Caitlin A. Stamatis, Nikki Puccetti, Caroline Charpentier, Aaron S. Heller, & Kiara R. Timpano


This document contains the code for analyses conducted for a manuscript in submission.

Data cleaning and set-up

Packages Used: stargazer, sjPlot, sjmisc, ggplot2, lmerTest, lme4, reshape2, tidyr, doBy, psych, dplyr, nlme, summarytools, plyr, knitr, pander, DT, shiny

View Data

Description of Variables

Demographic Variables

  • ID: subject ID
  • Gender_1: gender (0 = male; 1 = female)
  • Age_1: age
  • CurrentMed_notBC_t3: current medications, excluding oral contraceptive
  • CurrentMed_BC_t3: current oral contraceptive
  • Smoker_0no_1yes_2yestoday_t3: smoking status (0 = no; 1 = yes; 2 = yes and smoked today)
  • TodayCaff_NumCups_t3: number of caffeinated beverages today
  • CurrentMenstr_t3: current menstruation (1 = yes)
  • HurricaneImpairment: continued impairment in day-to-day functioning after Hurricane Irma related to stress of hurricane experience

Self-Reported RNT

  • RNT_1: repetitive negative thinking on the Perseverative Thinking Questionnaire at Time 1

Gambling Task

  • muBL: choice consistency during baseline block
  • lambdaBL: loss aversion during baseline block
  • rhoBL: risk seeking during baseline block
  • muANT: choice consistency during stress anticipation block
  • lambdaANT: loss aversion during stress anticipation block
  • rhoANT: risk seeking during stress anticipation block
  • muREC: choice consistency during stress recovery block
  • lambdaREC: loss aversion during stress recovery block
  • rhoREC: risk seeking during stress recovery block

Cortisol and Momentary Negative Affect

  • cort0-cort2: cortisol measures over course of session (nmol/L)
  • na0-na2: negative affect measures over course of session (PANAS)

Descriptive Statistics & Basic Data Visualization


Cortisol Visualization

cortvalues <- NA
cortvalues[1] <- mean(df$cort0, na.rm=T)
cortvalues[2] <- mean(df$cort1, na.rm=T)
cortvalues[3] <- mean(df$cort2, na.rm=T)

sevalues <- NA
a <- summarySE(df, measurevar="cort0", na.rm=T)
sevalues[1] <- a$se
a2 <- summarySE(df, measurevar="cort1", na.rm=T)
sevalues[2] <- a2$se
a3 <- summarySE(df, measurevar="cort2", na.rm=T)
sevalues[3] <- a3$se

Cortvalues <- NA
Cortvalues <- cbind(as.data.frame(cortvalues),as.data.frame(sevalues))

Cortvalues$Time <- c(0,1,2)


p<- ggplot(Cortvalues, aes(x=Time, y=cortvalues)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(ymin=cortvalues-sevalues, ymax=cortvalues+sevalues), width=.2,
                 position=position_dodge(0.05))
# Finished line plot
p+labs(title="Average cortisol across course of experimental stress manipulation", x="Time Point", y = "Average Cortisol (nmol/L)")+ theme_classic() + scale_x_continuous(breaks=c(0,1,2), labels=c("Baseline","Stress","Recovery")) + theme(plot.title = element_text(hjust = 0.5))

The plot clearly indicates an average increase in cortisol during the stress induction, and a decrease in cortisol as individuals recovered from the stressor.

Negative Affect Visualization

Gambling Task Parameter Visualization

Rho

Lambda

Mu

Preliminary Analyses

t.test(df$cort1, df$cort0, paired=T)
## 
##  Paired t-test
## 
## data:  df$cort1 and df$cort0
## t = 2.719, df = 62, p-value = 0.008483
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.2448777 1.6046461
## sample estimates:
## mean of the differences 
##               0.9247619
t.test(df$cort2, df$cort0, paired=T)
## 
##  Paired t-test
## 
## data:  df$cort2 and df$cort0
## t = -3.5126, df = 62, p-value = 0.0008341
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.3209934 -0.6374193
## sample estimates:
## mean of the differences 
##               -1.479206
t.test(df$cort2, df$cort1, paired=T)
## 
##  Paired t-test
## 
## data:  df$cort2 and df$cort1
## t = -8.6171, df = 62, p-value = 0.000000000003386
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.961636 -1.846300
## sample estimates:
## mean of the differences 
##               -2.403968
t.test(df$na1, df$na0, paired=T)
## 
##  Paired t-test
## 
## data:  df$na1 and df$na0
## t = 8.3599, df = 62, p-value = 0.000000000009414
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.430030 5.585843
## sample estimates:
## mean of the differences 
##                4.507937
t.test(df$na2, df$na0, paired=T)
## 
##  Paired t-test
## 
## data:  df$na2 and df$na0
## t = -3.5411, df = 63, p-value = 0.0007558
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.3953757 -0.6671243
## sample estimates:
## mean of the differences 
##                -1.53125
t.test(df$na2, df$na1, paired=T)
## 
##  Paired t-test
## 
## data:  df$na2 and df$na1
## t = -9.645, df = 62, p-value = 0.00000000000005917
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.320183 -4.806801
## sample estimates:
## mean of the differences 
##               -6.063492
t.test(df$lambda_ANT_log, df$lambda_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$lambda_ANT_log and df$lambda_BL_log
## t = 1.1049, df = 55, p-value = 0.274
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05627334  0.19457168
## sample estimates:
## mean of the differences 
##              0.06914917
t.test(df$lambda_REC_log, df$lambda_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$lambda_REC_log and df$lambda_BL_log
## t = 1.2722, df = 55, p-value = 0.2087
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04383823  0.19625332
## sample estimates:
## mean of the differences 
##              0.07620755
t.test(df$lambda_REC_log, df$lambda_ANT_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$lambda_REC_log and df$lambda_ANT_log
## t = 0.1125, df = 55, p-value = 0.9108
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1186806  0.1327973
## sample estimates:
## mean of the differences 
##             0.007058376
t.test(df$rho_ANT_log, df$rho_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$rho_ANT_log and df$rho_BL_log
## t = -1.8751, df = 55, p-value = 0.06609
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.091170524  0.003030089
## sample estimates:
## mean of the differences 
##             -0.04407022
t.test(df$rho_REC_log, df$rho_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$rho_REC_log and df$rho_BL_log
## t = -2.7781, df = 55, p-value = 0.007465
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.11496782 -0.01860906
## sample estimates:
## mean of the differences 
##             -0.06678844
t.test(df$rho_REC_log, df$rho_ANT_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$rho_REC_log and df$rho_ANT_log
## t = -0.90377, df = 55, p-value = 0.3701
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.07309434  0.02765790
## sample estimates:
## mean of the differences 
##             -0.02271822
t.test(df$mu_ANT_log, df$mu_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$mu_ANT_log and df$mu_BL_log
## t = 0.56323, df = 55, p-value = 0.5756
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1158097  0.2063525
## sample estimates:
## mean of the differences 
##              0.04527141
t.test(df$mu_REC_log, df$mu_BL_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$mu_REC_log and df$mu_BL_log
## t = 2.195, df = 55, p-value = 0.0324
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01404581 0.30878720
## sample estimates:
## mean of the differences 
##               0.1614165
t.test(df$mu_REC_log, df$mu_ANT_log, paired=T)
## 
##  Paired t-test
## 
## data:  df$mu_REC_log and df$mu_ANT_log
## t = 1.3332, df = 55, p-value = 0.188
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05844067  0.29073084
## sample estimates:
## mean of the differences 
##               0.1161451

Regression Models

Aim 1

hurte1 <- lm(scale(HurricaneImpairment) ~ scale(RNT_1), data=df)
stargazer(hurte1, ci=TRUE, type="html")
Dependent variable:
scale(HurricaneImpairment)
scale(RNT_1) 0.257**
(0.055, 0.458)
Constant -0.000
(-0.201, 0.201)
Observations 90
R2 0.066
Adjusted R2 0.055
Residual Std. Error 0.972 (df = 88)
F Statistic 6.198** (df = 1; 88)
Note: p<0.1; p<0.05; p<0.01

Aim 2

Baseline Models
cortBLmod <- lm(scale(cort0) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=df)
stargazer(cortBLmod, ci=TRUE, type="html")
Dependent variable:
scale(cort0)
scale(RNT_1) 0.189
(-0.037, 0.415)
scale(Smoker_0no_1yes_2yestoday_t3) 0.479***
(0.255, 0.703)
Gender -0.075
(-0.591, 0.441)
scale(TodayCaff_NumCups_t3) 0.025
(-0.204, 0.254)
Constant 0.011
(-0.243, 0.265)
Observations 63
R2 0.274
Adjusted R2 0.224
Residual Std. Error 0.881 (df = 58)
F Statistic 5.468*** (df = 4; 58)
Note: p<0.1; p<0.05; p<0.01 
naBLmod <- lm(scale(na0) ~ scale(RNT_1) , data=df)
stargazer(naBLmod, ci=TRUE, type="html")
Dependent variable:
scale(na0)
scale(RNT_1) 0.469***
(0.241, 0.698)
Constant -0.014
(-0.234, 0.206)
Observations 64
R2 0.207
Adjusted R2 0.194
Residual Std. Error 0.898 (df = 62)
F Statistic 16.175*** (df = 1; 62)
Note: p<0.1; p<0.05; p<0.01 
rhoBLmod <- lm(scale(rho_BL_log) ~ scale(RNT_1) , data=df)
stargazer(rhoBLmod, ci=TRUE, type="html")
Dependent variable:
scale(rho_BL_log)
scale(RNT_1) 0.091
(-0.185, 0.367)
Constant -0.008
(-0.272, 0.257)
Observations 56
R2 0.008
Adjusted R2 -0.011
Residual Std. Error 1.005 (df = 54)
F Statistic 0.418 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
lambdaBLmod <- lm(scale(lambda_BL_log) ~ scale(RNT_1) , data=df)
stargazer(lambdaBLmod, ci=TRUE, type="html")
Dependent variable:
scale(lambda_BL_log)
scale(RNT_1) -0.275**
(-0.543, -0.008)
Constant 0.024
(-0.232, 0.280)
Observations 56
R2 0.070
Adjusted R2 0.053
Residual Std. Error 0.973 (df = 54)
F Statistic 4.067** (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
muBLmod <- lm(scale(mu_BL_log) ~ scale(RNT_1) , data=df)
stargazer(muBLmod, ci=TRUE, type="html")
Dependent variable:
scale(mu_BL_log)
scale(RNT_1) 0.179
(-0.094, 0.452)
Constant -0.015
(-0.277, 0.246)
Observations 56
R2 0.030
Adjusted R2 0.012
Residual Std. Error 0.994 (df = 54)
F Statistic 1.649 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01

Aim 3

Stress Models

cortANTmod <- lm(scale(cort1) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=df)
stargazer(cortANTmod, ci=TRUE, type="html")
Dependent variable:
scale(cort1)
scale(RNT_1) 0.233**
(0.022, 0.445)
scale(Smoker_0no_1yes_2yestoday_t3) 0.438***
(0.228, 0.648)
Gender -0.002
(-0.485, 0.482)
scale(TodayCaff_NumCups_t3) 0.246**
(0.031, 0.461)
Constant -0.011
(-0.249, 0.227)
Observations 63
R2 0.363
Adjusted R2 0.319
Residual Std. Error 0.825 (df = 58)
F Statistic 8.253*** (df = 4; 58)
Note: p<0.1; p<0.05; p<0.01 
cortANTmod2 <- lm(scale(cort1) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3) + scale(cort0), data=df)
stargazer(cortANTmod2, ci=TRUE, type="html")
Dependent variable:
scale(cort1)
scale(RNT_1) 0.096
(-0.042, 0.235)
scale(Smoker_0no_1yes_2yestoday_t3) 0.092
(-0.062, 0.245)
Gender 0.053
(-0.257, 0.363)
scale(TodayCaff_NumCups_t3) 0.228***
(0.090, 0.365)
scale(cort0) 0.723***
(0.569, 0.878)
Constant -0.019
(-0.171, 0.134)
Observations 63
R2 0.742
Adjusted R2 0.720
Residual Std. Error 0.529 (df = 57)
F Statistic 32.866*** (df = 5; 57)
Note: p<0.1; p<0.05; p<0.01 
naANTmod <- lm(scale(na1) ~ scale(RNT_1) , data=df)
stargazer(naANTmod, ci=TRUE, type="html")
Dependent variable:
scale(na1)
scale(RNT_1) 0.591***
(0.374, 0.808)
Constant -0.032
(-0.238, 0.174)
Observations 63
R2 0.319
Adjusted R2 0.308
Residual Std. Error 0.832 (df = 61)
F Statistic 28.558*** (df = 1; 61)
Note: p<0.1; p<0.05; p<0.01 
confint.lm(naANTmod)
              2.5 %    97.5 %

(Intercept) -0.2420333 0.1778826 scale(RNT_1) 0.3697919 0.8119974

naANTmod2 <- lm(scale(na1) ~ scale(RNT_1) + scale(na0), data=df)
stargazer(naANTmod2, ci=TRUE, type="html")
Dependent variable:
scale(na1)
scale(RNT_1) 0.271***
(0.107, 0.435)
scale(na0) 0.682***
(0.526, 0.838)
Constant -0.023
(-0.162, 0.116)
Observations 63
R2 0.694
Adjusted R2 0.684
Residual Std. Error 0.562 (df = 60)
F Statistic 67.993*** (df = 2; 60)
Note: p<0.1; p<0.05; p<0.01 
rhoANTmod <- lm(scale(rho_ANT_log) ~ scale(RNT_1) , data=df)
stargazer(rhoANTmod, ci=TRUE, type="html")
Dependent variable:
scale(rho_ANT_log)
scale(RNT_1) 0.011
(-0.267, 0.288)
Constant -0.001
(-0.266, 0.264)
Observations 56
R2 0.0001
Adjusted R2 -0.018
Residual Std. Error 1.009 (df = 54)
F Statistic 0.006 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
rhoANTmod2 <- lm(scale(rho_ANT_log) ~ scale(RNT_1) + scale(rho_BL_log), data=df)
stargazer(rhoANTmod2, ci=TRUE, type="html")
Dependent variable:
scale(rho_ANT_log)
scale(RNT_1) -0.061
(-0.236, 0.114)
scale(rho_BL_log) 0.786***
(0.618, 0.954)
Constant 0.005
(-0.161, 0.172)
Observations 56
R2 0.613
Adjusted R2 0.599
Residual Std. Error 0.634 (df = 53)
F Statistic 42.008*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
lambdaANTmod <- lm(scale(lambda_ANT_log) ~ scale(RNT_1) , data=df)
stargazer(lambdaANTmod, ci=TRUE, type="html")
Dependent variable:
scale(lambda_ANT_log)
scale(RNT_1) 0.067
(-0.210, 0.343)
Constant -0.006
(-0.271, 0.259)
Observations 56
R2 0.004
Adjusted R2 -0.014
Residual Std. Error 1.007 (df = 54)
F Statistic 0.222 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
lambdaANTmod2 <- lm(scale(lambda_ANT_log) ~ scale(RNT_1) + scale(lambda_BL_log), data=df)
stargazer(lambdaANTmod2, ci=TRUE, type="html")
Dependent variable:
scale(lambda_ANT_log)
scale(RNT_1) 0.303***
(0.142, 0.465)
scale(lambda_BL_log) 0.859***
(0.704, 1.015)
Constant -0.026
(-0.175, 0.123)
Observations 56
R2 0.691
Adjusted R2 0.679
Residual Std. Error 0.567 (df = 53)
F Statistic 59.172*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
muANTmod <- lm(scale(mu_ANT_log) ~ scale(RNT_1) , data=df)
stargazer(muANTmod, ci=TRUE, type="html")
Dependent variable:
scale(mu_ANT_log)
scale(RNT_1) -0.135
(-0.411, 0.140)
Constant 0.012
(-0.252, 0.275)
Observations 56
R2 0.017
Adjusted R2 -0.001
Residual Std. Error 1.001 (df = 54)
F Statistic 0.932 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
muANTmod2 <- lm(scale(mu_ANT_log) ~ scale(RNT_1) + scale(mu_BL_log), data=df)
stargazer(muANTmod2, ci=TRUE, type="html")
Dependent variable:
scale(mu_ANT_log)
scale(RNT_1) -0.231*
(-0.470, 0.008)
scale(mu_BL_log) 0.534***
(0.304, 0.763)
Constant 0.020
(-0.205, 0.245)
Observations 56
R2 0.293
Adjusted R2 0.267
Residual Std. Error 0.856 (df = 53)
F Statistic 10.996*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01

Aim 4

Recovery Models

cortRECmod <- lm(scale(cort2) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3), data=df)
stargazer(cortRECmod, ci=TRUE, type="html")
Dependent variable:
scale(cort2)
scale(RNT_1) 0.321***
(0.103, 0.538)
scale(Smoker_0no_1yes_2yestoday_t3) 0.350***
(0.134, 0.566)
Gender 0.226
(-0.269, 0.721)
scale(TodayCaff_NumCups_t3) 0.225**
(0.006, 0.445)
Constant -0.066
(-0.307, 0.176)
Observations 64
R2 0.327
Adjusted R2 0.281
Residual Std. Error 0.848 (df = 59)
F Statistic 7.153*** (df = 4; 59)
Note: p<0.1; p<0.05; p<0.01 
cortRECmod2 <- lm(scale(cort2) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3) + scale(cort0), data=df)
stargazer(cortRECmod2, ci=TRUE, type="html")
Dependent variable:
scale(cort2)
scale(RNT_1) 0.216**
(0.042, 0.389)
scale(Smoker_0no_1yes_2yestoday_t3) 0.088
(-0.104, 0.281)
Gender 0.318
(-0.071, 0.706)
scale(TodayCaff_NumCups_t3) 0.234**
(0.062, 0.406)
scale(cort0) 0.551***
(0.357, 0.745)
Constant -0.115
(-0.306, 0.076)
Observations 63
R2 0.586
Adjusted R2 0.550
Residual Std. Error 0.663 (df = 57)
F Statistic 16.139*** (df = 5; 57)
Note: p<0.1; p<0.05; p<0.01 
cortRECmod3 <- lm(scale(cort2) ~ scale(RNT_1) +scale(Smoker_0no_1yes_2yestoday_t3) + Gender+ scale(TodayCaff_NumCups_t3) + scale(cort1), data=df)
stargazer(cortRECmod3, ci=TRUE, type="html")
Dependent variable:
scale(cort2)
scale(RNT_1) 0.129**
(0.005, 0.253)
scale(Smoker_0no_1yes_2yestoday_t3) -0.006
(-0.140, 0.128)
Gender 0.278**
(0.006, 0.549)
scale(TodayCaff_NumCups_t3) 0.047
(-0.079, 0.172)
scale(cort1) 0.818***
(0.673, 0.963)
Constant -0.100
(-0.234, 0.034)
Observations 63
R2 0.797
Adjusted R2 0.779
Residual Std. Error 0.464 (df = 57)
F Statistic 44.825*** (df = 5; 57)
Note: p<0.1; p<0.05; p<0.01 
naRECmod <- lm(scale(na2) ~ scale(RNT_1) , data=df)
stargazer(naRECmod, ci=TRUE, type="html")
Dependent variable:
scale(na2)
scale(RNT_1) 0.503***
(0.279, 0.727)
Constant -0.015
(-0.230, 0.201)
Observations 64
R2 0.238
Adjusted R2 0.225
Residual Std. Error 0.880 (df = 62)
F Statistic 19.333*** (df = 1; 62)
Note: p<0.1; p<0.05; p<0.01 
confint.lm(naRECmod)
              2.5 %    97.5 %

(Intercept) -0.2346433 0.2053861 scale(RNT_1) 0.2743333 0.7317067

naRECmod2 <- lm(scale(na2) ~ scale(RNT_1) + scale(na0), data=df)
stargazer(naRECmod2, ci=TRUE, type="html")
Dependent variable:
scale(na2)
scale(RNT_1) 0.149*
(-0.013, 0.311)
scale(na0) 0.754***
(0.597, 0.911)
Constant -0.004
(-0.143, 0.135)
Observations 64
R2 0.689
Adjusted R2 0.678
Residual Std. Error 0.567 (df = 61)
F Statistic 67.469*** (df = 2; 61)
Note: p<0.1; p<0.05; p<0.01 
naRECmod3 <- lm(scale(na2) ~ scale(RNT_1) + scale(na1), data=df)
stargazer(naRECmod3, ci=TRUE, type="html")
Dependent variable:
scale(na2)
scale(RNT_1) 0.104
(-0.114, 0.321)
scale(na1) 0.679***
(0.471, 0.887)
Constant 0.006
(-0.165, 0.177)
Observations 63
R2 0.543
Adjusted R2 0.528
Residual Std. Error 0.690 (df = 60)
F Statistic 35.624*** (df = 2; 60)
Note: p<0.1; p<0.05; p<0.01 
rhoRECmod <- lm(scale(rho_REC_log) ~ scale(RNT_1) , data=df)
stargazer(rhoRECmod, ci=TRUE, type="html")
Dependent variable:
scale(rho_REC_log)
scale(RNT_1) 0.177
(-0.097, 0.450)
Constant -0.015
(-0.277, 0.246)
Observations 56
R2 0.029
Adjusted R2 0.011
Residual Std. Error 0.995 (df = 54)
F Statistic 1.602 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
rhoRECmod2 <- lm(scale(rho_REC_log) ~ scale(RNT_1) + scale(rho_BL_log), data=df)
stargazer(rhoRECmod2, ci=TRUE, type="html")
Dependent variable:
scale(rho_REC_log)
scale(RNT_1) 0.105
(-0.064, 0.275)
scale(rho_BL_log) 0.782***
(0.619, 0.945)
Constant -0.009
(-0.171, 0.153)
Observations 56
R2 0.636
Adjusted R2 0.622
Residual Std. Error 0.615 (df = 53)
F Statistic 46.265*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
rhoRECmod3 <- lm(scale(rho_REC_log) ~ scale(RNT_1) + scale(rho_ANT_log), data=df)
stargazer(rhoRECmod3, ci=TRUE, type="html")
Dependent variable:
scale(rho_REC_log)
scale(RNT_1) 0.169*
(-0.015, 0.353)
scale(rho_ANT_log) 0.735***
(0.558, 0.912)
Constant -0.014
(-0.190, 0.162)
Observations 56
R2 0.569
Adjusted R2 0.552
Residual Std. Error 0.669 (df = 53)
F Statistic 34.918*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
lambdaRECmod <- lm(scale(lambda_REC_log) ~ scale(RNT_1) , data=df)
stargazer(lambdaRECmod, ci=TRUE, type="html")
Dependent variable:
scale(lambda_REC_log)
scale(RNT_1) 0.017
(-0.261, 0.294)
Constant -0.001
(-0.267, 0.264)
Observations 56
R2 0.0003
Adjusted R2 -0.018
Residual Std. Error 1.009 (df = 54)
F Statistic 0.014 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
lambdaRECmod2 <- lm(scale(lambda_REC_log) ~ scale(RNT_1) + scale(lambda_BL_log), data=df)
stargazer(lambdaRECmod2, ci=TRUE, type="html")
Dependent variable:
scale(lambda_REC_log)
scale(RNT_1) 0.269***
(0.134, 0.404)
scale(lambda_BL_log) 0.918***
(0.788, 1.048)
Constant -0.023
(-0.148, 0.101)
Observations 56
R2 0.784
Adjusted R2 0.776
Residual Std. Error 0.474 (df = 53)
F Statistic 96.097*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
lambdaRECmod3 <- lm(scale(lambda_REC_log) ~ scale(RNT_1) + scale(lambda_ANT_log), data=df)
stargazer(lambdaRECmod3, ci=TRUE, type="html")
Dependent variable:
scale(lambda_REC_log)
scale(RNT_1) -0.039
(-0.195, 0.118)
scale(lambda_ANT_log) 0.832***
(0.682, 0.982)
Constant 0.003
(-0.146, 0.153)
Observations 56
R2 0.690
Adjusted R2 0.678
Residual Std. Error 0.567 (df = 53)
F Statistic 58.919*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
muRECmod <- lm(scale(mu_REC_log) ~ scale(RNT_1) , data=df)
stargazer(muRECmod, ci=TRUE, type="html")
Dependent variable:
scale(mu_REC_log)
scale(RNT_1) -0.038
(-0.315, 0.240)
Constant 0.003
(-0.262, 0.268)
Observations 56
R2 0.001
Adjusted R2 -0.017
Residual Std. Error 1.009 (df = 54)
F Statistic 0.071 (df = 1; 54)
Note: p<0.1; p<0.05; p<0.01 
muRECmod2 <- lm(scale(mu_REC_log) ~ scale(RNT_1) + scale(mu_BL_log), data=df)
stargazer(muRECmod2, ci=TRUE, type="html")
Dependent variable:
scale(mu_REC_log)
scale(RNT_1) -0.160
(-0.370, 0.050)
scale(mu_BL_log) 0.684***
(0.482, 0.886)
Constant 0.014
(-0.184, 0.211)
Observations 56
R2 0.455
Adjusted R2 0.435
Residual Std. Error 0.752 (df = 53)
F Statistic 22.146*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01 
muRECmod3 <- lm(scale(mu_REC_log) ~ scale(RNT_1) + scale(mu_ANT_log), data=df)
stargazer(muRECmod3, ci=TRUE, type="html")
Dependent variable:
scale(mu_REC_log)
scale(RNT_1) 0.025
(-0.226, 0.276)
scale(mu_ANT_log) 0.460***
(0.219, 0.702)
Constant -0.002
(-0.240, 0.236)
Observations 56
R2 0.210
Adjusted R2 0.180
Residual Std. Error 0.906 (df = 53)
F Statistic 7.026*** (df = 2; 53)
Note: p<0.1; p<0.05; p<0.01