Total & Trajectory
- K-means Clustering
- Regression results
Trajectory
- K-means Clustering
- Regression results
Total
- K-means Clustering
- Regression results

In this script, you can find the results of the analysis based on all brain tracts. To examine individual differences, we used K-means clustering in an exploratory analysis:

K-means clustering was conducted based on the total level of brain connectivity (total; FA at imaging timepoint one plus timepoint two: higher FA total level implies greater connectivity, lower FA total levels imply lower connectivity) and the development of brain connectivity from imaging timepoint one to two (trajectory; FA at timepoint two minus timepoint one).
Multinomial logistic regression was used to predict cluster membership based on adversity experiences at baseline. To adjust for multiple comparisons, p-values were Bonferroni-corrected in the actual paper. We corrected for the number of brain regions (four) and adversity scales (five).

Total & Trajectory

K-means Clustering

set.seed(27)
Kmeans_AllTracts <- kmeans(AdvDTI[,19:20], 4, iter.max = 10, nstart = 50)

Groups <- as.factor(Kmeans_AllTracts$cluster)
samplesize <- 10808
id=factor(1:samplesize)

PlotCluster <- data.frame(c(AdvDTI$AllTracts_FA_t1),     #select FA at time 1
                              c(AdvDTI$AllTracts_FA_t2),     #select FA at time 2
                              as.factor(c(id,id)),         #Give ID to participants
                              as.factor(Groups))               #insert cluster number
colnames(PlotCluster)<-c('T1','T2','ID','Cluster')  #give column name

PlotAllClusters<-melt(PlotCluster,by='ID')

ggplot(PlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
  geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2","3","4")), 
             labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2", "3" = "Cluster 3", "4" = "Cluster 4")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2","3","4"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

AdvDTI$Groups <- as.factor(Kmeans_AllTracts$cluster)
AdvDTI[,2:20] <- apply(AdvDTI[,2:20],2, function(x) as.numeric(as.character(x)))

mod_PTSD_AllTracts = multinom(Groups ~ PTSDSum, data=AdvDTI)
mod_School_AllTracts = multinom(Groups ~ SchoolSum, data=AdvDTI)
mod_Family_AllTracts = multinom(Groups ~ FamilySum, data=AdvDTI)
mod_Safety_AllTracts = multinom(Groups ~ Safety, data=AdvDTI)
mod_SES_AllTracts = multinom(Groups ~ SES, data=AdvDTI)

options(scipen = 999)

Anova(mod_PTSD_AllTracts, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##         LR Chisq Df Pr(>Chisq)
## PTSDSum   2.8304  3     0.4185

summary(mod_PTSD_AllTracts)

## Call:
## multinom(formula = Groups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##    (Intercept)     PTSDSum
## 2 -0.005822554 -0.01273717
## 3 -0.937476354  0.03236716
## 4 -0.950441146  0.02157508
## 
## Std. Errors:
##   (Intercept)    PTSDSum
## 2  0.02268577 0.02364157
## 3  0.03019692 0.02933320
## 4  0.03033596 0.03003556
## 
## Residual Deviance: 27811.95 
## AIC: 27823.95

zPTAllTracts <- summary(mod_PTSD_AllTracts)$coefficients/summary(mod_PTSD_AllTracts)$standard.errors
zPTAllTracts

##   (Intercept)    PTSDSum
## 2  -0.2566611 -0.5387618
## 3 -31.0454345  1.1034310
## 4 -31.3305161  0.7183180

pPTAllTracts <- (1 - pnorm(abs(zPTAllTracts), 0, 1)) * 2
pPTAllTracts

##   (Intercept)   PTSDSum
## 2   0.7974404 0.5900512
## 3   0.0000000 0.2698400
## 4   0.0000000 0.4725612

Anova(mod_School_AllTracts, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum  0.17147  3     0.9821

summary(mod_School_AllTracts)

## Call:
## multinom(formula = Groups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##    (Intercept)   SchoolSum
## 2 -0.005612845 0.009078191
## 3 -0.937148039 0.001383108
## 4 -0.950300335 0.004986463
## 
## Std. Errors:
##   (Intercept)  SchoolSum
## 2  0.02268396 0.02284740
## 3  0.03019084 0.03045130
## 4  0.03033386 0.03056388
## 
## Residual Deviance: 27814.61 
## AIC: 27826.61

zSAllTracts <- summary(mod_School_AllTracts)$coefficients/summary(mod_School_AllTracts)$standard.errors
zSAllTracts

##   (Intercept)  SchoolSum
## 2  -0.2474367 0.39734019
## 3 -31.0408049 0.04542034
## 4 -31.3280402 0.16314889

pSAllTracts <- (1 - pnorm(abs(zSAllTracts), 0, 1)) * 2
pSAllTracts

##   (Intercept) SchoolSum
## 2   0.8045703 0.6911166
## 3   0.0000000 0.9637723
## 4   0.0000000 0.8704012

Anova(mod_Family_AllTracts, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##           LR Chisq Df Pr(>Chisq)   
## FamilySum   14.828  3    0.00197 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Family_AllTracts)

## Call:
## multinom(formula = Groups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##    (Intercept)   FamilySum
## 2 -0.006146027 -0.08666201
## 3 -0.936660296 -0.05799022
## 4 -0.949658294 -0.04421635
## 
## Std. Errors:
##   (Intercept)  FamilySum
## 2  0.02270519 0.02277359
## 3  0.03020029 0.03024979
## 4  0.03033979 0.03024833
## 
## Residual Deviance: 27799.96 
## AIC: 27811.96

zFAllTracts <- summary(mod_Family_AllTracts)$coefficients/summary(mod_Family_AllTracts)$standard.errors
zFAllTracts

##   (Intercept) FamilySum
## 2  -0.2706882 -3.805373
## 3 -31.0149419 -1.917045
## 4 -31.3007518 -1.461778

pFAllTracts <- (1 - pnorm(abs(zFAllTracts), 0, 1)) * 2
pFAllTracts

##   (Intercept)    FamilySum
## 2   0.7866309 0.0001415906
## 3   0.0000000 0.0552321820
## 4   0.0000000 0.1438019528

Anova(mod_Safety_AllTracts, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##        LR Chisq Df Pr(>Chisq)
## Safety   5.1884  3     0.1585

summary(mod_Safety_AllTracts)

## Call:
## multinom(formula = Groups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##    (Intercept)       Safety
## 2 -0.006025944 -0.045886379
## 3 -0.937280943  0.006819441
## 4 -0.950283841 -0.024853312
## 
## Std. Errors:
##   (Intercept)     Safety
## 2  0.02269015 0.02290123
## 3  0.03019564 0.03004247
## 4  0.03033536 0.03053461
## 
## Residual Deviance: 27809.6 
## AIC: 27821.6

zSafAllTracts <- summary(mod_Safety_AllTracts)$coefficients/summary(mod_Safety_AllTracts)$standard.errors
zSafAllTracts

##   (Intercept)     Safety
## 2  -0.2655753 -2.0036644
## 3 -31.0402713  0.2269934
## 4 -31.3259420 -0.8139390

pSafAllTracts <- (1 - pnorm(abs(zSafAllTracts), 0, 1)) * 2
pSafAllTracts

##   (Intercept)     Safety
## 2   0.7905663 0.04510603
## 3   0.0000000 0.82042887
## 4   0.0000000 0.41567989

Anova(mod_SES_AllTracts, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: Groups
##     LR Chisq Df Pr(>Chisq)    
## SES   24.309  3 0.00002153 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_SES_AllTracts)

## Call:
## multinom(formula = Groups ~ SES, data = AdvDTI)
## 
## Coefficients:
##    (Intercept)         SES
## 2 -0.007124469 -0.03855303
## 3 -0.940341713  0.10095172
## 4 -0.950492577  0.04607389
## 
## Std. Errors:
##   (Intercept)        SES
## 2  0.02270501 0.02328433
## 3  0.03026231 0.02938147
## 4  0.03034247 0.03018164
## 
## Residual Deviance: 27790.48 
## AIC: 27802.48

zSESAllTracts <- summary(mod_SES_AllTracts)$coefficients/summary(mod_SES_AllTracts)$standard.errors
zSESAllTracts

##   (Intercept)       SES
## 2   -0.313784 -1.655750
## 3  -31.073027  3.435897
## 4  -31.325480  1.526554

pSESAllTracts <- (1 - pnorm(abs(zSESAllTracts), 0, 1)) * 2
pSESAllTracts

##   (Intercept)          SES
## 2   0.7536851 0.0977724318
## 3   0.0000000 0.0005905949
## 4   0.0000000 0.1268720092

Trajectory

K-means Clustering

set.seed(4)
Kmeans_Shape <- kmeans(AdvDTI[,19], 3, iter.max = 10, nstart = 50)

ShapeGroups <- as.factor(Kmeans_Shape$cluster)

ShapePlotCluster <- data.frame(c(AdvDTI$AllTracts_FA_t1),     #select FA at time 1
                              c(AdvDTI$AllTracts_FA_t2),     #select FA at time 2
                              as.factor(c(id,id)),         #Give ID to participants
                              as.factor(ShapeGroups))               #insert cluster number
colnames(ShapePlotCluster)<-c('T1', 'T2','ID','Cluster')  #give column name

ShapePlotAllClusters<-melt(ShapePlotCluster,by='ID')

ggplot(ShapePlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
 geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2","3")), 
             labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2", "3" = "Cluster 3")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2","3"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

mod_PTSD_Shape = multinom(ShapeGroups ~ PTSDSum, data=AdvDTI)
mod_School_Shape = multinom(ShapeGroups ~ SchoolSum, data=AdvDTI)
mod_Family_Shape = multinom(ShapeGroups ~ FamilySum, data=AdvDTI)
mod_Safety_Shape = multinom(ShapeGroups ~ Safety, data=AdvDTI)
mod_SES_Shape = multinom(ShapeGroups ~ SES, data=AdvDTI)

options(scipen = 999)

Anova(mod_PTSD_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##         LR Chisq Df Pr(>Chisq)  
## PTSDSum    5.067  2    0.07938 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_PTSD_Shape)

## Call:
## multinom(formula = ShapeGroups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)   PTSDSum
## 2   -1.085692 0.0161057
## 3   -1.148634 0.0554215
## 
## Std. Errors:
##   (Intercept)    PTSDSum
## 2  0.02463092 0.02532587
## 3  0.02522796 0.02424196
## 
## Residual Deviance: 20429.51 
## AIC: 20437.51

zPTShape <- summary(mod_PTSD_Shape)$coefficients/summary(mod_PTSD_Shape)$standard.errors
zPTShape

##   (Intercept)   PTSDSum
## 2   -44.07842 0.6359386
## 3   -45.53018 2.2861810

pPTShape <- (1 - pnorm(abs(zPTShape), 0, 1)) * 2
pPTShape

##   (Intercept)    PTSDSum
## 2           0 0.52481644
## 3           0 0.02224367

Anova(mod_School_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum 0.059123  2     0.9709

summary(mod_School_Shape)

## Call:
## multinom(formula = ShapeGroups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)   SchoolSum
## 2   -1.085773 0.003553326
## 3   -1.148053 0.005733582
## 
## Std. Errors:
##   (Intercept)  SchoolSum
## 2  0.02462978 0.02480264
## 3  0.02521440 0.02537409
## 
## Residual Deviance: 20434.52 
## AIC: 20442.52

zSShape <- summary(mod_School_Shape)$coefficients/summary(mod_School_Shape)$standard.errors
zSShape

##   (Intercept) SchoolSum
## 2   -44.08373  0.143264
## 3   -45.53163  0.225962

pSShape <- (1 - pnorm(abs(zSShape), 0, 1)) * 2
pSShape

##   (Intercept) SchoolSum
## 2           0 0.8860817
## 3           0 0.8212309

Anova(mod_Family_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##           LR Chisq Df Pr(>Chisq)
## FamilySum  0.89571  2      0.639

summary(mod_Family_Shape)

## Call:
## multinom(formula = ShapeGroups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)  FamilySum
## 2   -1.085746 0.01364330
## 3   -1.147974 0.02220501
## 
## Std. Errors:
##   (Intercept)  FamilySum
## 2  0.02463064 0.02465685
## 3  0.02521510 0.02516377
## 
## Residual Deviance: 20433.68 
## AIC: 20441.68

zFShape <- summary(mod_Family_Shape)$coefficients/summary(mod_Family_Shape)$standard.errors
zFShape

##   (Intercept) FamilySum
## 2   -44.08112 0.5533272
## 3   -45.52726 0.8824198

pFShape <- (1 - pnorm(abs(zFShape), 0, 1)) * 2
pFShape

##   (Intercept) FamilySum
## 2           0 0.5800394
## 3           0 0.3775498

Anova(mod_Safety_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##        LR Chisq Df Pr(>Chisq)
## Safety   1.9329  2     0.3804

summary(mod_Safety_Shape)

## Call:
## multinom(formula = ShapeGroups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)       Safety
## 2   -1.085924 -0.006641791
## 3   -1.148280  0.031650394
## 
## Std. Errors:
##   (Intercept)     Safety
## 2  0.02463234 0.02492175
## 3  0.02521915 0.02514743
## 
## Residual Deviance: 20432.64 
## AIC: 20440.64

zSafShape <- summary(mod_Safety_Shape)$coefficients/summary(mod_Safety_Shape)$standard.errors
zSafShape

##   (Intercept)     Safety
## 2   -44.08530 -0.2665058
## 3   -45.53206  1.2585935

pSafShape <- (1 - pnorm(abs(zSafShape), 0, 1)) * 2
pSafShape

##   (Intercept)    Safety
## 2           0 0.7898497
## 3           0 0.2081772

Anova(mod_SES_Shape, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: ShapeGroups
##     LR Chisq Df  Pr(>Chisq)    
## SES   26.062  2 0.000002191 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_SES_Shape)

## Call:
## multinom(formula = ShapeGroups ~ SES, data = AdvDTI)
## 
## Coefficients:
##   (Intercept)        SES
## 2   -1.084748 0.05788339
## 3   -1.149835 0.12432410
## 
## Std. Errors:
##   (Intercept)        SES
## 2  0.02463816 0.02479614
## 3  0.02527547 0.02469067
## 
## Residual Deviance: 20408.51 
## AIC: 20416.51

zSESShape <- summary(mod_SES_Shape)$coefficients/summary(mod_SES_Shape)$standard.errors
zSESShape

##   (Intercept)      SES
## 2   -44.02713 2.334371
## 3   -45.49211 5.035265

pSESShape <- (1 - pnorm(abs(zSESShape), 0, 1)) * 2
pSESShape

##   (Intercept)             SES
## 2           0 0.0195763167781
## 3           0 0.0000004771871

Total

K-means Clustering

set.seed(123)
Kmeans_Magnitude <- kmeans(AdvDTI[,20], 2, iter.max = 10, nstart = 50)   #cluster

MagnitudeGroups <- as.factor(Kmeans_Magnitude$cluster)

MagnitudePlotCluster <- data.frame(c(AdvDTI$AllTracts_FA_t1),     #select FA at time 1
                              c(AdvDTI$AllTracts_FA_t2),     #select FA at time 2
                              as.factor(c(id,id)),         #Give ID to participants
                              as.factor(MagnitudeGroups))               #insert cluster number
colnames(MagnitudePlotCluster)<-c('T1', 'T2','ID','Cluster')  #give column name

MagnitudePlotAllClusters<-melt(MagnitudePlotCluster,by='ID')

ggplot(MagnitudePlotAllClusters,aes(variable,value,group=ID,col=Cluster))+
  geom_point(position = "jitter", size=1,alpha=.3)+
  geom_line(position = "jitter", alpha=.1)+
  stat_smooth(aes(group=Cluster), colour="black", method="loess", se=F, size=0.5, formula = 'y~x')+ 
  ylab('FA per participant')+
  xlab('Timepoints')+
  facet_grid(~factor(Cluster, levels=c("1","2")), labeller = as_labeller(c("1" = "Cluster 1", "2" = "Cluster 2")))+
  scale_color_manual(values = my_colors,
                     breaks =c("1","2"))+
  guides(col = FALSE)+
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(size = 12),
        strip.text = element_text(size = 14))

Regression results

mod_PTSD_Magnitude = multinom(MagnitudeGroups ~ PTSDSum, data=AdvDTI)
mod_School_Magnitude = multinom(MagnitudeGroups ~ SchoolSum, data=AdvDTI)
mod_Family_Magnitude = multinom(MagnitudeGroups ~ FamilySum, data=AdvDTI)
mod_Safety_Magnitude = multinom(MagnitudeGroups ~ Safety, data=AdvDTI)
mod_SES_Magnitude = multinom(MagnitudeGroups ~ SES, data=AdvDTI)

options(scipen = 999)

Anova(mod_PTSD_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##          LR Chisq Df Pr(>Chisq)
## PTSDSum 0.0017071  1      0.967

summary(mod_PTSD_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ PTSDSum, data = AdvDTI)
## 
## Coefficients:
##                    Values  Std. Err.
## (Intercept) -0.1074295514 0.01926572
## PTSDSum      0.0008087193 0.01957425
## 
## Residual Deviance: 14951.93 
## AIC: 14955.93

zPTMagnitude <- summary(mod_PTSD_Magnitude)$coefficients/summary(mod_PTSD_Magnitude)$standard.errors
zPTMagnitude

## (Intercept)     PTSDSum 
## -5.57620161  0.04131546

pPTMagnitude <- (1 - pnorm(abs(zPTMagnitude), 0, 1)) * 2
pPTMagnitude

##     (Intercept)         PTSDSum 
## 0.0000000245827 0.9670444055551

Anova(mod_School_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##           LR Chisq Df Pr(>Chisq)
## SchoolSum   1.0618  1     0.3028

summary(mod_School_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ SchoolSum, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.10754473 0.01926698
## SchoolSum   -0.02000606 0.01941959
## 
## Residual Deviance: 14950.87 
## AIC: 14954.87

zSMagnitude <- summary(mod_School_Magnitude)$coefficients/summary(mod_School_Magnitude)$standard.errors
zSMagnitude

## (Intercept)   SchoolSum 
##   -5.581817   -1.030200

pSMagnitude <- (1 - pnorm(abs(zSMagnitude), 0, 1)) * 2
pSMagnitude

##      (Intercept)        SchoolSum 
## 0.00000002380189 0.30291636150292

Anova(mod_Family_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##           LR Chisq Df Pr(>Chisq)    
## FamilySum   16.813  1 0.00004125 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Family_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ FamilySum, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.10697844 0.01928086
## FamilySum    0.07918894 0.01932407
## 
## Residual Deviance: 14935.12 
## AIC: 14939.12

zFMagnitude <- summary(mod_Family_Magnitude)$coefficients/summary(mod_Family_Magnitude)$standard.errors
zFMagnitude

## (Intercept)   FamilySum 
##   -5.548427    4.097944

pFMagnitude <- (1 - pnorm(abs(zFMagnitude), 0, 1)) * 2
pFMagnitude

##      (Intercept)        FamilySum 
## 0.00000002882511 0.00004168363914

Anova(mod_Safety_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##        LR Chisq Df Pr(>Chisq)  
## Safety   3.5816  1    0.05842 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(mod_Safety_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ Safety, data = AdvDTI)
## 
## Coefficients:
##                  Values  Std. Err.
## (Intercept) -0.10731769 0.01926889
## Safety       0.03669357 0.01938778
## 
## Residual Deviance: 14948.35 
## AIC: 14952.35

zSafMagnitude <- summary(mod_Safety_Magnitude)$coefficients/summary(mod_Safety_Magnitude)$standard.errors
zSafMagnitude

## (Intercept)      Safety 
##   -5.569479    1.892613

pSafMagnitude <- (1 - pnorm(abs(zSafMagnitude), 0, 1)) * 2
pSafMagnitude

##      (Intercept)           Safety 
## 0.00000002555026 0.05840932338287

Anova(mod_SES_Magnitude, type=3)

## Analysis of Deviance Table (Type III tests)
## 
## Response: MagnitudeGroups
##     LR Chisq Df Pr(>Chisq)
## SES  0.19948  1     0.6551

summary(mod_SES_Magnitude)

## Call:
## multinom(formula = MagnitudeGroups ~ SES, data = AdvDTI)
## 
## Coefficients:
##                   Values  Std. Err.
## (Intercept) -0.107333027 0.01926703
## SES          0.008673255 0.01941782
## 
## Residual Deviance: 14951.73 
## AIC: 14955.73

zSESMagnitude <- summary(mod_SES_Magnitude)$coefficients/summary(mod_SES_Magnitude)$standard.errors
zSESMagnitude

## (Intercept)         SES 
##  -5.5708138   0.4466646

pSESMagnitude <- (1 - pnorm(abs(zSESMagnitude), 0, 1)) * 2
pSESMagnitude

##      (Intercept)              SES 
## 0.00000002535522 0.65511722427027

Ayla Pollmann - 2023

All Tracts

Total & Trajectory

K-means Clustering

Regression results

Trajectory

K-means Clustering

Regression results

Total

K-means Clustering

Regression results