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Distribution of ptq scores

ptq_rrall <- lm(all4$ptq_total ~ all4$nd_resprate)
summary(ptq_rrall)
## 
## Call:
## lm(formula = all4$ptq_total ~ all4$nd_resprate)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.713  -8.399  -0.764   6.146  36.647 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        28.530      4.348   6.562 7.33e-10 ***
## all4$nd_resprate   -8.962      5.492  -1.632    0.105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.65 on 157 degrees of freedom
## Multiple R-squared:  0.01668,    Adjusted R-squared:  0.01041 
## F-statistic: 2.663 on 1 and 157 DF,  p-value: 0.1047
hist(all4$ptq_total, main = "Distribution of ptq scores for all subjects enrolled", xlab = "ptq total score")

ggplot(all4, aes(x=all4$ptq_total, y=all4$nd_resprate)) + 
  geom_point(shape=1) +
  geom_smooth(color= "#CC0066", method=lm) +
  labs(x = "PTQ score", y = "response rate", 
       title = "Relationship between total PTQ score and response rate") +
  theme_classic()

ptq_rr75 <- lm(all_75$ptq_total ~ all_75$nd_resprate)
summary(ptq_rr75)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$nd_resprate)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.727  -7.954  -0.940   5.453  33.381 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)          13.802     12.859   1.073    0.286
## all_75$nd_resprate    9.084     14.975   0.607    0.545
## 
## Residual standard error: 11.51 on 106 degrees of freedom
## Multiple R-squared:  0.00346,    Adjusted R-squared:  -0.005942 
## F-statistic: 0.368 on 1 and 106 DF,  p-value: 0.5454
hist(all_75$ptq_total, xlab = "ptq score", main ="Hist of ptq score distribution for 75% response rate")

ggplot(all_75, aes(x=all_75$ptq_total, y=all_75$nd_resprate)) + 
  geom_point(shape=1) +
  geom_smooth(color= "blue", method=lm) +
  labs(x = "PTQ score", y = "response rate", 
       title = "Relationship between total PTQ score and response rate for 75% responders") +
  theme_classic()

ptq_rr70 <- lm(all_70$ptq_total ~ all_70$nd_resprate)
summary(ptq_rr70)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$nd_resprate)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.605  -7.868  -1.862   4.976  32.997 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)           9.195     10.089   0.911    0.364
## all_70$nd_resprate   14.226     12.037   1.182    0.239
## 
## Residual standard error: 11.34 on 128 degrees of freedom
## Multiple R-squared:  0.0108, Adjusted R-squared:  0.003067 
## F-statistic: 1.397 on 1 and 128 DF,  p-value: 0.2394
hist(all_70$ptq_total, xlab = "ptq score", main ="Hist of ptq score distribution for 70% response rate")

ggplot(all_70, aes(x=all_70$ptq_total, y=all_70$nd_resprate)) + 
  geom_point(shape=1) +
  geom_smooth(color= "green", method=lm) +
  labs(x = "PTQ score", y = "response rate", 
       title = "Relationship between total PTQ score and response rate for 70% response rate") +
  theme_classic()

Analyses for 70% response rate

Skew & Kurtosis

skewness(all_70$ptq_total, na.rm=T)
## [1] 0.617506
skewness(all_70$PA_R_Mean)
## [1] 0.4981497
skewness(all_70$PA_R_MSSD)
## [1] 1.387316
skewness(all_70$NA_R_Mean)
## [1] -0.2691784
skewness(all_70$NA_R_MSSD)
## [1] 1.229989
kurtosis(all_70$ptq_total, na.rm = T)
## [1] 0.1395027
kurtosis(all_70$PA_R_Mean)
## [1] 0.4459975
kurtosis(all_70$PA_R_MSSD)
## [1] 2.023582
kurtosis(all_70$NA_R_Mean)
## [1] -0.4356086
kurtosis(all_70$NA_R_MSSD)
## [1] 1.263268

Descriptive Statistics

sd(all_70$ptq_total)
## [1] 11.35867
sd(all_70$PA_R_Mean)
## [1] 10.13314
sd(all_70$NA_R_Mean)
## [1] 12.9577
sd(all_70$PA_R_MSSD)
## [1] 257.7285
sd(all_70$NA_R_MSSD)
## [1] 278.3159
mean(all_70$ptq_total)
## [1] 21.06154
mean(all_70$PA_R_Mean)
## [1] 56.03002
mean(all_70$NA_R_Mean)
## [1] 37.70309
mean(all_70$PA_R_MSmean)
## Warning in mean.default(all_70$PA_R_MSmean): argument is not numeric or
## logical: returning NA
## [1] NA
sd(all_70$NA_R_MSSD)
## [1] 278.3159
col_means <- colMeans(all_70, na.rm=T)
col_sd <- apply(all_70,2, sd, na.rm=TRUE)

Aim 1 Analyses

Relationship between Mean NA & PA and PTQ

m.ptq2 <- lm(all_70$ptq_total ~ all_70$NA_R_Mean)
summary(m.ptq2)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.747  -7.743  -1.659   5.664  32.574 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      10.62165    2.92963   3.626 0.000415 ***
## all_70$NA_R_Mean  0.27690    0.07351   3.767 0.000251 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.82 on 128 degrees of freedom
## Multiple R-squared:  0.09978,    Adjusted R-squared:  0.09275 
## F-statistic: 14.19 on 1 and 128 DF,  p-value: 0.0002513
confint(m.ptq2, level=0.95)
##                      2.5 %     97.5 %
## (Intercept)      4.8248741 16.4184183
## all_70$NA_R_Mean 0.1314378  0.4223573
summary(influence.measures(m.ptq2))
## Potentially influential observations of
##   lm(formula = all_70$ptq_total ~ all_70$NA_R_Mean) :
## 
##     dfb.1_ dfb.a_70 dffit   cov.r   cook.d hat    
## 1   -0.28   0.26    -0.28    1.06_*  0.04   0.06_*
## 14  -0.04   0.12     0.27    0.91_*  0.04   0.01  
## 29   0.45  -0.38     0.46_*  0.92_*  0.10   0.03  
## 33   0.02  -0.02     0.02    1.05_*  0.00   0.03  
## 47  -0.02   0.02    -0.02    1.07_*  0.00   0.05_*
## 49  -0.02   0.02     0.03    1.06_*  0.00   0.04  
## 76   0.20  -0.14     0.25    0.95_*  0.03   0.01  
## 108 -0.04   0.05     0.06    1.05_*  0.00   0.03  
## 110 -0.01   0.01    -0.01    1.09_*  0.00   0.06_*
## 113 -0.08   0.18     0.33    0.89_*  0.05   0.01  
## 120  0.07  -0.07     0.08    1.05_*  0.00   0.03
m.ptq4 <- lm(all_70$ptq_total ~ all_70$PA_R_Mean)
summary(m.ptq4)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.444  -8.290  -1.089   6.398  34.960 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      32.62162    5.54436   5.884  3.3e-08 ***
## all_70$PA_R_Mean -0.20632    0.09739  -2.119   0.0361 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.21 on 128 degrees of freedom
## Multiple R-squared:  0.03388,    Adjusted R-squared:  0.02633 
## F-statistic: 4.488 on 1 and 128 DF,  p-value: 0.03606
confint(m.ptq4, level=0.95)
##                       2.5 %      97.5 %
## (Intercept)      21.6511594 43.59207306
## all_70$PA_R_Mean -0.3990133 -0.01362536
summary(influence.measures(m.ptq4))
## Potentially influential observations of
##   lm(formula = all_70$ptq_total ~ all_70$PA_R_Mean) :
## 
##     dfb.1_ dfb.a_70 dffit   cov.r   cook.d hat    
## 1    0.41  -0.44    -0.46_*  1.12_*  0.10   0.12_*
## 3    0.01  -0.01    -0.01    1.07_*  0.00   0.05_*
## 9    0.04  -0.05    -0.05    1.05_*  0.00   0.04  
## 14  -0.15   0.20     0.33    0.90_*  0.05   0.01  
## 70  -0.09   0.12     0.23    0.95_*  0.03   0.01  
## 76  -0.39   0.43     0.48_*  0.97    0.11   0.04  
## 85   0.37  -0.35     0.38_*  1.01    0.07   0.04  
## 103 -0.08   0.12     0.25    0.93_*  0.03   0.01  
## 113  0.05   0.00     0.29    0.87_*  0.04   0.01

Aim 2 Analyses

Relationship between NA & PA MSSD and PTQ

m.ptq1 <- lm(all_70$ptq_total ~ all_70$NA_R_MSSD)
summary(m.ptq1)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NA_R_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.441  -8.283  -1.288   6.467  29.126 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      16.853431   1.713226   9.837  < 2e-16 ***
## all_70$NA_R_MSSD  0.010382   0.003489   2.976  0.00349 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.03 on 128 degrees of freedom
## Multiple R-squared:  0.06471,    Adjusted R-squared:  0.0574 
## F-statistic: 8.856 on 1 and 128 DF,  p-value: 0.003494
confint(m.ptq1, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)      13.46352198 20.24334083
## all_70$NA_R_MSSD  0.00347878  0.01728459
summary(influence.measures(m.ptq1))
## Potentially influential observations of
##   lm(formula = all_70$ptq_total ~ all_70$NA_R_MSSD) :
## 
##     dfb.1_ dfb.a_70 dffit   cov.r   cook.d hat    
## 14  -0.09   0.27     0.35    0.93_*  0.06   0.02  
## 19   0.29  -0.21     0.30    0.94_*  0.04   0.02  
## 29   0.23  -0.14     0.25    0.94_*  0.03   0.01  
## 76  -0.18   0.31     0.33    1.04    0.05   0.05_*
## 85   0.20  -0.10     0.23    0.94_*  0.03   0.01  
## 87   0.04  -0.07    -0.07    1.09_*  0.00   0.07_*
## 93   0.17  -0.28    -0.29    1.07_*  0.04   0.06_*
## 103  0.17  -0.05     0.23    0.93_*  0.03   0.01  
## 106 -0.04   0.07     0.07    1.09_*  0.00   0.07_*
## 113 -0.27   0.50     0.56_*  0.94_*  0.15   0.04  
## 118  0.21  -0.32    -0.33    1.10_*  0.05   0.09_*
## 122  0.03  -0.05    -0.06    1.06_*  0.00   0.04  
## 123  0.00   0.00     0.00    1.06_*  0.00   0.04  
## 124  0.15  -0.24    -0.25    1.09_*  0.03   0.08_*
m.ptq3 <- lm(all_70$ptq_total ~ all_70$PA_R_MSSD)
summary(m.ptq3)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PA_R_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.790  -8.493  -0.740   6.508  32.585 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      18.390229   1.725584  10.657   <2e-16 ***
## all_70$PA_R_MSSD  0.007250   0.003842   1.887   0.0615 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.25 on 128 degrees of freedom
## Multiple R-squared:  0.02706,    Adjusted R-squared:  0.01946 
## F-statistic:  3.56 on 1 and 128 DF,  p-value: 0.06145
confint(m.ptq3, level=0.95)
##                          2.5 %      97.5 %
## (Intercept)      14.9758649027 21.80459219
## all_70$PA_R_MSSD -0.0003530751  0.01485264
summary(influence.measures(m.ptq3))
## Potentially influential observations of
##   lm(formula = all_70$ptq_total ~ all_70$PA_R_MSSD) :
## 
##     dfb.1_ dfb.a_70 dffit   cov.r   cook.d hat    
## 2    0.21  -0.31    -0.32    1.12_*  0.05   0.11_*
## 14  -0.15   0.34     0.41_*  0.94_*  0.08   0.02  
## 29   0.12  -0.02     0.19    0.95_*  0.02   0.01  
## 32   0.10  -0.18    -0.20    1.05_*  0.02   0.04  
## 76  -0.25   0.41     0.43_*  1.06_*  0.09   0.07_*
## 85   0.30  -0.22     0.30    0.95_*  0.04   0.02  
## 93   0.11  -0.18    -0.20    1.07_*  0.02   0.06_*
## 103  0.24  -0.13     0.26    0.93_*  0.03   0.01  
## 106 -0.09   0.14     0.14    1.10_*  0.01   0.08_*
## 113 -0.12   0.34     0.43_*  0.90_*  0.09   0.02  
## 118  0.09  -0.14    -0.15    1.08_*  0.01   0.07_*
## 123 -0.02   0.04     0.04    1.06_*  0.00   0.04  
## 124  0.11  -0.17    -0.18    1.11_*  0.02   0.09_*

Aim 3 Analyses

Relationship between Mean and MSSD as they relate to PTQ

m.NA_all <- lm(all_70$ptq_total ~ all_70$NA_R_MSSD + all_70$NA_R_Mean)
summary(m.NA_all)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NA_R_MSSD + all_70$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.679  -7.507  -1.719   5.869  30.859 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      7.263466   3.082049   2.357 0.019967 *  
## all_70$NA_R_MSSD 0.009562   0.003338   2.865 0.004881 ** 
## all_70$NA_R_Mean 0.263163   0.071688   3.671 0.000354 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.53 on 127 degrees of freedom
## Multiple R-squared:  0.1544, Adjusted R-squared:  0.1411 
## F-statistic:  11.6 on 2 and 127 DF,  p-value: 2.366e-05
confint(m.NA_all, level=0.95)
##                        2.5 %     97.5 %
## (Intercept)      1.164646581 13.3622854
## all_70$NA_R_MSSD 0.002957848  0.0161669
## all_70$NA_R_Mean 0.121305879  0.4050204
m.PA_all <- lm(all_70$ptq_total ~ all_70$PA_R_MSSD + all_70$PA_R_Mean)
summary(m.PA_all)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PA_R_MSSD + all_70$PA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.240  -8.127  -1.593   6.442  32.453 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      30.387013   5.585791   5.440 2.64e-07 ***
## all_70$PA_R_MSSD  0.007724   0.003788   2.039   0.0435 *  
## all_70$PA_R_Mean -0.217234   0.096355  -2.255   0.0259 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.07 on 127 degrees of freedom
## Multiple R-squared:  0.0645, Adjusted R-squared:  0.04977 
## F-statistic: 4.378 on 2 and 127 DF,  p-value: 0.0145
confint(m.PA_all, level=0.95)
##                          2.5 %      97.5 %
## (Intercept)      19.3337399920 41.44028593
## all_70$PA_R_MSSD  0.0002277189  0.01522085
## all_70$PA_R_Mean -0.4079033429 -0.02656460
m_all <-lm(all_70$ptq_total ~ all_70$PA_R_MSSD + all_70$PA_R_Mean + all_70$NA_R_MSSD + all_70$NA_R_Mean)
summary(m_all)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PA_R_MSSD + all_70$PA_R_Mean + 
##     all_70$NA_R_MSSD + all_70$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.345  -6.963  -1.990   6.074  30.477 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      11.643253   8.153949   1.428  0.15581   
## all_70$PA_R_MSSD -0.003932   0.006084  -0.646  0.51926   
## all_70$PA_R_Mean -0.057586   0.107369  -0.536  0.59268   
## all_70$NA_R_MSSD  0.012643   0.005645   2.240  0.02687 * 
## all_70$NA_R_Mean  0.237886   0.084093   2.829  0.00544 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.58 on 125 degrees of freedom
## Multiple R-squared:  0.1593, Adjusted R-squared:  0.1324 
## F-statistic: 5.922 on 4 and 125 DF,  p-value: 0.0002136
confint(m_all, level=0.95)
##                         2.5 %       97.5 %
## (Intercept)      -4.494423560 27.780928892
## all_70$PA_R_MSSD -0.015973766  0.008109051
## all_70$PA_R_Mean -0.270082829  0.154910920
## all_70$NA_R_MSSD  0.001471469  0.023814656
## all_70$NA_R_Mean  0.071454743  0.404317494

Aim 4 Analyses

Correlation matrix of the individual item means

#### Correlation matrix of the individual item mssd #### Correlation matrix of the individual item means and mssd

PCA for item means

means.pca <- prcomp(na.omit(indiv_means),
                    center = TRUE,
                    scale = TRUE)

print(means.pca)
## Standard deviations (1, .., p=10):
##  [1] 2.4314404 1.4560817 0.8282742 0.6657619 0.5304782 0.4939907 0.3839744
##  [8] 0.2750958 0.2233084 0.2005800
## 
## Rotation (n x k) = (10 x 10):
##                       PC1        PC2          PC3         PC4         PC5
## anxious_mean    0.3288094 -0.3162490  0.251999500 -0.37479636  0.14788507
## nervous_mean    0.3291338 -0.3326932  0.224616318 -0.16367606  0.09674457
## upset_mean      0.3428561 -0.2704493 -0.003171761  0.48517546 -0.05170728
## sluggish_mean   0.3018074 -0.2095316 -0.613274781 -0.19606325  0.12240297
## irritable_mean  0.3444031 -0.2761856 -0.178622381  0.26653394  0.12332698
## content_mean   -0.3403434 -0.3094750 -0.126066245 -0.31309278  0.21995902
## relaxed_mean   -0.3274357 -0.1575449 -0.459800059  0.39729640  0.25862471
## excited_mean   -0.2104801 -0.5095268  0.006499109  0.06413939 -0.79977709
## happy_mean     -0.3396801 -0.3249538 -0.094695226 -0.32434484  0.11799716
## attentive_mean -0.2712409 -0.3319176  0.491633576  0.35025206  0.40874123
##                        PC6         PC7         PC8         PC9
## anxious_mean   -0.19218768  0.02887847 -0.40139279 -0.26993827
## nervous_mean   -0.39526389  0.36301498  0.14659770  0.33977975
## upset_mean     -0.16981279 -0.13475205  0.60574012 -0.33361373
## sluggish_mean   0.51781077  0.39352968  0.10012930 -0.04173951
## irritable_mean  0.13240365 -0.62407196 -0.40652534  0.24277066
## content_mean   -0.06541904 -0.30833760  0.39282730  0.50463524
## relaxed_mean   -0.49586590  0.28915957 -0.30339555 -0.01782858
## excited_mean    0.08903964  0.10741780 -0.14717361  0.09169001
## happy_mean     -0.04270792 -0.25431502  0.06987057 -0.61336254
## attentive_mean  0.48210018  0.22359914 -0.04773894  0.01048353
##                        PC10
## anxious_mean   -0.542468428
## nervous_mean    0.519066180
## upset_mean     -0.214509289
## sluggish_mean  -0.026793660
## irritable_mean  0.236125370
## content_mean   -0.343173637
## relaxed_mean   -0.099775777
## excited_mean   -0.052414976
## happy_mean      0.450928800
## attentive_mean -0.001875378
biplot(means.pca, scale = 0)

screeplot(means.pca)

#### PCA for item mssd

mssd.pca <- prcomp(na.omit(indiv_mssd),
                    center = TRUE,
                    scale. = TRUE)
print(mssd.pca)
## Standard deviations (1, .., p=10):
##  [1] 2.4459791 1.2642974 0.7374592 0.6525924 0.6378996 0.5651048 0.4925840
##  [8] 0.4468800 0.4174235 0.3258416
## 
## Rotation (n x k) = (10 x 10):
##                      PC1         PC2         PC3          PC4         PC5
## anxious_mssd   0.3546079 -0.04611443  0.01898725 -0.277634466  0.46200992
## nervous_mssd   0.1781945 -0.63727121  0.32467451 -0.140119744  0.03304126
## upset_mssd     0.3245190  0.22020145  0.33296354  0.516243494 -0.17141724
## sluggish_mssd  0.1582614 -0.66860405  0.02272665  0.197739199 -0.06954507
## irritable_mssd 0.3419508  0.04746217 -0.09204012  0.647395461  0.27156888
## content_mssd   0.3639421  0.12472986  0.25313005 -0.199876382 -0.37944890
## relaxed_mssd   0.3434314  0.14959138 -0.10369632 -0.197001435  0.57070530
## excited_mssd   0.3574752  0.06779624 -0.15494906 -0.200009642 -0.16930006
## happy_mssd     0.3543192  0.18650605  0.25519473 -0.244458107 -0.26164847
## attentive_mssd 0.3024578 -0.12911310 -0.78128870  0.005725822 -0.33268096
##                        PC6          PC7         PC8         PC9
## anxious_mssd   -0.35852032  0.242072789 -0.20582851 -0.58544330
## nervous_mssd   -0.33964052 -0.100023263 -0.23064157  0.50728384
## upset_mssd     -0.33297756 -0.496817195  0.06918617 -0.18201683
## sluggish_mssd   0.45246366 -0.032825697  0.33850803 -0.39936396
## irritable_mssd  0.15978642  0.460616220 -0.22426101  0.25208364
## content_mssd   -0.03932306  0.163236568  0.19855328 -0.08190800
## relaxed_mssd    0.17607491 -0.376696049  0.46600508  0.29592554
## excited_mssd    0.46285885 -0.374454493 -0.64698275 -0.04356097
## happy_mssd      0.19088505  0.403141792  0.18711210  0.20044482
## attentive_mssd -0.36297972  0.006196424  0.15681177  0.08599333
##                         PC10
## anxious_mssd   -0.0947989560
## nervous_mssd    0.0100267606
## upset_mssd     -0.2093432058
## sluggish_mssd  -0.0598999017
## irritable_mssd  0.1671432739
## content_mssd    0.7277753549
## relaxed_mssd    0.0823121705
## excited_mssd    0.0009152523
## happy_mssd     -0.6100654663
## attentive_mssd -0.0833678625
biplot(mssd.pca, scale = 0)

screeplot(mssd.pca)

Correlation map of the 4 circumplex groups’ means and mssd

circum_group<- all_70[c("NAD_Mean", "NAA_Mean", "PAA_Mean", "PAD_Mean", "NAD_MSSD", "NAA_MSSD",
                      "PAA_MSSD", "PAD_MSSD")]

circum_group <- data.frame(circum_group)
View(indiv_means)

circum_group_cor <- cor(circum_group, y= NULL, use="complete.obs", method = "pearson")
corrplot(circum_group_cor, type = "upper", order = "hclust", 
         tl.col = "black")

View(circum_group_cor)

Visualizing the distribution of the circumplex groups

Relationship between emotion group means and PTQ

ptq_paa <- lm(all_70$ptq_total ~ all_70$PAA_Mean)
summary(ptq_paa)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PAA_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.547  -8.838  -0.792   6.441  34.679 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      31.1772     5.2973   5.885 3.27e-08 ***
## all_70$PAA_Mean  -0.1850     0.0952  -1.944   0.0541 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.24 on 128 degrees of freedom
## Multiple R-squared:  0.02866,    Adjusted R-squared:  0.02108 
## F-statistic: 3.777 on 1 and 128 DF,  p-value: 0.05415
confint(ptq_paa, level=0.95)
##                      2.5 %       97.5 %
## (Intercept)     20.6955422 41.658804746
## all_70$PAA_Mean -0.3733804  0.003346216
ptq_pad <- lm(all_70$ptq_total ~ all_70$PAD_Mean)
summary(ptq_pad)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.187  -8.497  -1.647   6.000  35.365 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     31.55340    5.17972   6.092 1.22e-08 ***
## all_70$PAD_Mean -0.18069    0.08758  -2.063   0.0411 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.22 on 128 degrees of freedom
## Multiple R-squared:  0.03218,    Adjusted R-squared:  0.02462 
## F-statistic: 4.256 on 1 and 128 DF,  p-value: 0.04112
confint(ptq_pad, level=0.95)
##                     2.5 %       97.5 %
## (Intercept)     21.304430 41.802369501
## all_70$PAD_Mean -0.353993 -0.007396728
ptq_naa <- lm(all_70$ptq_total ~ all_70$NAA_Mean)
summary(ptq_naa)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAA_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.010  -7.530  -1.885   5.465  32.211 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     10.49141    2.76439   3.795 0.000227 ***
## all_70$NAA_Mean  0.28757    0.07072   4.067 8.28e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.73 on 128 degrees of freedom
## Multiple R-squared:  0.1144, Adjusted R-squared:  0.1075 
## F-statistic: 16.54 on 1 and 128 DF,  p-value: 8.281e-05
confint(ptq_naa, level=0.95)
##                     2.5 %     97.5 %
## (Intercept)     5.0215925 15.9612371
## all_70$NAA_Mean 0.1476497  0.4274956
ptq_nad <- lm(all_70$ptq_total ~ all_70$NAD_Mean)
summary(ptq_nad)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.199  -8.879  -1.868   6.472  31.247 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     14.41464    3.99031   3.612 0.000518 ***
## all_70$NAD_Mean  0.12159    0.08862   1.372 0.173761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.57 on 83 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.02218,    Adjusted R-squared:  0.0104 
## F-statistic: 1.882 on 1 and 83 DF,  p-value: 0.1738
confint(ptq_nad, level=0.95)
##                       2.5 %     97.5 %
## (Intercept)      6.47806010 22.3512118
## all_70$NAD_Mean -0.05467812  0.2978649

Relationship between emotion group MSSD and PTQ

ptq_paa_mssd <- lm(all_70$ptq_total ~ all_70$PAA_MSSD)
summary(ptq_paa_mssd)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PAA_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.139  -7.964  -0.903   6.694  33.900 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     18.859352   1.765708  10.681   <2e-16 ***
## all_70$PAA_MSSD  0.005396   0.003580   1.507    0.134    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.3 on 128 degrees of freedom
## Multiple R-squared:  0.01744,    Adjusted R-squared:  0.009761 
## F-statistic: 2.272 on 1 and 128 DF,  p-value: 0.1342
confint(ptq_paa_mssd, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)     15.365596337 22.35310672
## all_70$PAA_MSSD -0.001688138  0.01248028
ptq_pad_mssd <- lm(all_70$ptq_total ~ all_70$PAD_MSSD)
summary(ptq_pad_mssd)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$PAD_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.930  -8.044  -1.068   6.144  32.222 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     18.069991   1.763150  10.249   <2e-16 ***
## all_70$PAD_MSSD  0.005336   0.002609   2.045   0.0429 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.22 on 128 degrees of freedom
## Multiple R-squared:  0.03164,    Adjusted R-squared:  0.02407 
## F-statistic: 4.182 on 1 and 128 DF,  p-value: 0.04291
confint(ptq_pad_mssd, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)     1.458130e+01 21.55868494
## all_70$PAD_MSSD 1.728478e-04  0.01049818
ptq_naa_mssd <- lm(all_70$ptq_total ~ all_70$NAA_MSSD)
summary(ptq_naa_mssd)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAA_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.352  -8.277  -1.453   6.491  30.716 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     17.326331   1.808940   9.578   <2e-16 ***
## all_70$NAA_MSSD  0.008244   0.003360   2.454   0.0155 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.14 on 128 degrees of freedom
## Multiple R-squared:  0.04493,    Adjusted R-squared:  0.03747 
## F-statistic: 6.022 on 1 and 128 DF,  p-value: 0.01548
confint(ptq_naa_mssd, level=0.95)
##                        2.5 %     97.5 %
## (Intercept)     13.747034510 20.9056267
## all_70$NAA_MSSD  0.001596512  0.0148917
ptq_nad_mssd <- lm(all_70$ptq_total ~ all_70$NAD_MSSD)
summary(ptq_nad_mssd)
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAD_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.637  -7.295  -1.542   5.883  30.226 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     16.927566   2.404211   7.041 5.09e-10 ***
## all_70$NAD_MSSD  0.002841   0.002170   1.309    0.194    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.58 on 83 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.02023,    Adjusted R-squared:  0.008429 
## F-statistic: 1.714 on 1 and 83 DF,  p-value: 0.1941
confint(ptq_nad_mssd, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)     12.145687294 21.70944436
## all_70$NAD_MSSD -0.001475257  0.00715817

Relationship between the four groups together (as mean and MSSD) as they relate to PTQ

ptq_circumplex_mean <- lm(all_70$ptq_total ~ all_70$NAD_Mean + all_70$NAA_Mean + all_70$PAA_Mean + all_70$PAD_Mean)
summary(ptq_circumplex_mean )
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAD_Mean + all_70$NAA_Mean + 
##     all_70$PAA_Mean + all_70$PAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.863  -7.844  -1.970   5.664  30.340 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      8.16487   10.56336   0.773  0.44183   
## all_70$NAD_Mean -0.15790    0.13208  -1.195  0.23543   
## all_70$NAA_Mean  0.43724    0.16106   2.715  0.00812 **
## all_70$PAA_Mean -0.06709    0.23111  -0.290  0.77235   
## all_70$PAD_Mean  0.10995    0.23351   0.471  0.63904   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.15 on 80 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.1242, Adjusted R-squared:  0.0804 
## F-statistic: 2.836 on 4 and 80 DF,  p-value: 0.02966
confint(ptq_circumplex_mean, level=0.95)
##                       2.5 %     97.5 %
## (Intercept)     -12.8568901 29.1866232
## all_70$NAD_Mean  -0.4207388  0.1049447
## all_70$NAA_Mean   0.1167243  0.7577618
## all_70$PAA_Mean  -0.5270186  0.3928419
## all_70$PAD_Mean  -0.3547627  0.5746549
ptq_circumplex_mssd <- lm(all_70$ptq_total ~ all_70$NAD_MSSD + all_70$NAA_MSSD + all_70$PAA_MSSD + all_70$PAD_MSSD)
summary(ptq_circumplex_mssd )
## 
## Call:
## lm(formula = all_70$ptq_total ~ all_70$NAD_MSSD + all_70$NAA_MSSD + 
##     all_70$PAA_MSSD + all_70$PAD_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.912  -8.210  -1.764   6.832  29.447 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     15.7377527  2.8333837   5.554 3.53e-07 ***
## all_70$NAD_MSSD  0.0023547  0.0028763   0.819    0.415    
## all_70$NAA_MSSD  0.0059701  0.0086314   0.692    0.491    
## all_70$PAA_MSSD -0.0026433  0.0086458  -0.306    0.761    
## all_70$PAD_MSSD  0.0003792  0.0063676   0.060    0.953    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.73 on 80 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.03012,    Adjusted R-squared:  -0.01837 
## F-statistic: 0.6211 on 4 and 80 DF,  p-value: 0.6487
confint(ptq_circumplex_mssd, level=0.95)
##                       2.5 %       97.5 %
## (Intercept)     10.09913941 21.376365963
## all_70$NAD_MSSD -0.00336928  0.008078686
## all_70$NAA_MSSD -0.01120704  0.023147193
## all_70$PAA_MSSD -0.01984896  0.014562450
## all_70$PAD_MSSD -0.01229277  0.013051226

75% repsonse rate analyses

Skew & Kurtosis

skewness(all_75$ptq_total, na.rm=T)
## [1] 0.5481804
skewness(all_75$PA_R_Mean)
## [1] 0.6161088
skewness(all_75$PA_R_MSSD)
## [1] 1.352792
skewness(all_75$NA_R_Mean)
## [1] -0.2841484
skewness(all_75$NA_R_MSSD)
## [1] 1.080293
kurtosis(all_75$ptq_total, na.rm = T)
## [1] 0.1651424
kurtosis(all_75$PA_R_Mean)
## [1] 0.7519026
kurtosis(all_75$PA_R_MSSD)
## [1] 1.649127
kurtosis(all_75$NA_R_Mean)
## [1] -0.3973586
kurtosis(all_75$NA_R_MSSD)
## [1] 0.7081455

Descriptive Statistics

sd(all_75$ptq_total)
## [1] 11.48034
sd(all_75$PA_R_Mean)
## [1] 9.971135
sd(all_75$NA_R_Mean)
## [1] 13.16381
sd(all_75$PA_R_MSSD)
## [1] 270.7738
sd(all_75$NA_R_MSSD)
## [1] 293.6723
mean(all_75$ptq_total)
## [1] 21.57407
mean(all_75$PA_R_Mean)
## [1] 55.97847
mean(all_75$NA_R_Mean)
## [1] 37.73603
mean(all_75$PA_R_MSmean)
## Warning in mean.default(all_75$PA_R_MSmean): argument is not numeric or
## logical: returning NA
## [1] NA
sd(all_75$NA_R_MSSD)
## [1] 293.6723
col_means <- colMeans(all_75, na.rm=T)
col_sd <- apply(all_75,2, sd, na.rm=TRUE)

Aim 1 Analyses

Relationship between Mean NA & PA and PTQ

m.ptq2 <- lm(all_75$ptq_total ~ all_75$NA_R_Mean)
summary(m.ptq2)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.234  -7.228  -1.929   5.351  31.854 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      10.16317    3.17381   3.202 0.001801 ** 
## all_75$NA_R_Mean  0.30239    0.07945   3.806 0.000237 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.82 on 106 degrees of freedom
## Multiple R-squared:  0.1202, Adjusted R-squared:  0.1119 
## F-statistic: 14.48 on 1 and 106 DF,  p-value: 0.0002368
confint(m.ptq2, level=0.95)
##                      2.5 %     97.5 %
## (Intercept)      3.8707830 16.4555589
## all_75$NA_R_Mean 0.1448655  0.4599095
summary(influence.measures(m.ptq2))
## Potentially influential observations of
##   lm(formula = all_75$ptq_total ~ all_75$NA_R_Mean) :
## 
##    dfb.1_ dfb.a_75 dffit   cov.r   cook.d hat    
## 1  -0.30   0.28    -0.30    1.07_*  0.04   0.07_*
## 12 -0.04   0.13     0.29    0.90_*  0.04   0.01  
## 23  0.49  -0.42     0.50_*  0.91_*  0.12   0.03  
## 38 -0.02   0.01    -0.02    1.08_*  0.00   0.06  
## 40  0.00   0.00     0.00    1.07_*  0.00   0.05  
## 60  0.21  -0.15     0.27    0.94_*  0.03   0.01  
## 86 -0.03   0.04     0.04    1.06_*  0.00   0.04  
## 88  0.00   0.00     0.00    1.10_*  0.00   0.08_*
## 91 -0.09   0.19     0.36    0.87_*  0.06   0.01
m.ptq4 <- lm(all_75$ptq_total ~ all_75$PA_R_Mean)
summary(m.ptq4)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.835  -8.067  -1.279   6.182  34.448 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       29.6691     6.3073   4.704 7.73e-06 ***
## all_75$PA_R_Mean  -0.1446     0.1109  -1.303    0.195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.44 on 106 degrees of freedom
## Multiple R-squared:  0.01578,    Adjusted R-squared:  0.00649 
## F-statistic: 1.699 on 1 and 106 DF,  p-value: 0.1952
confint(m.ptq4, level=0.95)
##                       2.5 %      97.5 %
## (Intercept)      17.1641854 42.17401416
## all_75$PA_R_Mean -0.3645672  0.07534791
summary(influence.measures(m.ptq4))
## Potentially influential observations of
##   lm(formula = all_75$ptq_total ~ all_75$PA_R_Mean) :
## 
##    dfb.1_ dfb.a_75 dffit   cov.r   cook.d hat    
## 1   0.57  -0.61    -0.63_*  1.14_*  0.19   0.14_*
## 3   0.04  -0.05    -0.05    1.08_*  0.00   0.06_*
## 12 -0.16   0.21     0.35    0.89_*  0.06   0.01  
## 48 -0.04   0.04     0.05    1.06_*  0.00   0.04  
## 60 -0.40   0.45     0.50_*  0.98    0.12   0.05  
## 81 -0.08   0.13     0.27    0.92_*  0.03   0.01  
## 91  0.05   0.00     0.30    0.86_*  0.04   0.01

Aim 2 Analyses

Relationship between NA & PA MSSD and PTQ

m.ptq1 <- lm(all_75$ptq_total ~ all_75$NA_R_MSSD)
summary(m.ptq1)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NA_R_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.517  -8.018  -1.041   6.403  29.203 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      17.406770   1.907097   9.127 5.16e-15 ***
## all_75$NA_R_MSSD  0.009729   0.003678   2.645   0.0094 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.17 on 106 degrees of freedom
## Multiple R-squared:  0.06193,    Adjusted R-squared:  0.05308 
## F-statistic: 6.999 on 1 and 106 DF,  p-value: 0.009399
confint(m.ptq1, level=0.95)
##                         2.5 %      97.5 %
## (Intercept)      13.625764127 21.18777581
## all_75$NA_R_MSSD  0.002437723  0.01701982
summary(influence.measures(m.ptq1))
## Potentially influential observations of
##   lm(formula = all_75$ptq_total ~ all_75$NA_R_MSSD) :
## 
##     dfb.1_ dfb.a_75 dffit   cov.r   cook.d hat    
## 12  -0.07   0.26     0.36    0.92_*  0.06   0.02  
## 15   0.32  -0.23     0.32    0.94_*  0.05   0.02  
## 23   0.26  -0.16     0.28    0.93_*  0.04   0.01  
## 60  -0.18   0.31     0.34    1.04    0.06   0.06_*
## 65   0.04  -0.06    -0.07    1.09_*  0.00   0.07_*
## 71   0.16  -0.27    -0.29    1.07_*  0.04   0.07_*
## 81   0.20  -0.07     0.25    0.92_*  0.03   0.01  
## 84  -0.04   0.07     0.08    1.10_*  0.00   0.07_*
## 91  -0.26   0.50     0.56_*  0.92_*  0.15   0.04  
## 96   0.20  -0.31    -0.33    1.11_*  0.05   0.10_*
## 100  0.03  -0.05    -0.05    1.06_*  0.00   0.04  
## 101  0.00   0.01     0.01    1.06_*  0.00   0.04  
## 102  0.15  -0.23    -0.25    1.10_*  0.03   0.08_*
m.ptq3 <- lm(all_75$ptq_total ~ all_75$PA_R_MSSD)
summary(m.ptq3)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PA_R_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.295  -8.262  -0.798   6.654  31.904 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      18.528294   1.881022   9.850   <2e-16 ***
## all_75$PA_R_MSSD  0.008033   0.004043   1.987   0.0495 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.33 on 106 degrees of freedom
## Multiple R-squared:  0.0359, Adjusted R-squared:  0.0268 
## F-statistic: 3.947 on 1 and 106 DF,  p-value: 0.04955
confint(m.ptq3, level=0.95)
##                         2.5 %      97.5 %
## (Intercept)      1.479898e+01 22.25760390
## all_75$PA_R_MSSD 1.616691e-05  0.01604943
summary(influence.measures(m.ptq3))
## Potentially influential observations of
##   lm(formula = all_75$ptq_total ~ all_75$PA_R_MSSD) :
## 
##     dfb.1_ dfb.a_75 dffit   cov.r   cook.d hat    
## 2    0.23  -0.36    -0.38    1.13_*  0.07   0.12_*
## 12  -0.13   0.34     0.42_*  0.93_*  0.08   0.03  
## 60  -0.24   0.39     0.42_*  1.06_*  0.09   0.08_*
## 71   0.12  -0.21    -0.23    1.07_*  0.03   0.06_*
## 81   0.26  -0.15     0.28    0.92_*  0.04   0.01  
## 84  -0.07   0.11     0.12    1.11_*  0.01   0.09_*
## 91  -0.11   0.33     0.44_*  0.89_*  0.09   0.02  
## 96   0.10  -0.17    -0.18    1.09_*  0.02   0.07_*
## 101 -0.01   0.02     0.03    1.06_*  0.00   0.04  
## 102  0.13  -0.21    -0.22    1.12_*  0.02   0.10_*

Aim 3 Analyses

Relationship between Mean and MSSD as they relate to PTQ

m.NA_all <- lm(all_75$ptq_total ~ all_75$NA_R_MSSD + all_75$NA_R_Mean)
summary(m.NA_all)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NA_R_MSSD + all_75$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.030  -7.121  -1.624   4.970  30.842 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      7.076782   3.340926   2.118 0.036517 *  
## all_75$NA_R_MSSD 0.008643   0.003491   2.476 0.014883 *  
## all_75$NA_R_Mean 0.286065   0.077876   3.673 0.000379 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.57 on 105 degrees of freedom
## Multiple R-squared:  0.1688, Adjusted R-squared:  0.1529 
## F-statistic: 10.66 on 2 and 105 DF,  p-value: 6.106e-05
confint(m.NA_all, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)      0.452342688 13.70122103
## all_75$NA_R_MSSD 0.001721725  0.01556488
## all_75$NA_R_Mean 0.131650761  0.44047867
m.PA_all <- lm(all_75$ptq_total ~ all_75$PA_R_MSSD + all_75$PA_R_Mean)
summary(m.PA_all)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PA_R_MSSD + all_75$PA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.589  -8.291  -1.117   6.287  31.863 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      27.002447   6.350049   4.252  4.6e-05 ***
## all_75$PA_R_MSSD  0.008238   0.004028   2.045   0.0433 *  
## all_75$PA_R_Mean -0.152773   0.109388  -1.397   0.1655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.28 on 105 degrees of freedom
## Multiple R-squared:  0.05348,    Adjusted R-squared:  0.03545 
## F-statistic: 2.966 on 2 and 105 DF,  p-value: 0.05583
confint(m.PA_all, level=0.95)
##                         2.5 %      97.5 %
## (Intercept)      14.411472867 39.59342126
## all_75$PA_R_MSSD  0.000250959  0.01622521
## all_75$PA_R_Mean -0.369669254  0.06412355
m_all <-lm(all_75$ptq_total ~ all_75$PA_R_MSSD + all_75$PA_R_Mean + all_75$NA_R_MSSD + all_75$NA_R_Mean)
summary(m_all)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PA_R_MSSD + all_75$PA_R_Mean + 
##     all_75$NA_R_MSSD + all_75$NA_R_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.889  -7.219  -1.657   4.732  31.505 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       4.1748667  8.8743624   0.470  0.63903   
## all_75$PA_R_MSSD -0.0009589  0.0065291  -0.147  0.88353   
## all_75$PA_R_Mean  0.0429910  0.1187514   0.362  0.71807   
## all_75$NA_R_MSSD  0.0092613  0.0060369   1.534  0.12807   
## all_75$NA_R_Mean  0.3018110  0.0902477   3.344  0.00115 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.66 on 103 degrees of freedom
## Multiple R-squared:   0.17,  Adjusted R-squared:  0.1377 
## F-statistic: 5.273 on 4 and 103 DF,  p-value: 0.0006643
confint(m_all, level=0.95)
##                          2.5 %      97.5 %
## (Intercept)      -13.425338093 21.77507156
## all_75$PA_R_MSSD  -0.013907867  0.01199016
## all_75$PA_R_Mean  -0.192524362  0.27850642
## all_75$NA_R_MSSD  -0.002711561  0.02123410
## all_75$NA_R_Mean   0.122826096  0.48079600

Aim 4 Analyses

Correlation matrix of the individual item means

#### Correlation matrix of the individual item mssd #### Correlation matrix of the individual item means and mssd

PCA for item means

means.pca <- prcomp(na.omit(indiv_means),
                    center = TRUE,
                    scale = TRUE)

print(means.pca)
## Standard deviations (1, .., p=10):
##  [1] 2.4071722 1.5168778 0.8620196 0.5914544 0.5543271 0.4399505 0.3719951
##  [8] 0.2758663 0.2325851 0.2056562
## 
## Rotation (n x k) = (10 x 10):
##                       PC1        PC2          PC3        PC4         PC5
## anxious_mean    0.3429960 -0.2910562  0.240679246 -0.2957904  0.22132551
## nervous_mean    0.3311355 -0.3276556  0.191594521 -0.1375269  0.16224056
## upset_mean      0.3340716 -0.3133791 -0.075090620  0.4432986 -0.14192391
## sluggish_mean   0.3262648 -0.1648100 -0.535448571 -0.1938690  0.04302294
## irritable_mean  0.3520405 -0.2634761 -0.195713445  0.2310694  0.10350021
## content_mean   -0.3366075 -0.3125597 -0.142830965 -0.2657013  0.37160499
## relaxed_mean   -0.3222487 -0.1702536 -0.533836511  0.4287283  0.12644630
## excited_mean   -0.2006664 -0.4890027 -0.002717079 -0.2189678 -0.80216521
## happy_mean     -0.3383382 -0.3256250 -0.078239677 -0.2923411  0.25659768
## attentive_mean -0.2425476 -0.3759049  0.513036626  0.4672121  0.16910657
##                         PC6          PC7         PC8         PC9
## anxious_mean   -0.148622564  0.014483331 -0.40908920  0.11156735
## nervous_mean   -0.401652479  0.468608134  0.09748159 -0.11402628
## upset_mean     -0.222532322 -0.201839705  0.60266535  0.21047994
## sluggish_mean   0.613034779  0.376661220  0.12920402  0.05361063
## irritable_mean  0.133651452 -0.583877720 -0.42099916 -0.20127172
## content_mean   -0.049246701 -0.202393608  0.34482876 -0.60937056
## relaxed_mean   -0.359314721  0.333756080 -0.35192092  0.03423183
## excited_mean   -0.005712282  0.005456563 -0.13169981 -0.10844133
## happy_mean      0.008862621 -0.242733863  0.06351919  0.70845383
## attentive_mean  0.491678912  0.212989958 -0.04323349 -0.02634147
##                        PC10
## anxious_mean   -0.633353649
## nervous_mean    0.545549316
## upset_mean     -0.264816725
## sluggish_mean  -0.054722089
## irritable_mean  0.357368402
## content_mean   -0.162129362
## relaxed_mean   -0.129888876
## excited_mean    0.004390894
## happy_mean      0.239016084
## attentive_mean -0.009975005
biplot(means.pca, scale = 0)

screeplot(means.pca)

#### PCA for item mssd

mssd.pca <- prcomp(na.omit(indiv_mssd),
                    center = TRUE,
                    scale. = TRUE)
print(mssd.pca)
## Standard deviations (1, .., p=10):
##  [1] 2.4839896 1.2608644 0.7396627 0.6492347 0.5889651 0.5135119 0.4779734
##  [8] 0.4330519 0.3926851 0.3010669
## 
## Rotation (n x k) = (10 x 10):
##                      PC1         PC2         PC3        PC4          PC5
## anxious_mssd   0.3553322 -0.03824830  0.02799643 -0.3032687  0.436098725
## nervous_mssd   0.1751495 -0.65338615 -0.23728026 -0.1844803  0.025252375
## upset_mssd     0.3214444  0.21844987 -0.37906270  0.5126974 -0.000852351
## sluggish_mssd  0.1726716 -0.66095029 -0.06796161  0.1926518 -0.078399921
## irritable_mssd 0.3421569  0.03298689  0.04325102  0.5748360  0.379397794
## content_mssd   0.3634925  0.09795696 -0.24049882 -0.1236704 -0.437929998
## relaxed_mssd   0.3421517  0.14616308  0.09403607 -0.3752331  0.483523750
## excited_mssd   0.3570387  0.10416539  0.11537631 -0.1408225 -0.251644100
## happy_mssd     0.3553763  0.18712365 -0.22397123 -0.2131638 -0.311809880
## attentive_mssd 0.2998080 -0.09557685  0.81398959  0.1490682 -0.270115169
##                        PC6          PC7         PC8        PC9        PC10
## anxious_mssd   -0.38922695  0.273558110 -0.11548638  0.5911431  0.01481138
## nervous_mssd   -0.28194478  0.006369209  0.50482927 -0.3382721 -0.05121090
## upset_mssd     -0.32395213 -0.516513326  0.08616220  0.1501320 -0.20176903
## sluggish_mssd   0.38878587 -0.161820158 -0.49251167  0.2494333 -0.05765658
## irritable_mssd  0.23202776  0.500022541  0.10601116 -0.2525698  0.16340584
## content_mssd   -0.15000595  0.063565543 -0.23852953 -0.1335002  0.70149833
## relaxed_mssd    0.24846610 -0.480032848 -0.16432781 -0.3971219  0.03541662
## excited_mssd    0.51368888 -0.087996707  0.58144703  0.3913975  0.04809571
## happy_mssd      0.07981097  0.359743557 -0.21503579 -0.2099302 -0.64789567
## attentive_mssd -0.32340371 -0.102470054 -0.04857218 -0.1215853 -0.10481096
biplot(mssd.pca, scale = 0)

screeplot(mssd.pca)

Correlation map of the 4 circumplex groups’ means and mssd

circum_group<- all_75[c("NAD_Mean", "NAA_Mean", "PAA_Mean", "PAD_Mean", "NAD_MSSD", "NAA_MSSD",
                      "PAA_MSSD", "PAD_MSSD")]

circum_group <- data.frame(circum_group)
View(indiv_means)

circum_group_cor <- cor(circum_group, y= NULL, use="complete.obs", method = "pearson")
corrplot(circum_group_cor, type = "upper", order = "hclust", 
         tl.col = "black")

View(circum_group_cor)

Visualizing the distribution of the circumplex groups

Relationship between emotion group means and PTQ

ptq_paa <- lm(all_75$ptq_total ~ all_75$PAA_Mean)
summary(ptq_paa)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PAA_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.915  -7.750  -1.153   6.135  34.244 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      28.6727     5.9212   4.842 4.39e-06 ***
## all_75$PAA_Mean  -0.1298     0.1064  -1.220    0.225    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.45 on 106 degrees of freedom
## Multiple R-squared:  0.01385,    Adjusted R-squared:  0.004548 
## F-statistic: 1.489 on 1 and 106 DF,  p-value: 0.2251
confint(ptq_paa, level=0.95)
##                      2.5 %    97.5 %
## (Intercept)     16.9333365 40.412090
## all_75$PAA_Mean -0.3408219  0.081133
ptq_pad <- lm(all_75$ptq_total ~ all_75$PAD_Mean)
summary(ptq_pad)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.643  -7.635  -1.801   6.049  34.725 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     28.55653    5.88372   4.853  4.2e-06 ***
## all_75$PAD_Mean -0.12051    0.09975  -1.208     0.23    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.46 on 106 degrees of freedom
## Multiple R-squared:  0.01358,    Adjusted R-squared:  0.004277 
## F-statistic:  1.46 on 1 and 106 DF,  p-value: 0.2297
confint(ptq_pad, level=0.95)
##                      2.5 %      97.5 %
## (Intercept)     16.8914826 40.22157205
## all_75$PAD_Mean -0.3182728  0.07725204
ptq_naa <- lm(all_75$ptq_total ~ all_75$NAA_Mean)
summary(ptq_naa)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAA_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.911  -7.472  -1.930   4.933  31.487 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       9.9051     3.0361   3.262  0.00149 ** 
## all_75$NAA_Mean   0.3159     0.0773   4.087 8.54e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.72 on 106 degrees of freedom
## Multiple R-squared:  0.1361, Adjusted R-squared:  0.128 
## F-statistic:  16.7 on 1 and 106 DF,  p-value: 8.536e-05
confint(ptq_naa, level=0.95)
##                     2.5 %     97.5 %
## (Intercept)     3.8856817 15.9244731
## all_75$NAA_Mean 0.1626369  0.4691533
ptq_nad <- lm(all_75$ptq_total ~ all_75$NAD_Mean)
summary(ptq_nad)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.791  -8.136  -1.644   8.035  30.524 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     13.63516    4.44345   3.069  0.00321 **
## all_75$NAD_Mean  0.15043    0.09912   1.518  0.13427   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.89 on 61 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.03638,    Adjusted R-squared:  0.02059 
## F-statistic: 2.303 on 1 and 61 DF,  p-value: 0.1343
confint(ptq_nad, level=0.95)
##                       2.5 %     97.5 %
## (Intercept)      4.74992179 22.5203948
## all_75$NAD_Mean -0.04777227  0.3486301

Relationship between emotion group MSSD and PTQ

ptq_paa_mssd <- lm(all_75$ptq_total ~ all_75$PAA_MSSD)
summary(ptq_paa_mssd)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PAA_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.010  -8.226  -0.929   7.017  33.043 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     18.557596   1.905125   9.741   <2e-16 ***
## all_75$PAA_MSSD  0.007325   0.003793   1.931   0.0561 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.34 on 106 degrees of freedom
## Multiple R-squared:  0.03399,    Adjusted R-squared:  0.02488 
## F-statistic:  3.73 on 1 and 106 DF,  p-value: 0.05612
confint(ptq_paa_mssd, level=0.95)
##                         2.5 %      97.5 %
## (Intercept)     14.7805003831 22.33469122
## all_75$PAA_MSSD -0.0001945665  0.01484428
ptq_pad_mssd <- lm(all_75$ptq_total ~ all_75$PAD_MSSD)
summary(ptq_pad_mssd)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$PAD_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.215  -8.315  -1.127   6.368  32.108 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     18.711230   1.961445   9.540  6.1e-16 ***
## all_75$PAD_MSSD  0.004843   0.002754   1.759   0.0815 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.37 on 106 degrees of freedom
## Multiple R-squared:  0.02835,    Adjusted R-squared:  0.01918 
## F-statistic: 3.092 on 1 and 106 DF,  p-value: 0.08154
confint(ptq_pad_mssd, level=0.95)
##                         2.5 %      97.5 %
## (Intercept)     14.8224733896 22.59998588
## all_75$PAD_MSSD -0.0006171291  0.01030361
ptq_naa_mssd <- lm(all_75$ptq_total ~ all_75$NAA_MSSD)
summary(ptq_naa_mssd)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAA_MSSD)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.4931  -8.0571  -0.9548   6.3283  30.7711 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     18.080073   2.004812   9.018 9.06e-15 ***
## all_75$NAA_MSSD  0.007406   0.003569   2.075   0.0404 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.31 on 106 degrees of freedom
## Multiple R-squared:  0.03903,    Adjusted R-squared:  0.02997 
## F-statistic: 4.305 on 1 and 106 DF,  p-value: 0.04041
confint(ptq_naa_mssd, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)     1.410534e+01 22.05480772
## all_75$NAA_MSSD 3.296686e-04  0.01448295
ptq_nad_mssd <- lm(all_75$ptq_total ~ all_75$NAD_MSSD)
summary(ptq_nad_mssd)
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAD_MSSD)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.3588  -7.7981  -0.7063   6.9006  28.5986 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     15.911876   2.732602   5.823 2.31e-07 ***
## all_75$NAD_MSSD  0.004391   0.002472   1.777   0.0806 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.81 on 61 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.0492, Adjusted R-squared:  0.03361 
## F-statistic: 3.157 on 1 and 61 DF,  p-value: 0.08061
confint(ptq_nad_mssd, level=0.95)
##                         2.5 %       97.5 %
## (Intercept)     10.4477000082 21.376051548
## all_75$NAD_MSSD -0.0005510315  0.009333487

Relationship between the four groups together (as mean and MSSD) as they relate to PTQ

ptq_circumplex_mean <- lm(all_75$ptq_total ~ all_75$NAD_Mean + all_75$NAA_Mean + all_75$PAA_Mean + all_75$PAD_Mean)
summary(ptq_circumplex_mean )
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAD_Mean + all_75$NAA_Mean + 
##     all_75$PAA_Mean + all_75$PAD_Mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.906  -7.095  -1.318   4.742  32.026 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -5.2325    11.8546  -0.441  0.66057   
## all_75$NAD_Mean  -0.1942     0.1601  -1.213  0.23008   
## all_75$NAA_Mean   0.6163     0.1965   3.136  0.00269 **
## all_75$PAA_Mean  -0.1312     0.2557  -0.513  0.60984   
## all_75$PAD_Mean   0.3274     0.2524   1.297  0.19962   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.13 on 58 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.1972, Adjusted R-squared:  0.1418 
## F-statistic: 3.562 on 4 and 58 DF,  p-value: 0.01151
confint(ptq_circumplex_mean, level=0.95)
##                       2.5 %     97.5 %
## (Intercept)     -28.9620606 18.4969993
## all_75$NAD_Mean  -0.5147861  0.1263177
## all_75$NAA_Mean   0.2228876  1.0096748
## all_75$PAA_Mean  -0.6431120  0.3806882
## all_75$PAD_Mean  -0.1777427  0.8325954
ptq_circumplex_mssd <- lm(all_75$ptq_total ~ all_75$NAD_MSSD + all_75$NAA_MSSD + all_75$PAA_MSSD + all_75$PAD_MSSD)
summary(ptq_circumplex_mssd )
## 
## Call:
## lm(formula = all_75$ptq_total ~ all_75$NAD_MSSD + all_75$NAA_MSSD + 
##     all_75$PAA_MSSD + all_75$PAD_MSSD)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.746  -7.742  -1.016   7.081  27.992 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     16.047538   3.245876   4.944 6.88e-06 ***
## all_75$NAD_MSSD  0.003111   0.003628   0.857    0.395    
## all_75$NAA_MSSD -0.000930   0.009831  -0.095    0.925    
## all_75$PAA_MSSD  0.007423   0.010866   0.683    0.497    
## all_75$PAD_MSSD -0.002669   0.007138  -0.374    0.710    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.06 on 58 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.05691,    Adjusted R-squared:  -0.008126 
## F-statistic: 0.8751 on 4 and 58 DF,  p-value: 0.4845
confint(ptq_circumplex_mssd, level=0.95)
##                        2.5 %      97.5 %
## (Intercept)      9.550211147 22.54486384
## all_75$NAD_MSSD -0.004151555  0.01037273
## all_75$NAA_MSSD -0.020608830  0.01874889
## all_75$PAA_MSSD -0.014327983  0.02917468
## all_75$PAD_MSSD -0.016956952  0.01161928