820 45.05119746 68.64481971 <1e-04
successSPN -0.38872771 -3.22962646 2.42991838 0.7738
AIES 0.03343999 -0.27795672 0.33760905 0.8478
Age -0.01457382 -0.25661809 0.22632210 0.9164
SexMale -5.20642616 -10.82664391 0.47911259 0.0714
successSPN:AIES 0.01206470 -0.07114781 0.09718801 0.7822vif(model22)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif successSPN AIES Age Sex successSPN:AIES
12.872040 2.350901 1.077333 1.016544 14.165941 10.16 Success SPN amplitudes predict PROMIS Depression moderated by historical loss
model23<-lm(promisDep~successSPN*HLS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model23)
Call:
lm(formula = promisDep ~ successSPN * HLS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-19.5914 -3.8796 -0.2954 4.8182 17.5846
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.96448 19.75483 2.630 0.0122 *
successSPN -1.98929 3.77158 -0.527 0.6010
HLS 0.11948 0.41674 0.287 0.7759
Age 0.01726 0.12560 0.137 0.8914
SexMale -5.08088 2.86061 -1.776 0.0837 .
successSPN:HLS 0.04604 0.08756 0.526 0.6021
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.786 on 38 degrees of freedom
Multiple R-squared: 0.118, Adjusted R-squared: 0.001917
F-statistic: 1.017 on 5 and 38 DF, p-value: 0.4215boot_model23<-boot_summary(model23, type='perc',method='residual',R=10000)
boot_model23 Estimate Lower.bound Upper.bound p.value
(Intercept) 51.96448215 13.2208954 91.5779392 0.0089
successSPN -1.98928614 -9.4681510 5.6825392 0.5856
HLS 0.11947662 -0.7112846 0.9392881 0.7983
Age 0.01726263 -0.2350217 0.2663162 0.8864
SexMale -5.08088404 -10.7517142 0.4794239 0.0688
successSPN:HLS 0.04604312 -0.1294742 0.2200223 0.5905johnson_neyman(
model23,
pred=successSPN,
modx=HLS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of SPN amplitudes to Success x PROMIS Depression",
y.label="Slope of SPN amplitudes to Success",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
The Johnson-Neyman interval could not be found. Is the p value for your
interaction term below the specified alpha?
vif(model23)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif successSPN HLS Age Sex successSPN:HLS
87.066436 2.821552 1.162154 1.043172 89.727405 10.17 Success SPN amplitudes predict PROMIS Depression moderated by spirituality
model24<-lm(promisDep~successSPN*NASS+Age+Sex, data=cleanedTibble4MIDcalc)
summary(model24)
Call:
lm(formula = promisDep ~ successSPN * NASS + Age + Sex, data = cleanedTibble4MIDcalc)
Residuals:
Min 1Q Median 3Q Max
-16.9474 -3.9560 0.5244 3.5296 13.2158
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.95456 7.07702 6.917 3.18e-08 ***
successSPN 4.53880 1.55951 2.910 0.00601 **
NASS 0.06268 0.18200 0.344 0.73246
Age 0.19437 0.11422 1.702 0.09697 .
SexMale -7.17869 2.46867 -2.908 0.00605 **
successSPN:NASS -0.14434 0.04556 -3.168 0.00302 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.605 on 38 degrees of freedom
Multiple R-squared: 0.3652, Adjusted R-squared: 0.2817
F-statistic: 4.372 on 5 and 38 DF, p-value: 0.003056boot_model24<-boot_summary(model24, type='perc',method='residual',R=10000)
boot_model24 Estimate Lower.bound Upper.bound p.value
(Intercept) 48.95455598 35.08549122 62.74279924 <1e-04
successSPN 4.53879903 1.49000652 7.57662924 0.0048
NASS 0.06267679 -0.29154197 0.41292081 0.7438
Age 0.19437411 -0.02950125 0.41480830 0.0903
SexMale -7.17868852 -11.98658786 -2.39185896 0.002
successSPN:NASS -0.14434358 -0.23367438 -0.05417985 0.0021johnson_neyman(
model24,
pred=successSPN,
modx=NASS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of SPN amplitudes to Success x PROMIS Depression",
y.label="Slope of SPN amplitudes to Success",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When NASS is OUTSIDE the interval [23.31, 37.00], the slope of successSPN
is p < .05.
Note: The range of observed values of NASS is [9.00, 49.00]
vif(model24)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif successSPN NASS Age Sex successSPN:NASS
20.683471 2.832194 1.335320 1.079460 19.492861 11 Multiple linear regression and moderation analyses in CPV
data4matrixH2<-tibble(
Age=dfLPP$Age,
Sex=dfLPP$Sex,
#CDDRposRein=dfLPP$CDDR_PosReinforcement,
#CDDRnegRein=dfLPP$CDDR_NegReinforcement,
promisDep=dfLPP$PROMIS_DepressTscore,
promisAnx=dfLPP$PROMIS_AnxietyTscore,
promisAlcUse=dfLPP$PROMIS_AlcoUseTscore,
#promisAppeal30=dfLPP$PROMIS_AppealSubUse30DaysTscore,
#promisAppeal3=dfLPP$PROMIS_AppealSubUse3MonthTscore,
eCulturalLPP=dfLPP$Early_LPPPz.Average_MastRef_Cultural,
eNeutralLPP=dfLPP$Early_LPPPz.Average_MastRef_Neutral,
CulturalLPP=dfLPP$LPP_Pz_Cultural,
NeutralLPP=dfLPP$LPP_Pz_Neutral,
mCulturalLPP=dfLPP$Mid_LPP_Pz.Average_MastRef_Cultural,
mNeutralLPP=dfLPP$Mid_LPP_Pz.Average_MastRef_Neutral,
HLS=dfLPP$hlsTot,
AIES=dfLPP$AIES_scale,
NAAS=dfLPP$NAAS_scale,
NASS=dfLPP$NASS_scale
)
cleanedData4matrixH2<-na.omit(data4matrixH2) # retains 40 participants (did not drop any of them)
# create residualized (to culture) LPP, early LPP, and mid LPP amplitudes according to method proposed by Meyer et al., 2017
residualCulturalPzEarlyLPP<-lm(eCulturalLPP~eNeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residEarlyLPP<-resid(residualCulturalPzEarlyLPP)
residualCulturalPzLPP<-lm(CulturalLPP~NeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residLPP<-resid(residualCulturalPzLPP)
residualCulturalPzMidLPP<-lm(mCulturalLPP~mNeutralLPP,data=cleanedData4matrixH2)
cleanedData4matrixH2$residMidLPP<-resid(residualCulturalPzMidLPP)11.1 Residualized Mid-LPP and enculturation predict PROMIS Depression
model25<-lm(promisDep~residMidLPP+AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model25)
Call:
lm(formula = promisDep ~ residMidLPP + AIES + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-14.237 -5.633 -1.497 5.837 22.353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.40653 8.02122 5.785 1.48e-06 ***
residMidLPP 2.71848 0.91712 2.964 0.00543 **
AIES 0.08913 0.07110 1.254 0.21831
Age -0.03040 0.14023 -0.217 0.82962
Sex 2.19102 3.44802 0.635 0.52927
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.628 on 35 degrees of freedom
Multiple R-squared: 0.2319, Adjusted R-squared: 0.1441
F-statistic: 2.641 on 4 and 35 DF, p-value: 0.05boot_model25<-boot_summary(model25, type='perc',method='residual',R=10000)
boot_model25 Estimate Lower.bound Upper.bound p.value
(Intercept) 46.40653489 30.1016739 62.8830141 <1e-04
residMidLPP 2.71847701 0.9234620 4.6209178 0.0026
AIES 0.08913160 -0.0483153 0.2391236 0.2137
Age -0.03040393 -0.3165184 0.2498817 0.829
Sex 2.19101749 -4.6639245 9.3759590 0.5554vif(model25)residMidLPP AIES Age Sex
1.002998 1.072813 1.054592 1.022219 11.2 Residualized Mid-LPP and acculturation predict PROMIS Depression
model26<-lm(promisDep~residMidLPP+NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model26)
Call:
lm(formula = promisDep ~ residMidLPP + NAAS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-15.321 -5.643 -1.637 4.187 21.230
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.76988 12.84524 3.641 0.00087 ***
residMidLPP 2.68482 0.93409 2.874 0.00684 **
NAAS 1.54023 3.11316 0.495 0.62387
Age -0.07386 0.13931 -0.530 0.59932
Sex 1.28636 3.53420 0.364 0.71807
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.788 on 35 degrees of freedom
Multiple R-squared: 0.203, Adjusted R-squared: 0.1119
F-statistic: 2.228 on 4 and 35 DF, p-value: 0.08591boot_model26<-boot_summary(model26, type='perc',method='residual',R=10000)
boot_model26 Estimate Lower.bound Upper.bound p.value
(Intercept) 46.76988025 21.7682409 72.571535 <1e-04
residMidLPP 2.68481572 0.8280632 4.516280 0.0043
NAAS 1.54023025 -4.6553197 7.557135 0.6101
Age -0.07386367 -0.3528512 0.203196 0.5994
Sex 1.28635868 -5.3606843 8.665983 0.7261vif(model26)residMidLPP NAAS Age Sex
1.002717 1.034570 1.003007 1.035003 11.3 Residualized Mid-LPP and spirituality predict PROMIS Depression
model27<-lm(promisDep~residMidLPP+NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model27)
Call:
lm(formula = promisDep ~ residMidLPP + NASS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-13.642 -5.940 -1.161 4.561 20.427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.7906 7.5618 7.378 1.25e-08 ***
residMidLPP 2.6938 0.9247 2.913 0.00619 **
NASS -0.1313 0.1341 -0.979 0.33416
Age -0.0332 0.1429 -0.232 0.81755
Sex 1.1396 3.4753 0.328 0.74494
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.701 on 35 degrees of freedom
Multiple R-squared: 0.2188, Adjusted R-squared: 0.1295
F-statistic: 2.451 on 4 and 35 DF, p-value: 0.06416boot_model27<-boot_summary(model27, type='perc',method='residual',R=10000)
boot_model27 Estimate Lower.bound Upper.bound p.value
(Intercept) 55.79063197 40.8822058 70.9039207 <1e-04
residMidLPP 2.69384361 0.8764395 4.5528256 0.0046
NASS -0.13130954 -0.3942072 0.1364749 0.3218
Age -0.03320403 -0.3110901 0.2552429 0.8031
Sex 1.13956152 -5.3969575 8.4848973 0.76vif(model27)residMidLPP NASS Age Sex
1.002473 1.095221 1.075917 1.021053 11.4 Residualized Mid-LPP and historical loss predict PROMIS Depression
model28<-lm(promisDep~residMidLPP+HLS+Age+Sex, data=cleanedData4matrixH2)
summary(model28)
Call:
lm(formula = promisDep ~ residMidLPP + HLS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-15.231 -5.941 -1.519 4.305 21.145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.12704 7.56621 6.757 7.88e-08 ***
residMidLPP 2.69174 0.93600 2.876 0.00682 **
HLS 0.03234 0.10648 0.304 0.76315
Age -0.06832 0.13958 -0.490 0.62754
Sex 1.17430 3.75129 0.313 0.75611
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.807 on 35 degrees of freedom
Multiple R-squared: 0.1995, Adjusted R-squared: 0.108
F-statistic: 2.181 on 4 and 35 DF, p-value: 0.09145boot_model28<-boot_summary(model28, type='perc',method='residual',R=10000)
boot_model28 Estimate Lower.bound Upper.bound p.value
(Intercept) 51.12704417 36.0473657 66.4825388 <1e-04
residMidLPP 2.69173616 0.8152190 4.5954159 0.0045
HLS 0.03233764 -0.1882496 0.2421706 0.7556
Age -0.06832390 -0.3484189 0.2116720 0.6229
Sex 1.17430086 -6.1768084 8.8191735 0.7632vif(model28)residMidLPP HLS Age Sex
1.002470 1.160990 1.002458 1.161014 11.5 Residualized Mid-LPP predicts PROMIS Depression moderated by enculturation
model29<-lm(promisDep~residMidLPP*AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model29)
Call:
lm(formula = promisDep ~ residMidLPP * AIES + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-15.328 -5.241 -1.670 5.403 22.094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.46973 8.13744 5.833 1.42e-06 ***
residMidLPP 0.45108 2.73629 0.165 0.870
AIES 0.08675 0.07138 1.215 0.233
Age -0.06003 0.14466 -0.415 0.681
Sex 2.28933 3.46100 0.661 0.513
residMidLPP:AIES 0.06223 0.07073 0.880 0.385
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.656 on 34 degrees of freedom
Multiple R-squared: 0.249, Adjusted R-squared: 0.1385
F-statistic: 2.254 on 5 and 34 DF, p-value: 0.07117boot_model29<-boot_summary(model29, type='perc',method='residual',R=10000)
boot_model29 Estimate Lower.bound Upper.bound p.value
(Intercept) 47.46973315 30.87048340 63.8711233 <1e-04
residMidLPP 0.45107644 -4.86306251 6.0092023 0.868
AIES 0.08675490 -0.05136370 0.2356704 0.2277
Age -0.06002721 -0.34541339 0.2353341 0.6899
Sex 2.28933055 -4.20621843 9.5023317 0.5286
residMidLPP:AIES 0.06223198 -0.08218566 0.2034996 0.3927johnson_neyman(
model29,
pred=residMidLPP,
modx=AIES,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Depression",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When AIES is INSIDE the interval [28.25, 64.77], the slope of residMidLPP
is p < .05.
Note: The range of observed values of AIES is [20.00, 108.00]
vif(model29)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP AIES Age Sex
8.870702 1.074352 1.114985 1.023286
residMidLPP:AIES
8.934265 11.6 Residualized Mid-LPP predicts PROMIS Depression moderated by acculturation
model30<-lm(promisDep~residMidLPP*NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model30)
Call:
lm(formula = promisDep ~ residMidLPP * NAAS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-14.681 -5.470 -1.467 4.189 20.944
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.41028 13.03959 3.636 0.000907 ***
residMidLPP -1.90345 8.87616 -0.214 0.831481
NAAS 1.26461 3.19049 0.396 0.694311
Age -0.05094 0.14753 -0.345 0.731987
Sex 0.95206 3.62906 0.262 0.794640
residMidLPP:NAAS 1.15165 2.21528 0.520 0.606524
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.881 on 34 degrees of freedom
Multiple R-squared: 0.2093, Adjusted R-squared: 0.09296
F-statistic: 1.799 on 5 and 34 DF, p-value: 0.1394boot_model30<-boot_summary(model30, type='perc',method='residual',R=10000)
boot_model30 Estimate Lower.bound Upper.bound p.value
(Intercept) 47.41027739 22.3362943 73.9689752 6e-04
residMidLPP -1.90344888 -19.2507262 15.8245539 0.8351
NAAS 1.26460693 -5.3739676 7.4347993 0.6786
Age -0.05094371 -0.3415113 0.2430178 0.7407
Sex 0.95205731 -6.0486834 8.3751765 0.818
residMidLPP:NAAS 1.15165110 -3.2851993 5.5310844 0.6167johnson_neyman(
model30,
pred=residMidLPP,
modx=NAAS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Depression",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When NAAS is INSIDE the interval [3.69, 4.61], the slope of residMidLPP is
p < .05.
Note: The range of observed values of NAAS is [2.40, 4.50]
vif(model30)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP NAAS Age Sex
88.654305 1.063950 1.101367 1.068554
residMidLPP:NAAS
88.665328 11.7 Residualized Mid-LPP predicts PROMIS Depression moderated by spirituality
model31<-lm(promisDep~residMidLPP*NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model31)
Call:
lm(formula = promisDep ~ residMidLPP * NASS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-13.892 -5.831 -1.180 4.428 20.538
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.66375 7.67589 7.252 2.15e-08 ***
residMidLPP 1.96523 2.71913 0.723 0.475
NASS -0.12776 0.13645 -0.936 0.356
Age -0.04125 0.14748 -0.280 0.781
Sex 1.38293 3.62352 0.382 0.705
residMidLPP:NASS 0.02397 0.08398 0.285 0.777
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.817 on 34 degrees of freedom
Multiple R-squared: 0.2207, Adjusted R-squared: 0.1061
F-statistic: 1.925 on 5 and 34 DF, p-value: 0.1158boot_model31<-boot_summary(model31, type='perc',method='residual',R=10000)
boot_model31 Estimate Lower.bound Upper.bound p.value
(Intercept) 55.66374674 40.2520245 70.6274586 <1e-04
residMidLPP 1.96522561 -3.3942423 7.6074496 0.4913
NASS -0.12776249 -0.3960965 0.1505926 0.372
Age -0.04124870 -0.3339270 0.2615704 0.7824
Sex 1.38293145 -5.5832550 9.0408729 0.7244
residMidLPP:NASS 0.02397283 -0.1482469 0.1886008 0.7736johnson_neyman(
model31,
pred=residMidLPP,
modx=NASS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Depression",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When NASS is INSIDE the interval [21.15, 44.16], the slope of residMidLPP
is p < .05.
Note: The range of observed values of NASS is [14.00, 54.00]
vif(model31)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP NASS Age Sex
8.441581 1.104381 1.116698 1.080896
residMidLPP:NASS
8.597148 11.8 Residualized Mid-LPP predicts PROMIS Depression moderated by historical loss
model32<-lm(promisDep~residMidLPP*HLS+Age+Sex, data=cleanedData4matrixH2)
summary(model32)
Call:
lm(formula = promisDep ~ residMidLPP * HLS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-15.681 -5.734 -1.365 4.099 21.136
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.31516 7.69425 6.669 1.18e-07 ***
residMidLPP 3.68466 3.53785 1.041 0.305
HLS 0.03447 0.10814 0.319 0.752
Age -0.07730 0.14475 -0.534 0.597
Sex 1.19486 3.80198 0.314 0.755
residMidLPP:HLS -0.02098 0.07202 -0.291 0.773
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.925 on 34 degrees of freedom
Multiple R-squared: 0.2015, Adjusted R-squared: 0.08407
F-statistic: 1.716 on 5 and 34 DF, p-value: 0.1576boot_model32<-boot_summary(model32, type='perc',method='residual',R=10000)
boot_model32 Estimate Lower.bound Upper.bound p.value
(Intercept) 51.31516203 35.6580496 67.1313974 <1e-04
residMidLPP 3.68466038 -3.7306753 10.3596048 0.3041
HLS 0.03446590 -0.1959752 0.2450764 0.759
Age -0.07729905 -0.3743266 0.2152668 0.6111
Sex 1.19486292 -6.2231333 9.0575040 0.7675
residMidLPP:HLS -0.02098009 -0.1572402 0.1295173 0.7626johnson_neyman(
model32,
pred=residMidLPP,
modx=HLS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Depression",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
When HLS is INSIDE the interval [31.39, 57.88], the slope of residMidLPP is
p < .05.
Note: The range of observed values of HLS is [13.00, 68.00]
vif(model32)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS Age Sex residMidLPP:HLS
13.947209 1.166312 1.050021 1.161414 13.982784 11.9 Residualized Mid-LPP predicts PROMIS Depression moderated by enculturation and historical loss
model33<-lm(promisDep~residMidLPP*HLS*AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model33)
Call:
lm(formula = promisDep ~ residMidLPP * HLS * AIES + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-11.069 -6.123 -1.530 5.717 21.024
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.050058 13.638684 3.303 0.00248 **
residMidLPP -26.687566 15.954520 -1.673 0.10478
HLS -0.053279 0.256195 -0.208 0.83666
AIES 0.103380 0.204141 0.506 0.61627
Age 0.063816 0.157090 0.406 0.68745
Sex 0.986992 3.731707 0.264 0.79321
residMidLPP:HLS 0.579549 0.316150 1.833 0.07672 .
residMidLPP:AIES 0.644440 0.323760 1.990 0.05572 .
HLS:AIES 0.001165 0.004724 0.247 0.80687
residMidLPP:HLS:AIES -0.013130 0.006757 -1.943 0.06144 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.625 on 30 degrees of freedom
Multiple R-squared: 0.3419, Adjusted R-squared: 0.1445
F-statistic: 1.732 on 9 and 30 DF, p-value: 0.125boot_model33<-boot_summary(model33, type='perc',method='residual',R=10000)
boot_model33 Estimate Lower.bound Upper.bound p.value
(Intercept) 45.050057822 16.884709239 7.189807e+01 0.0026
residMidLPP -26.687566451 -58.488324200 5.815209e+00 0.1055
HLS -0.053279003 -0.554682339 4.811467e-01 0.8228
AIES 0.103379511 -0.271973772 5.513693e-01 0.6626
Age 0.063816331 -0.252847897 3.811787e-01 0.686
Sex 0.986992047 -6.392795271 8.756160e+00 0.8047
residMidLPP:HLS 0.579548555 -0.076836023 1.213648e+00 0.079
residMidLPP:AIES 0.644440256 -0.026012385 1.291856e+00 0.0586
HLS:AIES 0.001165143 -0.008713429 1.024923e-02 0.7776
residMidLPP:HLS:AIES -0.013129825 -0.026448949 9.044147e-04 0.0641vif(model33)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS AIES
303.683855 7.008091 8.847353
Age Sex residMidLPP:HLS
1.323962 1.197921 288.507959
residMidLPP:AIES HLS:AIES residMidLPP:HLS:AIES
188.512969 9.233125 169.203610 11.10 Residualized Mid-LPP predicts PROMIS Depression moderated by spirituality and historical loss
model34<-lm(promisDep~residMidLPP*HLS*NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model34)
Call:
lm(formula = promisDep ~ residMidLPP * HLS * NASS + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-13.4847 -5.0240 -0.6538 4.8517 19.7259
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.264e+01 2.267e+01 1.881 0.0698 .
residMidLPP -1.108e+01 1.381e+01 -0.802 0.4288
HLS 2.832e-01 4.619e-01 0.613 0.5444
NASS 2.800e-01 5.579e-01 0.502 0.6195
Age 7.402e-04 1.589e-01 0.005 0.9963
Sex 3.744e-01 4.119e+00 0.091 0.9282
residMidLPP:HLS 2.736e-01 2.771e-01 0.987 0.3315
residMidLPP:NASS 4.016e-01 3.189e-01 1.259 0.2176
HLS:NASS -9.062e-03 1.175e-02 -0.771 0.4465
residMidLPP:HLS:NASS -8.099e-03 6.668e-03 -1.215 0.2340
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.955 on 30 degrees of freedom
Multiple R-squared: 0.2907, Adjusted R-squared: 0.07791
F-statistic: 1.366 on 9 and 30 DF, p-value: 0.2468boot_model34<-boot_summary(model34, type='perc',method='residual',R=10000)
boot_model34 Estimate Lower.bound Upper.bound p.value
(Intercept) 42.636737084 -1.03418450 87.298075260 0.0544
residMidLPP -11.077089084 -38.49850491 15.265757405 0.4211
HLS 0.283157467 -0.61422608 1.174751082 0.5343
NASS 0.279979827 -0.81362048 1.369250505 0.6043
Age 0.000740153 -0.30293592 0.311515294 0.9992
Sex 0.374382652 -7.48953334 8.585558117 0.9364
residMidLPP:HLS 0.273554367 -0.25732760 0.829077154 0.3205
residMidLPP:NASS 0.401625163 -0.22410741 1.026978322 0.2073
HLS:NASS -0.009061664 -0.03206689 0.013741621 0.4386
residMidLPP:HLS:NASS -0.008098765 -0.02113574 0.005009047 0.2263vif(model34)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS NASS
211.11513 21.13192 17.90118
Age Sex residMidLPP:HLS
1.25728 1.35410 205.67374
residMidLPP:NASS HLS:NASS residMidLPP:HLS:NASS
120.17522 26.29927 114.41911 11.11 Residualized Mid-LPP predicts PROMIS Depression moderated by acculturation and historical loss
model35<-lm(promisDep~residMidLPP*HLS*NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model35)
Call:
lm(formula = promisDep ~ residMidLPP * HLS * NAAS + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-14.1398 -5.7443 -0.6476 4.0531 19.3593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.648568 33.326289 1.940 0.0618 .
residMidLPP 79.485184 50.107447 1.586 0.1232
HLS -0.278032 0.725381 -0.383 0.7042
NAAS -3.073737 8.887161 -0.346 0.7319
Age 0.006648 0.155515 0.043 0.9662
Sex 0.022506 4.046308 0.006 0.9956
residMidLPP:HLS -1.665867 0.981074 -1.698 0.0999 .
residMidLPP:NAAS -19.790182 13.240106 -1.495 0.1454
HLS:NAAS 0.065808 0.191589 0.343 0.7336
residMidLPP:HLS:NAAS 0.424312 0.256076 1.657 0.1079
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.974 on 30 degrees of freedom
Multiple R-squared: 0.2877, Adjusted R-squared: 0.07398
F-statistic: 1.346 on 9 and 30 DF, p-value: 0.2558boot_model35<-boot_summary(model35, type='perc',method='residual',R=10000)
boot_model35 Estimate Lower.bound Upper.bound p.value
(Intercept) 64.648568115 2.68113870 133.2875892 0.037
residMidLPP 79.485184106 -18.63490864 176.1165566 0.1132
HLS -0.278032199 -1.74346180 1.0849387 0.7258
NAAS -3.073737500 -21.63806770 13.3451131 0.7688
Age 0.006648346 -0.29008673 0.3069925 0.9827
Sex 0.022505767 -7.81364916 8.0217120 0.9983
residMidLPP:HLS -1.665867323 -3.55458452 0.2476027 0.0876
residMidLPP:NAAS -19.790181889 -45.35708563 6.3629822 0.1348
HLS:NAAS 0.065808075 -0.29560735 0.4518689 0.7588
residMidLPP:HLS:NAAS 0.424311853 -0.08008402 0.9173062 0.0944vif(model35)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS NAAS
2767.316948 51.903069 8.086057
Age Sex residMidLPP:HLS
1.198742 1.301161 2566.702533
residMidLPP:NAAS HLS:NAAS residMidLPP:HLS:NAAS
3102.318113 74.250655 2915.385817 11.12 Residualized Mid-LPP and enculturation predict PROMIS Anxiety
model36<-lm(promisAnx~residMidLPP+AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model36)
Call:
lm(formula = promisAnx ~ residMidLPP + AIES + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-13.4852 -5.6776 0.1472 5.6243 10.6887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.43145 6.67991 9.346 4.82e-11 ***
residMidLPP 2.31677 0.76376 3.033 0.00454 **
AIES 0.09791 0.05921 1.653 0.10719
Age -0.21988 0.11678 -1.883 0.06806 .
Sex -4.08776 2.87144 -1.424 0.16342
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.185 on 35 degrees of freedom
Multiple R-squared: 0.3664, Adjusted R-squared: 0.294
F-statistic: 5.061 on 4 and 35 DF, p-value: 0.002519boot_model36<-boot_summary(model36, type='perc',method='residual',R=10000)
boot_model36 Estimate Lower.bound Upper.bound p.value
(Intercept) 62.43144726 49.42943045 75.623067061 <1e-04
residMidLPP 2.31677246 0.79103863 3.804537466 0.0021
AIES 0.09790552 -0.01845937 0.211982894 0.1044
Age -0.21987943 -0.44883148 0.007331221 0.0569
Sex -4.08776112 -9.67956610 1.550394980 0.1561vif(model36)residMidLPP AIES Age Sex
1.002998 1.072813 1.054592 1.022219 11.13 Residualized Mid-LPP and acculturation predict PROMIS Anxiety
model37<-lm(promisAnx~residMidLPP+NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model37)
Call:
lm(formula = promisAnx ~ residMidLPP + NAAS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.699 -5.093 -0.119 5.832 12.322
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.0966 10.8943 6.526 1.58e-07 ***
residMidLPP 2.2909 0.7922 2.892 0.00655 **
NAAS -0.6535 2.6403 -0.248 0.80597
Age -0.2622 0.1182 -2.219 0.03307 *
Sex -4.6116 2.9974 -1.539 0.13291
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.454 on 35 degrees of freedom
Multiple R-squared: 0.3181, Adjusted R-squared: 0.2402
F-statistic: 4.083 on 4 and 35 DF, p-value: 0.008072boot_model37<-boot_summary(model37, type='perc',method='residual',R=10000)
boot_model37 Estimate Lower.bound Upper.bound p.value
(Intercept) 71.0966050 49.6850211 92.00455186 <1e-04
residMidLPP 2.2908562 0.7278601 3.85903745 0.0033
NAAS -0.6534929 -5.7043719 4.48675844 0.8008
Age -0.2621719 -0.4895157 -0.03407162 0.0253
Sex -4.6116430 -10.4887092 1.18811194 0.1267vif(model37)residMidLPP NAAS Age Sex
1.002717 1.034570 1.003007 1.035003 11.14 Residualized Mid-LPP and spirituality predict PROMIS Anxiety
model38<-lm(promisAnx~residMidLPP+NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model38)
Call:
lm(formula = promisAnx ~ residMidLPP + NASS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-11.2870 -5.6392 -0.2723 6.0783 11.8760
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.0215 6.4113 10.298 3.91e-12 ***
residMidLPP 2.2864 0.7840 2.916 0.00614 **
NASS 0.1013 0.1137 0.891 0.37889
Age -0.2923 0.1211 -2.413 0.02117 *
Sex -4.3912 2.9465 -1.490 0.14510
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.377 on 35 degrees of freedom
Multiple R-squared: 0.3321, Adjusted R-squared: 0.2558
F-statistic: 4.351 on 4 and 35 DF, p-value: 0.005829boot_model38<-boot_summary(model38, type='perc',method='residual',R=10000)
boot_model38 Estimate Lower.bound Upper.bound p.value
(Intercept) 66.0215206 53.6983263 78.76909841 <1e-04
residMidLPP 2.2864127 0.7324329 3.84744461 0.0034
NASS 0.1013212 -0.1235872 0.32721576 0.3821
Age -0.2923036 -0.5339884 -0.05511209 0.0139
Sex -4.3912013 -10.1985148 1.39583671 0.1415vif(model38)residMidLPP NASS Age Sex
1.002473 1.095221 1.075917 1.021053 11.15 Residualized Mid-LPP and historical loss predict PROMIS Anxiety
model39<-lm(promisAnx~residMidLPP+HLS+Age+Sex, data=cleanedData4matrixH2)
summary(model39)
Call:
lm(formula = promisAnx ~ residMidLPP + HLS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.2141 -5.3333 -0.2899 5.4601 12.7290
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.00090 6.37573 10.509 2.28e-12 ***
residMidLPP 2.28720 0.78873 2.900 0.00641 **
HLS 0.05411 0.08972 0.603 0.55037
Age -0.26040 0.11762 -2.214 0.03344 *
Sex -5.44630 3.16106 -1.723 0.09373 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.422 on 35 degrees of freedom
Multiple R-squared: 0.324, Adjusted R-squared: 0.2467
F-statistic: 4.193 on 4 and 35 DF, p-value: 0.007054boot_model39<-boot_summary(model39, type='perc',method='residual',R=10000)
boot_model39 Estimate Lower.bound Upper.bound p.value
(Intercept) 67.00089767 54.5043287 79.46502799 <1e-04
residMidLPP 2.28720275 0.7316661 3.84697787 0.0032
HLS 0.05410697 -0.1211656 0.23094084 0.5471
Age -0.26040353 -0.4876347 -0.03370913 0.0243
Sex -5.44630353 -11.5862691 0.86349353 0.0925vif(model39)residMidLPP HLS Age Sex
1.002470 1.160990 1.002458 1.161014 11.16 Residualized Mid-LPP predicts PROMIS Anxiety moderated by enculturation
model40<-lm(promisAnx~residMidLPP*AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model40)
Call:
lm(formula = promisAnx ~ residMidLPP * AIES + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.2244 -5.5032 0.9837 5.1249 10.4555
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 63.75580 6.68055 9.543 3.81e-11 ***
residMidLPP -0.50757 2.24639 -0.226 0.8226
AIES 0.09495 0.05860 1.620 0.1145
Age -0.25678 0.11876 -2.162 0.0377 *
Sex -3.96530 2.84136 -1.396 0.1719
residMidLPP:AIES 0.07752 0.05807 1.335 0.1907
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.106 on 34 degrees of freedom
Multiple R-squared: 0.398, Adjusted R-squared: 0.3095
F-statistic: 4.496 on 5 and 34 DF, p-value: 0.002982boot_model40<-boot_summary(model40, type='perc',method='residual',R=10000)
boot_model40 Estimate Lower.bound Upper.bound p.value
(Intercept) 63.75579829 50.60006677 76.64443604 <1e-04
residMidLPP -0.50756842 -5.18002647 3.98657522 0.8166
AIES 0.09494503 -0.02065803 0.21011343 0.1077
Age -0.25677907 -0.48935927 -0.02412983 0.031
Sex -3.96529947 -9.62685433 1.58443650 0.1739
residMidLPP:AIES 0.07751799 -0.03854705 0.19481589 0.1891johnson_neyman(
model40,
pred=residMidLPP,
modx=AIES,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Anxiety",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When AIES is INSIDE the interval [29.20, 89.04], the slope of residMidLPP
is p < .05.
Note: The range of observed values of AIES is [20.00, 108.00]
vif(model40)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP AIES Age Sex
8.870702 1.074352 1.114985 1.023286
residMidLPP:AIES
8.934265 11.17 Residualized Mid-LPP predicts PROMIS Anxiety moderated by acculturation
model41<-lm(promisAnx~residMidLPP*NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model41)
Call:
lm(formula = promisAnx ~ residMidLPP * NAAS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.385 -6.178 0.295 5.478 12.164
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 70.2708 11.0014 6.387 2.72e-07 ***
residMidLPP 8.2074 7.4887 1.096 0.2808
NAAS -0.2981 2.6918 -0.111 0.9125
Age -0.2917 0.1245 -2.344 0.0251 *
Sex -4.1806 3.0618 -1.365 0.1811
residMidLPP:NAAS -1.4850 1.8690 -0.795 0.4324
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.493 on 34 degrees of freedom
Multiple R-squared: 0.3306, Adjusted R-squared: 0.2321
F-statistic: 3.358 on 5 and 34 DF, p-value: 0.01429boot_model41<-boot_summary(model41, type='perc',method='residual',R=10000)
boot_model41 Estimate Lower.bound Upper.bound p.value
(Intercept) 70.2708208 49.0280995 92.14419986 <1e-04
residMidLPP 8.2073668 -6.2827901 22.79137086 0.2899
NAAS -0.2980800 -5.6321112 4.97777620 0.9003
Age -0.2917269 -0.5404699 -0.04319264 0.0216
Sex -4.1805655 -10.1622269 1.69017376 0.1782
residMidLPP:NAAS -1.4850399 -5.0886054 2.12591926 0.4467johnson_neyman(
model41,
pred=residMidLPP,
modx=NAAS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Anxiety",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When NAAS is INSIDE the interval [3.16, 4.25], the slope of residMidLPP is
p < .05.
Note: The range of observed values of NAAS is [2.40, 4.50]
vif(model41)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP NAAS Age Sex
88.654305 1.063950 1.101367 1.068554
residMidLPP:NAAS
88.665328 11.18 Residualized Mid-LPP predicts PROMIS Anxiety moderated by spirituality
model42<-lm(promisAnx~residMidLPP*NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model42)
Call:
lm(formula = promisAnx ~ residMidLPP * NASS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.4260 -4.9463 -0.5343 6.0627 11.7348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.66525 6.42985 10.213 6.79e-12 ***
residMidLPP 0.24060 2.27773 0.106 0.9165
NASS 0.11128 0.11430 0.974 0.3371
Age -0.31489 0.12354 -2.549 0.0155 *
Sex -3.70787 3.03531 -1.222 0.2303
residMidLPP:NASS 0.06731 0.07035 0.957 0.3454
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.386 on 34 degrees of freedom
Multiple R-squared: 0.3496, Adjusted R-squared: 0.254
F-statistic: 3.655 on 5 and 34 DF, p-value: 0.009391boot_model42<-boot_summary(model42, type='perc',method='residual',R=10000)
boot_model42 Estimate Lower.bound Upper.bound p.value
(Intercept) 65.66525290 52.86073576 78.20766117 <1e-04
residMidLPP 0.24060279 -4.33905011 4.70865884 0.9415
NASS 0.11128059 -0.11467334 0.33927485 0.3298
Age -0.31489139 -0.55859906 -0.07006242 0.0097
Sex -3.70786849 -9.64306387 2.26278060 0.2322
residMidLPP:NASS 0.06731078 -0.07049974 0.20761114 0.3505johnson_neyman(
model42,
pred=residMidLPP,
modx=NASS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Anxiety",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When NASS is INSIDE the interval [23.87, 56.26], the slope of residMidLPP
is p < .05.
Note: The range of observed values of NASS is [14.00, 54.00]
vif(model42)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP NASS Age Sex
8.441581 1.104381 1.116698 1.080896
residMidLPP:NASS
8.597148 11.19 Residualized Mid-LPP predicts PROMIS Anxiety moderated by historical loss
model43<-lm(promisAnx~residMidLPP*HLS+Age+Sex, data=cleanedData4matrixH2)
summary(model43)
Call:
lm(formula = promisAnx ~ residMidLPP * HLS + Age + Sex, data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-11.6257 -5.2679 0.1647 5.9699 12.6188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.30435 6.46203 10.415 4.07e-12 ***
residMidLPP 3.88887 2.97127 1.309 0.1994
HLS 0.05754 0.09082 0.634 0.5306
Age -0.27488 0.12157 -2.261 0.0303 *
Sex -5.41314 3.19309 -1.695 0.0992 .
residMidLPP:HLS -0.03384 0.06048 -0.560 0.5795
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.496 on 34 degrees of freedom
Multiple R-squared: 0.3301, Adjusted R-squared: 0.2316
F-statistic: 3.351 on 5 and 34 DF, p-value: 0.01442boot_model43<-boot_summary(model43, type='perc',method='residual',R=10000)
boot_model43 Estimate Lower.bound Upper.bound p.value
(Intercept) 67.30434658 54.5881249 79.83974588 <1e-04
residMidLPP 3.88886765 -1.7629749 9.55429016 0.199
HLS 0.05754002 -0.1187820 0.23350265 0.5319
Age -0.27488114 -0.5124144 -0.04033692 0.0216
Sex -5.41313531 -11.6141965 0.86037420 0.0962
residMidLPP:HLS -0.03384254 -0.1497799 0.08228499 0.5858johnson_neyman(
model43,
pred=residMidLPP,
modx=HLS,
alpha=0.05,
plot=TRUE,
title="Johnson-Neyman plot of Mid-LPP Amplitudes x PROMIS Anxiety",
y.label="Slope of Residual Mid-LPP Amplitudes to Cultural Cues",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
When HLS is INSIDE the interval [27.03, 56.54], the slope of residMidLPP is
p < .05.
Note: The range of observed values of HLS is [13.00, 68.00]
vif(model43)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS Age Sex residMidLPP:HLS
13.947209 1.166312 1.050021 1.161414 13.982784 11.20 Residualized Mid-LPP predicts PROMIS Anxiety moderated by enculturation and historical loss
model44<-lm(promisAnx~residMidLPP*HLS*AIES+Age+Sex, data=cleanedData4matrixH2)
summary(model44)
Call:
lm(formula = promisAnx ~ residMidLPP * HLS * AIES + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-11.3954 -4.6978 -0.1356 4.5531 10.8805
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.8522409 11.6060594 5.071 1.91e-05 ***
residMidLPP -8.5849787 13.5767581 -0.632 0.532
HLS 0.0544633 0.2180129 0.250 0.804
AIES 0.1197099 0.1737173 0.689 0.496
Age -0.1943291 0.1336780 -1.454 0.156
Sex -5.3153069 3.1755565 -1.674 0.105
residMidLPP:HLS 0.1920480 0.2690328 0.714 0.481
residMidLPP:AIES 0.2690118 0.2755090 0.976 0.337
HLS:AIES 0.0005532 0.0040201 0.138 0.891
residMidLPP:HLS:AIES -0.0048763 0.0057501 -0.848 0.403
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.34 on 30 degrees of freedom
Multiple R-squared: 0.4332, Adjusted R-squared: 0.2632
F-statistic: 2.548 on 9 and 30 DF, p-value: 0.02629boot_model44<-boot_summary(model44, type='perc',method='residual',R=10000)
boot_model44 Estimate Lower.bound Upper.bound p.value
(Intercept) 58.8522409180 34.501220559 82.926260433 <1e-04
residMidLPP -8.5849786611 -36.683581033 19.963316161 0.5543
HLS 0.0544632515 -0.402713390 0.509863374 0.8083
AIES 0.1197098950 -0.238901481 0.472965305 0.5392
Age -0.1943290933 -0.467747566 0.087155416 0.1739
Sex -5.3153069041 -12.105951164 1.438917745 0.1241
residMidLPP:HLS 0.1920479715 -0.369516797 0.750951035 0.4968
residMidLPP:AIES 0.2690118491 -0.308775662 0.841378073 0.3563
HLS:AIES 0.0005531891 -0.007762533 0.008902042 0.9312
residMidLPP:HLS:AIES -0.0048762755 -0.016934820 0.007190083 0.4207vif(model44)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS AIES
303.683855 7.008091 8.847353
Age Sex residMidLPP:HLS
1.323962 1.197921 288.507959
residMidLPP:AIES HLS:AIES residMidLPP:HLS:AIES
188.512969 9.233125 169.203610 11.21 Residualized Mid-LPP predicts PROMIS Anxiety moderated by spirituality and historical loss
model45<-lm(promisAnx~residMidLPP*HLS*NASS+Age+Sex, data=cleanedData4matrixH2)
summary(model45)
Call:
lm(formula = promisAnx ~ residMidLPP * HLS * NASS + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-11.8401 -4.9224 0.0652 4.9278 11.5232
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.993517 18.032921 1.331 0.19337
residMidLPP 12.862992 10.984270 1.171 0.25080
HLS 0.907152 0.367348 2.469 0.01944 *
NASS 1.166636 0.443754 2.629 0.01337 *
Age -0.360840 0.126405 -2.855 0.00774 **
Sex -4.253063 3.276102 -1.298 0.20411
residMidLPP:HLS -0.245226 0.220415 -1.113 0.27473
residMidLPP:NASS -0.098004 0.253642 -0.386 0.70194
HLS:NASS -0.022220 0.009344 -2.378 0.02397 *
residMidLPP:HLS:NASS 0.003184 0.005304 0.600 0.55277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.122 on 30 degrees of freedom
Multiple R-squared: 0.4664, Adjusted R-squared: 0.3063
F-statistic: 2.913 on 9 and 30 DF, p-value: 0.01327boot_model45<-boot_summary(model45, type='perc',method='residual',R=10000)
boot_model45 Estimate Lower.bound Upper.bound p.value
(Intercept) 23.993516687 -11.973900817 59.390650952 0.1942
residMidLPP 12.862992431 -9.068095085 34.666385480 0.2461
HLS 0.907151569 0.181894466 1.640717993 0.0125
NASS 1.166635508 0.311781526 2.053523780 0.0069
Age -0.360840324 -0.612351688 -0.111856439 0.0049
Sex -4.253062929 -10.752646557 2.134147683 0.1898
residMidLPP:HLS -0.245226203 -0.681415896 0.199697897 0.2713
residMidLPP:NASS -0.098003512 -0.598472887 0.406756438 0.6914
HLS:NASS -0.022219915 -0.040657972 -0.003810854 0.0166
residMidLPP:HLS:NASS 0.003184131 -0.007390416 0.013689820 0.5418vif(model45)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS NASS
211.11513 21.13192 17.90118
Age Sex residMidLPP:HLS
1.25728 1.35410 205.67374
residMidLPP:NASS HLS:NASS residMidLPP:HLS:NASS
120.17522 26.29927 114.41911 11.22 Residualized Mid-LPP predicts PROMIS Anxiety moderated by acculturation and historical loss
model46<-lm(promisAnx~residMidLPP*HLS*NAAS+Age+Sex, data=cleanedData4matrixH2)
summary(model46)
Call:
lm(formula = promisAnx ~ residMidLPP * HLS * NAAS + Age + Sex,
data = cleanedData4matrixH2)
Residuals:
Min 1Q Median 3Q Max
-12.5622 -5.7972 0.4721 5.6481 11.9055
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 79.99283 28.60623 2.796 0.00893 **
residMidLPP 61.42393 43.01065 1.428 0.16359
HLS -0.10427 0.62264 -0.167 0.86813
NAAS -3.00815 7.62846 -0.394 0.69613
Age -0.24813 0.13349 -1.859 0.07290 .
Sex -5.12407 3.47322 -1.475 0.15055
residMidLPP:HLS -1.11020 0.84212 -1.318 0.19737
residMidLPP:NAAS -15.13926 11.36489 -1.332 0.19286
HLS:NAAS 0.03008 0.16445 0.183 0.85612
residMidLPP:HLS:NAAS 0.28180 0.21981 1.282 0.20965
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.703 on 30 degrees of freedom
Multiple R-squared: 0.3758, Adjusted R-squared: 0.1885
F-statistic: 2.007 on 9 and 30 DF, p-value: 0.07391boot_model46<-boot_summary(model46, type='perc',method='residual',R=10000)
boot_model46 Estimate Lower.bound Upper.bound p.value
(Intercept) 79.99283052 24.8160998 1.302521e+02 0.001
residMidLPP 61.42393010 -23.1817706 1.456651e+02 0.151
HLS -0.10427116 -1.2645890 1.109199e+00 0.8648
NAAS -3.00814758 -16.6786470 1.190656e+01 0.7169
Age -0.24812539 -0.5146762 9.898977e-03 0.0604
Sex -5.12406833 -12.0546566 1.720713e+00 0.1392
residMidLPP:HLS -1.11019598 -2.7540855 5.372264e-01 0.1934
residMidLPP:NAAS -15.13926068 -37.3995681 7.222409e+00 0.184
HLS:NAAS 0.03007568 -0.2943133 3.363590e-01 0.8632
residMidLPP:HLS:NAAS 0.28180429 -0.1455141 7.135203e-01 0.2049vif(model46)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif residMidLPP HLS NAAS
2767.316948 51.903069 8.086057
Age Sex residMidLPP:HLS
1.198742 1.301161 2566.702533
residMidLPP:NAAS HLS:NAAS residMidLPP:HLS:NAAS
3102.318113 74.250655 2915.385817