Culture & cognition: An event-related potential (ERP) study of error processing and reward anticipation and their conditional associations with depression and anxiety in American Indians
Wesley J.B. Vaught & Makiah P. Torres
November 13, 2025
Analysis prepared by Wesley J.B. Vaught & Makiah P. Torres | secondary data analysis of AI participants’ EEG data from T1000 and K99.
1 Preparation
1.1 Packages
library(psych) # for descriptive statistics and internacy consistency calcs
library(huxtable) # converting simple slopes into a table
library(gmodels) # for frequency calculation and tables for categorical variables
library(dplyr) # for creating tibbles and matrices for correlation matrices
library(corrr) # for calculating (Spearman's) correlations
library(corrplot) # for visualizing correlation plots
library(interactions) # for creating a Johnson-Neyman plot
library(ggplot2) # for data visualization with scatterplots and histograms
library(ggpmisc) # an extension of ggplot2
library(ggpubr) # for data visualization simplified
library(car) # for calculating VIF (variance inflation factor)
library(boot.pval) # for bootstrapping the multiple linear regression and moderation
library(stringr) # for replacing the written scoring for the HLS to numbers and replacing the binary code for sex from "1" to "Female" for Table 1
library(QuantPsyc) # for standardized betas in linear modeling
library(table1) # to create Table 11.2 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)
dfSSTdemo<-read.csv("T1000_redcap_wide-S.T0-2022-01-24.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)
dfMerged<-read.csv("dfMerged.csv",header=TRUE)
dfMergedAies<-read.csv("dfMergedAies.csv",header=TRUE)
dfMergedNass<-read.csv("dfMergedNass.csv",header=TRUE)
dfMergedNaas<-read.csv("dfMergedNaas.csv",header=TRUE)
dfMergedHls<-read.csv("dfMergedHls.csv",header=TRUE)# tibble for SST, clinical, and cultural variables
simpleTibble4SST<-tibble(
id=dfSST$id,
SexSST=dfSST$Gender,
AgeSST=dfSST$Age,
goIncorrERN_FCz=dfSST$ERNFCz_GoIncorr,
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$PePz_GoCorr,
sstPROMISanx=dfSST$PROMIS_AnxietyTscore,
sstPROMISalcUse=dfSST$PROMIS_AlcoUseTscore,
sstPROMISdepress=dfSST$PROMIS_DepressTscore,
sstAIES=dfSST$AIES_scale,
sstNAAS=dfSST$NAAS_scale,
sstNASS=dfSST$NASS_scale,
sstHLS=dfSST$HLS_scale
)
cleanedSimpleTibble4SST<-na.omit(simpleTibble4SST) # retains 51 participants
# residualized ERN
residual_incorrectFCzERN<-lm(goIncorrERN_FCz~goCorrERN_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$ERNresid<-resid(residual_incorrectFCzERN)
# residualized correct N2
residual_correctFCzN2<-lm(allCorrN2_FCz~allIncorrN2_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$N2corrResid<-resid(residual_correctFCzN2)
# residualized incorrect N2
residual_incorrectFCzN2<-lm(allIncorrN2_FCz~allCorrN2_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$N2resid<-resid(residual_incorrectFCzN2)
# residualized correct P3
residual_correctPzP3<-lm(allCorrP3_Pz~allIncorrP3_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$P3corrResid<-resid(residual_correctPzP3)
# residualized incorrect P3
residual_incorrectPzP3<-lm(allIncorrP3_Pz~allCorrP3_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$P3resid<-resid(residual_incorrectPzP3)
# residualized Pe
residual_incorrectPzPe<-lm(goIncorrPe_Pz~goCorrPe_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$PeResid<-resid(residual_incorrectPzPe)
# tibble for MID, clinical, and cultural variables
simpleTibble4MID<-tibble(
id=dfMID$id,
SexMID=dfMID$Gender,
AgeMID=dfMID$Age,
P3pzMIDcueLoss=dfMID$P3pzMIDcueLoss,
P3pzMIDcueNogain=dfMID$P3pzMIDcueNogain,
P3pzMIDcueGain=dfMID$P3pzMIDcueGain,
SPNfzMIDfeedbackSuccess=dfMID$SPNfzMIDfeedbackSuccess,
SPNfzMIDfeedbackFail=dfMID$SPNfzMIDfeedbackFail,
RewPfczMIDfeedbackGain=dfMID$RewPfczMIDfeedbackGain,
midPROMISanx=dfMID$PROMIS_AnxietyTscore,
midPROMISalcUse=dfMID$PROMIS_AlcoUseTscore,
midPROMISdepress=dfMID$PROMIS_DepressTscore,
midAIES=dfMID$AIES_scale,
midNAAS=dfMID$NAAS_scale,
midNASS=dfMID$NASS_scale,
midHLS=dfMID$HLS_scale
)
cleanedSimpleTibble4MID<-na.omit(simpleTibble4MID) # retains 44 participants
# merging tibbles by "id"
dfERPsMerged<-merge(cleanedSimpleTibble4SST,cleanedSimpleTibble4MID,by='id') # retains 39 participants total2 Descriptive Statistics
2.1 Descriptives in SST sample
Descriptive statistics of Age in SST
describeAgeSST<-describe(cleanedSimpleTibble4SST$AgeSST)
describeAgeSST| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 35.4 | 10.4 | 33 | 35.1 | 11.2 | 18.4 | 54.9 | 36.5 | 0.204 | -1.13 | 1.46 |
Descriptive statistics of Sex in SST
freqSexSST<-CrossTable(cleanedSimpleTibble4SST$SexSST, 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 SST
describeAIESsst<-describe(cleanedSimpleTibble4SST$sstAIES)
describeAIESsst| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 37.4 | 14 | 35 | 36 | 13.3 | 18 | 75 | 57 | 0.811 | -0.00515 | 1.96 |
Descriptive statistics of NASS in SST
describeNASSsst<-describe(cleanedSimpleTibble4SST$sstNASS)
describeNASSsst| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 35 | 8.37 | 37 | 35.6 | 5.93 | 11 | 50 | 39 | -0.755 | 0.1 | 1.17 |
Descriptive statistics of NAAS in SST
describeNAASsst<-describe(cleanedSimpleTibble4SST$sstNAAS)
describeNAASsst| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 3.86 | 0.345 | 3.9 | 3.86 | 0.371 | 3.05 | 4.45 | 1.4 | -0.121 | -0.771 | 0.0482 |
Descriptive statistics of HLS in SST
describeHLSsst<-describe(cleanedSimpleTibble4SST$sstHLS)
describeHLSsst| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 42.1 | 4.68 | 41 | 42.3 | 5.93 | 30 | 50 | 20 | -0.326 | -0.398 | 0.656 |
Descriptive statistics of PROMIS Depression in SST
describePROMISdepSST<-describe(cleanedSimpleTibble4SST$sstPROMISdepress)
describePROMISdepSST| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 55.3 | 7.61 | 56.3 | 55.9 | 6.38 | 34.2 | 69.5 | 35.3 | -0.596 | 0.109 | 1.07 |
Descriptive statistics of PROMIS Anxiety in SST
describePROMISanxSST<-describe(cleanedSimpleTibble4SST$sstPROMISanx)
describePROMISanxSST| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 57.4 | 9.09 | 58 | 58.2 | 7.86 | 32.9 | 75.3 | 42.4 | -0.748 | 0.568 | 1.27 |
Descriptive statistics of PROMIS Alcohol Use in SST
describePROMISalcUseSST<-describe(cleanedSimpleTibble4SST$sstPROMISalcUse)
describePROMISalcUseSST| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 49.2 | 5.07 | 50 | 49.3 | 0 | 37.5 | 62.4 | 24.9 | -0.0968 | 1 | 0.71 |
Descriptive statistics of Correct-Stop N200 amplitudes in SST
describeCorrN2<-describe(cleanedSimpleTibble4SST$allCorrN2_FCz)
describeCorrN2| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -4.12 | 4.53 | -3.51 | -3.86 | 4.41 | -15.7 | 4 | 19.7 | -0.498 | -0.356 | 0.634 |
Descriptive statistics of Residualized Incorrect-Stop N200 amplitudes in SST
describeResidIncorrN2<-describe(cleanedSimpleTibble4SST$N2resid)
describeResidIncorrN2| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -3.26e-17 | 3.38 | -0.151 | -0.0328 | 3.17 | -10.4 | 7.76 | 18.2 | -0.125 | 0.568 | 0.473 |
Descriptive statistics of Correct-Stop P300 amplitudes in SST
describeCorrP3<-describe(cleanedSimpleTibble4SST$allCorrP3_Pz)
describeCorrP3| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 5.32 | 4.52 | 5.22 | 5.12 | 5.57 | -2.84 | 17.1 | 19.9 | 0.388 | -0.529 | 0.632 |
Descriptive statistics of Residualized Incorrect-Stop P300 amplitudes in SST
describeResidIncorrP3<-describe(cleanedSimpleTibble4SST$P3resid)
describeResidIncorrP3| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -4.37e-17 | 2.98 | -0.36 | -0.0949 | 2.74 | -8.4 | 7.36 | 15.8 | 0.154 | 0.124 | 0.418 |
Descriptive statistics of CRN amplitudes in SST
describeCRN<-describe(cleanedSimpleTibble4SST$goCorrERN_FCz)
describeCRN| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -1.78 | 3.27 | -1.26 | -1.49 | 2.73 | -15.9 | 3.26 | 19.2 | -1.59 | 4.81 | 0.458 |
Descriptive statistics of Residualized ERN amplitudes in SST
describeResidERN<-describe(cleanedSimpleTibble4SST$ERNresid)
describeResidERN| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 1.65e-16 | 5.41 | -0.00317 | -0.0895 | 5.1 | -14.5 | 14.2 | 28.7 | 0.107 | 0.435 | 0.758 |
Descriptive statistics of Correct-Pe amplitudes in SST
describeCorrPe<-describe(cleanedSimpleTibble4SST$goCorrPe_Pz)
describeCorrPe| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -0.273 | 2.42 | -0.457 | -0.281 | 1.74 | -6.42 | 6.15 | 12.6 | 0.0104 | 0.798 | 0.339 |
Descriptive statistics of Residualized Incorrect-Pe in SST
describeResidIncorrPe<-describe(cleanedSimpleTibble4SST$PeResid)
describeResidIncorrPe| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | -2.9e-16 | 6.26 | 1.56 | 0.733 | 4.12 | -21.5 | 9.32 | 30.8 | -1.38 | 2.39 | 0.876 |
2.2 Descriptive statistics in MID sample
Descriptive statistics of Age in MID
describeAgeMID<-describe(cleanedSimpleTibble4MID$AgeMID)
describeAgeMID| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 34.3 | 10.2 | 32.8 | 33.9 | 12.1 | 18.4 | 54.9 | 36.5 | 0.242 | -1.02 | 1.54 |
Descriptive statistics of Sex in MID
freqSexMID<-CrossTable(cleanedSimpleTibble4MID$SexMID, format="SPSS")
Cell Contents
|-------------------------|
| Count |
| Row Percent |
|-------------------------|
Total Observations in Table: 44
| Female | Male |
|-----------|-----------|
| 34 | 10 |
| 77.273% | 22.727% |
|-----------|-----------|
freqSexMIDNULLDescriptive statistics of AIES in MID
describeAIESmid<-describe(cleanedSimpleTibble4MID$midAIES)
describeAIESmid| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 34.3 | 11.7 | 32 | 33.1 | 11.9 | 19 | 66 | 47 | 0.818 | 0.0828 | 1.76 |
Descriptive statistics NASS in MID
describeNASSmid<-describe(cleanedSimpleTibble4MID$midNASS)
describeNASSmid| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 33.4 | 9.31 | 36 | 34.3 | 6.67 | 9 | 49 | 40 | -0.948 | 0.195 | 1.4 |
Descriptive statistics NAAS in MID
describeNAASmid<-describe(cleanedSimpleTibble4MID$midNAAS)
describeNAASmid| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 3.95 | 0.331 | 4 | 3.95 | 0.371 | 3.3 | 4.5 | 1.2 | -0.181 | -1.04 | 0.0499 |
Descriptive statistics of HLS in MID
describeHLSmid<-describe(cleanedSimpleTibble4MID$midHLS)
describeHLSmid| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 42.1 | 4.79 | 41.5 | 42.4 | 5.19 | 30 | 50 | 20 | -0.436 | -0.45 | 0.721 |
Descriptive statistics of PROMIS Depression in MID
describePROMISdepMID<-describe(cleanedSimpleTibble4MID$midPROMISdepress)
describePROMISdepMID| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 56.3 | 7.79 | 57.4 | 56.7 | 7.19 | 39 | 69.5 | 30.5 | -0.461 | -0.506 | 1.17 |
Descriptive statistics of PROMIS Anxiety in MID
describePROMISanxMID<-describe(cleanedSimpleTibble4MID$midPROMISanx)
describePROMISanxMID| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 57.6 | 9.41 | 57.9 | 58 | 8.08 | 32.9 | 75.3 | 42.4 | -0.424 | -0.224 | 1.42 |
Descriptive statistics of PROMIS Alcohol Use in MID
describePROMISalcUseMID<-describe(cleanedSimpleTibble4MID$midPROMISalcUse)
describePROMISalcUseMID| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 49.5 | 4.69 | 50 | 49.5 | 0 | 38.2 | 61.2 | 23 | -0.0917 | 1.04 | 0.707 |
Descriptive statistics of P300 amplitudes to Loss Cues in MID
describeCueLossP3<-describe(cleanedSimpleTibble4MID$P3pzMIDcueLoss)
describeCueLossP3| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 2.35 | 2.77 | 2.42 | 2.18 | 2.65 | -3.33 | 10.8 | 14.2 | 0.762 | 1.02 | 0.418 |
Descriptive statistics of P300 amplitudes to No Gain Cues in MID
describeCueNoGainP3<-describe(cleanedSimpleTibble4MID$P3pzMIDcueNogain)
describeCueNoGainP3| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 2.34 | 2.4 | 2.21 | 2.24 | 2.12 | -2.43 | 9.92 | 12.4 | 0.504 | 0.795 | 0.362 |
Descriptive statistics of P300 amplitudes to Gain Cues in MID
describeCueGainP3<-describe(cleanedSimpleTibble4MID$P3pzMIDcueGain)
describeCueGainP3| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 2.33 | 2.79 | 1.75 | 1.98 | 1.98 | -2.29 | 11.4 | 13.7 | 1.42 | 2.38 | 0.42 |
Descriptive statistics of SPN amplitudes to successful trials in MID
describeSuccessSPN<-describe(cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess)
describeSuccessSPN| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 2.86 | 2.94 | 2.61 | 2.86 | 2.75 | -4.05 | 9.3 | 13.3 | 0.0778 | -0.397 | 0.443 |
Descriptive statistics of SPN amplitudes to failed trials in MID
describeFailSPN<-describe(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
describeFailSPN| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 1.6 | 4.34 | 1.49 | 1.68 | 3.73 | -14 | 9.62 | 23.6 | -0.672 | 2.18 | 0.654 |
Descriptive statistics of RewP amplitudes to Gain trials in MID
describeGainRewP<-describe(cleanedSimpleTibble4MID$RewPfczMIDfeedbackGain)
describeGainRewP| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 44 | 3.33 | 2.75 | 2.99 | 3.17 | 2.5 | -1.47 | 10.3 | 11.8 | 0.518 | -0.00814 | 0.415 |
3 Tests of Normality
3.1 Shapiro-Wilk test and QQ plots in SST sample
American Indian Enculturation Scale
shapiro.test(cleanedSimpleTibble4SST$sstAIES)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstAIES
W = 0.93353, p-value = 0.006813qqnorm(cleanedSimpleTibble4SST$sstAIES)
qqline(cleanedSimpleTibble4SST$sstAIES)
Native American Acculturation Scale
shapiro.test(cleanedSimpleTibble4SST$sstNAAS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstNAAS
W = 0.97666, p-value = 0.408qqnorm(cleanedSimpleTibble4SST$sstNAAS)
qqline(cleanedSimpleTibble4SST$sstNAAS)
Native American Spirituality Scale
shapiro.test(cleanedSimpleTibble4SST$sstNASS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstNASS
W = 0.94552, p-value = 0.02057qqnorm(cleanedSimpleTibble4SST$sstNASS)
qqline(cleanedSimpleTibble4SST$sstNASS)
Historical Loss Scale
shapiro.test(cleanedSimpleTibble4SST$sstHLS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstHLS
W = 0.96236, p-value = 0.1052qqnorm(cleanedSimpleTibble4SST$sstHLS)
qqline(cleanedSimpleTibble4SST$sstHLS)
PROMIS Anxiety
shapiro.test(cleanedSimpleTibble4SST$sstPROMISanx)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstPROMISanx
W = 0.94945, p-value = 0.02989qqnorm(cleanedSimpleTibble4SST$sstPROMISanx)
qqline(cleanedSimpleTibble4SST$sstPROMISanx)
PROMIS Alcohol Use
shapiro.test(cleanedSimpleTibble4SST$sstPROMISalcUse)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstPROMISalcUse
W = 0.86064, p-value = 2.589e-05qqnorm(cleanedSimpleTibble4SST$sstPROMISalcUse)
qqline(cleanedSimpleTibble4SST$sstPROMISalcUse)
PROMIS Depression
shapiro.test(cleanedSimpleTibble4SST$sstPROMISdepress)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$sstPROMISdepress
W = 0.96982, p-value = 0.2173qqnorm(cleanedSimpleTibble4SST$sstPROMISdepress)
qqline(cleanedSimpleTibble4SST$sstPROMISdepress)
Correct-Stop N200 Amplitudes
shapiro.test(cleanedSimpleTibble4SST$allCorrN2_FCz)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$allCorrN2_FCz
W = 0.97078, p-value = 0.2383qqnorm(cleanedSimpleTibble4SST$allCorrN2_FCz)
qqline(cleanedSimpleTibble4SST$allCorrN2_FCz)
Correct-Stop P300 Amplitudes
shapiro.test(cleanedSimpleTibble4SST$allCorrP3_Pz)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$allCorrP3_Pz
W = 0.97678, p-value = 0.4123qqnorm(cleanedSimpleTibble4SST$allCorrP3_Pz)
qqline(cleanedSimpleTibble4SST$allCorrP3_Pz)
Correct-Related Negativity Amplitudes
shapiro.test(cleanedSimpleTibble4SST$goCorrERN_FCz)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$goCorrERN_FCz
W = 0.88863, p-value = 0.0001793qqnorm(cleanedSimpleTibble4SST$goCorrERN_FCz)
qqline(cleanedSimpleTibble4SST$goCorrERN_FCz)
Correct Error Positivity Amplitudes
shapiro.test(cleanedSimpleTibble4SST$goCorrPe_Pz)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$goCorrPe_Pz
W = 0.9589, p-value = 0.07485qqnorm(cleanedSimpleTibble4SST$goCorrPe_Pz)
qqline(cleanedSimpleTibble4SST$goCorrPe_Pz)
Residualized Incorrect-Stop N200 Amplitudes
shapiro.test(cleanedSimpleTibble4SST$N2resid)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$N2resid
W = 0.9765, p-value = 0.4026qqnorm(cleanedSimpleTibble4SST$N2resid)
qqline(cleanedSimpleTibble4SST$N2resid)
Residualized Incorrect-Stop P300 Amplitudes
shapiro.test(cleanedSimpleTibble4SST$P3resid)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$P3resid
W = 0.97318, p-value = 0.2983qqnorm(cleanedSimpleTibble4SST$P3resid)
qqline(cleanedSimpleTibble4SST$P3resid)
Residualized Error-Related Negativity Amplitudes
shapiro.test(cleanedSimpleTibble4SST$ERNresid)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$ERNresid
W = 0.9871, p-value = 0.8495qqnorm(cleanedSimpleTibble4SST$ERNresid)
qqline(cleanedSimpleTibble4SST$ERNresid)
Residualized Error Positivity Amplitudes
shapiro.test(cleanedSimpleTibble4SST$PeResid)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4SST$PeResid
W = 0.88705, p-value = 0.0001598qqnorm(cleanedSimpleTibble4SST$PeResid)
qqline(cleanedSimpleTibble4SST$PeResid)
3.2 Shapiro-Wilk test and QQ plots in MID sample
American Indian Enculturation Scale
shapiro.test(cleanedSimpleTibble4MID$midAIES)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midAIES
W = 0.92831, p-value = 0.009119qqnorm(cleanedSimpleTibble4MID$midAIES)
qqline(cleanedSimpleTibble4MID$midAIES)
Native American Acculturation Scale
shapiro.test(cleanedSimpleTibble4MID$midNAAS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midNAAS
W = 0.96191, p-value = 0.1533qqnorm(cleanedSimpleTibble4MID$midNAAS)
qqline(cleanedSimpleTibble4MID$midNAAS)
Native American Spirituality Scale
shapiro.test(cleanedSimpleTibble4MID$midNASS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midNASS
W = 0.90795, p-value = 0.001927qqnorm(cleanedSimpleTibble4MID$midNASS)
qqline(cleanedSimpleTibble4MID$midNASS)
Historical Loss Scale
shapiro.test(cleanedSimpleTibble4MID$midHLS)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midHLS
W = 0.96182, p-value = 0.1521qqnorm(cleanedSimpleTibble4MID$midHLS)
qqline(cleanedSimpleTibble4MID$midHLS)
PROMIS Anxiety
shapiro.test(cleanedSimpleTibble4MID$midPROMISanx)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midPROMISanx
W = 0.97504, p-value = 0.4492qqnorm(cleanedSimpleTibble4MID$midPROMISanx)
qqline(cleanedSimpleTibble4MID$midPROMISanx)
PROMIS Alcohol Use
shapiro.test(cleanedSimpleTibble4MID$midPROMISalcUse)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midPROMISalcUse
W = 0.849, p-value = 4.12e-05qqnorm(cleanedSimpleTibble4MID$midPROMISalcUse)
qqline(cleanedSimpleTibble4MID$midPROMISalcUse)
PROMIS Depression
shapiro.test(cleanedSimpleTibble4MID$midPROMISdepress)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$midPROMISdepress
W = 0.96353, p-value = 0.1762qqnorm(cleanedSimpleTibble4MID$midPROMISdepress)
qqline(cleanedSimpleTibble4MID$midPROMISdepress)
Loss Cue P300 Amplitudes
shapiro.test(cleanedSimpleTibble4MID$P3pzMIDcueLoss)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$P3pzMIDcueLoss
W = 0.9562, p-value = 0.09381qqnorm(cleanedSimpleTibble4MID$P3pzMIDcueLoss)
qqline(cleanedSimpleTibble4MID$P3pzMIDcueLoss)
No Gain Cue P300 Amplitudes
shapiro.test(cleanedSimpleTibble4MID$P3pzMIDcueNogain)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$P3pzMIDcueNogain
W = 0.97488, p-value = 0.4441qqnorm(cleanedSimpleTibble4MID$P3pzMIDcueNogain)
qqline(cleanedSimpleTibble4MID$P3pzMIDcueNogain)
Gain Cue P300 Amplitudes
shapiro.test(cleanedSimpleTibble4MID$P3pzMIDcueGain)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$P3pzMIDcueGain
W = 0.87887, p-value = 0.0002594qqnorm(cleanedSimpleTibble4MID$P3pzMIDcueGain)
qqline(cleanedSimpleTibble4MID$P3pzMIDcueGain)
Success SPN Amplitudes
shapiro.test(cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess
W = 0.98571, p-value = 0.8537qqnorm(cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess)
qqline(cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess)
Failure SPN Amplitudes
shapiro.test(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail
W = 0.93923, p-value = 0.02217qqnorm(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
qqline(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
Gain RewP Amplitudes
shapiro.test(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
Shapiro-Wilk normality test
data: cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail
W = 0.93923, p-value = 0.02217qqnorm(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
qqline(cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail)
3.3 Shapiro-Wilk test and QQ plots in Merged sample
American Indian Enculturation Scale
shapiro.test(dfERPsMerged$midAIES)
Shapiro-Wilk normality test
data: dfERPsMerged$midAIES
W = 0.93706, p-value = 0.03046qqnorm(dfERPsMerged$midAIES)
qqline(dfERPsMerged$midAIES)
Native American Acculturation Scale
shapiro.test(dfERPsMerged$midNAAS)
Shapiro-Wilk normality test
data: dfERPsMerged$midNAAS
W = 0.96086, p-value = 0.1905qqnorm(dfERPsMerged$midNAAS)
qqline(dfERPsMerged$midNAAS)
Native American Spirituality Scale
shapiro.test(dfERPsMerged$midNASS)
Shapiro-Wilk normality test
data: dfERPsMerged$midNASS
W = 0.9345, p-value = 0.02511qqnorm(dfERPsMerged$midNASS)
qqline(dfERPsMerged$midNASS)
Historical Loss Scale
shapiro.test(dfERPsMerged$midHLS)
Shapiro-Wilk normality test
data: dfERPsMerged$midHLS
W = 0.96318, p-value = 0.2274qqnorm(dfERPsMerged$midHLS)
qqline(dfERPsMerged$midHLS)
PROMIS Anxiety
shapiro.test(dfERPsMerged$midPROMISanx)
Shapiro-Wilk normality test
data: dfERPsMerged$midPROMISanx
W = 0.97314, p-value = 0.4661qqnorm(dfERPsMerged$midPROMISanx)
qqline(dfERPsMerged$midPROMISanx)
PROMIS Alcohol Use
shapiro.test(dfERPsMerged$midPROMISalcUse)
Shapiro-Wilk normality test
data: dfERPsMerged$midPROMISalcUse
W = 0.84736, p-value = 9.358e-05qqnorm(dfERPsMerged$midPROMISalcUse)
qqline(dfERPsMerged$midPROMISalcUse)
PROMIS Depression
shapiro.test(dfERPsMerged$midPROMISdepress)
Shapiro-Wilk normality test
data: dfERPsMerged$midPROMISdepress
W = 0.96386, p-value = 0.2394qqnorm(dfERPsMerged$midPROMISdepress)
qqline(dfERPsMerged$midPROMISdepress)
Correct-Stop N200 Amplitudes
shapiro.test(dfERPsMerged$allCorrN2_FCz)
Shapiro-Wilk normality test
data: dfERPsMerged$allCorrN2_FCz
W = 0.96846, p-value = 0.3364qqnorm(dfERPsMerged$allCorrN2_FCz)
qqline(dfERPsMerged$allCorrN2_FCz)
Correct-Stop P300 Amplitudes
shapiro.test(dfERPsMerged$allCorrP3_Pz)
Shapiro-Wilk normality test
data: dfERPsMerged$allCorrP3_Pz
W = 0.97147, p-value = 0.4163qqnorm(dfERPsMerged$allCorrP3_Pz)
qqline(dfERPsMerged$allCorrP3_Pz)
Correct-Related Negativity Amplitudes
shapiro.test(dfERPsMerged$goCorrERN_FCz)
Shapiro-Wilk normality test
data: dfERPsMerged$goCorrERN_FCz
W = 0.97161, p-value = 0.4203qqnorm(dfERPsMerged$goCorrERN_FCz)
qqline(dfERPsMerged$goCorrERN_FCz)
Correct Error Positivity Amplitudes
shapiro.test(dfERPsMerged$goCorrPe_Pz)
Shapiro-Wilk normality test
data: dfERPsMerged$goCorrPe_Pz
W = 0.94972, p-value = 0.08039qqnorm(dfERPsMerged$goCorrPe_Pz)
qqline(dfERPsMerged$goCorrPe_Pz)
Residualized Incorrect-Stop N200 Amplitudes
shapiro.test(dfERPsMerged$N2resid)
Shapiro-Wilk normality test
data: dfERPsMerged$N2resid
W = 0.97491, p-value = 0.5231qqnorm(dfERPsMerged$N2resid)
qqline(dfERPsMerged$N2resid)
Residualized Incorrect-Stop P300 Amplitudes
shapiro.test(dfERPsMerged$P3resid)
Shapiro-Wilk normality test
data: dfERPsMerged$P3resid
W = 0.97422, p-value = 0.5007qqnorm(dfERPsMerged$P3resid)
qqline(dfERPsMerged$P3resid)
Residualized Error-Related Negativity Amplitudes
shapiro.test(dfERPsMerged$ERNresid)
Shapiro-Wilk normality test
data: dfERPsMerged$ERNresid
W = 0.97476, p-value = 0.5183qqnorm(dfERPsMerged$ERNresid)
qqline(dfERPsMerged$ERNresid)
Residualized Error Positivity Amplitudes
shapiro.test(dfERPsMerged$PeResid)
Shapiro-Wilk normality test
data: dfERPsMerged$PeResid
W = 0.88606, p-value = 0.0009001qqnorm(dfERPsMerged$PeResid)
qqline(dfERPsMerged$PeResid)
Loss Cue P300 Amplitudes
shapiro.test(dfERPsMerged$P3pzMIDcueLoss)
Shapiro-Wilk normality test
data: dfERPsMerged$P3pzMIDcueLoss
W = 0.94213, p-value = 0.04479qqnorm(dfERPsMerged$P3pzMIDcueLoss)
qqline(dfERPsMerged$P3pzMIDcueLoss)
No Gain Cue P300 Amplitudes
shapiro.test(dfERPsMerged$P3pzMIDcueNogain)
Shapiro-Wilk normality test
data: dfERPsMerged$P3pzMIDcueNogain
W = 0.98886, p-value = 0.9612qqnorm(dfERPsMerged$P3pzMIDcueNogain)
qqline(dfERPsMerged$P3pzMIDcueNogain)
Gain Cue P300 Amplitudes
shapiro.test(dfERPsMerged$P3pzMIDcueGain)
Shapiro-Wilk normality test
data: dfERPsMerged$P3pzMIDcueGain
W = 0.88553, p-value = 0.0008709qqnorm(dfERPsMerged$P3pzMIDcueGain)
qqline(dfERPsMerged$P3pzMIDcueGain)
Success SPN Amplitudes
shapiro.test(dfERPsMerged$SPNfzMIDfeedbackSuccess)
Shapiro-Wilk normality test
data: dfERPsMerged$SPNfzMIDfeedbackSuccess
W = 0.96812, p-value = 0.3283qqnorm(dfERPsMerged$SPNfzMIDfeedbackSuccess)
qqline(dfERPsMerged$SPNfzMIDfeedbackSuccess)
Failure SPN Amplitudes
shapiro.test(dfERPsMerged$SPNfzMIDfeedbackFail)
Shapiro-Wilk normality test
data: dfERPsMerged$SPNfzMIDfeedbackFail
W = 0.91059, p-value = 0.004504qqnorm(dfERPsMerged$SPNfzMIDfeedbackFail)
qqline(dfERPsMerged$SPNfzMIDfeedbackFail)
Gain RewP Amplitudes
shapiro.test(dfERPsMerged$RewPfczMIDfeedbackGain)
Shapiro-Wilk normality test
data: dfERPsMerged$RewPfczMIDfeedbackGain
W = 0.95985, p-value = 0.1762qqnorm(dfERPsMerged$P3pzMIDcueGain)
qqline(dfERPsMerged$P3pzMIDcueGain)
4 Internal consistency
4.1 Cronbach’s alpha and MacDonald’s omega in SST sample for cultural self-reports
American Indian Enculturation Scale
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 0.52Historical Loss Scale
omega(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 0.27Native American Spirituality Scale
omega(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 0.96NAtive American Acculturation Scale
omega(dfSSTnaas)In smc, smcs < 0 were set to .0
In 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 fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : 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 in MID sample for cultural self-reports
American Indian Enculturation Scale
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 0.44Historical Loss Scale
omega(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 0.19Native American Spirituality Scale
omega(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 0.43Native American Acculturation Scale
omega(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 in Merged smaple for cultural self-reports
American Indian Enculturation Scale
omega(dfMergedAies)In smc, smcs < 0 were set to .0
In 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 used
In smc, smcs < 0 were set to .0Warning 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: 0.88
G.6: 0.94
Omega Hierarchical: 0.71
Omega H asymptotic: 0.78
Omega Total 0.92
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1- 0.24 -0.26 0.15 0.85 0.37 2.73
aies_2 0.72 0.28 0.59 0.59 0.41 0.00 1.31
aies_3- -0.70 0.52 0.52 0.48 0.06 1.13
aies_4- 0.27 -0.57 0.42 0.42 0.58 0.17 1.62
aies_5- 0.24 -0.60 0.44 0.44 0.56 0.13 1.42
aies_6- 0.27 -0.35 0.22 0.22 0.78 0.34 2.25
aies_7 0.75 0.27 0.63 0.63 0.37 0.00 1.26
aies_8- -0.48 0.27 0.27 0.73 0.05 1.35
aies_9- 0.23 -0.49 0.31 0.31 0.69 0.17 1.56
aies_10- -0.41 0.34 0.30 0.30 0.70 0.03 2.04
aies_11 0.69 0.49 0.49 0.51 0.01 1.05
aies_12- -0.38 0.38 0.31 0.31 0.69 0.07 2.30
aies_13 0.46 0.25 0.25 0.75 0.05 1.27
aies_14- -0.76 0.59 0.59 0.41 0.01 1.06
aies_15 0.25 -0.21 0.12 0.88 0.52 2.36
aies_16- -0.59 0.39 0.39 0.61 0.02 1.19
aies_17 0.24 0.08 0.92 0.02 1.71
naas_1 -0.21 0.43 0.24 0.24 0.76 0.01 1.48
naas_2 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_3 -0.20 -0.37 0.17 0.83 0.00 1.55
naas_4 0.05 0.95 0.21 1.70
naas_5- 0.25 0.22 0.11 0.89 0.53 2.22
naas_6- 0.25 0.10 0.90 0.65 1.85
naas_7- 0.33 0.31 0.21 0.21 0.79 0.53 2.01
naas_8- 0.29 0.09 0.91 0.09 1.28
naas_9 -0.26 0.10 0.90 0.08 1.91
naas_10 0.40 0.19 0.81 0.83 1.41
naas_11 -0.22 0.08 0.92 0.05 2.00
naas_12- 0.43 0.20 0.20 0.80 0.02 1.24
naas_13 -0.63 0.20 0.48 0.48 0.52 0.06 1.37
naas_14 -0.37 0.14 0.86 0.03 1.08
naas_15 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_16 0.46 -0.32 0.45 0.52 0.52 0.48 0.41 2.79
naas_17 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_18 0.23 -0.46 -0.31 0.35 0.35 0.65 0.15 2.27
naas_19 0.23 -0.23 -0.47 0.32 0.32 0.68 0.16 1.96
naas_20 -0.87 0.76 0.76 0.24 0.00 1.01
nass_1- 0.41 -0.53 0.47 0.47 0.53 0.36 2.05
nass_2- 0.33 -0.62 0.51 0.51 0.49 0.21 1.57
nass_3- 0.04 0.96 0.14 1.48
nass_4- 0.29 -0.33 -0.36 0.32 0.32 0.68 0.26 2.91
nass_5- -0.25 -0.51 0.35 0.35 0.65 0.09 1.72
nass_6- 0.24 -0.60 0.43 0.43 0.57 0.13 1.38
nass_7- -0.75 0.57 0.57 0.43 0.00 1.01
nass_8- -0.35 -0.34 0.24 0.24 0.76 0.02 2.09
nass_9- -0.31 -0.48 0.34 0.34 0.66 0.05 1.87
nass_10- 0.51 -0.41 0.44 0.44 0.56 0.59 2.04
nass_11- 0.30 -0.65 0.53 0.53 0.47 0.16 1.53
nass_12- 0.33 -0.21 -0.20 0.19 0.81 0.58 2.38
hls_1 0.67 0.47 0.47 0.53 0.95 1.11
hls_2 0.66 -0.20 0.25 0.53 0.53 0.47 0.81 1.48
hls_3 0.66 0.45 0.45 0.55 0.97 1.07
hls_4 0.72 0.55 0.55 0.45 0.95 1.11
hls_5 0.77 -0.21 0.66 0.66 0.34 0.91 1.20
hls_6 0.56 0.28 0.40 0.40 0.60 0.78 1.54
hls_7 0.74 -0.28 0.64 0.64 0.36 0.87 1.29
hls_8 0.74 0.58 0.58 0.42 0.94 1.12
hls_9 0.70 0.51 0.51 0.49 0.96 1.08
hls_10 0.78 0.63 0.63 0.37 0.96 1.08
hls_11 0.77 0.61 0.61 0.39 0.99 1.03
hls_12 0.78 -0.21 0.65 0.65 0.35 0.92 1.16
With Sums of squares of:
g F1* F2* F3* h2
9.04 8.94 0.04 5.07 10.98
general/max 0.82 max/min = 262.82
mean percent general = 0.34 with sd = 0.35 and cv of 1.03
Explained Common Variance of the general factor = 0.39
The degrees of freedom are 1650 and the fit is 497.67
The number of observations was 40 with Chi Square = 7879.75 with prob < 0
The root mean square of the residuals is 0.11
The df corrected root mean square of the residuals is 0.12
RMSEA index = 0.306 and the 90 % confidence intervals are 0.304 0.318
BIC = 1793.1
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 1769 and the fit is 512.35
The number of observations was 40 with Chi Square = 8795.28 with prob < 0
The root mean square of the residuals is 0.2
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.314 and the 90 % confidence intervals are 0.313 0.326
BIC = 2269.65
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.97 0.97 0.07 0.95
Multiple R square of scores with factors 0.95 0.95 0.00 0.90
Minimum correlation of factor score estimates 0.90 0.90 -0.99 0.80
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.92 0.77 NA 0.72
Omega general for total scores and subscales 0.71 0.58 NA 0.63
Omega group for total scores and subscales 0.08 0.19 NA 0.09Historical Loss Scale
omega(dfMergedHls)In smc, smcs < 0 were set to .0
In 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 used
In smc, smcs < 0 were set to .0Warning 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: 0.88
G.6: 0.94
Omega Hierarchical: 0.71
Omega H asymptotic: 0.78
Omega Total 0.92
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1- 0.24 -0.26 0.15 0.85 0.37 2.73
aies_2 0.72 0.28 0.59 0.59 0.41 0.00 1.31
aies_3- -0.70 0.52 0.52 0.48 0.06 1.13
aies_4- 0.27 -0.57 0.42 0.42 0.58 0.17 1.62
aies_5- 0.24 -0.60 0.44 0.44 0.56 0.13 1.42
aies_6- 0.27 -0.35 0.22 0.22 0.78 0.34 2.25
aies_7 0.75 0.27 0.63 0.63 0.37 0.00 1.26
aies_8- -0.48 0.27 0.27 0.73 0.05 1.35
aies_9- 0.23 -0.49 0.31 0.31 0.69 0.17 1.56
aies_10- -0.41 0.34 0.30 0.30 0.70 0.03 2.04
aies_11 0.69 0.49 0.49 0.51 0.01 1.05
aies_12- -0.38 0.38 0.31 0.31 0.69 0.07 2.30
aies_13 0.46 0.25 0.25 0.75 0.05 1.27
aies_14- -0.76 0.59 0.59 0.41 0.01 1.06
aies_15 0.25 -0.21 0.12 0.88 0.52 2.36
aies_16- -0.59 0.39 0.39 0.61 0.02 1.19
aies_17 0.24 0.08 0.92 0.02 1.71
naas_1 -0.21 0.43 0.24 0.24 0.76 0.01 1.48
naas_2 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_3 -0.20 -0.37 0.17 0.83 0.00 1.55
naas_4 0.05 0.95 0.21 1.70
naas_5- 0.25 0.22 0.11 0.89 0.53 2.22
naas_6- 0.25 0.10 0.90 0.65 1.85
naas_7- 0.33 0.31 0.21 0.21 0.79 0.53 2.01
naas_8- 0.29 0.09 0.91 0.09 1.28
naas_9 -0.26 0.10 0.90 0.08 1.91
naas_10 0.40 0.19 0.81 0.83 1.41
naas_11 -0.22 0.08 0.92 0.05 2.00
naas_12- 0.43 0.20 0.20 0.80 0.02 1.24
naas_13 -0.63 0.20 0.48 0.48 0.52 0.06 1.37
naas_14 -0.37 0.14 0.86 0.03 1.08
naas_15 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_16 0.46 -0.32 0.45 0.52 0.52 0.48 0.41 2.79
naas_17 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_18 0.23 -0.46 -0.31 0.35 0.35 0.65 0.15 2.27
naas_19 0.23 -0.23 -0.47 0.32 0.32 0.68 0.16 1.96
naas_20 -0.87 0.76 0.76 0.24 0.00 1.01
nass_1- 0.41 -0.53 0.47 0.47 0.53 0.36 2.05
nass_2- 0.33 -0.62 0.51 0.51 0.49 0.21 1.57
nass_3- 0.04 0.96 0.14 1.48
nass_4- 0.29 -0.33 -0.36 0.32 0.32 0.68 0.26 2.91
nass_5- -0.25 -0.51 0.35 0.35 0.65 0.09 1.72
nass_6- 0.24 -0.60 0.43 0.43 0.57 0.13 1.38
nass_7- -0.75 0.57 0.57 0.43 0.00 1.01
nass_8- -0.35 -0.34 0.24 0.24 0.76 0.02 2.09
nass_9- -0.31 -0.48 0.34 0.34 0.66 0.05 1.87
nass_10- 0.51 -0.41 0.44 0.44 0.56 0.59 2.04
nass_11- 0.30 -0.65 0.53 0.53 0.47 0.16 1.53
nass_12- 0.33 -0.21 -0.20 0.19 0.81 0.58 2.38
hls_1 0.67 0.47 0.47 0.53 0.95 1.11
hls_2 0.66 -0.20 0.25 0.53 0.53 0.47 0.81 1.48
hls_3 0.66 0.45 0.45 0.55 0.97 1.07
hls_4 0.72 0.55 0.55 0.45 0.95 1.11
hls_5 0.77 -0.21 0.66 0.66 0.34 0.91 1.20
hls_6 0.56 0.28 0.40 0.40 0.60 0.78 1.54
hls_7 0.74 -0.28 0.64 0.64 0.36 0.87 1.29
hls_8 0.74 0.58 0.58 0.42 0.94 1.12
hls_9 0.70 0.51 0.51 0.49 0.96 1.08
hls_10 0.78 0.63 0.63 0.37 0.96 1.08
hls_11 0.77 0.61 0.61 0.39 0.99 1.03
hls_12 0.78 -0.21 0.65 0.65 0.35 0.92 1.16
With Sums of squares of:
g F1* F2* F3* h2
9.04 8.94 0.04 5.07 10.98
general/max 0.82 max/min = 262.82
mean percent general = 0.34 with sd = 0.35 and cv of 1.03
Explained Common Variance of the general factor = 0.39
The degrees of freedom are 1650 and the fit is 497.67
The number of observations was 40 with Chi Square = 7879.75 with prob < 0
The root mean square of the residuals is 0.11
The df corrected root mean square of the residuals is 0.12
RMSEA index = 0.306 and the 90 % confidence intervals are 0.304 0.318
BIC = 1793.1
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 1769 and the fit is 512.35
The number of observations was 40 with Chi Square = 8795.28 with prob < 0
The root mean square of the residuals is 0.2
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.314 and the 90 % confidence intervals are 0.313 0.326
BIC = 2269.65
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.97 0.97 0.07 0.95
Multiple R square of scores with factors 0.95 0.95 0.00 0.90
Minimum correlation of factor score estimates 0.90 0.90 -0.99 0.80
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.92 0.77 NA 0.72
Omega general for total scores and subscales 0.71 0.58 NA 0.63
Omega group for total scores and subscales 0.08 0.19 NA 0.09Native American Spirituality Scale
omega(dfMergedNass)In smc, smcs < 0 were set to .0
In 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 used
In smc, smcs < 0 were set to .0Warning 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: 0.88
G.6: 0.94
Omega Hierarchical: 0.71
Omega H asymptotic: 0.78
Omega Total 0.92
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1- 0.24 -0.26 0.15 0.85 0.37 2.73
aies_2 0.72 0.28 0.59 0.59 0.41 0.00 1.31
aies_3- -0.70 0.52 0.52 0.48 0.06 1.13
aies_4- 0.27 -0.57 0.42 0.42 0.58 0.17 1.62
aies_5- 0.24 -0.60 0.44 0.44 0.56 0.13 1.42
aies_6- 0.27 -0.35 0.22 0.22 0.78 0.34 2.25
aies_7 0.75 0.27 0.63 0.63 0.37 0.00 1.26
aies_8- -0.48 0.27 0.27 0.73 0.05 1.35
aies_9- 0.23 -0.49 0.31 0.31 0.69 0.17 1.56
aies_10- -0.41 0.34 0.30 0.30 0.70 0.03 2.04
aies_11 0.69 0.49 0.49 0.51 0.01 1.05
aies_12- -0.38 0.38 0.31 0.31 0.69 0.07 2.30
aies_13 0.46 0.25 0.25 0.75 0.05 1.27
aies_14- -0.76 0.59 0.59 0.41 0.01 1.06
aies_15 0.25 -0.21 0.12 0.88 0.52 2.36
aies_16- -0.59 0.39 0.39 0.61 0.02 1.19
aies_17 0.24 0.08 0.92 0.02 1.71
naas_1 -0.21 0.43 0.24 0.24 0.76 0.01 1.48
naas_2 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_3 -0.20 -0.37 0.17 0.83 0.00 1.55
naas_4 0.05 0.95 0.21 1.70
naas_5- 0.25 0.22 0.11 0.89 0.53 2.22
naas_6- 0.25 0.10 0.90 0.65 1.85
naas_7- 0.33 0.31 0.21 0.21 0.79 0.53 2.01
naas_8- 0.29 0.09 0.91 0.09 1.28
naas_9 -0.26 0.10 0.90 0.08 1.91
naas_10 0.40 0.19 0.81 0.83 1.41
naas_11 -0.22 0.08 0.92 0.05 2.00
naas_12- 0.43 0.20 0.20 0.80 0.02 1.24
naas_13 -0.63 0.20 0.48 0.48 0.52 0.06 1.37
naas_14 -0.37 0.14 0.86 0.03 1.08
naas_15 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_16 0.46 -0.32 0.45 0.52 0.52 0.48 0.41 2.79
naas_17 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_18 0.23 -0.46 -0.31 0.35 0.35 0.65 0.15 2.27
naas_19 0.23 -0.23 -0.47 0.32 0.32 0.68 0.16 1.96
naas_20 -0.87 0.76 0.76 0.24 0.00 1.01
nass_1- 0.41 -0.53 0.47 0.47 0.53 0.36 2.05
nass_2- 0.33 -0.62 0.51 0.51 0.49 0.21 1.57
nass_3- 0.04 0.96 0.14 1.48
nass_4- 0.29 -0.33 -0.36 0.32 0.32 0.68 0.26 2.91
nass_5- -0.25 -0.51 0.35 0.35 0.65 0.09 1.72
nass_6- 0.24 -0.60 0.43 0.43 0.57 0.13 1.38
nass_7- -0.75 0.57 0.57 0.43 0.00 1.01
nass_8- -0.35 -0.34 0.24 0.24 0.76 0.02 2.09
nass_9- -0.31 -0.48 0.34 0.34 0.66 0.05 1.87
nass_10- 0.51 -0.41 0.44 0.44 0.56 0.59 2.04
nass_11- 0.30 -0.65 0.53 0.53 0.47 0.16 1.53
nass_12- 0.33 -0.21 -0.20 0.19 0.81 0.58 2.38
hls_1 0.67 0.47 0.47 0.53 0.95 1.11
hls_2 0.66 -0.20 0.25 0.53 0.53 0.47 0.81 1.48
hls_3 0.66 0.45 0.45 0.55 0.97 1.07
hls_4 0.72 0.55 0.55 0.45 0.95 1.11
hls_5 0.77 -0.21 0.66 0.66 0.34 0.91 1.20
hls_6 0.56 0.28 0.40 0.40 0.60 0.78 1.54
hls_7 0.74 -0.28 0.64 0.64 0.36 0.87 1.29
hls_8 0.74 0.58 0.58 0.42 0.94 1.12
hls_9 0.70 0.51 0.51 0.49 0.96 1.08
hls_10 0.78 0.63 0.63 0.37 0.96 1.08
hls_11 0.77 0.61 0.61 0.39 0.99 1.03
hls_12 0.78 -0.21 0.65 0.65 0.35 0.92 1.16
With Sums of squares of:
g F1* F2* F3* h2
9.04 8.94 0.04 5.07 10.98
general/max 0.82 max/min = 262.82
mean percent general = 0.34 with sd = 0.35 and cv of 1.03
Explained Common Variance of the general factor = 0.39
The degrees of freedom are 1650 and the fit is 497.67
The number of observations was 40 with Chi Square = 7879.75 with prob < 0
The root mean square of the residuals is 0.11
The df corrected root mean square of the residuals is 0.12
RMSEA index = 0.306 and the 90 % confidence intervals are 0.304 0.318
BIC = 1793.1
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 1769 and the fit is 512.35
The number of observations was 40 with Chi Square = 8795.28 with prob < 0
The root mean square of the residuals is 0.2
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.314 and the 90 % confidence intervals are 0.313 0.326
BIC = 2269.65
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.97 0.97 0.07 0.95
Multiple R square of scores with factors 0.95 0.95 0.00 0.90
Minimum correlation of factor score estimates 0.90 0.90 -0.99 0.80
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.92 0.77 NA 0.72
Omega general for total scores and subscales 0.71 0.58 NA 0.63
Omega group for total scores and subscales 0.08 0.19 NA 0.09Native American Acculturation Scale
omega(dfMergedNaas)In smc, smcs < 0 were set to .0
In 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 used
In smc, smcs < 0 were set to .0Warning 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: 0.88
G.6: 0.94
Omega Hierarchical: 0.71
Omega H asymptotic: 0.78
Omega Total 0.92
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* h2 h2 u2 p2 com
aies_1- 0.24 -0.26 0.15 0.85 0.37 2.73
aies_2 0.72 0.28 0.59 0.59 0.41 0.00 1.31
aies_3- -0.70 0.52 0.52 0.48 0.06 1.13
aies_4- 0.27 -0.57 0.42 0.42 0.58 0.17 1.62
aies_5- 0.24 -0.60 0.44 0.44 0.56 0.13 1.42
aies_6- 0.27 -0.35 0.22 0.22 0.78 0.34 2.25
aies_7 0.75 0.27 0.63 0.63 0.37 0.00 1.26
aies_8- -0.48 0.27 0.27 0.73 0.05 1.35
aies_9- 0.23 -0.49 0.31 0.31 0.69 0.17 1.56
aies_10- -0.41 0.34 0.30 0.30 0.70 0.03 2.04
aies_11 0.69 0.49 0.49 0.51 0.01 1.05
aies_12- -0.38 0.38 0.31 0.31 0.69 0.07 2.30
aies_13 0.46 0.25 0.25 0.75 0.05 1.27
aies_14- -0.76 0.59 0.59 0.41 0.01 1.06
aies_15 0.25 -0.21 0.12 0.88 0.52 2.36
aies_16- -0.59 0.39 0.39 0.61 0.02 1.19
aies_17 0.24 0.08 0.92 0.02 1.71
naas_1 -0.21 0.43 0.24 0.24 0.76 0.01 1.48
naas_2 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_3 -0.20 -0.37 0.17 0.83 0.00 1.55
naas_4 0.05 0.95 0.21 1.70
naas_5- 0.25 0.22 0.11 0.89 0.53 2.22
naas_6- 0.25 0.10 0.90 0.65 1.85
naas_7- 0.33 0.31 0.21 0.21 0.79 0.53 2.01
naas_8- 0.29 0.09 0.91 0.09 1.28
naas_9 -0.26 0.10 0.90 0.08 1.91
naas_10 0.40 0.19 0.81 0.83 1.41
naas_11 -0.22 0.08 0.92 0.05 2.00
naas_12- 0.43 0.20 0.20 0.80 0.02 1.24
naas_13 -0.63 0.20 0.48 0.48 0.52 0.06 1.37
naas_14 -0.37 0.14 0.86 0.03 1.08
naas_15 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_16 0.46 -0.32 0.45 0.52 0.52 0.48 0.41 2.79
naas_17 0.48 -0.41 0.45 0.61 0.61 0.39 0.38 2.95
naas_18 0.23 -0.46 -0.31 0.35 0.35 0.65 0.15 2.27
naas_19 0.23 -0.23 -0.47 0.32 0.32 0.68 0.16 1.96
naas_20 -0.87 0.76 0.76 0.24 0.00 1.01
nass_1- 0.41 -0.53 0.47 0.47 0.53 0.36 2.05
nass_2- 0.33 -0.62 0.51 0.51 0.49 0.21 1.57
nass_3- 0.04 0.96 0.14 1.48
nass_4- 0.29 -0.33 -0.36 0.32 0.32 0.68 0.26 2.91
nass_5- -0.25 -0.51 0.35 0.35 0.65 0.09 1.72
nass_6- 0.24 -0.60 0.43 0.43 0.57 0.13 1.38
nass_7- -0.75 0.57 0.57 0.43 0.00 1.01
nass_8- -0.35 -0.34 0.24 0.24 0.76 0.02 2.09
nass_9- -0.31 -0.48 0.34 0.34 0.66 0.05 1.87
nass_10- 0.51 -0.41 0.44 0.44 0.56 0.59 2.04
nass_11- 0.30 -0.65 0.53 0.53 0.47 0.16 1.53
nass_12- 0.33 -0.21 -0.20 0.19 0.81 0.58 2.38
hls_1 0.67 0.47 0.47 0.53 0.95 1.11
hls_2 0.66 -0.20 0.25 0.53 0.53 0.47 0.81 1.48
hls_3 0.66 0.45 0.45 0.55 0.97 1.07
hls_4 0.72 0.55 0.55 0.45 0.95 1.11
hls_5 0.77 -0.21 0.66 0.66 0.34 0.91 1.20
hls_6 0.56 0.28 0.40 0.40 0.60 0.78 1.54
hls_7 0.74 -0.28 0.64 0.64 0.36 0.87 1.29
hls_8 0.74 0.58 0.58 0.42 0.94 1.12
hls_9 0.70 0.51 0.51 0.49 0.96 1.08
hls_10 0.78 0.63 0.63 0.37 0.96 1.08
hls_11 0.77 0.61 0.61 0.39 0.99 1.03
hls_12 0.78 -0.21 0.65 0.65 0.35 0.92 1.16
With Sums of squares of:
g F1* F2* F3* h2
9.04 8.94 0.04 5.07 10.98
general/max 0.82 max/min = 262.82
mean percent general = 0.34 with sd = 0.35 and cv of 1.03
Explained Common Variance of the general factor = 0.39
The degrees of freedom are 1650 and the fit is 497.67
The number of observations was 40 with Chi Square = 7879.75 with prob < 0
The root mean square of the residuals is 0.11
The df corrected root mean square of the residuals is 0.12
RMSEA index = 0.306 and the 90 % confidence intervals are 0.304 0.318
BIC = 1793.1
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 1769 and the fit is 512.35
The number of observations was 40 with Chi Square = 8795.28 with prob < 0
The root mean square of the residuals is 0.2
The df corrected root mean square of the residuals is 0.2
RMSEA index = 0.314 and the 90 % confidence intervals are 0.313 0.326
BIC = 2269.65
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.97 0.97 0.07 0.95
Multiple R square of scores with factors 0.95 0.95 0.00 0.90
Minimum correlation of factor score estimates 0.90 0.90 -0.99 0.80
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.92 0.77 NA 0.72
Omega general for total scores and subscales 0.71 0.58 NA 0.63
Omega group for total scores and subscales 0.08 0.19 NA 0.095 Bivariate correlations
5.1 Correlation matrix for SST
labelCleanedSimpleTibble4SST<-tibble(
Age=cleanedSimpleTibble4SST$AgeSST,
'Corr N2 FCz'=cleanedSimpleTibble4SST$allCorrN2_FCz,
'Corr P3 Pz'=cleanedSimpleTibble4SST$allCorrP3_Pz,
'CRN FCz FCz'=cleanedSimpleTibble4SST$goCorrERN_FCz,
'Corr Pe Pz'=cleanedSimpleTibble4SST$goCorrPe_Pz,
'Resid. Incorr N2 FCz'=cleanedSimpleTibble4SST$N2resid,
'Resid. Incorr P3 Pz'=cleanedSimpleTibble4SST$P3resid,
'Resid. ERN FCz'=cleanedSimpleTibble4SST$ERNresid,
'Resid Pe Pz'=cleanedSimpleTibble4SST$PeResid,
'AIES'=cleanedSimpleTibble4SST$sstAIES,
'NAAS'=cleanedSimpleTibble4SST$sstNAAS,
'NASS'=cleanedSimpleTibble4SST$sstNASS,
'HLS'=cleanedSimpleTibble4SST$sstHLS,
'PROMIS Anxiety'=cleanedSimpleTibble4SST$sstPROMISanx,
'PROMIS Alcohol Use'=cleanedSimpleTibble4SST$sstPROMISalcUse,
'PROMIS Depression'=cleanedSimpleTibble4SST$sstPROMISdepress
)# correlate the measures in the tibble
pSST<-corr.test(labelCleanedSimpleTibble4SST,method='spearman',adjust='none')$p
# write.csv(rAllsub,file='r.csv')
rSST<-corr.test(labelCleanedSimpleTibble4SST,method='spearman',adjust='none')$r
# write.csv(pAllsub,file='p.csv')
# create correlation matrix for all variables
corrplot(rSST,p.mat=pSST,method='color',diag=FALSE,type='lower',sig.level = c(0.001,0.01,0.05),pch.cex = 0.75, insig = 'label_sig',pch.col = 'black')
5.2 Correlation matrix for MID
labelCleanedSimpleTibble4MID<-tibble(
Age=cleanedSimpleTibble4MID$AgeMID,
"Loss Cue P3 Pz"=cleanedSimpleTibble4MID$P3pzMIDcueLoss,
"No Gain Cue P3 Pz"=cleanedSimpleTibble4MID$P3pzMIDcueNogain,
"Gain Cue P3 Pz"=cleanedSimpleTibble4MID$P3pzMIDcueGain,
"Success SPN Fz"=cleanedSimpleTibble4MID$SPNfzMIDfeedbackSuccess,
"Failure SPN Fz"=cleanedSimpleTibble4MID$SPNfzMIDfeedbackFail,
"Gain RewP FCz"=cleanedSimpleTibble4MID$RewPfczMIDfeedbackGain,
"AIES"=cleanedSimpleTibble4MID$midAIES,
"NAAS"=cleanedSimpleTibble4MID$midNAAS,
"NASS"=cleanedSimpleTibble4MID$midNASS,
"HLS"=cleanedSimpleTibble4MID$midHLS,
"PROMIS Anxiety"=cleanedSimpleTibble4MID$midPROMISanx,
"PROMIS Alcohol Use"=cleanedSimpleTibble4MID$midPROMISalcUse,
"PROMIS Depression"=cleanedSimpleTibble4MID$midPROMISdepress,
)# correlate the measures in the tibble
pMID<-corr.test(labelCleanedSimpleTibble4MID,method='spearman',adjust='none')$p
# write.csv(rAllsub,file='r.csv')
rMID<-corr.test(labelCleanedSimpleTibble4MID,method='spearman',adjust='none')$r
# write.csv(pAllsub,file='p.csv')
# create correlation matrix for all variables
corrplot(rMID,p.mat=pMID,method='color',diag=FALSE,type='lower',sig.level = c(0.001,0.01,0.05),pch.cex = 0.75, insig = 'label_sig',pch.col = 'black')
5.3 Correlation matrix for SST + MID
labelsDfERPsMerged<-tibble(
'Age'=dfERPsMerged$AgeSST,
'Corr N2 FCz'=dfERPsMerged$allCorrN2_FCz,
'Corr P3 Pz'=dfERPsMerged$allCorrP3_Pz,
'CRN FCz FCz'=dfERPsMerged$goCorrERN_FCz,
'Corr Pe Pz'=dfERPsMerged$goCorrPe_Pz,
'Resid. Incorr N2 FCz'=dfERPsMerged$N2resid,
'Resid. Incorr P3 Pz'=dfERPsMerged$P3resid,
'Resid. ERN FCz'=dfERPsMerged$ERNresid,
'Resid Pe Pz'=dfERPsMerged$PeResid,
'AIES'=dfERPsMerged$sstAIES,
'NAAS'=dfERPsMerged$sstNAAS,
'NASS'=dfERPsMerged$sstNASS,
'HLS'=dfERPsMerged$sstHLS,
'Loss Cue P3 Pz'=dfERPsMerged$P3pzMIDcueLoss,
'No Gain Cue P3 Pz'=dfERPsMerged$P3pzMIDcueNogain,
'Gain Cue P3 Pz'=dfERPsMerged$P3pzMIDcueGain,
'Success SPN Fz'=dfERPsMerged$SPNfzMIDfeedbackSuccess,
'Failure SPN Fz'=dfERPsMerged$SPNfzMIDfeedbackFail,
'Gain RewP FCz'=dfERPsMerged$RewPfczMIDfeedbackGain,
'PROMIS Anxiety'=dfERPsMerged$midPROMISanx,
'PROMIS Alcohol Use'=dfERPsMerged$midPROMISalcUse,
'PROMIS Depression'=dfERPsMerged$midPROMISdepress
)# correlate the measures in the tibble
pMerged<-corr.test(labelsDfERPsMerged,method='spearman',adjust='none')$p
# write.csv(rAllsub,file='r.csv')
rMerged<-corr.test(labelsDfERPsMerged,method='spearman',adjust='none')$r
# write.csv(pAllsub,file='p.csv')
# create correlation matrix for all variables
corrplot(rMerged,p.mat=pMerged,method='color',diag=FALSE,type='lower',sig.level = c(0.001,0.01,0.05),pch.cex = 0.75, insig = 'label_sig',pch.col = 'black')
6 Table 1
# re/create tibble for SST ERP sample
simpleTibble4SST<-tibble(
id=dfSST$id,
SexSST=dfSST$Gender,
AgeSST=dfSST$Age,
goIncorrERN_FCz=dfSST$ERNFCz_GoIncorr,
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$PePz_GoCorr,
sstPROMISanx=dfSST$PROMIS_AnxietyTscore,
sstPROMISalcUse=dfSST$PROMIS_AlcoUseTscore,
sstPROMISdepress=dfSST$PROMIS_DepressTscore,
sstAIES=dfSST$AIES_scale,
sstNAAS=dfSST$NAAS_scale,
sstNASS=dfSST$NASS_scale,
sstHLS=dfSST$HLS_scale
)
cleanedSimpleTibble4SST<-na.omit(simpleTibble4SST) # retains 51 participants
# residualized ERN
residual_incorrectFCzERN<-lm(goIncorrERN_FCz~goCorrERN_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$ERNresid<-resid(residual_incorrectFCzERN)
# residualized correct N2
residual_correctFCzN2<-lm(allCorrN2_FCz~allIncorrN2_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$N2corrResid<-resid(residual_correctFCzN2)
# residualized incorrect N2
residual_incorrectFCzN2<-lm(allIncorrN2_FCz~allCorrN2_FCz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$N2resid<-resid(residual_incorrectFCzN2)
# residualized correct P3
residual_correctPzP3<-lm(allCorrP3_Pz~allIncorrP3_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$P3corrResid<-resid(residual_correctPzP3)
# residualized incorrect P3
residual_incorrectPzP3<-lm(allIncorrP3_Pz~allCorrP3_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$P3resid<-resid(residual_incorrectPzP3)
# residualized Pe
residual_incorrectPzPe<-lm(goIncorrPe_Pz~goCorrPe_Pz,data=cleanedSimpleTibble4SST)
cleanedSimpleTibble4SST$PeResid<-resid(residual_incorrectPzPe)
# re/create tibble for MID ERP sample
simpleTibble4MID<-tibble(
id=dfMID$id,
SexMID=dfMID$Gender,
AgeMID=dfMID$Age,
P3pzMIDcueLoss=dfMID$P3pzMIDcueLoss,
P3pzMIDcueNogain=dfMID$P3pzMIDcueNogain,
P3pzMIDcueGain=dfMID$P3pzMIDcueGain,
SPNfzMIDfeedbackSuccess=dfMID$SPNfzMIDfeedbackSuccess,
SPNfzMIDfeedbackFail=dfMID$SPNfzMIDfeedbackFail,
RewPfczMIDfeedbackGain=dfMID$RewPfczMIDfeedbackGain,
midPROMISanx=dfMID$PROMIS_AnxietyTscore,
midPROMISalcUse=dfMID$PROMIS_AlcoUseTscore,
midPROMISdepress=dfMID$PROMIS_DepressTscore,
midAIES=dfMID$AIES_scale,
midNAAS=dfMID$NAAS_scale,
midNASS=dfMID$NASS_scale,
midHLS=dfMID$HLS_scale
)
cleanedSimpleTibble4MID<-na.omit(simpleTibble4MID)
# re/merge tibble for SST + MID ERP sample
dfERPsMerged<-merge(cleanedSimpleTibble4SST,cleanedSimpleTibble4MID,by='id')
# loading appropriate, cleaned dataframes before tibbl'ing and merging
cleanedSimpleTibble4SST$Sample="SST"
cleanedSimpleTibble4MID$Sample="MID"
dfERPsMerged$Sample="SST+MID"
# merge dataframes by id
dfTibble4TableSST<-merge(cleanedSimpleTibble4SST,dfSSTdemo,by='id')
dfTibble4TableMID<-merge(cleanedSimpleTibble4MID,dfSSTdemo,by='id')
dfTibble4TableMerged<-merge(dfERPsMerged,dfSSTdemo,by='id')
# create tibble combining the three dataframes
tibble4preTable1<-tibble(
Sample=c(dfTibble4TableSST$Sample,dfTibble4TableMID$Sample,dfTibble4TableMerged$Sample),
Education=c(dfTibble4TableSST$Education_4level,dfTibble4TableMID$Education_4level,dfTibble4TableMerged$Education_4level),
Ethnicity=c(dfTibble4TableSST$Ethnicity,dfTibble4TableMID$Ethnicity,dfTibble4TableMerged$Ethnicity),
#RaceAI=c(dfTibble4TableSST$RACE_NativeAmerican,dfTibble4TableMID$RACE_NativeAmerican,dfTibble4TableMerged$raceAI),
#RaceAlaskanNative=c(dfTibble4TableSST$RACE_AlaskanNative,dfTibble4TableMID$RACE_AlaskanNative,dfTibble4TableMerged$raceAI), # for dfTibble4TableMerged, it includes both AI and Alaska Native
#RaceBlack=c(dfTibble4TableSST$RACE_AfricanAmerican,dfTibble4TableMID$RACE_AfricanAmerican,dfTibble4TableMerged$raceBlack),
#RaceAsian=c(dfTibble4TableSST$RACE_Asian,dfTibble4TableMID$RACE_Asian,dfTibble4TableMerged$raceAsian),
#RaceOther=c(dfTibble4TableSST$RACE_Other,dfTibble4TableMID$RACE_Other,dfTibble4TableMerged$raceOther),
#RacePacificIslander=c(dfTibble4TableSST$RACE_PacificIslander,dfTibble4TableMID$RACE_PacificIslander,dfTibble4TableMerged$raceHawaii),
#RaceWhite=c(dfTibble4TableSST$RACE_White,dfTibble4TableMID$RACE_White,dfTibble4TableMerged$raceWhite),
#RaceEthnicity=c(dfTibble4TableSST$RaceEthnicity,dfTibble4TableMID$RaceEthnicity,dfTibble4TableMerged$raceAI),
Sex=c(dfTibble4TableSST$SexSST,dfTibble4TableMID$SexMID,dfTibble4TableMerged$SexSST),
Age=c(dfTibble4TableSST$Age,dfTibble4TableMID$Age,dfTibble4TableMerged$AgeMID),
Income=c(dfTibble4TableSST$Income,dfTibble4TableMID$Income,dfTibble4TableMerged$Income),
PROMIS_Anxiety=c(dfTibble4TableSST$sstPROMISanx,dfTibble4TableMID$midPROMISanx,dfTibble4TableMerged$midPROMISanx),
PROMIS_Alcohol_Use=c(dfTibble4TableSST$sstPROMISalcUse,dfTibble4TableMID$midPROMISalcUse,dfTibble4TableMerged$midPROMISalcUse),
PROMIS_Depression=c(dfTibble4TableSST$sstPROMISdepress,dfTibble4TableMID$midPROMISdepress,dfTibble4TableMerged$midPROMISdepress),
AIES=c(dfTibble4TableSST$sstAIES,dfTibble4TableMID$midAIES,dfTibble4TableMerged$midAIES),
NAAS=c(dfTibble4TableSST$sstNAAS,dfTibble4TableMID$midNAAS,dfTibble4TableMerged$midNAAS),
HLS=c(dfTibble4TableSST$sstHLS,dfTibble4TableMID$midHLS,dfTibble4TableMerged$midHLS),
NASS=c(dfTibble4TableSST$sstNASS,dfTibble4TableMID$midNASS,dfTibble4TableMerged$midNASS)
)
write.csv(tibble4preTable1,"C:\\Users\\wvaught\\OneDrive - Laureate Institute for Brain Research\\Documents\\01. EEG CORE\\05. Papers\\Culture & Cognition\\finalAnalysis\\tibble4preTable1.csv")
# reworking inside of the csv file to match reporting guidelines on race
dfTable1practice<-read.csv("tibble4preTable1.csv",header=TRUE)# create correct labels for grouping and other categorical variables, another way to do this, see https://blog.djnavarro.net/posts/2024-06-21_table1/#table-annotations or https://benjaminrich.github.io/table1/vignettes/table1-examples.html for more
# function to apply only certain statistics to cont. variables
my.render.cont <- function(x, ...) { with(stats.apply.rounding(stats.default(x), digits=3), c("", "Mean (SD)"=sprintf("%s (± %s)", MEAN, SD))) }
no.missing <- function(x, ...) {
c()
} # only use this function when you already know the number of missing values
# function to do an anova for cont. variables and to do a chi-squared test for cat. variables
pvalue<- function(x, ...) {
y <- unlist(x)
g <- factor(rep(1:length(x), times=sapply(x,length)))
if (is.numeric(y)) {
ano <- aov(y ~ g)
p <- summary(ano)[[1]][[5]][1] # change to t.test from stats package to use for future, see https://stackoverflow.com/questions/67711459/anova-p-value-column-in-table1-package-r
} else {
p <- chisq.test(table(y,g))$p.value
}
c("", sub("<","<",format.pval(p, digits = 3,eps=0.001)))
}
# updating labels for variables
table1::label(dfTable1practice$PROMIS_Depression) <- "PROMIS Depression (T score)"
table1::label(dfTable1practice$PROMIS_Anxiety) <- "PROMIS Anxiety (T score)"
table1::label(dfTable1practice$PROMIS_Alcohol_Use) <- "PROMIS Alcohol Use (T score)"
# code for Table 1
tab1<-table1(~ Age + Education + Sex + Ethnicity + PROMIS_Anxiety + PROMIS_Depression + PROMIS_Alcohol_Use + AIES + NAAS + HLS + NASS | Sample,
data=dfTable1practice,
render.continuous = my.render.cont,
render.missing = no.missing,
caption = "Table 1. Demographic, clinical, and cultural characteristics per sample",
footnote = "Note:",
overall = F,
extra.col = list(`p-value`=pvalue))Warning in chisq.test(table(y, g)): Chi-squared approximation may be incorrect
Warning in chisq.test(table(y, g)): Chi-squared approximation may be incorrecttab1| MID (N=44) |
SST (N=51) |
SST+MID (N=39) |
p-value | |
|---|---|---|---|---|
Note: | ||||
| Age | ||||
| Mean (SD) | 34.3 (± 10.2) | 35.4 (± 10.4) | 34.9 (± 10.3) | 0.886 |
| Education | ||||
| CollegeOrHigher | 19 (43.2%) | 24 (47.1%) | 17 (43.6%) | 0.999 |
| HS | 5 (11.4%) | 5 (9.8%) | 5 (12.8%) | |
| NoHS | 2 (4.5%) | 2 (3.9%) | 2 (5.1%) | |
| SomeCollege | 18 (40.9%) | 20 (39.2%) | 15 (38.5%) | |
| Sex | ||||
| Female | 34 (77.3%) | 41 (80.4%) | 31 (79.5%) | 0.931 |
| Male | 10 (22.7%) | 10 (19.6%) | 8 (20.5%) | |
| Ethnicity | ||||
| Hispanic or Latine | 1 (2.3%) | 2 (3.9%) | 1 (2.6%) | 0.751 |
| Not Hispanic or Latine | 43 (97.7%) | 48 (94.1%) | 38 (97.4%) | |
| DON'T KNOW | 0 (0%) | 1 (2.0%) | 0 (0%) | |
| PROMIS Anxiety (T score) | ||||
| Mean (SD) | 57.6 (± 9.41) | 57.4 (± 9.09) | 57.4 (± 9.34) | 0.996 |
| PROMIS Depression (T score) | ||||
| Mean (SD) | 56.3 (± 7.79) | 55.3 (± 7.61) | 56.2 (± 7.59) | 0.79 |
| PROMIS Alcohol Use (T score) | ||||
| Mean (SD) | 49.5 (± 4.69) | 49.2 (± 5.07) | 49.3 (± 4.68) | 0.946 |
| AIES | ||||
| Mean (SD) | 34.3 (± 11.7) | 37.4 (± 14.0) | 35.1 (± 11.7) | 0.455 |
| NAAS | ||||
| Mean (SD) | 3.95 (± 0.331) | 3.86 (± 0.345) | 3.92 (± 0.323) | 0.441 |
| HLS | ||||
| Mean (SD) | 42.1 (± 4.79) | 42.1 (± 4.68) | 41.9 (± 4.92) | 0.989 |
| NASS | ||||
| Mean (SD) | 33.4 (± 9.31) | 35.0 (± 8.37) | 34.3 (± 8.26) | 0.67 |
7 Scatterplots for significant correlations in SST
7.1 Residualied ERN amplitudes predicting PROMIS Depression scores
ggplot(cleanedSimpleTibble4SST, aes(x = ERNresid, y = sstPROMISdepress)) +
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"),
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "ERNresid",
parse = TRUE) +
labs(x = "Residualized ERN Amplitudes @ FCz (μV)", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
7.2 Spirituality prediciting PROMIS Depression scores
ggplot(cleanedSimpleTibble4SST, aes(x = sstNASS, y = sstPROMISdepress)) +
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"),
eq.with.lhs = "promisDep~~`=`~~",
eq.x.rhs = "NASS",
parse = TRUE) +
labs(x = "NASS", y = "PROMIS Depression Score") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
7.3 Residualized Incorrect-Stop P300 amplitudes predicting PROMIS Alcohol Use
ggplot(cleanedSimpleTibble4SST, aes(x = P3resid, y = sstPROMISalcUse)) +
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"),
eq.with.lhs = "promisAlcUse~~`=`~~",
eq.x.rhs = "P3resid",
parse = TRUE) +
labs(x = "Residualized Incorrect-Stop P300 Amplitudes @ Pz (μV)", y = "PROMIS Alcohol Use Score") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
8 Scatterplots for significant correlations in MID
8.1 Cue loss P300 amplitudes predicting Alcohol Use scores
ggplot(cleanedSimpleTibble4MID, aes(x = P3pzMIDcueLoss , y = midPROMISalcUse)) +
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"),
eq.with.lhs = "promisAlcUse~~`=`~~",
eq.x.rhs = "cueLossP3",
parse = TRUE) +
labs(x = "P300 Amplitudes to Loss Cues @ Pz (μV)", y = "PROMIS Alcohol Use Score") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
8.2 Cue gain P300 amplitudes predicting Alcohol Use scores
ggplot(cleanedSimpleTibble4MID, aes(x = P3pzMIDcueGain, y = midPROMISalcUse)) +
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"),
eq.with.lhs = "promisAlcUse~~`=`~~",
eq.x.rhs = "cueGainP3",
parse = TRUE) +
labs(x = "P300 Amplitudes to Gain Cues @ Pz (μV)", y = "PROMIS Alcohol Use Score") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
8.3 SPN to successful trials predicting NASS scores
ggplot(cleanedSimpleTibble4MID, aes(x = SPNfzMIDfeedbackSuccess, y = midNASS)) +
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"),
eq.with.lhs = "NASS~~`=`~~",
eq.x.rhs = "successSPN",
parse = TRUE) +
labs(x = "SPN amplitudes to successful trials", y = "NASS") +
theme_classic(base_size = 15)`geom_smooth()` using formula = 'y ~ x'
9 Scatterplots for significant correlations in SST + MID
9.1 Gain RewP predicting Residualized Incorrect P300 amplitudes
9.2 SPN to successful trials predicting Residualized Incorrect Pe amplitudes
9.3 SPN to successful trials predicting NASS scores
9.4 Residualized Incorrect P300 amplitudes predicting PROMIS Alcohol Use
9.5 NAAS scores predicting PROMIS Alcohol Use scores
9.6 P300 to No gain trials predicting AIES scores
10 Multiple linear regression and moderation analyses in SST
10.1 Residualized ERN amplitudes and acculturation predict PROMIS Depression
model1<-lm(sstPROMISdepress~ERNresid+sstNAAS+AgeSST+SexSST, data=cleanedSimpleTibble4SST)
summary(model1)
Call:
lm(formula = sstPROMISdepress ~ ERNresid + sstNAAS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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 *
sstNAAS 3.07120 2.97380 1.033 0.3071
AgeSST 0.07219 0.09964 0.724 0.4724
SexSSTMale -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: 0.04683betaModel1<-lm.beta(model1)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel1) ERNresid sstNAAS AgeSST SexSSTMale
0.34167774 0.13898931 0.09886745 NA boot_model1<-boot_summary(model1, type='perc', method='residual',R = 10000)
boot_model1| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 41.9 | 17.8 | 66.5 | 7e-04 |
| 0.481 | 0.108 | 0.853 | 0.0129 |
| 3.07 | -2.79 | 8.9 | 0.3038 |
| 0.0722 | -0.126 | 0.264 | 0.4778 |
| -5.16 | -10.3 | -0.176 | 0.0424 |
vif(model1)ERNresid sstNAAS AgeSST SexSST
1.023638 1.023091 1.051987 1.028995 10.2 Residualized ERN amplitudes and enculturation predict PROMIS Depression
model2<-lm(sstPROMISdepress~ERNresid+sstAIES+AgeSST+SexSST, data=cleanedSimpleTibble4SST)
summary(model2)
Call:
lm(formula = sstPROMISdepress ~ ERNresid + sstAIES + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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 *
sstAIES -0.01299 0.07380 -0.176 0.8611
AgeSST 0.05997 0.10068 0.596 0.5544
SexSSTMale -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: 0.07187betaModel2<-lm.beta(model2)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel2) ERNresid sstAIES AgeSST SexSSTMale
0.34393391 -0.02391395 0.08212997 NA boot_model2<-boot_summary(model2, type='perc', method='residual',R = 10000)
boot_model2| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 54.7 | 46.4 | 63.2 | <1e-04 |
| 0.484 | 0.108 | 0.86 | 0.0106 |
| -0.013 | -0.155 | 0.133 | 0.8616 |
| 0.06 | -0.136 | 0.258 | 0.5449 |
| -5.04 | -10.2 | -0.074 | 0.0464 |
vif(model2)ERNresid sstAIES AgeSST SexSST
1.023972 1.020297 1.050407 1.028716 10.3 Residualized ERN amplitudes and historical loss predict PROMIS Depression
model3<-lm(sstPROMISdepress~ERNresid+sstHLS+AgeSST+SexSST, data=cleanedSimpleTibble4SST)
summary(model3)
Call:
lm(formula = sstPROMISdepress ~ ERNresid + sstHLS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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 *
sstHLS 0.34578 0.21494 1.609 0.114525
AgeSST 0.07536 0.09770 0.771 0.444468
SexSSTMale -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: 0.02504betaModel3<-lm.beta(model3)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel3) ERNresid sstHLS AgeSST SexSSTMale
0.3408525 0.2126938 0.1032087 NA boot_model3<-boot_summary(model3, type='perc', method='residual',R = 10000)
boot_model3| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 39 | 18.7 | 59 | <1e-04 |
| 0.479 | 0.113 | 0.846 | 0.0088 |
| 0.346 | -0.081 | 0.783 | 0.1177 |
| 0.0754 | -0.115 | 0.265 | 0.4533 |
| -4.77 | -9.83 | 0.262 | 0.0622 |
vif(model3)ERNresid sstHLS AgeSST SexSST
1.023627 1.019346 1.044088 1.029812 10.4 Residualized ERN amplitudes and spirituality predict PROMIS Depression
model4<-lm(sstPROMISdepress~ERNresid+sstNASS+AgeSST+SexSST, data=cleanedSimpleTibble4SST)
summary(model4)
Call:
lm(formula = sstPROMISdepress ~ ERNresid + sstNASS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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 *
sstNASS -0.3287 0.1350 -2.436 0.0188 *
AgeSST 0.1857 0.1076 1.725 0.0912 .
SexSSTMale -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: 0.006497betaModel4<-lm.beta(model4)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel4) ERNresid sstNASS AgeSST SexSSTMale
0.2906931 -0.3614772 0.2542889 NA boot_model4<-boot_summary(model4, type='perc', method='residual',R = 10000)
boot_model4| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 61.3 | 52.5 | 69.9 | <1e-04 |
| 0.409 | 0.0553 | 0.765 | 0.0225 |
| -0.329 | -0.586 | -0.0634 | 0.0148 |
| 0.186 | -0.026 | 0.394 | 0.0825 |
| -5.11 | -9.8 | -0.329 | 0.036 |
vif(model4)ERNresid sstNASS AgeSST SexSST
1.052677 1.372881 1.354225 1.026226 10.5 Residualized ERN amplitudes predict PROMIS Depression moderated by acculturation
model5<-lm(sstPROMISdepress~ERNresid*sstNAAS+AgeSST+SexSST,data= cleanedSimpleTibble4SST)
summary(model5)
Call:
lm(formula = sstPROMISdepress ~ ERNresid * sstNAAS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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
sstNAAS 3.13349 3.00274 1.044 0.30227
AgeSST 0.06194 0.10297 0.602 0.55046
SexSSTMale -4.89843 2.64650 -1.851 0.07075 .
ERNresid:sstNAAS 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: 0.08244betaModel5<-lm.beta(model5)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel5) ERNresid sstNAAS AgeSST SexSSTMale
-0.32997295 0.14180825 0.08483596 NA
ERNresid:sstNAAS
0.17761487 boot_model5<-boot_summary(model5, type='perc', method='residual',R = 10000)
boot_model5| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 42 | 17.9 | 66.6 | 0.001 |
| -0.464 | -4.55 | 3.75 | 0.8342 |
| 3.13 | -2.79 | 8.92 | 0.2973 |
| 0.0619 | -0.141 | 0.262 | 0.5387 |
| -4.9 | -10.1 | 0.387 | 0.0678 |
| 0.25 | -0.86 | 1.32 | 0.6512 |
johnson_neyman(
model5,
pred=ERNresid,
modx=sstNAAS,
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 sstNAAS is INSIDE the interval [3.60, 4.18], the slope of ERNresid is
p < .05.
Note: The range of observed values of sstNAAS is [3.05, 4.45]
vif(model5)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid sstNAAS AgeSST SexSST
120.251097 1.025191 1.104022 1.078638
ERNresid:sstNAAS
119.304341 10.6 Residualized ERN amplitudes predict PROMIS Depression moderated by enculturation
model6<-lm(sstPROMISdepress~ERNresid*sstAIES+AgeSST+SexSST, data=cleanedSimpleTibble4SST)
summary(model6)
Call:
lm(formula = sstPROMISdepress ~ ERNresid * sstAIES + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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
sstAIES -0.02037 0.07584 -0.269 0.7895
AgeSST 0.05926 0.10152 0.584 0.5623
SexSSTMale -4.64000 2.72967 -1.700 0.0961 .
ERNresid:sstAIES -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: 0.1187betaModel6<-lm.beta(model6)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel6) ERNresid sstAIES AgeSST SexSSTMale
0.559369196 -0.037510104 0.081164751 NA
ERNresid:sstAIES
-0.005154784 boot_model6<-boot_summary(model6, type = 'perc',method = 'residual',R = 10000)
boot_model6| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 54.9 | 46.5 | 63.4 | <1e-04 |
| 0.787 | -0.452 | 2.04 | 0.2081 |
| -0.0204 | -0.174 | 0.127 | 0.7848 |
| 0.0593 | -0.138 | 0.259 | 0.572 |
| -4.64 | -9.85 | 0.76 | 0.0864 |
| -0.00725 | -0.0355 | 0.0206 | 0.6013 |
johnson_neyman(
model6,
pred=ERNresid,
modx=sstAIES,
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 sstAIES is INSIDE the interval [26.20, 48.51], the slope of ERNresid
is p < .05.
Note: The range of observed values of sstAIES is [18.00, 75.00]
vif(model6)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid sstAIES AgeSST SexSST
10.991573 1.059997 1.050607 1.123325
ERNresid:sstAIES
11.285358 10.7 Residualized ERN amplitudes predict PROMIS Depression moderated by historical loss
model7<-lm(sstPROMISdepress~ERNresid*sstHLS+AgeSST+SexSST,data=cleanedSimpleTibble4SST)
summary(model7)
Call:
lm(formula = sstPROMISdepress ~ ERNresid * sstHLS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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
sstHLS 0.41868 0.21807 1.920 0.06122 .
AgeSST 0.11589 0.10040 1.154 0.25446
SexSSTMale -4.28827 2.51360 -1.706 0.09490 .
ERNresid:sstHLS 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: 0.02176betaModel7<-lm.beta(model7)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel7) ERNresid sstHLS AgeSST SexSSTMale ERNresid:sstHLS
-1.9082347 0.2575347 0.1587184 NA 0.0522158 boot_model7<-boot_summary(model7, type='perc', method='residual',R = 10000)
boot_model7| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 34.4 | 13.7 | 55.2 | 0.001 |
| -2.68 | -6.98 | 1.52 | 0.2095 |
| 0.419 | -0.0162 | 0.85 | 0.0565 |
| 0.116 | -0.087 | 0.31 | 0.2551 |
| -4.29 | -9.34 | 0.712 | 0.0913 |
| 0.0734 | -0.0238 | 0.173 | 0.1365 |
johnson_neyman(
model7,
pred=ERNresid,
modx=sstHLS,
alpha=0.05,
plot=TRUE,
title="Depression~ERNresid*HLS Johnson-Neyman Plot",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Historical Loss Thinking"
)JOHNSON-NEYMAN INTERVAL
When sstHLS is INSIDE the interval [41.86, 58.95], the slope of ERNresid is
p < .05.
Note: The range of observed values of sstHLS is [30.00, 50.00]
vif(model7)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid sstHLS AgeSST SexSST ERNresid:sstHLS
141.906411 1.075347 1.129906 1.047493 142.415303 10.8 Residualized ERN amplitudes predict PROMIS Depression moderated by spirituality
model8<-lm(sstPROMISdepress~ERNresid*sstNASS+AgeSST+SexSST,data= cleanedSimpleTibble4SST)
summary(model8)
Call:
lm(formula = sstPROMISdepress ~ ERNresid * sstNASS + AgeSST +
SexSST, data = cleanedSimpleTibble4SST)
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
sstNASS -0.30186 0.15110 -1.998 0.0518 .
AgeSST 0.17864 0.10996 1.625 0.1112
SexSSTMale -5.19141 2.46649 -2.105 0.0409 *
ERNresid:sstNASS -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: 0.01392betaModel8<-lm.beta(model8)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel8) ERNresid sstNASS AgeSST SexSSTMale
0.560012163 -0.331923813 0.244654266 NA
ERNresid:sstNASS
-0.007262459 boot_model8<-boot_summary(model8, type='perc', method='residual',R = 10000)
boot_model8| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 60.5 | 50.8 | 69.7 | <1e-04 |
| 0.788 | -1.04 | 2.6 | 0.3869 |
| -0.302 | -0.585 | -0.00864 | 0.0425 |
| 0.179 | -0.034 | 0.392 | 0.1008 |
| -5.19 | -10.1 | -0.589 | 0.0267 |
| -0.0102 | -0.0583 | 0.0377 | 0.6704 |
johnson_neyman(
model8,
pred=ERNresid,
modx=sstNASS,
alpha=0.05,
plot=TRUE,
title="Depression~ERNresid*NASS Johnson-Neyman Plot",
y.label="Slope of Residualized ERN amplitudes",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When sstNASS is INSIDE the interval [31.45, 39.24], the slope of ERNresid
is p < .05.
Note: The range of observed values of sstNASS is [11.00, 50.00]
vif(model8)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif ERNresid sstNASS AgeSST SexSST
27.345413 1.689483 1.387874 1.032801
ERNresid:sstNASS
26.522430 10.9 Residualized Incorrect-Stop P300 amplitudes predict PROMIS Alcohol Use moderated by enculturation
model62<-lm(sstPROMISalcUse~P3resid*sstAIES+AgeSST+SexSST,data=cleanedSimpleTibble4SST)
summary(model62)
Call:
lm(formula = sstPROMISalcUse ~ P3resid * sstAIES + AgeSST + SexSST,
data = cleanedSimpleTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-10.2285 -2.4457 -0.1932 1.3715 14.7921
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.50872 2.99031 17.560 <2e-16 ***
P3resid -0.64581 0.72536 -0.890 0.378
sstAIES -0.03777 0.05146 -0.734 0.467
AgeSST -0.06941 0.06928 -1.002 0.322
SexSSTMale 1.74700 1.78903 0.977 0.334
P3resid:sstAIES 0.02690 0.01736 1.549 0.128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.969 on 45 degrees of freedom
Multiple R-squared: 0.136, Adjusted R-squared: 0.04004
F-statistic: 1.417 on 5 and 45 DF, p-value: 0.2364betaModel62<-lm.beta(model62)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel62) P3resid sstAIES AgeSST SexSSTMale P3resid:sstAIES
-0.3799936 -0.1044193 -0.1426956 NA 0.0158289 boot_model62<-boot_summary(model62, type='perc', method='residual',R = 10000)
boot_model62| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52.5 | 46.5 | 58.3 | <1e-04 |
| -0.646 | -2.09 | 0.779 | 0.37 |
| -0.0378 | -0.135 | 0.0658 | 0.4649 |
| -0.0694 | -0.205 | 0.0674 | 0.3136 |
| 1.75 | -1.71 | 5.39 | 0.3332 |
| 0.0269 | -0.00729 | 0.0614 | 0.1159 |
johnson_neyman(
model62,
pred=P3resid,
modx=sstAIES,
alpha=0.05,
plot=TRUE,
title="AlcoholUse~P3resid*AIES Johnson-Neyman Plot",
y.label="Slope of Residualized Incorrect-Stop P300 Amplitudes",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When sstAIES is INSIDE the interval [42.39, 81.13], the slope of P3resid is
p < .05.
Note: The range of observed values of sstAIES is [18.00, 75.00]
vif(model62)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3resid sstAIES AgeSST SexSST P3resid:sstAIES
9.487752 1.054073 1.056530 1.042007 9.518321 10.10 Residualized Incorrect-Stop P300 amplitudes predict PROMIS Alcohol Use moderated by acculturation
model63<-lm(sstPROMISalcUse~P3resid*sstNAAS+AgeSST+SexSST,data=cleanedSimpleTibble4SST)
summary(model63)
Call:
lm(formula = sstPROMISalcUse ~ P3resid * sstNAAS + AgeSST + SexSST,
data = cleanedSimpleTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-10.5690 -2.1919 -0.0813 1.5525 14.5115
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.62515 8.56992 3.340 0.00169 **
P3resid 4.91692 2.34767 2.094 0.04189 *
sstNAAS 5.59870 2.05969 2.718 0.00929 **
AgeSST -0.04916 0.06482 -0.758 0.45217
SexSSTMale 1.88604 1.67084 1.129 0.26496
P3resid:sstNAAS -1.13584 0.60366 -1.882 0.06637 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.675 on 45 degrees of freedom
Multiple R-squared: 0.2355, Adjusted R-squared: 0.1505
F-statistic: 2.772 on 5 and 45 DF, p-value: 0.02887betaModel63<-lm.beta(model63)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel63) P3resid sstNAAS AgeSST SexSSTMale P3resid:sstNAAS
2.8930977 0.3803338 -0.1010594 NA -0.6683228 boot_model63<-boot_summary(model63, type='perc', method='residual',R = 10000)
boot_model63| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 28.6 | 11.9 | 45.3 | 0.0017 |
| 4.92 | 0.399 | 9.61 | 0.0356 |
| 5.6 | 1.57 | 9.58 | 0.0081 |
| -0.0492 | -0.174 | 0.0767 | 0.4517 |
| 1.89 | -1.25 | 5.23 | 0.2411 |
| -1.14 | -2.34 | 0.0356 | 0.0575 |
johnson_neyman(
model63,
pred=P3resid,
modx=sstNAAS,
alpha=0.05,
plot=TRUE,
title="AlcoholUse~P3resid*NAAS Johnson-Neyman Plot",
y.label="Slope of Residualized Incorrect-Stop P300 Amplitudes",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When sstNAAS is INSIDE the interval [-2.47, 3.92], the slope of P3resid is
p < .05.
Note: The range of observed values of sstNAAS is [3.05, 4.45]
vif(model63)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3resid sstNAAS AgeSST SexSST P3resid:sstNAAS
112.314082 1.152343 1.045165 1.027089 111.015216 10.11 Residualized Incorrect-Stop P300 amplitudes predict PROMIS Alcohol Use moderated by historical loss
model64<-lm(sstPROMISalcUse~P3resid*sstHLS+AgeSST+SexSST,data=cleanedSimpleTibble4SST)
summary(model64)
Call:
lm(formula = sstPROMISalcUse ~ P3resid * sstHLS + AgeSST + SexSST,
data = cleanedSimpleTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-10.9034 -1.8330 -0.0436 1.7095 14.7955
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.457702 7.435737 7.055 8.45e-09 ***
P3resid 0.058137 1.997814 0.029 0.977
sstHLS -0.033147 0.160340 -0.207 0.837
AgeSST -0.065613 0.074369 -0.882 0.382
SexSSTMale 2.114190 1.828589 1.156 0.254
P3resid:sstHLS 0.008329 0.048549 0.172 0.865
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.117 on 45 degrees of freedom
Multiple R-squared: 0.08403, Adjusted R-squared: -0.01774
F-statistic: 0.8257 on 5 and 45 DF, p-value: 0.5381betaModel64<-lm.beta(model64)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel64) P3resid sstHLS AgeSST SexSSTMale P3resid:sstHLS
0.03420780 -0.03060618 -0.13488968 NA 0.00490096 boot_model64<-boot_summary(model64, type='perc', method='residual',R = 10000)
boot_model64| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52.5 | 37.8 | 67.3 | <1e-04 |
| 0.0581 | -4 | 4.01 | 0.9784 |
| -0.0331 | -0.356 | 0.284 | 0.8234 |
| -0.0656 | -0.211 | 0.0789 | 0.3847 |
| 2.11 | -1.5 | 5.76 | 0.2535 |
| 0.00833 | -0.0875 | 0.107 | 0.8642 |
johnson_neyman(
model64,
pred=P3resid,
modx=sstHLS,
alpha=0.05,
plot=TRUE,
title="AlcoholUse~P3resid*HLS Johnson-Neyman Plot",
y.label="Slope of Residualized Incorrect-Stop P300 Amplitudes",
modx.label = "Historical Loss"
)JOHNSON-NEYMAN INTERVAL
The Johnson-Neyman interval could not be found. Is the p value for your
interaction term below the specified alpha?
vif(model64)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3resid sstHLS AgeSST SexSST P3resid:sstHLS
67.886556 1.076804 1.148396 1.026798 67.702727 10.12 Residualized Incorrect-Stop P300 amplitudes predict PROMIS Alcohol Use moderated by spirituality
model65<-lm(sstPROMISalcUse~P3resid*sstNASS+AgeSST+SexSST,data=cleanedSimpleTibble4SST)
summary(model65)
Call:
lm(formula = sstPROMISalcUse ~ P3resid * sstNASS + AgeSST + SexSST,
data = cleanedSimpleTibble4SST)
Residuals:
Min 1Q Median 3Q Max
-9.2773 -2.1602 0.1647 1.8805 13.3779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.30576 3.32426 15.735 <2e-16 ***
P3resid -1.01735 1.18636 -0.858 0.396
sstNASS -0.09261 0.10067 -0.920 0.363
AgeSST -0.01124 0.07836 -0.143 0.887
SexSSTMale 1.70242 1.79740 0.947 0.349
P3resid:sstNASS 0.04023 0.03202 1.257 0.215
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.956 on 45 degrees of freedom
Multiple R-squared: 0.1405, Adjusted R-squared: 0.04496
F-statistic: 1.471 on 5 and 45 DF, p-value: 0.2182betaModel65<-lm.beta(model65)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel65) P3resid sstNASS AgeSST SexSSTMale P3resid:sstNASS
-0.59860415 -0.15285162 -0.02311741 NA 0.02367214 boot_model65<-boot_summary(model65, type='perc', method='residual',R = 10000)
boot_model65| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52.3 | 46 | 58.9 | <1e-04 |
| -1.02 | -3.23 | 1.35 | 0.3846 |
| -0.0926 | -0.293 | 0.0996 | 0.3426 |
| -0.0112 | -0.163 | 0.142 | 0.9146 |
| 1.7 | -1.75 | 5.32 | 0.3431 |
| 0.0402 | -0.0236 | 0.101 | 0.2081 |
johnson_neyman(
model65,
pred=P3resid,
modx=sstNASS,
alpha=0.05,
plot=TRUE,
title="AlcoholUse~P3resid*NASS Johnson-Neyman Plot",
y.label="Slope of Residualized Incorrect-Stop P300 Amplitudes",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When sstNASS is INSIDE the interval [37.45, 49.14], the slope of P3resid is
p < .05.
Note: The range of observed values of sstNASS is [11.00, 50.00]
vif(model64)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3resid sstHLS AgeSST SexSST P3resid:sstHLS
67.886556 1.076804 1.148396 1.026798 67.702727 11 Multiple linear regression and moderation analyses in MID
11.1 Cue Loss P3 amplitudes and acculturation predict PROMIS Alcohol Use
model9<-lm(midPROMISalcUse~P3pzMIDcueLoss+midNAAS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model9)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss + midNAAS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss -0.50979 0.27607 -1.847 0.072408 .
midNAAS 3.67439 2.09913 1.750 0.087908 .
AgeMID -0.02814 0.07403 -0.380 0.705881
SexMIDMale 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: 0.1282betaModel9<-lm.beta(model9)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel9)P3pzMIDcueLoss midNAAS AgeMID SexMIDMale
-0.30139317 0.25945301 -0.06115804 NA boot_model9<-boot_summary(model9, type='perc', method='residual',R = 10000)
boot_model9| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 37.1 | 18.7 | 55 | <1e-04 |
| -0.51 | -1.06 | 0.0542 | 0.0749 |
| 3.67 | -0.478 | 7.81 | 0.0818 |
| -0.0281 | -0.177 | 0.123 | 0.7127 |
| 0.433 | -2.77 | 3.8 | 0.795 |
vif(model9)P3pzMIDcueLoss midNAAS AgeMID SexMID
1.242421 1.024610 1.206926 1.051933 11.2 Cue Loss P3 amplitudes and enculturation predit PROMIS Alcohol Use
model10<-lm(midPROMISalcUse~P3pzMIDcueLoss+midAIES+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model10)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss + midAIES + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss -0.55437 0.28511 -1.944 0.0591 .
midAIES -0.03910 0.06146 -0.636 0.5283
AgeMID -0.03993 0.07632 -0.523 0.6038
SexMIDMale 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: 0.338betaModel10<-lm.beta(model10)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel10)P3pzMIDcueLoss midAIES AgeMID SexMIDMale
-0.32775145 -0.09762002 -0.08675851 NA boot_model10<-boot_summary(model10, type='perc', method='residual',R = 10000)
boot_model10| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 53.4 | 46.3 | 60.5 | <1e-04 |
| -0.554 | -1.11 | 0.0397 | 0.0658 |
| -0.0391 | -0.163 | 0.0828 | 0.5167 |
| -0.0399 | -0.194 | 0.111 | 0.6157 |
| 0.603 | -2.77 | 4.05 | 0.7099 |
vif(model10)P3pzMIDcueLoss midAIES AgeMID SexMID
1.241356 1.028327 1.201669 1.047821 11.3 Cue Loss P3 amplitudes and historical loss predict PROMIS Alcohol Use
model11<-lm(midPROMISalcUse~P3pzMIDcueLoss+midHLS+SexMID+AgeMID, data=cleanedSimpleTibble4MID)
summary(model11)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss + midHLS + SexMID +
AgeMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss -0.542295 0.290182 -1.869 0.0692 .
midHLS -0.002512 0.160725 -0.016 0.9876
SexMIDMale 0.618344 1.730905 0.357 0.7228
AgeMID -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: 0.3892betaModel11<-lm.beta(model11)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel11)P3pzMIDcueLoss midHLS SexMIDMale AgeMID
-0.320612468 -0.002563895 NA -0.099857563 boot_model11<-boot_summary(model11, type='perc', method='residual',R = 10000)
boot_model11| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52.3 | 36.8 | 67.8 | <1e-04 |
| -0.542 | -1.12 | 0.0188 | 0.059 |
| -0.00251 | -0.323 | 0.309 | 0.9915 |
| 0.618 | -2.74 | 3.88 | 0.7131 |
| -0.046 | -0.197 | 0.11 | 0.57 |
vif(model11)P3pzMIDcueLoss midHLS SexMID AgeMID
1.272684 1.163234 1.058589 1.251430 11.4 Cue Loss P3 amplitudes and spirituality predict Alcohol Use
model12<-lm(midPROMISalcUse~P3pzMIDcueLoss+midNASS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model12)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss + midNASS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss -0.543003 0.286020 -1.898 0.0651 .
midNASS -0.005353 0.088189 -0.061 0.9519
AgeMID -0.043477 0.084317 -0.516 0.6090
SexMIDMale 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: 0.3887betaModel12<-lm.beta(model12)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel12)P3pzMIDcueLoss midNASS AgeMID SexMIDMale
-0.32103086 -0.01063218 -0.09447557 NA boot_model12<-boot_summary(model12, type='perc', method='residual',R = 10000)
boot_model12| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52.3 | 45.4 | 59 | <1e-04 |
| -0.543 | -1.1 | 0.0143 | 0.057 |
| -0.00535 | -0.177 | 0.168 | 0.9491 |
| -0.0435 | -0.21 | 0.121 | 0.6027 |
| 0.593 | -2.99 | 4.08 | 0.7079 |
vif(model12)P3pzMIDcueLoss midNASS AgeMID SexMID
1.236544 1.326642 1.451710 1.121310 11.5 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by acculturation
model13<-lm(midPROMISalcUse~P3pzMIDcueLoss*midNAAS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model13)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss * midNAAS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss -2.98274 2.83835 -1.051 0.299955
midNAAS 2.18570 2.70641 0.808 0.424347
AgeMID -0.03979 0.07544 -0.527 0.600913
SexMIDMale 0.32167 1.67130 0.192 0.848403
P3pzMIDcueLoss:midNAAS 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: 0.1651betaModel13<-lm.beta(model13)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel13) P3pzMIDcueLoss midNAAS AgeMID
-1.76343947 0.15433466 -0.08646966
SexMIDMale P3pzMIDcueLoss:midNAAS
NA 0.37031032 boot_model13<-boot_summary(model13, type='perc', method='residual',R = 10000)
boot_model13| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 43.4 | 19.7 | 66.9 | <1e-04 |
| -2.98 | -8.71 | 2.88 | 0.2868 |
| 2.19 | -3.23 | 7.68 | 0.4247 |
| -0.0398 | -0.193 | 0.108 | 0.5943 |
| 0.322 | -2.94 | 3.62 | 0.8312 |
| 0.626 | -0.846 | 2.08 | 0.3752 |
johnson_neyman(
model13,
pred=P3pzMIDcueLoss,
modx=midNAAS,
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 midNAAS is INSIDE the interval [3.70, 3.82], the slope of
P3pzMIDcueLoss is p < .05.
Note: The range of observed values of midNAAS is [3.30, 4.50]
vif(model13)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midNAAS AgeMID
130.540613 1.692996 1.245679
SexMID P3pzMIDcueLoss:midNAAS
1.058089 128.260553 11.6 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by enculturation
model14<-lm(midPROMISalcUse~P3pzMIDcueLoss*midAIES+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model14)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss * midAIES + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss 0.89188 0.75109 1.187 0.2424
midAIES 0.06222 0.07671 0.811 0.4224
AgeMID -0.03846 0.07331 -0.525 0.6029
SexMIDMale 0.51000 1.64612 0.310 0.7584
P3pzMIDcueLoss:midAIES -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: 0.1223betaModel14<-lm.beta(model14)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel14) P3pzMIDcueLoss midAIES AgeMID
0.52729246 0.15531608 -0.08356381
SexMIDMale P3pzMIDcueLoss:midAIES
NA -0.02609489 boot_model14<-boot_summary(model14, type='perc', method='residual',R = 10000)
boot_model14| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 49.9 | 42.3 | 57.6 | <1e-04 |
| 0.892 | -0.646 | 2.43 | 0.2406 |
| 0.0622 | -0.0957 | 0.215 | 0.4076 |
| -0.0385 | -0.187 | 0.11 | 0.5796 |
| 0.51 | -2.78 | 3.74 | 0.7458 |
| -0.0441 | -0.0887 | -0.000279 | 0.0486 |
johnson_neyman(
model14,
pred=P3pzMIDcueLoss,
modx=midAIES,
alpha=0.05,
plot=TRUE,
title="Alcohol_Use~P3Loss*AIES Johnson-Neyman Plot",
y.label="Slope of P300 Amplitudes to Loss",
modx.label = "Enculturation"
)JOHNSON-NEYMAN INTERVAL
When midAIES is OUTSIDE the interval [-570.93, 32.77], the slope of
P3pzMIDcueLoss is p < .05.
Note: The range of observed values of midAIES is [19.00, 66.00]
vif(model14)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midAIES AgeMID
9.338481 1.736885 1.201782
SexMID P3pzMIDcueLoss:midAIES
1.048610 9.266174 11.7 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by spirituality
model15<-lm(midPROMISalcUse~P3pzMIDcueLoss*midNASS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model15)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss * midNASS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss 0.39325 0.83084 0.473 0.639
midNASS 0.06502 0.10552 0.616 0.541
AgeMID -0.04579 0.08387 -0.546 0.588
SexMIDMale 0.46297 1.77477 0.261 0.796
P3pzMIDcueLoss:midNASS -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: 0.3533betaModel15<-lm.beta(model15)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel15) P3pzMIDcueLoss midNASS AgeMID
0.23249458 0.12913639 -0.09949477
SexMIDMale P3pzMIDcueLoss:midNASS
NA -0.01720345 boot_model15<-boot_summary(model15, type='perc', method='residual',R = 10000)
boot_model15| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 50.1 | 41.6 | 57.5 | <1e-04 |
| 0.393 | -1.19 | 2.17 | 0.5882 |
| 0.065 | -0.141 | 0.279 | 0.5185 |
| -0.0458 | -0.209 | 0.12 | 0.5742 |
| 0.463 | -3.01 | 3.86 | 0.7608 |
| -0.0291 | -0.0784 | 0.0169 | 0.2001 |
johnson_neyman(
model15,
pred=P3pzMIDcueLoss,
modx=midNASS,
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 midNASS is INSIDE the interval [33.41, 51.12], the slope of
P3pzMIDcueLoss is p < .05.
Note: The range of observed values of midNASS is [9.00, 49.00]
vif(model15)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midNASS AgeMID
10.551230 1.920540 1.452476
SexMID P3pzMIDcueLoss:midNASS
1.125534 10.483681 11.8 Cue Loss P3 amplitudes predict PROMIS Alcohol Use moderated by historical loss
model16<-lm(midPROMISalcUse~P3pzMIDcueLoss*midHLS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model16)
Call:
lm(formula = midPROMISalcUse ~ P3pzMIDcueLoss * midHLS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
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 ***
P3pzMIDcueLoss 1.61185 2.86056 0.563 0.576
midHLS 0.10551 0.21560 0.489 0.627
AgeMID -0.05418 0.07947 -0.682 0.499
SexMIDMale 0.83434 1.76369 0.473 0.639
P3pzMIDcueLoss:midHLS -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: 0.4583betaModel16<-lm.beta(model16)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel16) P3pzMIDcueLoss midHLS AgeMID
0.95294863 0.10767156 -0.11773605
SexMIDMale P3pzMIDcueLoss:midHLS
NA -0.02935267 boot_model16<-boot_summary(model16, type='perc', method='residual',R = 10000)
boot_model16| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 48 | 28.3 | 66.9 | <1e-04 |
| 1.61 | -4.01 | 7.1 | 0.5609 |
| 0.106 | -0.309 | 0.529 | 0.6016 |
| -0.0542 | -0.207 | 0.0994 | 0.4992 |
| 0.834 | -2.76 | 4.27 | 0.6205 |
| -0.0496 | -0.176 | 0.0778 | 0.4366 |
johnson_neyman(
model16,
pred=P3pzMIDcueLoss,
modx=midHLS,
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 P3pzMIDcueLoss midHLS AgeMID
122.320697 2.070136 1.275285
SexMID P3pzMIDcueLoss:midHLS
1.087040 129.702832 11.9 Success SPN amplitudes and acculturation predict PROMIS Depression
model17<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess+midNAAS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model17)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess + midNAAS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 ***
SPNfzMIDfeedbackSuccess 0.00477 0.40707 0.012 0.990710
midNAAS 0.01637 3.64479 0.004 0.996439
AgeMID -0.01016 0.11817 -0.086 0.931934
SexMIDMale -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: 0.4783betaModel17<-lm.beta(model17)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel17)SPNfzMIDfeedbackSuccess midNAAS AgeMID
0.0017979976 0.0006955949 -0.0132829507
SexMIDMale
NA boot_model17<-boot_summary(model17, type='perc',method='residual',R = 10000)
boot_model17| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 57.8 | 27.8 | 88.3 | <1e-04 |
| 0.00477 | -0.799 | 0.814 | 0.9966 |
| 0.0164 | -7.23 | 7.11 | 0.9896 |
| -0.0102 | -0.245 | 0.231 | 0.943 |
| -5.31 | -10.8 | 0.351 | 0.0663 |
vif(model17)SPNfzMIDfeedbackSuccess midNAAS AgeMID
1.002073 1.020930 1.016245
SexMID
1.009402 11.10 Success SPN amplitudes and enculturation predict PROMIS Depression
model18<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess+midAIES+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model18)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess + midAIES +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 ***
SPNfzMIDfeedbackSuccess 0.004629 0.404672 0.011 0.9909
midAIES 0.066432 0.102714 0.647 0.5216
AgeMID -0.021992 0.118129 -0.186 0.8533
SexMIDMale -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: 0.4164betaModel18<-lm.beta(model18)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel18)SPNfzMIDfeedbackSuccess midAIES AgeMID
0.001744687 0.099798417 -0.028757184
SexMIDMale
NA boot_model18<-boot_summary(model18, type='perc',method='residual',R=10000)
boot_model18| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 56 | 45.7 | 66.5 | <1e-04 |
| 0.00463 | -0.776 | 0.788 | 0.9859 |
| 0.0664 | -0.14 | 0.265 | 0.5251 |
| -0.022 | -0.251 | 0.218 | 0.8514 |
| -5.3 | -10.8 | 0.416 | 0.0638 |
vif(model18)SPNfzMIDfeedbackSuccess midAIES AgeMID
1.000918 1.024330 1.026521
SexMID
1.003047 11.11 Success SPN amplitudes and historical loss predict PROMIS Depression
model19<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess+midHLS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model19)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess + midHLS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 **
SPNfzMIDfeedbackSuccess -0.01756 0.40113 -0.044 0.96530
midHLS 0.28899 0.26163 1.105 0.27611
AgeMID 0.03141 0.12154 0.258 0.79741
SexMIDMale -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: 0.3163betaModel19<-lm.beta(model19)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel19)SPNfzMIDfeedbackSuccess midHLS AgeMID
-0.006620382 0.177465193 0.041078452
SexMIDMale
NA boot_model19<-boot_summary(model19, type='perc',method='residual',R=10000)
boot_model19| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 44.2 | 18 | 69.4 | 0.0014 |
| -0.0176 | -0.805 | 0.776 | 0.9718 |
| 0.289 | -0.214 | 0.821 | 0.2707 |
| 0.0314 | -0.211 | 0.271 | 0.8028 |
| -4.87 | -10.6 | 0.544 | 0.0769 |
vif(model19)SPNfzMIDfeedbackSuccess midHLS AgeMID
1.003482 1.133085 1.108794
SexMID
1.022976 11.12 Success SPN amplitudes and spirituality predict PROMIS Depression
model20<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess+midNASS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
summary(model20)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess + midNASS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 ***
SPNfzMIDfeedbackSuccess -0.2696 0.3982 -0.677 0.5024
midNASS -0.3400 0.1446 -2.351 0.0239 *
AgeMID 0.1284 0.1246 1.030 0.3094
SexMIDMale -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: 0.06645betaModel20<-lm.beta(model20)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel20)SPNfzMIDfeedbackSuccess midNASS AgeMID
-0.1016275 -0.4062958 0.1678591
SexMIDMale
NA boot_model20<-boot_summary(model20, type='perc',method='residual',R = 10000)
boot_model20| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 65.6 | 55.1 | 75.8 | <1e-04 |
| -0.27 | -1.05 | 0.518 | 0.5157 |
| -0.34 | -0.626 | -0.0486 | 0.0253 |
| 0.128 | -0.115 | 0.375 | 0.3023 |
| -7.01 | -12.5 | -1.69 | 0.0104 |
vif(model20)SPNfzMIDfeedbackSuccess midNASS AgeMID
1.095006 1.451333 1.290897
SexMID
1.078975 11.13 Success SPN amplitudes predict PROMIS Depression moderated by acculturation
model21<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess*midNAAS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model21)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess * midNAAS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 **
SPNfzMIDfeedbackSuccess -1.00122 5.25366 -0.191 0.84987
midNAAS -0.65350 5.07778 -0.129 0.89828
AgeMID -0.01091 0.11971 -0.091 0.92789
SexMIDMale -5.36472 2.88318 -1.861 0.07054 .
SPNfzMIDfeedbackSuccess:midNAAS 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: 0.6248betaModel21<-lm.beta(model21)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel21) SPNfzMIDfeedbackSuccess midNAAS
-0.37738211 -0.02776797
AgeMID SexMIDMale
-0.01426174 NA
SPNfzMIDfeedbackSuccess:midNAAS
0.09686456 boot_model21<-boot_summary(model21, type='perc',method='residual',R=10000)
boot_model21| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 60.4 | 19.8 | 102 | 0.0038 |
| -1 | -11.3 | 9.38 | 0.8378 |
| -0.654 | -10.8 | 9.29 | 0.8882 |
| -0.0109 | -0.246 | 0.226 | 0.9347 |
| -5.36 | -11.1 | 0.326 | 0.0649 |
| 0.257 | -2.4 | 2.9 | 0.8381 |
johnson_neyman(
model21,
pred=SPNfzMIDfeedbackSuccess,
modx=midNAAS,
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 SPNfzMIDfeedbackSuccess midNAAS
162.788582 1.932583
AgeMID SexMID
1.017323 1.021121
SPNfzMIDfeedbackSuccess:midNAAS
164.683434 11.14 Success SPN amplitudes predict PROMIS Depression moderated by enculturation
model22<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess*midAIES+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model22)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess * midAIES +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 ***
SPNfzMIDfeedbackSuccess -0.38873 1.46867 -0.265 0.7927
midAIES 0.03344 0.15748 0.212 0.8330
AgeMID -0.01457 0.12247 -0.119 0.9059
SexMIDMale -5.20643 2.85987 -1.821 0.0766 .
SPNfzMIDfeedbackSuccess:midAIES 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: 0.556betaModel22<-lm.beta(model22)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel22) SPNfzMIDfeedbackSuccess midAIES
-0.14652003 0.05023547
AgeMID SexMIDMale
-0.01905711 NA
SPNfzMIDfeedbackSuccess:midAIES
0.00454745 boot_model22<-boot_summary(model22, type='perc',method='residual',R=10000)
johnson_neyman(
model22,
pred=SPNfzMIDfeedbackSuccess,
modx=midAIES,
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 |
|---|---|---|---|
| 56.8 | 44.9 | 68.8 | <1e-04 |
| -0.389 | -3.3 | 2.54 | 0.7869 |
| 0.0334 | -0.274 | 0.349 | 0.8329 |
| -0.0146 | -0.251 | 0.232 | 0.8895 |
| -5.21 | -10.7 | 0.589 | 0.0737 |
| 0.0121 | -0.0737 | 0.097 | 0.7751 |
vif(model22)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif SPNfzMIDfeedbackSuccess midAIES
12.872040 2.350901
AgeMID SexMID
1.077333 1.016544
SPNfzMIDfeedbackSuccess:midAIES
14.165941 11.15 Success SPN amplitudes predict PROMIS Depression moderated by historical loss
model23<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess*midHLS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model23)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess * midHLS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 *
SPNfzMIDfeedbackSuccess -1.98929 3.77158 -0.527 0.6010
midHLS 0.11948 0.41674 0.287 0.7759
AgeMID 0.01726 0.12560 0.137 0.8914
SexMIDMale -5.08088 2.86061 -1.776 0.0837 .
SPNfzMIDfeedbackSuccess:midHLS 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: 0.4215betaModel23<-lm.beta(model23)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel23) SPNfzMIDfeedbackSuccess midHLS
-0.74980571 0.07336889
AgeMID SexMIDMale
0.02257307 NA
SPNfzMIDfeedbackSuccess:midHLS
0.01735466 boot_model23<-boot_summary(model23, type='perc',method='residual',R=10000)
boot_model23| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 52 | 13 | 91.4 | 0.0089 |
| -1.99 | -9.51 | 5.61 | 0.5976 |
| 0.119 | -0.714 | 0.942 | 0.7796 |
| 0.0173 | -0.23 | 0.266 | 0.87 |
| -5.08 | -10.8 | 0.464 | 0.0727 |
| 0.046 | -0.131 | 0.221 | 0.6024 |
johnson_neyman(
model23,
pred=SPNfzMIDfeedbackSuccess,
modx=midHLS,
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 SPNfzMIDfeedbackSuccess midHLS
87.066436 2.821552
AgeMID SexMID
1.162154 1.043172
SPNfzMIDfeedbackSuccess:midHLS
89.727405 11.16 Success SPN amplitudes predict PROMIS Depression moderated by spirituality
model24<-lm(midPROMISdepress~SPNfzMIDfeedbackSuccess*midNASS+AgeMID+SexMID, data=cleanedSimpleTibble4MID)
summary(model24)
Call:
lm(formula = midPROMISdepress ~ SPNfzMIDfeedbackSuccess * midNASS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
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 ***
SPNfzMIDfeedbackSuccess 4.53880 1.55951 2.910 0.00601 **
midNASS 0.06268 0.18200 0.344 0.73246
AgeMID 0.19437 0.11422 1.702 0.09697 .
SexMIDMale -7.17869 2.46867 -2.908 0.00605 **
SPNfzMIDfeedbackSuccess:midNASS -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: 0.003056betaModel24<-lm.beta(model24)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel24) SPNfzMIDfeedbackSuccess midNASS
1.71077322 0.07490792
AgeMID SexMIDMale
0.25416867 NA
SPNfzMIDfeedbackSuccess:midNASS
-0.05440627 boot_model24<-boot_summary(model24, type='perc',method='residual',R=10000)
boot_model24| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 49 | 34.8 | 62.4 | <1e-04 |
| 4.54 | 1.47 | 7.67 | 0.0027 |
| 0.0627 | -0.281 | 0.425 | 0.7195 |
| 0.194 | -0.0319 | 0.413 | 0.0875 |
| -7.18 | -12.1 | -2.33 | 0.0045 |
| -0.144 | -0.235 | -0.0552 | 0.0013 |
johnson_neyman(
model24,
pred=SPNfzMIDfeedbackSuccess,
modx=midNASS,
alpha=0.05,
plot=TRUE,
title="Depression~SPN*NASS Johnson-Neyman plot",
y.label="Slope of SPN amplitudes to Success",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When midNASS is OUTSIDE the interval [23.31, 37.00], the slope of
SPNfzMIDfeedbackSuccess is p < .05.
Note: The range of observed values of midNASS is [9.00, 49.00]
vif(model24)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif SPNfzMIDfeedbackSuccess midNASS
20.683471 2.832194
AgeMID SexMID
1.335320 1.079460
SPNfzMIDfeedbackSuccess:midNASS
19.492861 11.17 Gain RewP amplitudes predict PROMIS Alcohol Use moderated by enculturation
model47<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain*midAIES+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel47<-lm.beta(model47)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel47) RewPfczMIDfeedbackGain midAIES
-0.44771811 -0.37762874
AgeMID SexMIDMale
0.08213213 NA
RewPfczMIDfeedbackGain:midAIES
0.02094756 summary(model47)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain * midAIES +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-9.7911 -1.3748 0.1385 1.1697 8.8486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.61673 4.21199 12.255 9.01e-15 ***
RewPfczMIDfeedbackGain -0.76246 0.83577 -0.912 0.367
midAIES -0.15127 0.09527 -1.588 0.121
AgeMID 0.03780 0.07043 0.537 0.595
SexMIDMale 1.08382 1.66680 0.650 0.519
RewPfczMIDfeedbackGain:midAIES 0.03567 0.02256 1.582 0.122
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.577 on 38 degrees of freedom
Multiple R-squared: 0.158, Adjusted R-squared: 0.04726
F-statistic: 1.427 on 5 and 38 DF, p-value: 0.2369boot_model47<-boot_summary(model47, type='perc',method='residual',R=10000)
boot_model47| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 51.6 | 43.1 | 60.1 | <1e-04 |
| -0.762 | -2.41 | 0.922 | 0.3749 |
| -0.151 | -0.341 | 0.0411 | 0.1275 |
| 0.0378 | -0.101 | 0.177 | 0.6081 |
| 1.08 | -2.25 | 4.44 | 0.5116 |
| 0.0357 | -0.00837 | 0.0806 | 0.1201 |
vif(model47)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif RewPfczMIDfeedbackGain midAIES
10.870311 2.552968
AgeMID SexMID
1.057020 1.024596
RewPfczMIDfeedbackGain:midAIES
12.323393 11.18 Gain RewP amplitudes and enculturation predict PROMIS Alcohol Use
model55<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain+midAIES+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel55<-lm.beta(model55)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel55)RewPfczMIDfeedbackGain midAIES AgeMID
0.29068875 -0.08662268 0.08610610
SexMIDMale
NA summary(model55)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain + midAIES +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-10.9555 -1.4765 0.1493 1.8336 10.5526
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.35860 3.30096 14.347 <2e-16 ***
RewPfczMIDfeedbackGain 0.49504 0.26251 1.886 0.0668 .
midAIES -0.03470 0.06152 -0.564 0.5760
AgeMID 0.03963 0.07176 0.552 0.5840
SexMIDMale 1.45294 1.68185 0.864 0.3929
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.665 on 39 degrees of freedom
Multiple R-squared: 0.1026, Adjusted R-squared: 0.01058
F-statistic: 1.115 on 4 and 39 DF, p-value: 0.363411.19 Gain RewP amplitudes predict PROMIS Alcohol Use moderated by historical loss thinking
model48<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain*midHLS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel48<-lm.beta(model48)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel48) RewPfczMIDfeedbackGain midHLS
0.372171445 0.041774013
AgeMID SexMIDMale
0.084369199 NA
RewPfczMIDfeedbackGain:midHLS
-0.001798357 summary(model48)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain * midHLS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-11.1042 -1.3334 0.3449 1.8271 9.4852
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.420750 12.380560 3.588 0.000938 ***
RewPfczMIDfeedbackGain 0.633804 1.903550 0.333 0.740995
midHLS 0.040936 0.254199 0.161 0.872917
AgeMID 0.038826 0.078391 0.495 0.623249
SexMIDMale 1.505705 1.733435 0.869 0.390506
RewPfczMIDfeedbackGain:midHLS -0.003063 0.044226 -0.069 0.945156
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.743 on 38 degrees of freedom
Multiple R-squared: 0.09607, Adjusted R-squared: -0.02287
F-statistic: 0.8077 on 5 and 38 DF, p-value: 0.5514boot_model48<-boot_summary(model48, type='perc',method='residual',R=10000)
boot_model48| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 44.4 | 19.6 | 69.8 | 7e-04 |
| 0.634 | -3.41 | 4.48 | 0.7208 |
| 0.0409 | -0.489 | 0.561 | 0.8673 |
| 0.0388 | -0.119 | 0.194 | 0.6265 |
| 1.51 | -2.11 | 5.03 | 0.3893 |
| -0.00306 | -0.0918 | 0.0908 | 0.9319 |
vif(model48)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif RewPfczMIDfeedbackGain midHLS
52.523222 2.828829
AgeMID SexMID
1.219832 1.032176
RewPfczMIDfeedbackGain:midHLS
49.948261 11.20 Gain RewP amplitudes and historical loss thinking predict PROMIS Alcohol Use
model56<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain+midHLS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
summary(model56)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain + midHLS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-11.1881 -1.3045 0.3892 1.8146 9.4985
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.02764 8.63236 5.216 6.32e-06 ***
RewPfczMIDfeedbackGain 0.50339 0.27328 1.842 0.0731 .
midHLS 0.02765 0.16456 0.168 0.8675
AgeMID 0.03784 0.07610 0.497 0.6218
SexMIDMale 1.49964 1.70899 0.877 0.3856
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.682 on 39 degrees of freedom
Multiple R-squared: 0.09595, Adjusted R-squared: 0.003229
F-statistic: 1.035 on 4 and 39 DF, p-value: 0.4016betaModel56<-lm.beta(model56)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel56)RewPfczMIDfeedbackGain midHLS AgeMID
0.29559133 0.02821286 0.08222706
SexMIDMale
NA 11.21 Gain RewP amplitudes predict PROMIS Alcohol Use moderated by spirituality
model49<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain*midNASS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel49<-lm.beta(model49)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel49) RewPfczMIDfeedbackGain midNASS
-0.54234955 -0.28519972
AgeMID SexMIDMale
0.04862724 NA
RewPfczMIDfeedbackGain:midNASS
0.02519434 summary(model49)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain * midNASS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-10.9336 -1.4649 0.5595 1.9515 8.4907
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.67139 6.13939 8.416 3.27e-10 ***
RewPfczMIDfeedbackGain -0.92362 1.27425 -0.725 0.473
midNASS -0.14360 0.17000 -0.845 0.404
AgeMID 0.02238 0.07879 0.284 0.778
SexMIDMale 1.70055 1.75064 0.971 0.337
RewPfczMIDfeedbackGain:midNASS 0.04291 0.03750 1.144 0.260
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.662 on 38 degrees of freedom
Multiple R-squared: 0.1268, Adjusted R-squared: 0.01187
F-statistic: 1.103 on 5 and 38 DF, p-value: 0.3748boot_model49<-boot_summary(model49, type='perc',method='residual',R=10000)
boot_model49| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 51.7 | 39.6 | 63.7 | <1e-04 |
| -0.924 | -3.57 | 1.53 | 0.4609 |
| -0.144 | -0.485 | 0.19 | 0.3898 |
| 0.0224 | -0.137 | 0.173 | 0.7644 |
| 1.7 | -1.85 | 4.93 | 0.3293 |
| 0.0429 | -0.0313 | 0.12 | 0.2446 |
vif(model49)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif RewPfczMIDfeedbackGain midNASS
24.363428 4.961030
AgeMID SexMID
1.275774 1.089788
RewPfczMIDfeedbackGain:midNASS
24.255079 11.22 Gain RewP amplitudes and spirituality predict PROMIS Alcohol Use
model57<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain+midNASS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel57<-lm.beta(model57)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel57)RewPfczMIDfeedbackGain midNASS AgeMID
0.29479809 0.04370233 0.05388511
SexMIDMale
NA summary(model57)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain + midNASS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-11.4489 -1.4031 0.3087 1.7966 9.2508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.89204 3.50252 13.103 7.26e-16 ***
RewPfczMIDfeedbackGain 0.50204 0.26698 1.880 0.0675 .
midNASS 0.02200 0.08950 0.246 0.8071
AgeMID 0.02480 0.07908 0.314 0.7555
SexMIDMale 1.57255 1.75396 0.897 0.3754
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.68 on 39 degrees of freedom
Multiple R-squared: 0.0967, Adjusted R-squared: 0.004051
F-statistic: 1.044 on 4 and 39 DF, p-value: 0.397211.23 Gain RewP amplitudes predict PROMIS Alcohol Use moderated by acculturation
model50<-lm(midPROMISalcUse~RewPfczMIDfeedbackGain*midNAAS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel50<-lm.beta(model50)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel50) RewPfczMIDfeedbackGain midNAAS
1.3869028 0.4144334
AgeMID SexMIDMale
0.1244869 NA
RewPfczMIDfeedbackGain:midNAAS
-0.2799967 summary(model50)
Call:
lm(formula = midPROMISalcUse ~ RewPfczMIDfeedbackGain * midNAAS +
AgeMID + SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-8.9880 -2.2118 -0.3524 1.9536 11.3926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.46863 15.08384 1.490 0.145
RewPfczMIDfeedbackGain 2.36188 3.01126 0.784 0.438
midNAAS 5.86923 3.64049 1.612 0.115
AgeMID 0.05729 0.07026 0.815 0.420
SexMIDMale 1.29140 1.63690 0.789 0.435
RewPfczMIDfeedbackGain:midNAAS -0.47683 0.76880 -0.620 0.539
Residual standard error: 4.51 on 38 degrees of freedom
Multiple R-squared: 0.1827, Adjusted R-squared: 0.07517
F-statistic: 1.699 on 5 and 38 DF, p-value: 0.1585boot_model50<-boot_summary(model50, type='perc',method='residual',R=10000)
boot_model50| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 22.5 | -7.66 | 53.3 | 0.146 |
| 2.36 | -3.62 | 8.42 | 0.4548 |
| 5.87 | -1.48 | 13.2 | 0.1207 |
| 0.0573 | -0.0849 | 0.197 | 0.4271 |
| 1.29 | -1.88 | 4.53 | 0.4345 |
| -0.477 | -2.02 | 1.06 | 0.5536 |
vif(model50)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif RewPfczMIDfeedbackGain midNAAS
145.371295 3.072361
AgeMID SexMID
1.083923 1.017989
RewPfczMIDfeedbackGain:midNAAS
146.348857 11.24 Loss P3 amplitudes predict PROMIS Depression moderated by enculturation
model51<-lm(midPROMISdepress~P3pzMIDcueLoss*midAIES+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel51<-lm.beta(model51)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel51) P3pzMIDcueLoss midAIES AgeMID
0.81676384 0.27277052 0.08106910
SexMIDMale P3pzMIDcueLoss:midAIES
NA -0.01616418 summary(model51)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss * midAIES + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-14.2195 -4.4710 -0.6825 4.5082 17.5590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.04291 6.38562 7.367 7.88e-09 ***
P3pzMIDcueLoss 2.29577 1.25055 1.836 0.0742 .
midAIES 0.18157 0.12772 1.422 0.1633
AgeMID 0.06200 0.12206 0.508 0.6144
SexMIDMale -4.38097 2.74075 -1.598 0.1182
P3pzMIDcueLoss:midAIES -0.04543 0.03554 -1.278 0.2088
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.44 on 38 degrees of freedom
Multiple R-squared: 0.1945, Adjusted R-squared: 0.08855
F-statistic: 1.836 on 5 and 38 DF, p-value: 0.1291boot_model51<-boot_summary(model51, type='perc',method='residual',R=10000)
boot_model51| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 47 | 34.4 | 59.5 | <1e-04 |
| 2.3 | -0.181 | 4.77 | 0.0702 |
| 0.182 | -0.0647 | 0.438 | 0.155 |
| 0.062 | -0.173 | 0.303 | 0.5946 |
| -4.38 | -9.8 | 1.23 | 0.1158 |
| -0.0454 | -0.116 | 0.0246 | 0.205 |
vif(model51)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midAIES AgeMID
9.338481 1.736885 1.201782
SexMID P3pzMIDcueLoss:midAIES
1.048610 9.266174 11.25 Loss P3 amplitudes and enculturation predict PROMIS Depression
model58<-lm(midPROMISdepress~P3pzMIDcueLoss+midAIES+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel58<-lm.beta(model58)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel58)P3pzMIDcueLoss midAIES AgeMID SexMIDMale
0.28711675 0.11609217 0.07909018 NA summary(model58)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss + midAIES + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-14.672 -4.292 -0.992 5.429 17.192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.65248 5.77395 8.773 9.11e-11 ***
P3pzMIDcueLoss 0.80703 0.45964 1.756 0.087 .
midAIES 0.07728 0.09907 0.780 0.440
AgeMID 0.06048 0.12304 0.492 0.626
SexMIDMale -4.28483 2.76192 -1.551 0.129
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.5 on 39 degrees of freedom
Multiple R-squared: 0.1599, Adjusted R-squared: 0.07372
F-statistic: 1.856 on 4 and 39 DF, p-value: 0.1378boot_model58<-boot_summary(model58, type='perc',method='residual',R=10000)
boot_model58| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 50.7 | 39.3 | 62.2 | <1e-04 |
| 0.807 | -0.0975 | 1.72 | 0.0788 |
| 0.0773 | -0.115 | 0.275 | 0.4401 |
| 0.0605 | -0.182 | 0.306 | 0.6337 |
| -4.28 | -9.68 | 1.22 | 0.1241 |
vif(model58)P3pzMIDcueLoss midAIES AgeMID SexMID
1.241356 1.028327 1.201669 1.047821 11.26 Loss P3 amplitudes predict PROMIS Depression moderated by historical loss thinking
model52<-lm(midPROMISdepress~P3pzMIDcueLoss*midHLS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel52<-lm.beta(model52)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel52) P3pzMIDcueLoss midHLS AgeMID
0.2600130525 0.1364073757 0.1264059235
SexMIDMale P3pzMIDcueLoss:midHLS
NA -0.0001111579 summary(model52)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss * midHLS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-15.974 -4.406 -1.164 5.224 16.302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.8820126 15.7670361 2.720 0.0098 **
P3pzMIDcueLoss 0.7308478 4.6146146 0.158 0.8750
midHLS 0.2221308 0.3477957 0.639 0.5269
AgeMID 0.0966682 0.1281958 0.754 0.4555
SexMIDMale -4.0748829 2.8451638 -1.432 0.1603
P3pzMIDcueLoss:midHLS -0.0003124 0.1058016 -0.003 0.9977
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.586 on 38 degrees of freedom
Multiple R-squared: 0.1627, Adjusted R-squared: 0.05251
F-statistic: 1.477 on 5 and 38 DF, p-value: 0.2202boot_model52<-boot_summary(model52, type='perc',method='residual',R=10000)
boot_model52| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 42.9 | 10.9 | 74.5 | 0.0074 |
| 0.731 | -8.81 | 9.83 | 0.8814 |
| 0.222 | -0.486 | 0.922 | 0.5414 |
| 0.0967 | -0.157 | 0.35 | 0.4499 |
| -4.07 | -9.63 | 1.6 | 0.1636 |
| -0.000312 | -0.209 | 0.217 | 0.9944 |
vif(model52)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midHLS AgeMID
122.320697 2.070136 1.275285
SexMID P3pzMIDcueLoss:midHLS
1.087040 129.702832 11.27 Loss P3 amplitudes and historical loss thinking predict PROMIS Depression
model59<-lm(midPROMISdepress~P3pzMIDcueLoss+midAIES+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel59<-lm.beta(model59)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel59)P3pzMIDcueLoss midAIES AgeMID SexMIDMale
0.28711675 0.11609217 0.07909018 NA summary(model59)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss + midAIES + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-14.672 -4.292 -0.992 5.429 17.192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.65248 5.77395 8.773 9.11e-11 ***
P3pzMIDcueLoss 0.80703 0.45964 1.756 0.087 .
midAIES 0.07728 0.09907 0.780 0.440
AgeMID 0.06048 0.12304 0.492 0.626
SexMIDMale -4.28483 2.76192 -1.551 0.129
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.5 on 39 degrees of freedom
Multiple R-squared: 0.1599, Adjusted R-squared: 0.07372
F-statistic: 1.856 on 4 and 39 DF, p-value: 0.1378boot_model59<-boot_summary(model59, type='perc',method='residual',R=10000)
boot_model59| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 50.7 | 39.1 | 62 | <1e-04 |
| 0.807 | -0.0858 | 1.72 | 0.0784 |
| 0.0773 | -0.115 | 0.275 | 0.4431 |
| 0.0605 | -0.18 | 0.306 | 0.639 |
| -4.28 | -9.61 | 1.27 | 0.1267 |
vif(model59)P3pzMIDcueLoss midAIES AgeMID SexMID
1.241356 1.028327 1.201669 1.047821 11.28 Loss P3 amplitudes predict PROMIS Depression moderated by spirituality
model53<-lm(midPROMISdepress~P3pzMIDcueLoss*midNASS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel53<-lm.beta(model53)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel53) P3pzMIDcueLoss midNASS AgeMID
0.93851499 -0.20675789 0.25524036
SexMIDMale P3pzMIDcueLoss:midNASS
NA -0.02045584 summary(model53)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss * midNASS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-15.0695 -4.3158 -0.9275 4.3682 14.4364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.91366 5.99950 9.153 3.76e-11 ***
P3pzMIDcueLoss 2.63799 1.24085 2.126 0.0401 *
midNASS -0.17300 0.15759 -1.098 0.2792
AgeMID 0.19519 0.12526 1.558 0.1274
SexMIDMale -6.19421 2.65061 -2.337 0.0248 *
P3pzMIDcueLoss:midNASS -0.05750 0.03624 -1.587 0.1208
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.945 on 38 degrees of freedom
Multiple R-squared: 0.2981, Adjusted R-squared: 0.2058
F-statistic: 3.228 on 5 and 38 DF, p-value: 0.01587boot_model53<-boot_summary(model53, type='perc',method='residual',R=10000)
boot_model53| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 54.9 | 43.2 | 67.1 | <1e-04 |
| 2.64 | 0.161 | 5 | 0.0341 |
| -0.173 | -0.485 | 0.128 | 0.2745 |
| 0.195 | -0.0503 | 0.439 | 0.1184 |
| -6.19 | -11.4 | -0.929 | 0.0209 |
| -0.0575 | -0.128 | 0.0154 | 0.1211 |
vif(model53)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midNASS AgeMID
10.551230 1.920540 1.452476
SexMID P3pzMIDcueLoss:midNASS
1.125534 10.483681 johnson_neyman(
model53,
pred=P3pzMIDcueLoss,
modx=midNASS,
alpha=0.05,
plot=TRUE,
title="Depression~P3Loss*NASS Johnson-Neyman Plot",
y.label="Slope of P300 Amplitudes to Loss Cues",
modx.label = "Spirituality"
)JOHNSON-NEYMAN INTERVAL
When midNASS is INSIDE the interval [-10.15, 30.83], the slope of
P3pzMIDcueLoss is p < .05.
Note: The range of observed values of midNASS is [9.00, 49.00]
11.29 Loss P3 amplitudes and spirituality predict PROMIS Depression
model60<-lm(midPROMISdepress~P3pzMIDcueLoss+midNASS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel60<-lm.beta(model60)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel60)P3pzMIDcueLoss midNASS AgeMID SexMIDMale
0.2803432 -0.3729503 0.2612085 NA summary(model60)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss + midNASS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-14.9914 -3.9947 -0.6678 4.4713 13.3230
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 59.3567 5.4083 10.975 1.72e-13 ***
P3pzMIDcueLoss 0.7880 0.4330 1.820 0.0764 .
midNASS -0.3121 0.1335 -2.337 0.0246 *
AgeMID 0.1998 0.1276 1.565 0.1257
SexMIDMale -5.9365 2.6966 -2.201 0.0337 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.079 on 39 degrees of freedom
Multiple R-squared: 0.2516, Adjusted R-squared: 0.1749
F-statistic: 3.278 on 4 and 39 DF, p-value: 0.02073boot_model60<-boot_summary(model60, type='perc',method='residual',R=10000)
boot_model60| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 59.4 | 48.5 | 70.1 | <1e-04 |
| 0.788 | -0.0761 | 1.65 | 0.0744 |
| -0.312 | -0.579 | -0.041 | 0.0228 |
| 0.2 | -0.0533 | 0.456 | 0.1204 |
| -5.94 | -11.3 | -0.629 | 0.0288 |
vif(model60)P3pzMIDcueLoss midNASS AgeMID SexMID
1.236544 1.326642 1.451710 1.121310 11.30 Loss P3 amplitudes predict PROMIS Depression moderated by acculturation
model54<-lm(midPROMISdepress~P3pzMIDcueLoss*midNAAS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel54<-lm.beta(model54)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionWarning in b * sx: longer object length is not a multiple of shorter object
lengthprint(betaModel54) P3pzMIDcueLoss midNAAS AgeMID
-3.40557345 -0.24672626 0.03280874
SexMIDMale P3pzMIDcueLoss:midNAAS
NA 0.93362996 summary(model54)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss * midNAAS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-12.848 -4.254 -0.304 4.307 18.473
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.68121 18.53529 4.191 0.00016 ***
P3pzMIDcueLoss -9.57243 4.50512 -2.125 0.04017 *
midNAAS -5.80655 4.29569 -1.352 0.18446
AgeMID 0.02509 0.11974 0.210 0.83514
SexMIDMale -4.80945 2.65274 -1.813 0.07773 .
P3pzMIDcueLoss:midNAAS 2.62426 1.13562 2.311 0.02636 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.169 on 38 degrees of freedom
Multiple R-squared: 0.2522, Adjusted R-squared: 0.1538
F-statistic: 2.563 on 5 and 38 DF, p-value: 0.04295boot_model54<-boot_summary(model54, type='perc',method='residual',R=10000)
boot_model54| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 77.7 | 40.2 | 114 | 3e-04 |
| -9.57 | -18.1 | -0.419 | 0.0398 |
| -5.81 | -14.1 | 2.82 | 0.189 |
| 0.0251 | -0.21 | 0.263 | 0.8411 |
| -4.81 | -9.93 | 0.465 | 0.071 |
| 2.62 | 0.317 | 4.82 | 0.0226 |
vif(model54)there are higher-order terms (interactions) in this model
consider setting type = 'predictor'; see ?vif P3pzMIDcueLoss midNAAS AgeMID
130.540613 1.692996 1.245679
SexMID P3pzMIDcueLoss:midNAAS
1.058089 128.260553 johnson_neyman(
model54,
pred=P3pzMIDcueLoss,
modx=midNAAS,
alpha=0.05,
plot=TRUE,
title="Depression~P3Loss*NAAS Johnson-Neyman Plot",
y.label="Slope of P300 Amplitudes to Loss Cues",
modx.label = "Acculturation"
)JOHNSON-NEYMAN INTERVAL
When midNAAS is OUTSIDE the interval [1.32, 3.99], the slope of
P3pzMIDcueLoss is p < .05.
Note: The range of observed values of midNAAS is [3.30, 4.50]
11.31 Loss P3 amplitudes and acculturation predict PROMIS Depression
model61<-lm(midPROMISdepress~P3pzMIDcueLoss+midNAAS+AgeMID+SexMID,data=cleanedSimpleTibble4MID)
betaModel61<-lm.beta(model61)Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
na.rm): NAs introduced by coercionprint(betaModel61)P3pzMIDcueLoss midNAAS AgeMID SexMIDMale
0.28055184 0.01829915 0.09662463 NA summary(model61)
Call:
lm(formula = midPROMISdepress ~ P3pzMIDcueLoss + midNAAS + AgeMID +
SexMID, data = cleanedSimpleTibble4MID)
Residuals:
Min 1Q Median 3Q Max
-15.0728 -4.9259 -0.8268 5.2124 16.6225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.19969 15.35746 3.334 0.00189 **
P3pzMIDcueLoss 0.78858 0.46332 1.702 0.09671 .
midNAAS 0.43066 3.52287 0.122 0.90333
AgeMID 0.07389 0.12424 0.595 0.55545
SexMIDMale -4.34186 2.78830 -1.557 0.12751
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.557 on 39 degrees of freedom
Multiple R-squared: 0.1471, Adjusted R-squared: 0.05963
F-statistic: 1.682 on 4 and 39 DF, p-value: 0.1738boot_model61<-boot_summary(model61, type='perc',method='residual',R=10000)
boot_model61| Estimate | Lower.bound | Upper.bound | p.value |
|---|---|---|---|
| 51.2 | 20.6 | 81.7 | 0.0016 |
| 0.789 | -0.129 | 1.71 | 0.0997 |
| 0.431 | -6.54 | 7.47 | 0.8841 |
| 0.0739 | -0.171 | 0.321 | 0.5602 |
| -4.34 | -9.84 | 1.2 | 0.1305 |
vif(model61)P3pzMIDcueLoss midNAAS AgeMID SexMID
1.242421 1.024610 1.206926 1.051933 12 Multiple linear regression and moderation analysis in SST + MID
TBD