HealthyU_10072018

Examine PTQ and hurricane related stress data

Plot of PTQ over semester

PTQvals <- NA
PTQvals[1] <- mean(rntadditional$PTQ_tot_0, na.rm=T)
PTQvals[2] <- mean(rntadditional$PTQ_tot_1, na.rm=T)
PTQvals[3] <- mean(rntadditional$PTQ_tot_2, na.rm=T)
PTQvals[4] <- mean(rntadditional$PTQ_tot_3, na.rm=T)
plot(PTQvals)

Plot of PTQ over semester

PTQ0 vs PTQ1

t.test(cortdata$PTQ1, cortdata$PTQ0, paired=T)

    Paired t-test

data:  cortdata$PTQ1 and cortdata$PTQ0
t = 0.52735, df = 87, p-value = 0.5993
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.006937  1.734210
sample estimates:
mean of the differences 
              0.3636364 

PTQ high at both time 0 (beginning of school stress) and time 1 (hurricane stress)

PTQ1 vs PTQ3

t.test(cortdata$PTQ3, cortdata$PTQ1, paired=T)

    Paired t-test

data:  cortdata$PTQ3 and cortdata$PTQ1
t = -3.0768, df = 63, p-value = 0.003095
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -5.541272 -1.177478
sample estimates:
mean of the differences 
              -3.359375 

PTQ significantly lower at PTQ3 (“true” baseline) compared to PTQ1

Associations of PTQ with Hurricane stress

##Hurricane-related impairment in relation to PTQ0 and PTQ1; PTQ2, PTQ3
hurte0 <- lm(scale(currentImpairment) ~ scale(PTQ0), data=cortdata)
summary(hurte0)

Call:
lm(formula = scale(currentImpairment) ~ scale(PTQ0), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0727 -0.7195 -0.4918  0.6836  3.1223 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   0.0154     0.1065   0.145    0.885
scale(PTQ0)   0.1616     0.1071   1.509    0.135

Residual standard error: 0.9988 on 86 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.02579,   Adjusted R-squared:  0.01446 
F-statistic: 2.277 on 1 and 86 DF,  p-value: 0.135

hurte1 <- lm(scale(currentImpairment) ~ scale(PTQ1), data=cortdata)
summary(hurte1)

Call:
lm(formula = scale(currentImpairment) ~ scale(PTQ1), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2095 -0.6612 -0.3556  0.6255  3.2431 

Coefficients:
                           Estimate              Std. Error t value Pr(>|t|)  
(Intercept) -0.00000000000000009393  0.10245989413103204213   0.000   1.0000  
scale(PTQ1)  0.25650203472239918590  0.10303390363146079645   2.489   0.0147 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.972 on 88 degrees of freedom
Multiple R-squared:  0.06579,   Adjusted R-squared:  0.05518 
F-statistic: 6.198 on 1 and 88 DF,  p-value: 0.01467

hurte2 <- lm(scale(currentImpairment) ~ scale(PTQ2), data=cortdata)
summary(hurte2)

Call:
lm(formula = scale(currentImpairment) ~ scale(PTQ2), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1397 -0.6880 -0.4370  0.7487  3.1743 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) 0.001461   0.106850   0.014   0.9891  
scale(PTQ2) 0.201946   0.107469   1.879   0.0637 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9966 on 85 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.03988,   Adjusted R-squared:  0.02859 
F-statistic: 3.531 on 1 and 85 DF,  p-value: 0.06366

hurte3 <- lm(scale(currentImpairment) ~ scale(PTQ3), data=cortdata)
summary(hurte3)

Call:
lm(formula = scale(currentImpairment) ~ scale(PTQ3), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.8706 -0.5541 -0.4031  0.5494  2.2736 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.1193     0.1047  -1.139    0.259
scale(PTQ3)   0.1643     0.1055   1.557    0.125

Residual standard error: 0.8377 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.03763,   Adjusted R-squared:  0.02211 
F-statistic: 2.424 on 1 and 62 DF,  p-value: 0.1245

##Change score from pretesting to Time 1
cortdata$PTQpre1change <- cortdata$PTQ1 - cortdata$PTQ0
##Change score from Time 1 to Time 3
cortdata$PTQ31change <- cortdata$PTQ3 - cortdata$PTQ1
##Does hurricane-related impairment predict change in PTQ from pretesting to Time 1?
hurtePTQchange <- lm(scale(PTQpre1change) ~ scale(currentImpairment), data=cortdata)
summary(hurtePTQchange)

Call:
lm(formula = scale(PTQpre1change) ~ scale(currentImpairment), 
    data = cortdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.37288 -0.52133 -0.05399  0.62387  2.46626 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.003485   0.104416  -0.033   0.9735  
scale(currentImpairment)  0.226244   0.104368   2.168   0.0329 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9794 on 86 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.05181,   Adjusted R-squared:  0.04078 
F-statistic: 4.699 on 1 and 86 DF,  p-value: 0.03294

##Does hurricane-related impairment predict change in PTQ from Time 1 to Time 3?
hurtePTQ31change <- lm(scale(PTQ31change) ~ scale(currentImpairment), data=cortdata)
summary(hurtePTQ31change)

Call:
lm(formula = scale(PTQ31change) ~ scale(currentImpairment), data = cortdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.39157 -0.49088 -0.03294  0.44776  2.60019 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)
(Intercept)               0.00863    0.12703   0.068    0.946
scale(currentImpairment)  0.07234    0.14964   0.483    0.630

Residual standard error: 1.006 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.003756,  Adjusted R-squared:  -0.01231 
F-statistic: 0.2337 on 1 and 62 DF,  p-value: 0.6305

##Is hurricane-related impairment associated with depression at Time 1 and Time 3?
hurte1PHQ9 <- lm(scale(currentImpairment) ~ scale(PHQ91), data=cortdata)
summary(hurte1PHQ9)

Call:
lm(formula = scale(currentImpairment) ~ scale(PHQ91), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2918 -0.5821 -0.4490  0.6117  3.2744 

Coefficients:
                           Estimate             Std. Error t value Pr(>|t|)  
(Intercept)  -0.0000000000000001107  0.1027585453475831773   0.000   1.0000  
scale(PHQ91)  0.2456405603641604041  0.1033342279771623101   2.377   0.0196 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9749 on 88 degrees of freedom
Multiple R-squared:  0.06034,   Adjusted R-squared:  0.04966 
F-statistic: 5.651 on 1 and 88 DF,  p-value: 0.01961

hurte3PHQ9 <- lm(scale(currentImpairment) ~ scale(PHQ93), data=cortdata)
summary(hurte3PHQ9)

Call:
lm(formula = scale(currentImpairment) ~ scale(PHQ93), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0788 -0.5338 -0.4028  0.5916  2.4701 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)   -0.1193     0.1045  -1.141    0.258
scale(PHQ93)   0.1712     0.1054   1.625    0.109

Residual standard error: 0.8363 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.04084,   Adjusted R-squared:  0.02537 
F-statistic:  2.64 on 1 and 62 DF,  p-value: 0.1093

Hurricane-related impairment predicts PTQ1 but not PTQ0 or PTQ3. Trending association with PTQ2 (beginning of “return to baseline”). In addition, hurricane-related impairment associated with greater increases in PTQ from Time 0 to Time 1. Finally, hurricane-related impairment is associated with depressive symptoms on the PHQ9 at Time 1 but not at Time 3.

Cortisol data

#colnames(cortdata)
colnames(cortdata)[14:19] <- c("Cort0","Cort1","Cort2","Cort3","Cort4","Cort5")
#Create change scores
cortdata$RRS_change <- cortdata$RRS1 - cortdata$RRS3
cortdata$PSWQ_change <- cortdata$PSWQ1 - cortdata$PSWQ3
cortdata$PEPQ_change <- cortdata$PEPQ1 - cortdata$PEPQ3
#write.csv(cortdata, "cortdata.csv", row.names=F)

Consolidate cortisol information:

Plot cortisol data

plot(cortvalues, xlab = "Time point", ylab = "Average Cortisol (nmol/L)", xaxt = 'n', cex.lab = 0.75, cex.axis=0.75)
axis(1, at=1:3, labels=c("Baseline","Stress","Recovery"), cex.axis=0.75)

consolidate PANAS information

colnames(cortdata)

  [1] "ID"                           "SES"                          "Age"                         
  [4] "Gender"                       "Ethnicity"                    "PTQ1"                        
  [7] "PTQ3"                         "Time0"                        "Time1"                       
 [10] "Time2"                        "Time3"                        "Time4"                       
 [13] "Time5"                        "Cort0"                        "Cort1"                       
 [16] "Cort2"                        "Cort3"                        "Cort4"                       
 [19] "Cort5"                        "cortresponder"                "CurrentMed_notBC_t3"         
 [22] "CurrentMed_BC_t3"             "Smoker_0no_1yes_2yestoday_t3" "TodayCaff_NumCups_t3"        
 [25] "CurrentMenstr_t3"             "altresponder"                 "PTQ_change"                  
 [28] "PTQ2"                         "PSWQ1"                        "RRS1"                        
 [31] "PEPQ1"                        "PSWQ2"                        "RRS2"                        
 [34] "PEPQ2"                        "PSWQ3"                        "RRS3"                        
 [37] "PEPQ3"                        "PTQ0"                         "PHQ91"                       
 [40] "IES1"                         "GAD71"                        "PTSD3"                       
 [43] "PHQ93"                        "GAD73"                        "pan_p1"                      
 [46] "pan_p2"                       "pan_p3"                       "pan_p4"                      
 [49] "pan_p5"                       "pan_p6"                       "pan_p7"                      
 [52] "pan_n1"                       "pan_n2"                       "pan_n3"                      
 [55] "pan_n4"                       "pan_n5"                       "pan_n6"                      
 [58] "pan_n7"                       "HURTE_before12"               "HURTE_before13"              
 [61] "HURTE_before14"               "HURTE_before15"               "HURTE_before16"              
 [64] "HURTE_before17"               "HURTE_before18"               "HURTE_during_left1"          
 [67] "HURTE_during_left2"           "HURTE_during_left3"           "HURTE_during_left4"          
 [70] "HURTE_during_left5"           "HURTE_during_left6"           "HURTE_during_left7"          
 [73] "HURTE_during_left8"           "HURTE_during_stayed14"        "HURTE_during_stayed15"       
 [76] "HURTE_during_stayed16"        "HURTE_during_stayed17"        "HURTE_during_stayed18"       
 [79] "HURTE_during_stayed19"        "HURTE_during_stayed20"        "HURTE_during_stayed21"       
 [82] "HURTE_after16_1"              "HURTE_after16_2"              "HURTE_after16_3"             
 [85] "HURTE_after16_4"              "HURTE_after16_5"              "HURTE_after16_6"             
 [88] "HURTE_curr_1"                 "HURTE_curr_2"                 "HURTE_curr_3"                
 [91] "HURTE_curr_4"                 "beforeDistress"               "duringLeftDistress"          
 [94] "duringStayedDistress"         "afterDistress"                "currentImpairment"           
 [97] "PTQpre1change"                "PTQ31change"                  "RRS_change"                  
[100] "PSWQ_change"                  "PEPQ_change"                  "cortBL"                      
[103] "cortStress"                   "cortRec"                     

panasdata <- cortdata[c(1:7,27:58,96:101)]
#colnames(panasdata)
colnames(panasdata)[c(19:39)] <- sub("^[[:alnum:]]+_","",(colnames(panasdata)[c(19:39)]))
#colnames(panasdata)
#PA
#Baseline: select higher value between p1 and p2, the 2 baseline measures of positive affect
panasdata$paBL <- ifelse(panasdata$p1 > panasdata$p2, panasdata$p1, panasdata$p2)
#Stress: select lower value between p3 and p4, the 2 stress measures
panasdata$paStress <- ifelse(panasdata$p3 < panasdata$p4, panasdata$p3, panasdata$p4)
#Recovery: select higher value between p5 and p6, the 2 recovery measures. Then if p7 is highest, select that one instead.
panasdata$paRec <- ifelse(panasdata$p5 > panasdata$p6, panasdata$p5, panasdata$p6)
#panasdata$paRec <- ifelse(panasdata$p7 > panasdata$paRec0, panasdata$p7, panasdata$paRec0)
#NA
#Baseline: select lower value between n1 and n2, the 2 baseline measures of negative affect
panasdata$naBL <- ifelse(panasdata$n1 < panasdata$n2, panasdata$n1, panasdata$n2)
#Stress: select higher value between n3 and n4, the 2 stress measures
panasdata$naStress <- ifelse(panasdata$n3 > panasdata$n4, panasdata$n3, panasdata$n4)
#Recovery: select lower value between n5 and n6, the 2 recovery measures. Then if n7 is lowest, select that one instead.
panasdata$naRec <- ifelse(panasdata$n5 < panasdata$n6, panasdata$n5, panasdata$n6)
#panasdata$naRec <- ifelse(panasdata$n7 < panasdata$naRec0, panasdata$n7, panasdata$naRec0)

Plot PANAS data

plot(panas_n, xlab = "Time point", ylab = "Average Negative Affect", xaxt = 'n', cex.lab = 0.75, cex.axis=0.75)
axis(1, at=1:3, labels=c("Baseline","Stress","Recovery"), cex.axis=0.75)

plot(panas_p, xlab = "Time point", ylab = "Average Positive Affect", xaxt = 'n', cex.lab = 0.75, cex.axis=0.75)
axis(1, at=1:3, labels=c("Baseline","Stress","Recovery"), cex.axis=0.75)

naplot <- ggplot(data = panas.db, aes(x = time, y = na, group = ID))
naplot + geom_line()

paplot <- ggplot(data = panas.db, aes(x = time, y = pa, group = ID))
paplot + geom_line()

Repeated measures ANOVAs

Test effects of time on cortisol, NA, PA

timeCortModel <- lme(scale(cort) ~ time + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), random = ~1|ID/time, data=cort.db2, na.action = na.omit)
summary(timeCortModel)

Linear mixed-effects model fit by REML
 Data: cort.db2 
       AIC      BIC    logLik
  413.5871 442.5216 -197.7936

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7374085

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4169686 0.2096236

Fixed effects: scale(cort) ~ time + scale(Smoker_0no_1yes_2yestoday_t3) + Gender +      scale(TodayCaff_NumCups_t3) 
                                         Value  Std.Error  DF   t-value p-value
(Intercept)                          0.0386319 0.12412479 124  0.311234  0.7561
time1                                0.2183737 0.08315312 124  2.626163  0.0097
time2                               -0.3424861 0.08306123 124 -4.123297  0.0001
scale(Smoker_0no_1yes_2yestoday_t3)  0.4136064 0.10192969  60  4.057762  0.0001
Gender                               0.0617730 0.23290594  60  0.265227  0.7917
scale(TodayCaff_NumCups_t3)          0.1653129 0.10329758  60  1.600356  0.1148
 Correlation: 
                                    (Intr) time1  time2  s(S_0_ Gender
time1                               -0.335                            
time2                               -0.341  0.501                     
scale(Smoker_0no_1yes_2yestoday_t3)  0.062  0.000  0.000              
Gender                              -0.471  0.000  0.004 -0.131       
scale(TodayCaff_NumCups_t3)         -0.097  0.000  0.004 -0.228  0.212

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.97620285 -0.26764819 -0.04103578  0.21320734  1.12727444 

Number of Observations: 190
Number of Groups: 
          ID time %in% ID 
          64          190 

summary(glht(timeCortModel,linfct=mcp(time="Tukey")))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lme.formula(fixed = scale(cort) ~ time + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3), data = cort.db2, random = ~1 | 
    ID/time, na.action = na.omit)

Linear Hypotheses:
           Estimate Std. Error z value Pr(>|z|)    
1 - 0 == 0  0.21837    0.08315   2.626   0.0235 *  
2 - 0 == 0 -0.34249    0.08306  -4.123   <0.001 ***
2 - 1 == 0 -0.56086    0.08306  -6.752   <0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

timeNAModel <- lme(scale(na) ~ time, random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(timeNAModel)

Linear mixed-effects model fit by REML
 Data: panas.db2 
      AIC      BIC    logLik
  418.303 437.7216 -203.1515

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7823731

 Formula: ~1 | time %in% ID
        (Intercept) Residual
StdDev:   0.4250258 0.216859

Fixed effects: scale(na) ~ time 
                 Value  Std.Error  DF   t-value p-value
(Intercept) -0.1572686 0.11454955 125 -1.372931  0.1722
time1        0.7141996 0.08482399 125  8.419784  0.0000
time2       -0.2414496 0.08434949 125 -2.862491  0.0049
 Correlation: 
      (Intr) time1 
time1 -0.366       
time2 -0.368  0.497

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-1.35905547 -0.21694541 -0.02282059  0.23362659  1.18507915 

Number of Observations: 191
Number of Groups: 
          ID time %in% ID 
          64          191 

summary(glht(timeNAModel,linfct=mcp(time="Tukey")))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lme.formula(fixed = scale(na) ~ time, data = panas.db2, random = ~1 | 
    ID/time, na.action = na.omit)

Linear Hypotheses:
           Estimate Std. Error z value Pr(>|z|)    
1 - 0 == 0  0.71420    0.08482   8.420   <0.001 ***
2 - 0 == 0 -0.24145    0.08435  -2.862   0.0118 *  
2 - 1 == 0 -0.95565    0.08482 -11.266   <0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

timePAModel <- lme(scale(pa) ~ time, random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(timePAModel)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC      BIC    logLik
  438.1242 457.4138 -213.0621

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7473613

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:    0.486039 0.2339577

Fixed effects: scale(pa) ~ time 
                 Value  Std.Error  DF    t-value p-value
(Intercept)  0.5867623 0.11703474 121   5.013574       0
time1       -1.0651259 0.09755107 121 -10.918649       0
time2       -0.6516620 0.09809358 121  -6.643268       0
 Correlation: 
      (Intr) time1 
time1 -0.435       
time2 -0.433  0.520

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.89992664 -0.23461635 -0.04203701  0.20186024  1.32698968 

Number of Observations: 187
Number of Groups: 
          ID time %in% ID 
          64          187 

summary(glht(timePAModel,linfct=mcp(time="Tukey")))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lme.formula(fixed = scale(pa) ~ time, data = panas.db2, random = ~1 | 
    ID/time, na.action = na.omit)

Linear Hypotheses:
           Estimate Std. Error z value  Pr(>|z|)    
1 - 0 == 0 -1.06513    0.09755 -10.919 < 0.00001 ***
2 - 0 == 0 -0.65166    0.09809  -6.643 < 0.00001 ***
2 - 1 == 0  0.41346    0.09588   4.312  0.000043 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

Test effects of PTQ on cortisol, NA, PA overall

#PTQ at Time 1 - cort
PTQ1model <- lme(scale(cort) ~ time + scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), random = ~1|ID/time, data=cort.db2, na.action = na.omit)
summary(PTQ1model)

Linear mixed-effects model fit by REML
 Data: cort.db2 
       AIC      BIC    logLik
  412.9798 445.0747 -196.4899

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7063202

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4182942 0.2072122

Fixed effects: scale(cort) ~ time + scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) +      Gender + scale(TodayCaff_NumCups_t3) 
                                         Value  Std.Error  DF   t-value p-value
(Intercept)                          0.0500602 0.12035649 124  0.415933  0.6782
time1                                0.2183737 0.08317259 124  2.625549  0.0097
time2                               -0.3420816 0.08307487 124 -4.117750  0.0001
scale(PTQ1)                          0.2274169 0.09582278  59  2.373307  0.0209
scale(Smoker_0no_1yes_2yestoday_t3)  0.4159225 0.09815613  59  4.237356  0.0001
Gender                               0.0147224 0.22517454  59  0.065382  0.9481
scale(TodayCaff_NumCups_t3)          0.1464767 0.09979631  59  1.467756  0.1475
 Correlation: 
                                    (Intr) time1  time2  s(PTQ1 s(S_0_ Gender
time1                               -0.346                                   
time2                               -0.351  0.501                            
scale(PTQ1)                          0.042  0.000  0.000                     
scale(Smoker_0no_1yes_2yestoday_t3)  0.062  0.000  0.000  0.010              
Gender                              -0.470  0.000  0.004 -0.089 -0.131       
scale(TodayCaff_NumCups_t3)         -0.100  0.000  0.004 -0.080 -0.228  0.218

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.94349380 -0.25477933 -0.04640205  0.20268351  1.12043504 

Number of Observations: 190
Number of Groups: 
          ID time %in% ID 
          64          190 

#Look at PTQ at Time 0 - cort
PTQ0model <- lme(scale(cort) ~ time + scale(PTQ0) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), random = ~1|ID/time, data=cort.db2, na.action = na.omit)
summary(PTQ0model)

Linear mixed-effects model fit by REML
 Data: cort.db2 
       AIC      BIC  logLik
  410.2401 442.2801 -195.12

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7085451

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4172721 0.2072473

Fixed effects: scale(cort) ~ time + scale(PTQ0) + scale(Smoker_0no_1yes_2yestoday_t3) +      Gender + scale(TodayCaff_NumCups_t3) 
                                         Value  Std.Error  DF   t-value p-value
(Intercept)                          0.0189250 0.12122376 124  0.156116  0.8762
time1                                0.2183425 0.08301222 124  2.630245  0.0096
time2                               -0.3492506 0.08301222 124 -4.207219  0.0000
scale(PTQ0)                          0.1812139 0.09661547  58  1.875620  0.0657
scale(Smoker_0no_1yes_2yestoday_t3)  0.4140830 0.09864664  58  4.197639  0.0001
Gender                               0.0972997 0.22553516  58  0.431417  0.6678
scale(TodayCaff_NumCups_t3)          0.1564330 0.10097474  58  1.549229  0.1268
 Correlation: 
                                    (Intr) time1  time2  s(PTQ0 s(S_0_ Gender
time1                               -0.342                                   
time2                               -0.342  0.500                            
scale(PTQ0)                         -0.004  0.000  0.000                     
scale(Smoker_0no_1yes_2yestoday_t3)  0.061  0.000  0.000 -0.012              
Gender                              -0.473  0.000  0.000  0.009 -0.130       
scale(TodayCaff_NumCups_t3)         -0.101  0.000  0.000 -0.124 -0.223  0.214

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.94100321 -0.26053106 -0.05318282  0.20600903  1.12407133 

Number of Observations: 189
Number of Groups: 
          ID time %in% ID 
          63          189 

#Look at PTQ at Time 3 - cort
PTQ3model <- lme(scale(cort) ~ time + scale(PTQ3) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), random = ~1|ID/time, data=cort.db2, na.action = na.omit)
summary(PTQ3model)

Linear mixed-effects model fit by REML
 Data: cort.db2 
       AIC     BIC    logLik
  415.8721 447.967 -197.9361

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7268424

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:    0.417478 0.2087699

Fixed effects: scale(cort) ~ time + scale(PTQ3) + scale(Smoker_0no_1yes_2yestoday_t3) +      Gender + scale(TodayCaff_NumCups_t3) 
                                         Value  Std.Error  DF   t-value p-value
(Intercept)                          0.0370822 0.12280871 124  0.301951  0.7632
time1                                0.2183737 0.08316607 124  2.625755  0.0097
time2                               -0.3422375 0.08307226 124 -4.119756  0.0001
scale(PTQ3)                          0.1582925 0.10013968  59  1.580717  0.1193
scale(Smoker_0no_1yes_2yestoday_t3)  0.4231892 0.10082632  59  4.197210  0.0001
Gender                               0.0678744 0.23000287  59  0.295102  0.7690
scale(TodayCaff_NumCups_t3)          0.1302631 0.10438818  59  1.247872  0.2170
 Correlation: 
                                    (Intr) time1  time2  s(PTQ3 s(S_0_ Gender
time1                               -0.339                                   
time2                               -0.344  0.501                            
scale(PTQ3)                         -0.007  0.000  0.001                     
scale(Smoker_0no_1yes_2yestoday_t3)  0.062  0.000  0.001  0.060              
Gender                              -0.470  0.000  0.004  0.016 -0.129       
scale(TodayCaff_NumCups_t3)         -0.093  0.000  0.004 -0.213 -0.235  0.204

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.96399562 -0.26721093 -0.04487948  0.22013418  1.11938377 

Number of Observations: 190
Number of Groups: 
          ID time %in% ID 
          64          190 

#NA models
PTQ1modelNA <- lme(scale(na) ~ time + scale(PTQ1), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ1modelNA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC     BIC    logLik
  400.8713 423.489 -193.4356

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.6431133

 Formula: ~1 | time %in% ID
        (Intercept) Residual
StdDev:   0.4303822 0.205853

Fixed effects: scale(na) ~ time + scale(PTQ1) 
                 Value  Std.Error  DF   t-value p-value
(Intercept) -0.1533661 0.10009649 125 -1.532183  0.1280
time1        0.7121978 0.08480249 125  8.398313  0.0000
time2       -0.2414496 0.08433643 125 -2.862934  0.0049
scale(PTQ1)  0.4505852 0.08741767  62  5.154395  0.0000
 Correlation: 
            (Intr) time1  time2 
time1       -0.419              
time2       -0.421  0.497       
scale(PTQ1)  0.008 -0.006  0.000

Standardized Within-Group Residuals:
         Min           Q1          Med           Q3          Max 
-1.247508145 -0.252140115 -0.002562848  0.216564008  1.170065514 

Number of Observations: 191
Number of Groups: 
          ID time %in% ID 
          64          191 

PTQ0modelNA <- lme(scale(na) ~ time + scale(PTQ0), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ0modelNA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC      BIC    logLik
  396.2847 418.7892 -191.1423

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.6793328

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4216155 0.2063103

Fixed effects: scale(na) ~ time + scale(PTQ0) 
                 Value  Std.Error  DF   t-value p-value
(Intercept) -0.1614936 0.10403486 123 -1.552303  0.1232
time1        0.7288247 0.08410610 123  8.665539  0.0000
time2       -0.2336321 0.08363252 123 -2.793556  0.0060
scale(PTQ0)  0.3993728 0.09200253  61  4.340889  0.0001
 Correlation: 
            (Intr) time1  time2 
time1       -0.400              
time2       -0.402  0.497       
scale(PTQ0)  0.008 -0.006  0.000

Standardized Within-Group Residuals:
         Min           Q1          Med           Q3          Max 
-1.312759749 -0.252315281 -0.005210294  0.222057071  1.158352163 

Number of Observations: 188
Number of Groups: 
          ID time %in% ID 
          63          188 

PTQ3modelNA <- lme(scale(na) ~ time + scale(PTQ3), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ3modelNA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC     BIC    logLik
  383.0612 405.679 -184.5306

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.5396926

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4345089 0.1969835

Fixed effects: scale(na) ~ time + scale(PTQ3) 
                 Value  Std.Error  DF   t-value p-value
(Intercept) -0.1525628 0.09004286 125 -1.694336  0.0927
time1        0.7113517 0.08479119 125  8.389453  0.0000
time2       -0.2414496 0.08433573 125 -2.862958  0.0049
scale(PTQ3)  0.5672581 0.07577764  62  7.485825  0.0000
 Correlation: 
            (Intr) time1  time2 
time1       -0.466              
time2       -0.468  0.497       
scale(PTQ3)  0.007 -0.006  0.000

Standardized Within-Group Residuals:
         Min           Q1          Med           Q3          Max 
-1.304674935 -0.239265947 -0.003492833  0.199392842  1.009504310 

Number of Observations: 191
Number of Groups: 
          ID time %in% ID 
          64          191 

#PA models
PTQ1modelPA <- lme(scale(pa) ~ time + scale(PTQ1), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ1modelPA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC     BIC    logLik
  442.2445 464.711 -214.1223

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7493757

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4861518 0.2340715

Fixed effects: scale(pa) ~ time + scale(PTQ1) 
                 Value  Std.Error  DF    t-value p-value
(Intercept)  0.5872851 0.11724997 121   5.008829  0.0000
time1       -1.0643397 0.09758404 121 -10.906903  0.0000
time2       -0.6509131 0.09812639 121  -6.633415  0.0000
scale(PTQ1) -0.0803089 0.10037914  62  -0.800056  0.4267
 Correlation: 
            (Intr) time1  time2 
time1       -0.435              
time2       -0.432  0.520       
scale(PTQ1) -0.005 -0.010 -0.010

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.89657251 -0.23906409 -0.04591769  0.20251529  1.30900026 

Number of Observations: 187
Number of Groups: 
          ID time %in% ID 
          64          187 

PTQ0modelPA <- lme(scale(pa) ~ time + scale(PTQ0), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ0modelPA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC      BIC    logLik
  426.8402 449.1909 -206.4201

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7550802

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4673454 0.2282147

Fixed effects: scale(pa) ~ time + scale(PTQ0) 
                 Value  Std.Error  DF    t-value p-value
(Intercept)  0.6005520 0.11727109 119   5.121058  0.0000
time1       -1.0656306 0.09485146 119 -11.234730  0.0000
time2       -0.6863469 0.09539172 119  -7.195036  0.0000
scale(PTQ0) -0.1331617 0.10106829  61  -1.317542  0.1926
 Correlation: 
            (Intr) time1  time2 
time1       -0.423              
time2       -0.421  0.520       
scale(PTQ0) -0.004 -0.010 -0.011

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.95093173 -0.24317540 -0.03761091  0.20359792  1.36527698 

Number of Observations: 184
Number of Groups: 
          ID time %in% ID 
          63          184 

PTQ3modelPA <- lme(scale(pa) ~ time + scale(PTQ3), random = ~1|ID/time, data=panas.db2, na.action = na.omit)
summary(PTQ3modelPA)

Linear mixed-effects model fit by REML
 Data: panas.db2 
       AIC     BIC    logLik
  442.7646 465.231 -214.3823

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.7539358

 Formula: ~1 | time %in% ID
        (Intercept)  Residual
StdDev:   0.4857548 0.2344235

Fixed effects: scale(pa) ~ time + scale(PTQ3) 
                 Value  Std.Error  DF    t-value p-value
(Intercept)  0.5867534 0.11768948 121   4.985606  0.0000
time1       -1.0650828 0.09754387 121 -10.919013  0.0000
time2       -0.6515705 0.09808747 121  -6.642750  0.0000
scale(PTQ3) -0.0316298 0.10188250  62  -0.310453  0.7573
 Correlation: 
            (Intr) time1  time2 
time1       -0.433              
time2       -0.431  0.520       
scale(PTQ3)  0.001 -0.002 -0.004

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-0.89600883 -0.23311783 -0.04384287  0.20316802  1.32376753 

Number of Observations: 187
Number of Groups: 
          ID time %in% ID 
          64          187 

PTQ associated with greater cortisol and NA overall, but not PA.

Test influence of PTQ on Cortisol, PA, NA at specific time points

Baseline model

#cort
cortBL_PTQ1model <- lm(scale(cortBL) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=cortdata)
summary(cortBL_PTQ1model)

Call:
lm(formula = scale(cortBL) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7820 -0.5820 -0.1870  0.4045  2.0597 

Coefficients:
                                    Estimate Std. Error t value  Pr(>|t|)    
(Intercept)                          0.01083    0.12956   0.084     0.934    
scale(PTQ1)                          0.18929    0.11522   1.643     0.106    
scale(Smoker_0no_1yes_2yestoday_t3)  0.47910    0.11445   4.186 0.0000976 ***
Gender                              -0.07512    0.26321  -0.285     0.776    
scale(TodayCaff_NumCups_t3)          0.02532    0.11690   0.217     0.829    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.881 on 58 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.2739,    Adjusted R-squared:  0.2238 
F-statistic: 5.468 on 4 and 58 DF,  p-value: 0.000834

#NA
naBL_PTQ1model <- lm(scale(naBL) ~ scale(PTQ1), data=panasdata)
summary(naBL_PTQ1model)

Call:
lm(formula = scale(naBL) ~ scale(PTQ1), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5109 -0.4716 -0.1489  0.2861  3.0212 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01365    0.11227  -0.122 0.903632    
scale(PTQ1)  0.46931    0.11669   4.022 0.000159 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8977 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.2069,    Adjusted R-squared:  0.1941 
F-statistic: 16.17 on 1 and 62 DF,  p-value: 0.0001593

#PA
paBL_PTQ1model <- lm(scale(paBL) ~ scale(PTQ1), data=panasdata)
summary(paBL_PTQ1model)

Call:
lm(formula = scale(paBL) ~ scale(PTQ1), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2792 -0.6977  0.1208  0.7871  1.6269 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.006451   0.121794  -0.053  0.95794   
scale(PTQ1) -0.393545   0.136497  -2.883  0.00551 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9433 on 58 degrees of freedom
  (30 observations deleted due to missingness)
Multiple R-squared:  0.1254,    Adjusted R-squared:  0.1103 
F-statistic: 8.313 on 1 and 58 DF,  p-value: 0.005515

PTQ associated with greater self-reported NA and lower self-reported PA at baseline, but not associated with cortisol at baseline.

Stress models

#Cort - Stress
cortStress_PTQ1model <- lm(scale(cortStress) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=cortdata)
summary(cortStress_PTQ1model)

Call:
lm(formula = scale(cortStress) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3), data = cortdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.74548 -0.57011 -0.06136  0.36719  2.22983 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         -0.011115   0.121370  -0.092 0.927348    
scale(PTQ1)                          0.233305   0.107939   2.161 0.034800 *  
scale(Smoker_0no_1yes_2yestoday_t3)  0.438062   0.107219   4.086 0.000137 ***
Gender                              -0.001594   0.246580  -0.006 0.994863    
scale(TodayCaff_NumCups_t3)          0.246117   0.109509   2.247 0.028429 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8254 on 58 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.3627,    Adjusted R-squared:  0.3188 
F-statistic: 8.253 on 4 and 58 DF,  p-value: 0.00002436

##Cort - Stress controlling for cort BL
cortStress_PTQ1model_BL <- lm(scale(cortStress) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3) + scale(cortBL), data=cortdata)
summary(cortStress_PTQ1model_BL)

Call:
lm(formula = scale(cortStress) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3) + scale(cortBL), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.9137 -0.3542 -0.1531  0.2373  1.6083 

Coefficients:
                                    Estimate Std. Error t value          Pr(>|t|)    
(Intercept)                         -0.01895    0.07783  -0.243           0.80856    
scale(PTQ1)                          0.09642    0.07081   1.362           0.17865    
scale(Smoker_0no_1yes_2yestoday_t3)  0.09160    0.07846   1.168           0.24784    
Gender                               0.05273    0.15823   0.333           0.74019    
scale(TodayCaff_NumCups_t3)          0.22780    0.07025   3.243           0.00198 ** 
scale(cortBL)                        0.72315    0.07888   9.168 0.000000000000823 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5293 on 57 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.7425,    Adjusted R-squared:  0.7199 
F-statistic: 32.87 on 5 and 57 DF,  p-value: 0.000000000000001285

#NA - Stress
naStress_PTQ1model <- lm(scale(naStress) ~ scale(PTQ1), data=panasdata)
summary(naStress_PTQ1model)

Call:
lm(formula = scale(naStress) ~ scale(PTQ1), data = panasdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.54800 -0.63772 -0.06647  0.50661  2.08805 

Coefficients:
            Estimate Std. Error t value   Pr(>|t|)    
(Intercept) -0.03208    0.10500  -0.305      0.761    
scale(PTQ1)  0.59089    0.11057   5.344 0.00000143 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.832 on 61 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.3189,    Adjusted R-squared:  0.3077 
F-statistic: 28.56 on 1 and 61 DF,  p-value: 0.00000143

#NA - Stress controlling for NA BL
naStress_PTQ1model <- lm(scale(naStress) ~ scale(PTQ1) + scale(naBL), data=panasdata)
summary(naStress_PTQ1model)

Call:
lm(formula = scale(naStress) ~ scale(PTQ1) + scale(naBL), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1076 -0.4127 -0.2100  0.4329  1.3887 

Coefficients:
            Estimate Std. Error t value        Pr(>|t|)    
(Intercept) -0.02300    0.07099  -0.324         0.74708    
scale(PTQ1)  0.27115    0.08354   3.246         0.00192 ** 
scale(naBL)  0.68213    0.07957   8.573 0.0000000000052 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5625 on 60 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.6939,    Adjusted R-squared:  0.6836 
F-statistic: 67.99 on 2 and 60 DF,  p-value: 0.0000000000000003783

#PA - Stress
paStress_PTQ1model <- lm(scale(paStress) ~ scale(PTQ1), data=panasdata)
summary(paStress_PTQ1model)

Call:
lm(formula = scale(paStress) ~ scale(PTQ1), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1745 -0.8064 -0.2381  0.6239  3.9314 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.001132   0.125972  -0.009    0.993
scale(PTQ1)  0.038940   0.130937   0.297    0.767

Residual standard error: 1.007 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.001424,  Adjusted R-squared:  -0.01468 
F-statistic: 0.08844 on 1 and 62 DF,  p-value: 0.7672

#PA - Stress controlling for PA BL
paStress_PTQ1model <- lm(scale(paStress) ~ scale(PTQ1) + scale(paBL), data=panasdata)
summary(paStress_PTQ1model)

Call:
lm(formula = scale(paStress) ~ scale(PTQ1) + scale(paBL), data = panasdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.50286 -0.42912 -0.06479  0.43940  1.75655 

Coefficients:
            Estimate Std. Error t value    Pr(>|t|)    
(Intercept) -0.01458    0.09185  -0.159       0.874    
scale(PTQ1)  0.17397    0.11007   1.581       0.120    
scale(paBL)  0.54388    0.09902   5.492 0.000000959 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7113 on 57 degrees of freedom
  (30 observations deleted due to missingness)
Multiple R-squared:  0.3472,    Adjusted R-squared:  0.3243 
F-statistic: 15.16 on 2 and 57 DF,  p-value: 0.000005259

Recovery models

#PTQ and Rec
cortRec_PTQ1model <- lm(scale(cortRec) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=cortdata)
summary(cortRec_PTQ1model)

Call:
lm(formula = scale(cortRec) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3), data = cortdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6739 -0.4501 -0.0466  0.4608  3.3995 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)                         -0.06586    0.12325  -0.534  0.59509   
scale(PTQ1)                          0.32057    0.11090   2.891  0.00537 **
scale(Smoker_0no_1yes_2yestoday_t3)  0.34972    0.11015   3.175  0.00238 **
Gender                               0.22616    0.25238   0.896  0.37385   
scale(TodayCaff_NumCups_t3)          0.22536    0.11208   2.011  0.04893 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.848 on 59 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.3266,    Adjusted R-squared:  0.2809 
F-statistic: 7.153 on 4 and 59 DF,  p-value: 0.00009145

##PTQ and Rec controlling for cort BL
cortRec_PTQ1model_BL <- lm(scale(cortRec) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3) + scale(cortBL), data=cortdata)
summary(cortRec_PTQ1model_BL)

Call:
lm(formula = scale(cortRec) ~ scale(PTQ1) + scale(Smoker_0no_1yes_2yestoday_t3) + 
    Gender + scale(TodayCaff_NumCups_t3) + scale(cortBL), data = cortdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.40354 -0.35987 -0.08598  0.33943  2.93451 

Coefficients:
                                    Estimate Std. Error t value    Pr(>|t|)    
(Intercept)                         -0.11493    0.09745  -1.179      0.2432    
scale(PTQ1)                          0.21551    0.08866   2.431      0.0182 *  
scale(Smoker_0no_1yes_2yestoday_t3)  0.08847    0.09823   0.901      0.3716    
Gender                               0.31762    0.19812   1.603      0.1144    
scale(TodayCaff_NumCups_t3)          0.23392    0.08796   2.659      0.0101 *  
scale(cortBL)                        0.55104    0.09876   5.579 0.000000695 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6627 on 57 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.586, Adjusted R-squared:  0.5497 
F-statistic: 16.14 on 5 and 57 DF,  p-value: 0.0000000006873

#NA - Recovery
naRec_PTQ1model <- lm(scale(naRec) ~ scale(PTQ1), data=panasdata)
summary(naRec_PTQ1model)

Call:
lm(formula = scale(naRec) ~ scale(PTQ1), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5341 -0.4353 -0.1165  0.3640  3.1080 

Coefficients:
            Estimate Std. Error t value  Pr(>|t|)    
(Intercept) -0.01463    0.11006  -0.133     0.895    
scale(PTQ1)  0.50302    0.11440   4.397 0.0000439 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8801 on 62 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.2377,    Adjusted R-squared:  0.2254 
F-statistic: 19.33 on 1 and 62 DF,  p-value: 0.00004388

#NA - Recovery controlling for NA BL
naRec_PTQ1model <- lm(scale(naRec) ~ scale(PTQ1) + scale(naBL), data=panasdata)
summary(naRec_PTQ1model)

Call:
lm(formula = scale(naRec) ~ scale(PTQ1) + scale(naBL), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9517 -0.2868 -0.0564  0.2015  1.5508 

Coefficients:
             Estimate Std. Error t value         Pr(>|t|)    
(Intercept) -0.004337   0.070920  -0.061           0.9514    
scale(PTQ1)  0.149129   0.082765   1.802           0.0765 .  
scale(naBL)  0.754073   0.080219   9.400 0.00000000000018 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.567 on 61 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.6887,    Adjusted R-squared:  0.6785 
F-statistic: 67.47 on 2 and 61 DF,  p-value: 0.0000000000000003491

#PA - Recovery
paRec_PTQ1model <- lm(scale(paRec) ~ scale(PTQ1), data=panasdata)
summary(paRec_PTQ1model)

Call:
lm(formula = scale(paRec) ~ scale(PTQ1), data = panasdata)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2675 -0.8261 -0.2642  0.6410  3.1549 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.0009899  0.1269811   0.008    0.994
scale(PTQ1) -0.0371303  0.1309874  -0.283    0.778

Residual standard error: 1.008 on 61 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.001316,  Adjusted R-squared:  -0.01506 
F-statistic: 0.08035 on 1 and 61 DF,  p-value: 0.7778

#PA - Recovery controlling for PA BL
paRec_PTQ1model <- lm(scale(paRec) ~ scale(PTQ1) + scale(paBL), data=panasdata)
summary(paRec_PTQ1model)

Call:
lm(formula = scale(paRec) ~ scale(PTQ1) + scale(paBL), data = panasdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.68120 -0.39305 -0.09109  0.46449  2.07580 

Coefficients:
             Estimate Std. Error t value  Pr(>|t|)    
(Intercept) -0.004133   0.102901  -0.040     0.968    
scale(PTQ1)  0.092644   0.122346   0.757     0.452    
scale(paBL)  0.527658   0.110035   4.795 0.0000124 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7902 on 56 degrees of freedom
  (31 observations deleted due to missingness)
Multiple R-squared:  0.3001,    Adjusted R-squared:  0.2751 
F-statistic: 12.01 on 2 and 56 DF,  p-value: 0.00004578