Create data frame with unequal n’s for DSF, DSB, & Trails B (our original analyses)
TeloCogSPSS_exclude <- TeloCogSPSS[ which(TeloCogSPSS$MedHxAlzheimersDisease==0&TeloCogSPSS$MedHxDementiaAny==0&
TeloCogSPSS$MedHxCVstroke==0&TeloCogSPSS$MedHxEpilepsy==0&
TeloCogSPSS$MedHxMultipleSclerosis==0&TeloCogSPSS$MedHxParkinsonDisease==0),]
varNames = c('tLength', 'LgTrailA', 'LgTrailB', 'DSF', 'DSB', 'TeloPov', 'TeloRace', 'RacePov', 'TeloRacePov', 'Educ', 'CRPdi3', 'HTNDic', 'DiabDic', 'BMI', 'Age', 'Sex', 'Race', 'PovStat')
zNamesCheck(TeloCogSPSS_exclude, varNames)
TeloCogR=TeloCogSPSS_exclude[,varNames]
zQuick(TeloCogR)## Dimensions: 339 18
##
## tLength LgTrailA LgTrailB DSF
## Min. :2.600 Min. :1.146 Min. :1.491 Min. : 0.000
## 1st Qu.:5.200 1st Qu.:1.380 1st Qu.:1.785 1st Qu.: 6.000
## Median :5.670 Median :1.491 Median :1.929 Median : 7.000
## Mean :5.645 Mean :1.514 Mean :2.017 Mean : 7.074
## 3rd Qu.:6.080 3rd Qu.:1.602 3rd Qu.:2.152 3rd Qu.: 9.000
## Max. :8.500 Max. :2.778 Max. :2.778 Max. :14.000
## NA's :14 NA's :14 NA's :3
## DSB TeloPov TeloRace RacePov
## Min. : 0.000 Min. : 2.600 Min. : 3.770 Min. :1.000
## 1st Qu.: 4.000 1st Qu.: 5.690 1st Qu.: 5.600 1st Qu.:1.000
## Median : 5.000 Median : 6.840 Median : 7.600 Median :2.000
## Mean : 5.507 Mean : 8.349 Mean : 8.438 Mean :2.215
## 3rd Qu.: 7.000 3rd Qu.:11.190 3rd Qu.:11.360 3rd Qu.:2.000
## Max. :13.000 Max. :16.600 Max. :17.000 Max. :4.000
## NA's :2
## TeloRacePov Educ CRPdi3 HTNDic DiabDic
## Min. : 3.770 HS+ :253 <3 :190 Min. :0.0000 No :286
## 1st Qu.: 6.825 <HS : 85 3+ :134 1st Qu.:0.0000 Yes : 52
## Median :11.200 NA's: 1 NA's: 15 Median :0.0000 NA's: 1
## Mean :12.465 Mean :0.4036
## 3rd Qu.:14.780 3rd Qu.:1.0000
## Max. :30.800 Max. :1.0000
## NA's :2
## BMI Age Sex Race PovStat
## < 30 :202 Min. :30.16 Women:167 White:171 Above:175
## >= 30:137 1st Qu.:41.07 Men :172 AfrAm:168 Below:164
## Median :48.72
## Mean :48.04
## 3rd Qu.:55.32
## Max. :64.99
## Create Bulk Formulas & Tables for analyses with unequal n’s for DSF, DSB, & Trails B (our original analyses)
bulkFormula1a= 'DSF + DSB + LgTrailB ~ Race*PovStat*tLength + tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR, bulkFormula1a)
| Effects | DSF | DSB | LgTrailB | R**2 | 0.109** | 0.160*** | 0.188*** | RaceAfrAm | -6.790* | -4.374 | 0.453 | PovStatBelow | -9.983** | -8.782** | 0.766 | tLength | -0.802 | -0.540 | 0.022 |
Educ|
-0.725*
|
-1.385***
|
0.144***
|
CRPdi33+
|
-0.327
|
0.110
|
-0.076*
|
HTNDic
|
0.780*
|
0.415
|
0.061
|
DiabDicYes
|
-0.595
|
-1.011*
|
0.020
|
BMI>= 30
|
-0.268
|
-0.233
|
0.038
|
Age
|
-0.040*
|
-0.027
|
0.004
|
SexMen
|
-0.292
|
0.019
|
0.020
|
RaceAfrAm:PovStatBelow
|
10.778*
|
8.139
|
-0.153
|
RaceAfrAm:tLength
|
1.114
|
0.613
|
-0.052
|
PovStatBelow:tLength
|
1.691**
|
1.481**
|
-0.117
|
RaceAfrAm:PovStatBelow:tLength
|
-1.984*
|
-1.460*
|
0.029
|
|
|---|
3-way interaction of Telo x Race x Poverty Status was significant for DSF & DSB, but not for Trails B, so we backward eliminate.
bulkFormula1b= 'LgTrailB ~ tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR, bulkFormula1b)
| Effects | LgTrailB | R**2 | 0.187*** | tLength | 0.014 | RaceAfrAm | 0.362 | PovStatBelow | 0.684* |
Educ|
0.145***
|
CRPdi33+
|
-0.077*
|
HTNDic
|
0.062
|
DiabDicYes
|
0.020
|
BMI>= 30
|
0.038
|
Age
|
0.004
|
SexMen
|
0.021
|
tLength:RaceAfrAm
|
-0.036
|
tLength:PovStatBelow
|
-0.102*
|
RaceAfrAm:PovStatBelow
|
0.010
|
|
|---|
bulkFormula1c= 'LgTrailB ~ tLength*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR, bulkFormula1c)
| Effects | LgTrailB | R**2 | 0.186*** | tLength | -0.006 | PovStatBelow | 0.670* | RaceAfrAm | 0.161*** |
Educ|
0.145***
|
CRPdi33+
|
-0.078*
|
HTNDic
|
0.065
|
DiabDicYes
|
0.020
|
BMI>= 30
|
0.036
|
Age
|
0.004
|
SexMen
|
0.017
|
tLength:PovStatBelow
|
-0.099*
|
|
|---|
2-way interaction of Telo x Poverty Status was significant for Trails B
Create data frame with equal n’s for all cognitive outcomes (i.e., equal n for DSF, DSB, & Trails B)
TeloCogSPSS_equaln <- TeloCogSPSS[ which(TeloCogSPSS$MedHxAlzheimersDisease==0&TeloCogSPSS$MedHxDementiaAny==0&
TeloCogSPSS$MedHxCVstroke==0&TeloCogSPSS$MedHxEpilepsy==0&
TeloCogSPSS$MedHxMultipleSclerosis==0&TeloCogSPSS$MedHxParkinsonDisease==0&TeloCogSPSS$valicog==3),]
varNames = c('tLength', 'LgTrailA', 'LgTrailB', 'DSF', 'DSB', 'TeloPov', 'TeloRace', 'RacePov', 'TeloRacePov', 'Educ', 'CRPdi3', 'HTNDic', 'DiabDic', 'BMI', 'Age', 'Sex', 'Race', 'PovStat')
zNamesCheck(TeloCogSPSS_equaln, varNames)
TeloCogR_equaln=TeloCogSPSS_equaln[,varNames] Create Bulk Formulas & Tables for analyses with equal n’s for DSF, DSB, & Trails B
bulkFormula2a= 'DSF + DSB + LgTrailB ~ Race*PovStat*tLength + tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_equaln, bulkFormula2a)
| Effects | DSF | DSB | LgTrailB | R**2 | 0.119*** | 0.181*** | 0.188*** | RaceAfrAm | -6.510* | -4.024 | 0.446 | PovStatBelow | -9.803** | -8.779** | 0.777 | tLength | -0.853* | -0.576 | 0.022 |
Educ|
-0.809**
|
-1.486***
|
0.143***
|
CRPdi33+
|
0.049
|
0.417
|
-0.077*
|
HTNDic
|
0.799**
|
0.418
|
0.061
|
DiabDicYes
|
-0.344
|
-0.855*
|
0.020
|
BMI>= 30
|
-0.343
|
-0.295
|
0.037
|
Age
|
-0.030*
|
-0.019
|
0.004
|
SexMen
|
-0.236
|
0.090
|
0.020
|
RaceAfrAm:PovStatBelow
|
9.339*
|
7.030
|
-0.155
|
RaceAfrAm:tLength
|
1.061
|
0.546
|
-0.051
|
PovStatBelow:tLength
|
1.677**
|
1.490**
|
-0.118
|
RaceAfrAm:PovStatBelow:tLength
|
-1.750*
|
-1.273
|
0.029
|
|
|---|
3-way interaction of Telo x Race x Poverty Status was significant for DSF, but not for DSB or Trails B, so we backward eliminate.
bulkFormula2b= 'DSB + LgTrailB ~ tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_equaln, bulkFormula2b)
| Effects | DSB | LgTrailB | R**2 | 0.172*** | 0.188*** | tLength | -0.192 | 0.014 | RaceAfrAm | -0.024 | 0.356 | PovStatBelow | -5.139* | 0.695* |
Educ|
-1.512***
|
0.144***
|
CRPdi33+
|
0.435
|
-0.078*
|
HTNDic
|
0.387
|
0.062
|
DiabDicYes
|
-0.860*
|
0.020
|
BMI>= 30
|
-0.288
|
0.037
|
Age
|
-0.019
|
0.004
|
SexMen
|
0.073
|
0.021
|
tLength:RaceAfrAm
|
-0.157
|
-0.035
|
tLength:PovStatBelow
|
0.850*
|
-0.104*
|
RaceAfrAm:PovStatBelow
|
-0.161
|
0.006
|
|
|---|
bulkFormula2c= 'DSB + LgTrailB ~ tLength*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_equaln, bulkFormula2c)
| Effects | DSB | LgTrailB | R**2 | 0.171*** | 0.186*** | tLength | -0.279 | -0.005 | PovStatBelow | -5.324** | 0.681* | RaceAfrAm | -0.986*** | 0.161*** |
Educ|
-1.505***
|
0.144***
|
CRPdi33+
|
0.430
|
-0.079*
|
HTNDic
|
0.392
|
0.065
|
DiabDicYes
|
-0.854*
|
0.020
|
BMI>= 30
|
-0.288
|
0.035
|
Age
|
-0.019
|
0.004
|
SexMen
|
0.057
|
0.017
|
tLength:PovStatBelow
|
0.869*
|
-0.101*
|
|
|---|
2-way interaction of Telo x Poverty was significant for both DSB & Trails B
Create data frame with equal n’s for DSB & DSF but not for Trails B
TeloCogSPSS_unequaln <- TeloCogSPSS[which(TeloCogSPSS$MedHxAlzheimersDisease==0&TeloCogSPSS$MedHxDementiaAny==0&
TeloCogSPSS$MedHxCVstroke==0&TeloCogSPSS$MedHxEpilepsy==0&
TeloCogSPSS$MedHxMultipleSclerosis==0&TeloCogSPSS$MedHxParkinsonDisease==0),]
varNames = c('tLength', 'LgTrailA', 'LgTrailB', 'DSF', 'DSB', 'TeloPov', 'TeloRace', 'RacePov', 'TeloRacePov', 'Educ', 'CRPdi3', 'HTNDic', 'DiabDic', 'BMI', 'Age', 'Sex', 'Race', 'PovStat')
zNamesCheck(TeloCogSPSS_unequaln, varNames)
TeloCogR_unequaln=TeloCogSPSS_unequaln[,varNames]
zQuick(zVsel(TeloCogSPSS_unequaln, DSF, DSB))## Dimensions: 339 2
##
## DSF DSB
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 6.000 1st Qu.: 4.000
## Median : 7.000 Median : 5.000
## Mean : 7.074 Mean : 5.507
## 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :14.000 Max. :13.000
## NA's :3 NA's :2TeloCogSPSS_unequaln$DSB[is.na(TeloCogSPSS_unequaln$DSF)] = NA
TeloCogSPSS_unequaln$DSF[is.na(TeloCogSPSS_unequaln$DSB)] = NA
zQuick(zVsel(TeloCogSPSS_unequaln, DSF, DSB))## Dimensions: 339 2
##
## DSF DSB
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 6.000 1st Qu.: 4.000
## Median : 7.000 Median : 5.000
## Mean : 7.074 Mean : 5.506
## 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :14.000 Max. :13.000
## NA's :3 NA's :3Create Regression Formulas & Tables for analyses with equal n’s for DSF & DSB, but with a different n for Trails B
bulkFormula3a= 'DSF + DSB + LgTrailB ~ Race*PovStat*tLength + tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_unequaln, bulkFormula3a)
| Effects | DSF | DSB | LgTrailB | R**2 | 0.109** | 0.160*** | 0.188*** | RaceAfrAm | -6.790* | -4.374 | 0.453 | PovStatBelow | -9.983** | -8.782** | 0.766 | tLength | -0.802 | -0.540 | 0.022 |
Educ|
-0.725*
|
-1.385***
|
0.144***
|
CRPdi33+
|
-0.327
|
0.110
|
-0.076*
|
HTNDic
|
0.780*
|
0.415
|
0.061
|
DiabDicYes
|
-0.595
|
-1.011*
|
0.020
|
BMI>= 30
|
-0.268
|
-0.233
|
0.038
|
Age
|
-0.040*
|
-0.027
|
0.004
|
SexMen
|
-0.292
|
0.019
|
0.020
|
RaceAfrAm:PovStatBelow
|
10.778*
|
8.139
|
-0.153
|
RaceAfrAm:tLength
|
1.114
|
0.613
|
-0.052
|
PovStatBelow:tLength
|
1.691**
|
1.481**
|
-0.117
|
RaceAfrAm:PovStatBelow:tLength
|
-1.984*
|
-1.460*
|
0.029
|
|
|---|
Like in the original analyses, the 3-way interaction of Telo x Race x Poverty was significant for DSF & DSB when those two tests had equal n’s. This was non-significant for Trails B, but just like in the original analyses, the 2-way interaction of Telo x Poverty significant for Trails B when we backward eliminate (shown again below)
bulkFormula3b= 'LgTrailB ~ tLength*Race + tLength*PovStat + Race*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_equaln, bulkFormula3b)
| Effects | LgTrailB | R**2 | 0.188*** | tLength | 0.014 | RaceAfrAm | 0.356 | PovStatBelow | 0.695* |
Educ|
0.144***
|
CRPdi33+
|
-0.078*
|
HTNDic
|
0.062
|
DiabDicYes
|
0.020
|
BMI>= 30
|
0.037
|
Age
|
0.004
|
SexMen
|
0.021
|
tLength:RaceAfrAm
|
-0.035
|
tLength:PovStatBelow
|
-0.104*
|
RaceAfrAm:PovStatBelow
|
0.006
|
|
|---|
bulkFormula3c= 'LgTrailB ~ tLength*PovStat + tLength + Race + PovStat + Educ + CRPdi3 + HTNDic + DiabDic + BMI + Age + Sex'
zBulkReg(TeloCogR_equaln, bulkFormula3c)
| Effects | LgTrailB | R**2 | 0.186*** | tLength | -0.005 | PovStatBelow | 0.681* | RaceAfrAm | 0.161*** |
Educ|
0.144***
|
CRPdi33+
|
-0.079*
|
HTNDic
|
0.065
|
DiabDicYes
|
0.020
|
BMI>= 30
|
0.035
|
Age
|
0.004
|
SexMen
|
0.017
|
tLength:PovStatBelow
|
-0.101*
|
|
|---|