setwd("/Users/fazlisalim/Desktop/Projects/Study 3")
S3<- read_excel("Study3.xlsx")
variables <- c("IN1", "IN2",
"Degro1", "Degro2",
"EB1", "EB2", "EB3",
"SR1", "SR2",
"HA1", "HA2",
"ISM1", "ISM1", "ISM3")
S3 %>%
count(Condition)
## # A tibble: 9 × 2
## Condition n
## <chr> <int>
## 1 Flam 400
## 2 Flam + BE 403
## 3 Flam + Norm 404
## 4 Flam + Norm + BE 397
## 5 Harm 404
## 6 Harm + BE 402
## 7 Harm + Norm 400
## 8 Harm + Norm + BE 398
## 9 <NA> 3
S3 <- S3 %>%
filter(!is.na(Condition))
S3$Condition <- factor(S3$Condition,
levels = c("Flam",
"Flam + BE",
"Flam + Norm",
"Flam + Norm + BE",
"Harm",
"Harm + BE",
"Harm + Norm",
"Harm + Norm + BE"),
labels = c("Flam",
"FlamBE",
"FlamNorm",
"FlamNormBE",
"Harm",
"HarmBE",
"HarmNorm",
"HarmNormBE"))
S3$Condition <- relevel(S3$Condition, ref = "Flam")
#IN1
describe(S3$IN1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 2.47 1.16 2 2.39 1.48 1 5 4 0.44 -0.65 0.02
tapply(S3$IN1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 1.0 2.0 2.3 3.0 5.0
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.447 3.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.287 3.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.542 3.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.412 3.000 5.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.428 3.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.618 3.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.696 4.000 5.000
model_IN1 <- lm(IN1 ~ Condition, data = S3)
summary(model_IN1)
##
## Call:
## lm(formula = IN1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.696 -0.696 -0.300 0.700 2.713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30000 0.05771 39.855 < 2e-16 ***
## ConditionFlamBE 0.14665 0.08146 1.800 0.071918 .
## ConditionFlamNorm -0.01287 0.08141 -0.158 0.874387
## ConditionFlamNormBE 0.24156 0.08177 2.954 0.003157 **
## ConditionHarm 0.11191 0.08146 1.374 0.169606
## ConditionHarmBE 0.12786 0.08151 1.569 0.116838
## ConditionHarmNorm 0.31750 0.08161 3.890 0.000102 ***
## ConditionHarmNormBE 0.39598 0.08172 4.846 1.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01351, Adjusted R-squared: 0.01135
## F-statistic: 6.258 on 7 and 3199 DF, p-value: 2.617e-07
#IN2
describe(S3$IN2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 2.87 1.25 3 2.83 1.48 1 5 4 0.12 -1 0.02
tapply(S3$IN2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.68 3.25 5.00
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.896 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.500 2.653 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.97 4.00 5.00
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.821 4.000 5.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.853 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.005 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 3.05 4.00 5.00
model_IN2 <- lm(IN2 ~ Condition, data = S3)
summary(model_IN2)
##
## Call:
## lm(formula = IN2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05025 -0.89578 0.03023 1.03023 2.34653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.68000 0.06197 43.246 < 2e-16 ***
## ConditionFlamBE 0.21578 0.08748 2.467 0.013688 *
## ConditionFlamNorm -0.02653 0.08742 -0.304 0.761515
## ConditionFlamNormBE 0.28977 0.08781 3.300 0.000977 ***
## ConditionHarm 0.14134 0.08748 1.616 0.106252
## ConditionHarmBE 0.17323 0.08753 1.979 0.047891 *
## ConditionHarmNorm 0.32500 0.08764 3.708 0.000212 ***
## ConditionHarmNormBE 0.37025 0.08775 4.219 2.52e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.239 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01183, Adjusted R-squared: 0.009667
## F-statistic: 5.471 on 7 and 3199 DF, p-value: 2.903e-06
#Degro1
describe(S3$Degro1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 3.15 1.33 3 3.18 1.48 1 5 4 -0.18 -1.12 0.02
tapply(S3$Degro1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.973 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.236 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.002 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.111 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.101 4.000 5.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.274 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.158 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.317 5.000 5.000
model_Degro1 <- lm(Degro1 ~ Condition, data = S3)
summary(model_Degro1)
##
## Call:
## lm(formula = Degro1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31658 -1.11083 -0.00248 0.99752 2.02750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.97250 0.06642 44.751 < 2e-16 ***
## ConditionFlamBE 0.26323 0.09376 2.807 0.005023 **
## ConditionFlamNorm 0.02998 0.09370 0.320 0.749069
## ConditionFlamNormBE 0.13833 0.09411 1.470 0.141704
## ConditionHarm 0.12899 0.09370 1.377 0.168755
## ConditionHarmBE 0.30113 0.09382 3.210 0.001342 **
## ConditionHarmNorm 0.18500 0.09394 1.969 0.048991 *
## ConditionHarmNormBE 0.34408 0.09405 3.658 0.000258 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.328 on 3200 degrees of freedom
## Multiple R-squared: 0.007571, Adjusted R-squared: 0.0054
## F-statistic: 3.487 on 7 and 3200 DF, p-value: 0.0009934
#Degro2
describe(S3$Degro2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 3.26 1.36 3 3.33 1.48 1 5 4 -0.29 -1.11 0.02
tapply(S3$Degro2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 3.12 4.00 5.00
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.407 5.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.082 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.242 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.198 4.000 5.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.361 4.750 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.315 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.364 5.000 5.000
model_Degro2 <- lm(Degro2 ~ Condition, data = S3)
summary(model_Degro2)
##
## Call:
## lm(formula = Degro2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40695 -1.19802 -0.08168 0.91832 1.91832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12000 0.06773 46.065 < 2e-16 ***
## ConditionFlamBE 0.28695 0.09561 3.001 0.00271 **
## ConditionFlamNorm -0.03832 0.09555 -0.401 0.68843
## ConditionFlamNormBE 0.12181 0.09597 1.269 0.20441
## ConditionHarm 0.07802 0.09555 0.817 0.41425
## ConditionHarmBE 0.24070 0.09567 2.516 0.01192 *
## ConditionHarmNorm 0.19500 0.09579 2.036 0.04185 *
## ConditionHarmNormBE 0.24432 0.09591 2.548 0.01090 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 3200 degrees of freedom
## Multiple R-squared: 0.006881, Adjusted R-squared: 0.004709
## F-statistic: 3.168 on 7 and 3200 DF, p-value: 0.002428
#EB1
describe(S3$EB1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 2.86 1.2 3 2.82 1.48 1 5 4 0.15 -0.88 0.02
tapply(S3$EB1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.772 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.958 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.688 3.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.909 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.809 4.000 5.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.871 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.797 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.055 4.000 5.000
model_EB1 <- lm(EB1 ~ Condition, data = S3)
summary(model_EB1)
##
## Call:
## lm(formula = EB1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05528 -0.87065 0.09068 1.09068 2.31188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.77250 0.05994 46.254 < 2e-16 ***
## ConditionFlamBE 0.18532 0.08461 2.190 0.028582 *
## ConditionFlamNorm -0.08438 0.08456 -0.998 0.318409
## ConditionFlamNormBE 0.13682 0.08493 1.611 0.107284
## ConditionHarm 0.03691 0.08456 0.436 0.662541
## ConditionHarmBE 0.09815 0.08466 1.159 0.246443
## ConditionHarmNorm 0.02500 0.08477 0.295 0.768077
## ConditionHarmNormBE 0.28278 0.08488 3.332 0.000873 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.199 on 3200 degrees of freedom
## Multiple R-squared: 0.008112, Adjusted R-squared: 0.005943
## F-statistic: 3.739 on 7 and 3200 DF, p-value: 0.0004862
#EB2
describe(S3$EB2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 2.97 1.17 3 2.96 1.48 1 5 4 0.01 -0.81 0.02
tapply(S3$EB2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.868 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.119 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.804 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.023 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.911 4.000 5.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 3.04 4.00 5.00
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.89 4.00 5.00
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.106 4.000 5.000
model_EB2 <- lm(EB2 ~ Condition, data = S3)
summary(model_EB2)
##
## Call:
## lm(formula = EB2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11911 -0.91089 -0.02267 0.96020 2.19554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.86750 0.05816 49.305 < 2e-16 ***
## ConditionFlamBE 0.25161 0.08210 3.065 0.00220 **
## ConditionFlamNorm -0.06304 0.08204 -0.768 0.44230
## ConditionFlamNormBE 0.15517 0.08240 1.883 0.05979 .
## ConditionHarm 0.04339 0.08204 0.529 0.59693
## ConditionHarmBE 0.17230 0.08215 2.097 0.03603 *
## ConditionHarmNorm 0.02250 0.08225 0.274 0.78444
## ConditionHarmNormBE 0.23803 0.08235 2.890 0.00387 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.163 on 3200 degrees of freedom
## Multiple R-squared: 0.008831, Adjusted R-squared: 0.006663
## F-statistic: 4.073 on 7 and 3200 DF, p-value: 0.0001855
#EB3
describe(S3$EB3)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 2.86 1.26 3 2.83 1.48 1 5 4 0.08 -1.04 0.02
tapply(S3$EB3, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.692 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.983 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.73 4.00 5.00
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.924 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.856 4.000 5.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.93 4.00 5.00
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.757 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.035 4.000 5.000
model_EB3 <- lm(EB3 ~ Condition, data = S3)
summary(model_EB3)
##
## Call:
## lm(formula = EB3 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03518 -0.93035 0.06965 1.06965 2.30750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.69250 0.06305 42.707 < 2e-16 ***
## ConditionFlamBE 0.29013 0.08899 3.260 0.001125 **
## ConditionFlamNorm 0.03770 0.08894 0.424 0.671693
## ConditionFlamNormBE 0.23193 0.08933 2.596 0.009463 **
## ConditionHarm 0.16358 0.08899 1.838 0.066140 .
## ConditionHarmBE 0.23785 0.08905 2.671 0.007601 **
## ConditionHarmNorm 0.06500 0.08916 0.729 0.466038
## ConditionHarmNormBE 0.34268 0.08927 3.839 0.000126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.008598, Adjusted R-squared: 0.006429
## F-statistic: 3.964 on 7 and 3199 DF, p-value: 0.0002547
#HA1
describe(S3$HA1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 4.42 1.27 5 4.58 1.48 1 6 5 -0.9 0.47 0.02
tapply(S3$HA1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.500 4.348 5.000 6.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.563 5.000 6.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.354 5.000 6.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.451 5.000 6.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.332 5.000 6.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.408 5.000 6.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.503 5.000 6.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.432 5.000 6.000
model_HA1 <- lm(HA1 ~ Condition, data = S3)
summary(model_HA1)
##
## Call:
## lm(formula = HA1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5633 -0.4509 0.4367 0.6525 1.6683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.34750 0.06353 68.428 <2e-16 ***
## ConditionFlamBE 0.21577 0.08968 2.406 0.0162 *
## ConditionFlamNorm 0.00646 0.08963 0.072 0.9425
## ConditionFlamNormBE 0.10338 0.09002 1.148 0.2509
## ConditionHarm -0.01582 0.08963 -0.176 0.8599
## ConditionHarmBE 0.06046 0.08974 0.674 0.5005
## ConditionHarmNorm 0.15500 0.08985 1.725 0.0846 .
## ConditionHarmNormBE 0.08466 0.08996 0.941 0.3467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 3200 degrees of freedom
## Multiple R-squared: 0.003561, Adjusted R-squared: 0.001381
## F-statistic: 1.634 on 7 and 3200 DF, p-value: 0.1212
#HA2
describe(S3$HA2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 4.39 1.39 5 4.57 1.48 1 6 5 -0.94 0.25 0.02
tapply(S3$HA2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.357 5.000 6.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.454 5.000 6.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.347 5.000 6.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.411 5.000 6.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 5.000 4.315 5.000 6.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.413 5.000 6.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.372 6.000 6.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 5.00 4.44 6.00 6.00
model_HA2 <- lm(HA2 ~ Condition, data = S3)
summary(model_HA2)
##
## Call:
## lm(formula = HA2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4541 -0.4129 0.5603 0.6849 1.6849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.35750 0.06973 62.493 <2e-16 ***
## ConditionFlamBE 0.09659 0.09843 0.981 0.326
## ConditionFlamNorm -0.01097 0.09837 -0.111 0.911
## ConditionFlamNormBE 0.05308 0.09880 0.537 0.591
## ConditionHarm -0.04236 0.09843 -0.430 0.667
## ConditionHarmBE 0.05544 0.09849 0.563 0.574
## ConditionHarmNorm 0.01500 0.09861 0.152 0.879
## ConditionHarmNormBE 0.08220 0.09873 0.833 0.405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.00113
## F-statistic: 0.4832 on 7 and 3199 DF, p-value: 0.8474
#SR1
describe(S3$SR1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 4.21 1.38 4 4.35 1.48 1 6 5 -0.8 -0.07 0.02
tapply(S3$SR1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 4.00 4.16 5.00 6.00
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.231 5.000 6.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.191 5.000 6.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.217 5.000 6.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.500 4.000 4.144 5.000 6.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.318 5.000 6.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 3.00 4.00 4.18 5.00 6.00
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.274 5.000 6.000
model_SR1 <- lm(SR1 ~ Condition, data = S3)
summary(model_SR1)
##
## Call:
## lm(formula = SR1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3184 -0.3184 -0.1439 0.8200 1.8561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.16000 0.06919 60.123 <2e-16 ***
## ConditionFlamBE 0.07077 0.09767 0.725 0.469
## ConditionFlamNorm 0.03059 0.09761 0.313 0.754
## ConditionFlamNormBE 0.05662 0.09804 0.578 0.564
## ConditionHarm -0.01608 0.09767 -0.165 0.869
## ConditionHarmBE 0.15841 0.09773 1.621 0.105
## ConditionHarmNorm 0.02000 0.09785 0.204 0.838
## ConditionHarmNormBE 0.11387 0.09797 1.162 0.245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.00159, Adjusted R-squared: -0.000595
## F-statistic: 0.7276 on 7 and 3199 DF, p-value: 0.6486
#SR2
describe(S3$SR2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 4.21 1.43 5 4.35 1.48 1 6 5 -0.77 -0.21 0.03
tapply(S3$SR2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 4.035 5.000 6.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.333 5.000 6.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 4.131 5.000 6.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.282 5.000 6.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 4.084 5.000 6.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.251 5.000 6.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.253 5.000 6.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.276 5.000 6.000
model_SR2 <- lm(SR2 ~ Condition, data = S3)
summary(model_SR2)
##
## Call:
## lm(formula = SR2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3325 -0.3325 0.6675 0.9158 1.9650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.03500 0.07165 56.315 < 2e-16 ***
## ConditionFlamBE 0.29751 0.10114 2.942 0.00329 **
## ConditionFlamNorm 0.09619 0.10108 0.952 0.34136
## ConditionFlamNormBE 0.24712 0.10152 2.434 0.01498 *
## ConditionHarm 0.04916 0.10108 0.486 0.62676
## ConditionHarmBE 0.21624 0.10120 2.137 0.03270 *
## ConditionHarmNorm 0.21750 0.10133 2.146 0.03191 *
## ConditionHarmNormBE 0.24138 0.10146 2.379 0.01741 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.433 on 3200 degrees of freedom
## Multiple R-squared: 0.004901, Adjusted R-squared: 0.002724
## F-statistic: 2.251 on 7 and 3200 DF, p-value: 0.02767
#ISM1
describe(S3$ISM1)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3207 3.05 1.27 3 3.06 1.48 1 5 4 -0.1 -1.03 0.02
tapply(S3$ISM1, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.882 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.109 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 2.871 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.068 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.062 4.000 5.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.184 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.013 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.201 4.000 5.000
model_ISM1 <- lm(ISM1 ~ Condition, data = S3)
summary(model_ISM1)
##
## Call:
## lm(formula = ISM1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.20101 -1.06203 -0.06203 0.93797 2.12871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.88250 0.06353 45.375 < 2e-16 ***
## ConditionFlamBE 0.22668 0.08967 2.528 0.011522 *
## ConditionFlamNorm -0.01121 0.08962 -0.125 0.900436
## ConditionFlamNormBE 0.18551 0.09001 2.061 0.039381 *
## ConditionHarm 0.17953 0.08967 2.002 0.045354 *
## ConditionHarmBE 0.30158 0.08973 3.361 0.000785 ***
## ConditionHarmNorm 0.13000 0.08984 1.447 0.147985
## ConditionHarmNormBE 0.31851 0.08995 3.541 0.000405 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.008179, Adjusted R-squared: 0.006009
## F-statistic: 3.769 on 7 and 3199 DF, p-value: 0.0004465
#ISM2
describe(S3$ISM2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3206 3.6 1.16 4 3.7 1.48 1 5 4 -0.58 -0.49 0.02
tapply(S3$ISM2, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 3.00 4.00 3.57 4.00 5.00
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.623 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.588 4.000 5.000 1
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.698 5.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.479 4.000 5.000 1
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.614 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.607 4.250 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.593 5.000 5.000
model_ISM2 <- lm(ISM2 ~ Condition, data = S3)
summary(model_ISM2)
##
## Call:
## lm(formula = ISM2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6977 -0.6144 0.3856 0.5211 1.5211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57000 0.05796 61.594 <2e-16 ***
## ConditionFlamBE 0.05283 0.08182 0.646 0.519
## ConditionFlamNorm 0.01809 0.08182 0.221 0.825
## ConditionFlamNormBE 0.12773 0.08212 1.555 0.120
## ConditionHarm -0.09109 0.08182 -1.113 0.266
## ConditionHarmBE 0.04443 0.08187 0.543 0.587
## ConditionHarmNorm 0.03750 0.08197 0.457 0.647
## ConditionHarmNormBE 0.02296 0.08207 0.280 0.780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.159 on 3198 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002417, Adjusted R-squared: 0.0002336
## F-statistic: 1.107 on 7 and 3198 DF, p-value: 0.3556
#ISM3
describe(S3$ISM3)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3208 3.35 1.23 3 3.42 1.48 1 5 4 -0.3 -0.88 0.02
tapply(S3$ISM3, S3$Condition, summary)
## $Flam
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.235 4.000 5.000
##
## $FlamBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.469 4.000 5.000
##
## $FlamNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.255 4.000 5.000
##
## $FlamNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.393 4.000 5.000
##
## $Harm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.307 4.000 5.000
##
## $HarmBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.435 4.000 5.000
##
## $HarmNorm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.297 4.000 5.000
##
## $HarmNormBE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 4.00 3.41 4.00 5.00
model_ISM3 <- lm(ISM3 ~ Condition, data = S3)
summary(model_ISM3)
##
## Call:
## lm(formula = ISM3 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.469 -1.235 -0.235 0.745 1.765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23500 0.06132 52.760 <2e-16 ***
## ConditionFlamBE 0.23398 0.08655 2.703 0.0069 **
## ConditionFlamNorm 0.01995 0.08650 0.231 0.8176
## ConditionFlamNormBE 0.15795 0.08688 1.818 0.0691 .
## ConditionHarm 0.07193 0.08650 0.832 0.4057
## ConditionHarmBE 0.20032 0.08661 2.313 0.0208 *
## ConditionHarmNorm 0.06250 0.08671 0.721 0.4711
## ConditionHarmNormBE 0.17455 0.08682 2.010 0.0445 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.226 on 3200 degrees of freedom
## Multiple R-squared: 0.004462, Adjusted R-squared: 0.002284
## F-statistic: 2.049 on 7 and 3200 DF, p-value: 0.04577
##Interactions
S3$Condition <- as.factor(S3$Condition)
#Harm
S3$H <- ifelse (S3$Condition=="Harm", 1,0)
S3$HN <- ifelse (S3$Condition== "HarmNorm", 1,0)
S3$HBE <- ifelse(S3$Condition=="HarmBE", 1,0)
S3$HNBE <- ifelse (S3$Condition== "HarmNormBE", 1,0)
S3$Harm<-S3$H+S3$HN+S3$HBE+S3$HNBE
#Norm
S3$FN <- ifelse(S3$Condition=="FlamNorm", 1,0)
S3$FNBE <- ifelse(S3$Condition=="FlamNormBE", 1,0)
S3$Norm<-S3$HN+S3$HNBE+S3$FN+S3$FNBE
#Broader Effort
S3$FBE <- ifelse(S3$Condition=="FlamBE", 1,0)
S3$BE<-S3$HBE+S3$HNBE+S3$FBE+S3$FNBE
#ClimNormS3
S3$ClimNorm<-NA
S3[S3$Condition %in% c("HarmNorm", "HarmNormBE"),]$ClimNorm<-1
S3[S3$Condition %in% c("Harm", "HarmBE"),]$ClimNorm<-0
# Average pairs
S3$IN <- rowMeans(S3[, c("IN1", "IN2")], na.rm = TRUE)
S3$Degro <- rowMeans(S3[, c("Degro1", "Degro2")], na.rm = TRUE)
S3$HA <- rowMeans(S3[, c("HA1", "HA2")], na.rm = TRUE)
S3$EB <- rowMeans(S3[, c("EB1", "EB2", "EB3")], na.rm = TRUE)
S3$SR <- rowMeans(S3[, c("SR1", "SR2")], na.rm = TRUE)
S3$ISM <- rowMeans(S3[, c("ISM1", "ISM2", "ISM3")], na.rm = TRUE)
#main affects
S3IN1 <- lm(IN1 ~ Harm*Norm*BE, data = S3)
summary(S3IN1)
##
## Call:
## lm(formula = IN1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.696 -0.696 -0.300 0.700 2.713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30000 0.05771 39.855 <2e-16 ***
## Harm 0.11191 0.08146 1.374 0.1696
## Norm -0.01287 0.08141 -0.158 0.8744
## BE 0.14665 0.08146 1.800 0.0719 .
## Harm:Norm 0.21846 0.11517 1.897 0.0579 .
## Harm:BE -0.13070 0.11513 -1.135 0.2564
## Norm:BE 0.10778 0.11528 0.935 0.3499
## Harm:Norm:BE -0.04525 0.16305 -0.278 0.7814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01351, Adjusted R-squared: 0.01135
## F-statistic: 6.258 on 7 and 3199 DF, p-value: 2.617e-07
S3IN1 <- lm(IN1 ~ Harm*Norm*BE, data = S3)
summary(S3IN1)
##
## Call:
## lm(formula = IN1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.696 -0.696 -0.300 0.700 2.713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30000 0.05771 39.855 <2e-16 ***
## Harm 0.11191 0.08146 1.374 0.1696
## Norm -0.01287 0.08141 -0.158 0.8744
## BE 0.14665 0.08146 1.800 0.0719 .
## Harm:Norm 0.21846 0.11517 1.897 0.0579 .
## Harm:BE -0.13070 0.11513 -1.135 0.2564
## Norm:BE 0.10778 0.11528 0.935 0.3499
## Harm:Norm:BE -0.04525 0.16305 -0.278 0.7814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01351, Adjusted R-squared: 0.01135
## F-statistic: 6.258 on 7 and 3199 DF, p-value: 2.617e-07
S3IN2 <- lm(IN2 ~ Harm*Norm*BE, data = S3)
summary(S3IN2)
##
## Call:
## lm(formula = IN2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05025 -0.89578 0.03023 1.03023 2.34653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.68000 0.06197 43.246 <2e-16 ***
## Harm 0.14134 0.08748 1.616 0.1063
## Norm -0.02653 0.08742 -0.304 0.7615
## BE 0.21578 0.08748 2.467 0.0137 *
## Harm:Norm 0.21019 0.12367 1.700 0.0893 .
## Harm:BE -0.18389 0.12363 -1.487 0.1370
## Norm:BE 0.10053 0.12379 0.812 0.4168
## Harm:Norm:BE -0.08717 0.17509 -0.498 0.6186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.239 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01183, Adjusted R-squared: 0.009667
## F-statistic: 5.471 on 7 and 3199 DF, p-value: 2.903e-06
S3Degro1 <- lm(Degro1 ~ Harm*Norm*BE, data = S3)
summary(S3Degro1)
##
## Call:
## lm(formula = Degro1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31658 -1.11083 -0.00248 0.99752 2.02750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.97250 0.06642 44.751 < 2e-16 ***
## Harm 0.12899 0.09370 1.377 0.16875
## Norm 0.02998 0.09370 0.320 0.74907
## BE 0.26323 0.09376 2.807 0.00502 **
## Harm:Norm 0.02604 0.13252 0.197 0.84423
## Harm:BE -0.09109 0.13247 -0.688 0.49177
## Norm:BE -0.15488 0.13268 -1.167 0.24319
## Harm:Norm:BE 0.14181 0.18764 0.756 0.44985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.328 on 3200 degrees of freedom
## Multiple R-squared: 0.007571, Adjusted R-squared: 0.0054
## F-statistic: 3.487 on 7 and 3200 DF, p-value: 0.0009934
# S3Degro1HN <- lm(Degro1 ~ Harm*Norm, data = S3)
# summary(S3Degro1HN)
S3Degro2 <- lm(Degro2 ~ Harm*Norm*BE, data = S3)
summary(S3Degro2)
##
## Call:
## lm(formula = Degro2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40695 -1.19802 -0.08168 0.91832 1.91832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12000 0.06773 46.065 < 2e-16 ***
## Harm 0.07802 0.09555 0.817 0.41425
## Norm -0.03832 0.09555 -0.401 0.68843
## BE 0.28695 0.09561 3.001 0.00271 **
## Harm:Norm 0.15530 0.13513 1.149 0.25053
## Harm:BE -0.12427 0.13508 -0.920 0.35766
## Norm:BE -0.12682 0.13530 -0.937 0.34866
## Harm:Norm:BE 0.01346 0.19134 0.070 0.94391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 3200 degrees of freedom
## Multiple R-squared: 0.006881, Adjusted R-squared: 0.004709
## F-statistic: 3.168 on 7 and 3200 DF, p-value: 0.002428
S3HA <- lm(HA ~ Harm*Norm*BE, data = S3)
summary(S3HA)
##
## Call:
## lm(formula = HA ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5087 -0.4375 0.1498 0.6770 1.6770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.352500 0.062067 70.126 <2e-16 ***
## Harm -0.029480 0.087559 -0.337 0.7364
## Norm -0.002252 0.087559 -0.026 0.9795
## BE 0.156185 0.087613 1.783 0.0747 .
## Harm:Norm 0.116733 0.123827 0.943 0.3459
## Harm:BE -0.068757 0.123787 -0.555 0.5786
## Norm:BE -0.075702 0.123982 -0.611 0.5415
## Harm:Norm:BE -0.013296 0.175337 -0.076 0.9396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 3200 degrees of freedom
## Multiple R-squared: 0.002112, Adjusted R-squared: -7.094e-05
## F-statistic: 0.9675 on 7 and 3200 DF, p-value: 0.4531
S3HA2 <- lm(HA2 ~ Harm*Norm*BE, data = S3)
summary(S3HA2)
##
## Call:
## lm(formula = HA2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4541 -0.4129 0.5603 0.6849 1.6849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.357500 0.069728 62.493 <2e-16 ***
## Harm -0.042364 0.098426 -0.430 0.667
## Norm -0.010965 0.098365 -0.111 0.911
## BE 0.096594 0.098426 0.981 0.326
## Harm:Norm 0.068329 0.139152 0.491 0.623
## Harm:BE 0.001205 0.139108 0.009 0.993
## Norm:BE -0.032550 0.139284 -0.234 0.815
## Harm:Norm:BE 0.001949 0.197007 0.010 0.992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.00113
## F-statistic: 0.4832 on 7 and 3199 DF, p-value: 0.8474
S3EB <- lm(EB ~ Harm*Norm*BE, data = S3)
summary(S3EB)
##
## Call:
## lm(formula = EB ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.06533 -0.77750 -0.01985 0.80652 2.25908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.777500 0.052586 52.818 < 2e-16 ***
## Harm 0.082649 0.074184 1.114 0.26532
## Norm -0.036576 0.074184 -0.493 0.62202
## BE 0.242351 0.074230 3.265 0.00111 **
## Harm:Norm -0.008573 0.104912 -0.082 0.93488
## Harm:BE -0.155568 0.104879 -1.483 0.13809
## Norm:BE -0.031134 0.105044 -0.296 0.76695
## Harm:Norm:BE 0.194677 0.148554 1.310 0.19013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.052 on 3200 degrees of freedom
## Multiple R-squared: 0.01072, Adjusted R-squared: 0.008557
## F-statistic: 4.954 on 7 and 3200 DF, p-value: 1.376e-05
S3EB2 <- lm(EB2 ~ Harm*Norm*BE, data = S3)
summary(S3EB2)
##
## Call:
## lm(formula = EB2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11911 -0.91089 -0.02267 0.96020 2.19554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.86750 0.05816 49.305 <2e-16 ***
## Harm 0.04339 0.08204 0.529 0.5969
## Norm -0.06304 0.08204 -0.768 0.4423
## BE 0.25161 0.08210 3.065 0.0022 **
## Harm:Norm 0.04215 0.11603 0.363 0.7164
## Harm:BE -0.12270 0.11599 -1.058 0.2902
## Norm:BE -0.03339 0.11617 -0.287 0.7738
## Harm:Norm:BE 0.12001 0.16430 0.730 0.4652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.163 on 3200 degrees of freedom
## Multiple R-squared: 0.008831, Adjusted R-squared: 0.006663
## F-statistic: 4.073 on 7 and 3200 DF, p-value: 0.0001855
S3EB3 <- lm(EB3 ~ Harm*Norm*BE, data = S3)
summary(S3EB3)
##
## Call:
## lm(formula = EB3 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03518 -0.93035 0.06965 1.06965 2.30750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.69250 0.06305 42.707 < 2e-16 ***
## Harm 0.16358 0.08899 1.838 0.06614 .
## Norm 0.03770 0.08894 0.424 0.67169
## BE 0.29013 0.08899 3.260 0.00113 **
## Harm:Norm -0.13628 0.12582 -1.083 0.27883
## Harm:BE -0.21586 0.12578 -1.716 0.08622 .
## Norm:BE -0.09590 0.12594 -0.761 0.44644
## Harm:Norm:BE 0.29930 0.17813 1.680 0.09300 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.008598, Adjusted R-squared: 0.006429
## F-statistic: 3.964 on 7 and 3199 DF, p-value: 0.0002547
S3SR<- lm(SR ~ Harm*Norm*BE, data = S3)
summary(S3SR)
##
## Call:
## lm(formula = SR ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2848 -0.6609 0.2506 0.8391 1.9025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.09750 0.06621 61.887 <2e-16 ***
## Harm 0.01760 0.09340 0.188 0.8506
## Norm 0.06339 0.09340 0.679 0.4974
## BE 0.18414 0.09346 1.970 0.0489 *
## Harm:Norm 0.03776 0.13209 0.286 0.7750
## Harm:BE -0.01441 0.13205 -0.109 0.9131
## Norm:BE -0.09566 0.13226 -0.723 0.4696
## Harm:Norm:BE -0.01519 0.18704 -0.081 0.9353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.324 on 3200 degrees of freedom
## Multiple R-squared: 0.002899, Adjusted R-squared: 0.0007178
## F-statistic: 1.329 on 7 and 3200 DF, p-value: 0.232
S3SR2 <- lm(SR2 ~ Harm*Norm*BE, data = S3)
summary(S3SR2)
##
## Call:
## lm(formula = SR2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3325 -0.3325 0.6675 0.9158 1.9650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.035000 0.071651 56.315 < 2e-16 ***
## Harm 0.049158 0.101078 0.486 0.62676
## Norm 0.096188 0.101078 0.952 0.34136
## BE 0.297506 0.101140 2.942 0.00329 **
## Harm:Norm 0.072153 0.142946 0.505 0.61376
## Harm:BE -0.130421 0.142901 -0.913 0.36149
## Norm:BE -0.146578 0.143126 -1.024 0.30585
## Harm:Norm:BE 0.003375 0.202409 0.017 0.98670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.433 on 3200 degrees of freedom
## Multiple R-squared: 0.004901, Adjusted R-squared: 0.002724
## F-statistic: 2.251 on 7 and 3200 DF, p-value: 0.02767
S3ISM <- lm(ISM ~ Harm*Norm*BE, data = S3)
summary(S3ISM)
##
## Call:
## lm(formula = ISM ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4113 -0.7337 0.0953 0.7145 1.7708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.229167 0.051847 62.282 <2e-16 ***
## Harm 0.056312 0.073141 0.770 0.4414
## Norm 0.008870 0.073141 0.121 0.9035
## BE 0.171164 0.073187 2.339 0.0194 *
## Harm:Norm 0.011485 0.103438 0.111 0.9116
## Harm:BE -0.045366 0.103405 -0.439 0.6609
## Norm:BE -0.022970 0.103568 -0.222 0.8245
## Harm:Norm:BE -0.007489 0.146466 -0.051 0.9592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 3200 degrees of freedom
## Multiple R-squared: 0.004751, Adjusted R-squared: 0.002574
## F-statistic: 2.182 on 7 and 3200 DF, p-value: 0.03291
S3ISM2 <- lm(ISM2 ~ Harm*Norm*BE, data = S3)
summary(S3ISM2)
##
## Call:
## lm(formula = ISM2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6977 -0.6144 0.3856 0.5211 1.5211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57000 0.05796 61.594 <2e-16 ***
## Harm -0.09109 0.08182 -1.113 0.266
## Norm 0.01809 0.08182 0.221 0.825
## BE 0.05283 0.08182 0.646 0.519
## Harm:Norm 0.11050 0.11570 0.955 0.340
## Harm:BE 0.08269 0.11563 0.715 0.475
## Norm:BE 0.05681 0.11581 0.491 0.624
## Harm:Norm:BE -0.20687 0.16378 -1.263 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.159 on 3198 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002417, Adjusted R-squared: 0.0002336
## F-statistic: 1.107 on 7 and 3198 DF, p-value: 0.3556
S3ISM3 <- lm(ISM3 ~ Harm*Norm*BE, data = S3)
summary(S3ISM3)
##
## Call:
## lm(formula = ISM3 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.469 -1.235 -0.235 0.745 1.765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23500 0.06132 52.760 <2e-16 ***
## Harm 0.07193 0.08650 0.832 0.4057
## Norm 0.01995 0.08650 0.231 0.8176
## BE 0.23398 0.08655 2.703 0.0069 **
## Harm:Norm -0.02938 0.12233 -0.240 0.8102
## Harm:BE -0.10559 0.12229 -0.863 0.3880
## Norm:BE -0.09599 0.12248 -0.784 0.4333
## Harm:Norm:BE 0.07964 0.17321 0.460 0.6457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.226 on 3200 degrees of freedom
## Multiple R-squared: 0.004462, Adjusted R-squared: 0.002284
## F-statistic: 2.049 on 7 and 3200 DF, p-value: 0.04577
S3ISM4 <- lm(ISM3 ~ Condition, data = S3)
summary(S3ISM4)
##
## Call:
## lm(formula = ISM3 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.469 -1.235 -0.235 0.745 1.765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23500 0.06132 52.760 <2e-16 ***
## ConditionFlamBE 0.23398 0.08655 2.703 0.0069 **
## ConditionFlamNorm 0.01995 0.08650 0.231 0.8176
## ConditionFlamNormBE 0.15795 0.08688 1.818 0.0691 .
## ConditionHarm 0.07193 0.08650 0.832 0.4057
## ConditionHarmBE 0.20032 0.08661 2.313 0.0208 *
## ConditionHarmNorm 0.06250 0.08671 0.721 0.4711
## ConditionHarmNormBE 0.17455 0.08682 2.010 0.0445 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.226 on 3200 degrees of freedom
## Multiple R-squared: 0.004462, Adjusted R-squared: 0.002284
## F-statistic: 2.049 on 7 and 3200 DF, p-value: 0.04577
S3DG1.c <- lm(Degro1 ~ Condition, data = S3)
summary(S3DG1.c)
##
## Call:
## lm(formula = Degro1 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31658 -1.11083 -0.00248 0.99752 2.02750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.97250 0.06642 44.751 < 2e-16 ***
## ConditionFlamBE 0.26323 0.09376 2.807 0.005023 **
## ConditionFlamNorm 0.02998 0.09370 0.320 0.749069
## ConditionFlamNormBE 0.13833 0.09411 1.470 0.141704
## ConditionHarm 0.12899 0.09370 1.377 0.168755
## ConditionHarmBE 0.30113 0.09382 3.210 0.001342 **
## ConditionHarmNorm 0.18500 0.09394 1.969 0.048991 *
## ConditionHarmNormBE 0.34408 0.09405 3.658 0.000258 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.328 on 3200 degrees of freedom
## Multiple R-squared: 0.007571, Adjusted R-squared: 0.0054
## F-statistic: 3.487 on 7 and 3200 DF, p-value: 0.0009934
S3DG2.c <- lm(Degro2 ~ Condition, data = S3)
summary(S3DG2.c)
##
## Call:
## lm(formula = Degro2 ~ Condition, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40695 -1.19802 -0.08168 0.91832 1.91832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12000 0.06773 46.065 < 2e-16 ***
## ConditionFlamBE 0.28695 0.09561 3.001 0.00271 **
## ConditionFlamNorm -0.03832 0.09555 -0.401 0.68843
## ConditionFlamNormBE 0.12181 0.09597 1.269 0.20441
## ConditionHarm 0.07802 0.09555 0.817 0.41425
## ConditionHarmBE 0.24070 0.09567 2.516 0.01192 *
## ConditionHarmNorm 0.19500 0.09579 2.036 0.04185 *
## ConditionHarmNormBE 0.24432 0.09591 2.548 0.01090 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 3200 degrees of freedom
## Multiple R-squared: 0.006881, Adjusted R-squared: 0.004709
## F-statistic: 3.168 on 7 and 3200 DF, p-value: 0.002428
#discrete beliefs and coalescent beliefs
#harm attribution
summary(lm(ISM1~HA1, data = S3))
##
## Call:
## lm(formula = ISM1 ~ HA1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0454 -0.7807 0.1164 0.5870 4.1164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25120 0.06320 3.975 7.2e-05 ***
## HA1 0.63237 0.01373 46.056 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9887 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3983, Adjusted R-squared: 0.3981
## F-statistic: 2121 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~HA1, data = S3))
##
## Call:
## lm(formula = ISM2 ~ HA1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2072 -0.8197 0.1803 0.7928 2.7303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.88223 0.06709 28.06 <2e-16 ***
## HA1 0.38750 0.01458 26.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.05 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1807, Adjusted R-squared: 0.1805
## F-statistic: 706.8 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~HA1, data = S3))
##
## Call:
## lm(formula = ISM3 ~ HA1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3664 -0.7217 0.2126 0.6336 3.8574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.49789 0.05842 8.522 <2e-16 ***
## HA1 0.64476 0.01269 50.797 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.914 on 3206 degrees of freedom
## Multiple R-squared: 0.4459, Adjusted R-squared: 0.4458
## F-statistic: 2580 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(ISM1~HA2, data = S3))
##
## Call:
## lm(formula = ISM1 ~ HA2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9828 -0.6640 0.0172 0.5969 3.9157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50456 0.05751 8.774 <2e-16 ***
## HA2 0.57971 0.01249 46.417 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9856 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.402, Adjusted R-squared: 0.4018
## F-statistic: 2155 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~HA2, data = S3))
##
## Call:
## lm(formula = ISM2 ~ HA2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1619 -0.8111 0.1889 0.8381 2.5923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.05688 0.06135 33.53 <2e-16 ***
## HA2 0.35084 0.01332 26.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1779, Adjusted R-squared: 0.1776
## F-statistic: 693.3 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~HA2, data = S3))
##
## Call:
## lm(formula = ISM3 ~ HA2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3342 -0.7232 -0.1121 0.6658 3.7210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.66801 0.05159 12.95 <2e-16 ***
## HA2 0.61103 0.01120 54.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8842 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4813, Adjusted R-squared: 0.4811
## F-statistic: 2974 on 1 and 3205 DF, p-value: < 2.2e-16
#norms
summary(lm(ISM1~IN1, data = S3))
##
## Call:
## lm(formula = ISM1 ~ IN1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3267 -0.8139 0.1776 0.6904 2.6904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.80537 0.04694 38.46 <2e-16 ***
## IN1 0.50426 0.01723 29.27 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.132 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.211, Adjusted R-squared: 0.2107
## F-statistic: 857 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~IN1, data = S3))
##
## Call:
## lm(formula = ISM2 ~ IN1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2173 -0.7273 0.2727 0.7626 1.7626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.99243 0.04661 64.21 <2e-16 ***
## IN1 0.24497 0.01710 14.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.124 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.06018, Adjusted R-squared: 0.05988
## F-statistic: 205.2 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~IN1, data = S3))
##
## Call:
## lm(formula = ISM3 ~ IN1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4444 -0.7165 -0.0124 0.8516 2.2835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.28448 0.04646 49.17 <2e-16 ***
## IN1 0.43198 0.01705 25.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.121 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1669, Adjusted R-squared: 0.1666
## F-statistic: 641.9 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM1~IN2, data = S3))
##
## Call:
## lm(formula = ISM1 ~ IN2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3062 -0.7170 -0.1278 0.6938 3.0505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.36033 0.04617 29.46 <2e-16 ***
## IN2 0.58917 0.01478 39.87 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.042 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3316, Adjusted R-squared: 0.3314
## F-statistic: 1590 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~IN2, data = S3))
##
## Call:
## lm(formula = ISM2 ~ IN2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2562 -0.6380 0.0529 0.7438 1.9801
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.71081 0.04846 55.94 <2e-16 ***
## IN2 0.30907 0.01551 19.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1103, Adjusted R-squared: 0.11
## F-statistic: 397 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~IN2, data = S3))
##
## Call:
## lm(formula = ISM3 ~ IN2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4906 -0.8868 0.0440 0.5786 2.6478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.81757 0.04570 39.77 <2e-16 ***
## IN2 0.53461 0.01463 36.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.031 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2942, Adjusted R-squared: 0.294
## F-statistic: 1336 on 1 and 3205 DF, p-value: < 2.2e-16
#efficacy beliefs
summary(lm(ISM1~EB1, data = S3))
##
## Call:
## lm(formula = ISM1 ~ EB1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6407 -0.6697 -0.1552 0.5876 3.3303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92697 0.04141 22.38 <2e-16 ***
## EB1 0.74274 0.01336 55.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9095 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4908, Adjusted R-squared: 0.4906
## F-statistic: 3089 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~EB1, data = S3))
##
## Call:
## lm(formula = ISM2 ~ EB1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4848 -0.6560 -0.0704 0.7584 2.1728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41279 0.04768 50.60 <2e-16 ***
## EB1 0.41439 0.01539 26.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1846, Adjusted R-squared: 0.1843
## F-statistic: 725.3 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~EB1, data = S3))
##
## Call:
## lm(formula = ISM3 ~ EB1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8388 -0.7545 -0.0597 0.5507 2.9403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.36497 0.04096 33.32 <2e-16 ***
## EB1 0.69476 0.01321 52.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8998 on 3206 degrees of freedom
## Multiple R-squared: 0.463, Adjusted R-squared: 0.4628
## F-statistic: 2764 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(ISM1~EB2, data = S3))
##
## Call:
## lm(formula = ISM1 ~ EB2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4676 -0.6724 -0.0700 0.6288 3.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.97353 0.04728 20.59 <2e-16 ***
## EB2 0.69882 0.01482 47.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9793 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4096, Adjusted R-squared: 0.4094
## F-statistic: 2224 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~EB2, data = S3))
##
## Call:
## lm(formula = ISM2 ~ EB2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4025 -0.6086 -0.0056 0.7883 2.1853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41780 0.05132 47.11 <2e-16 ***
## EB2 0.39695 0.01609 24.68 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.063 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1597, Adjusted R-squared: 0.1594
## F-statistic: 608.9 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~EB2, data = S3))
##
## Call:
## lm(formula = ISM3 ~ EB2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.05573 -0.68581 -0.05573 0.62923 2.99915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.31589 0.04499 29.25 <2e-16 ***
## EB2 0.68496 0.01410 48.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9319 on 3206 degrees of freedom
## Multiple R-squared: 0.424, Adjusted R-squared: 0.4238
## F-statistic: 2360 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(ISM1~EB3, data = S3))
##
## Call:
## lm(formula = ISM1 ~ EB3, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6135 -0.6135 0.1189 0.5837 3.3161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.95149 0.03825 24.88 <2e-16 ***
## EB3 0.73239 0.01222 59.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8751 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5285, Adjusted R-squared: 0.5284
## F-statistic: 3593 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~EB3, data = S3))
##
## Call:
## lm(formula = ISM2 ~ EB3, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4119 -0.6485 -0.0302 0.7332 2.1149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.50341 0.04607 54.34 <2e-16 ***
## EB3 0.38171 0.01472 25.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.054 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1735, Adjusted R-squared: 0.1733
## F-statistic: 672.7 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~EB3, data = S3))
##
## Call:
## lm(formula = ISM3 ~ EB3, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7848 -0.7696 -0.0978 0.5587 2.9022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.42610 0.03872 36.83 <2e-16 ***
## EB3 0.67173 0.01237 54.30 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8861 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4792, Adjusted R-squared: 0.479
## F-statistic: 2948 on 1 and 3205 DF, p-value: < 2.2e-16
#system responsibility beliefs
summary(lm(ISM1~SR1, data = S3))
##
## Call:
## lm(formula = ISM1 ~ SR1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0587 -0.4931 0.0725 0.6381 3.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.66497 0.05696 11.67 <2e-16 ***
## SR1 0.56563 0.01284 44.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.006 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.377, Adjusted R-squared: 0.3769
## F-statistic: 1940 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~SR1, data = S3))
##
## Call:
## lm(formula = ISM2 ~ SR1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2586 -0.7755 0.1122 0.7414 2.5953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.03388 0.05888 34.54 <2e-16 ***
## SR1 0.37079 0.01328 27.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.04 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1958, Adjusted R-squared: 0.1956
## F-statistic: 780.1 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~SR1, data = S3))
##
## Call:
## lm(formula = ISM3 ~ SR1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3769 -0.6510 0.1984 0.6231 3.4995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92516 0.05293 17.48 <2e-16 ***
## SR1 0.57529 0.01193 48.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9348 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4203, Adjusted R-squared: 0.4201
## F-statistic: 2324 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM1~SR1, data = S3))
##
## Call:
## lm(formula = ISM1 ~ SR1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0587 -0.4931 0.0725 0.6381 3.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.66497 0.05696 11.67 <2e-16 ***
## SR1 0.56563 0.01284 44.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.006 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.377, Adjusted R-squared: 0.3769
## F-statistic: 1940 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM2~SR2, data = S3))
##
## Call:
## lm(formula = ISM2 ~ SR2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2307 -0.8172 0.1227 0.7693 2.5362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.11042 0.05701 37.02 <2e-16 ***
## SR2 0.35339 0.01283 27.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1914, Adjusted R-squared: 0.1912
## F-statistic: 758.4 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(ISM3~SR2, data = S3))
##
## Call:
## lm(formula = ISM3 ~ SR2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3749 -0.5195 0.1962 0.6251 3.4805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.94836 0.04999 18.97 <2e-16 ***
## SR2 0.57110 0.01125 50.76 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9143 on 3206 degrees of freedom
## Multiple R-squared: 0.4456, Adjusted R-squared: 0.4454
## F-statistic: 2576 on 1 and 3206 DF, p-value: < 2.2e-16
# coalescent beliefs and lifestyle action
#Degrowth / Lifestyle actions
summary(lm(Degro1~ISM1, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ISM1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6711 -0.5447 -0.1079 0.4553 3.4553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.76317 0.04052 18.84 <2e-16 ***
## ISM1 0.78158 0.01226 63.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8848 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.559, Adjusted R-squared: 0.5588
## F-statistic: 4062 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(Degro1~ISM2, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ISM2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8773 -0.8348 0.1652 1.1227 3.2078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.27097 0.06837 18.59 <2e-16 ***
## ISM2 0.52127 0.01809 28.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.188 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2058, Adjusted R-squared: 0.2055
## F-statistic: 830 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(Degro1~ISM3, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ISM3, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4858 -0.6739 0.1380 0.5142 3.7619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42615 0.04535 9.397 <2e-16 ***
## ISM3 0.81194 0.01271 63.879 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8837 on 3206 degrees of freedom
## Multiple R-squared: 0.56, Adjusted R-squared: 0.5599
## F-statistic: 4081 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro2~ISM1, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ISM1, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6955 -0.7543 0.0398 0.5104 3.2457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.01898 0.04501 22.64 <2e-16 ***
## ISM1 0.73531 0.01362 53.97 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.983 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4761, Adjusted R-squared: 0.476
## F-statistic: 2913 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(Degro2~ISM2, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ISM2, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9905 -0.9091 0.0502 1.0095 3.0909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.38868 0.07004 19.83 <2e-16 ***
## ISM2 0.52037 0.01854 28.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.217 on 3204 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1974, Adjusted R-squared: 0.1972
## F-statistic: 788.1 on 1 and 3204 DF, p-value: < 2.2e-16
summary(lm(Degro2~ISM3, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ISM3, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5310 -0.5310 0.0086 0.4690 3.5481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.68212 0.05004 13.63 <2e-16 ***
## ISM3 0.76977 0.01402 54.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9751 on 3206 degrees of freedom
## Multiple R-squared: 0.4844, Adjusted R-squared: 0.4843
## F-statistic: 3013 on 1 and 3206 DF, p-value: < 2.2e-16
#interaction
S3IN <- lm(IN ~ Harm*Norm*BE, data = S3)
summary(S3IN)
##
## Call:
## lm(formula = IN ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8731 -0.8113 0.0100 0.7443 2.5297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.49000 0.05399 46.118 <2e-16 ***
## Harm 0.12663 0.07621 1.661 0.0967 .
## Norm -0.01970 0.07617 -0.259 0.7959
## BE 0.18122 0.07621 2.378 0.0175 *
## Harm:Norm 0.21433 0.10775 1.989 0.0468 *
## Harm:BE -0.15729 0.10772 -1.460 0.1443
## Norm:BE 0.10415 0.10785 0.966 0.3343
## Harm:Norm:BE -0.06621 0.15255 -0.434 0.6643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.08 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01526, Adjusted R-squared: 0.01311
## F-statistic: 7.084 on 7 and 3199 DF, p-value: 2.032e-08
S3IN1 <- lm(IN1 ~ Harm*Norm*BE, data = S3)
summary(S3IN1)
##
## Call:
## lm(formula = IN1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.696 -0.696 -0.300 0.700 2.713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30000 0.05771 39.855 <2e-16 ***
## Harm 0.11191 0.08146 1.374 0.1696
## Norm -0.01287 0.08141 -0.158 0.8744
## BE 0.14665 0.08146 1.800 0.0719 .
## Harm:Norm 0.21846 0.11517 1.897 0.0579 .
## Harm:BE -0.13070 0.11513 -1.135 0.2564
## Norm:BE 0.10778 0.11528 0.935 0.3499
## Harm:Norm:BE -0.04525 0.16305 -0.278 0.7814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01351, Adjusted R-squared: 0.01135
## F-statistic: 6.258 on 7 and 3199 DF, p-value: 2.617e-07
S3IN2 <- lm(IN2 ~ Harm*Norm*BE, data = S3)
summary(S3IN2)
##
## Call:
## lm(formula = IN2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05025 -0.89578 0.03023 1.03023 2.34653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.68000 0.06197 43.246 <2e-16 ***
## Harm 0.14134 0.08748 1.616 0.1063
## Norm -0.02653 0.08742 -0.304 0.7615
## BE 0.21578 0.08748 2.467 0.0137 *
## Harm:Norm 0.21019 0.12367 1.700 0.0893 .
## Harm:BE -0.18389 0.12363 -1.487 0.1370
## Norm:BE 0.10053 0.12379 0.812 0.4168
## Harm:Norm:BE -0.08717 0.17509 -0.498 0.6186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.239 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01183, Adjusted R-squared: 0.009667
## F-statistic: 5.471 on 7 and 3199 DF, p-value: 2.903e-06
S3Degro1 <- lm(Degro1 ~ Harm*Norm*BE, data = S3)
summary(S3Degro1)
##
## Call:
## lm(formula = Degro1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31658 -1.11083 -0.00248 0.99752 2.02750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.97250 0.06642 44.751 < 2e-16 ***
## Harm 0.12899 0.09370 1.377 0.16875
## Norm 0.02998 0.09370 0.320 0.74907
## BE 0.26323 0.09376 2.807 0.00502 **
## Harm:Norm 0.02604 0.13252 0.197 0.84423
## Harm:BE -0.09109 0.13247 -0.688 0.49177
## Norm:BE -0.15488 0.13268 -1.167 0.24319
## Harm:Norm:BE 0.14181 0.18764 0.756 0.44985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.328 on 3200 degrees of freedom
## Multiple R-squared: 0.007571, Adjusted R-squared: 0.0054
## F-statistic: 3.487 on 7 and 3200 DF, p-value: 0.0009934
S3Degro2 <- lm(Degro2 ~ Harm*Norm*BE, data = S3)
summary(S3Degro2)
##
## Call:
## lm(formula = Degro2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40695 -1.19802 -0.08168 0.91832 1.91832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12000 0.06773 46.065 < 2e-16 ***
## Harm 0.07802 0.09555 0.817 0.41425
## Norm -0.03832 0.09555 -0.401 0.68843
## BE 0.28695 0.09561 3.001 0.00271 **
## Harm:Norm 0.15530 0.13513 1.149 0.25053
## Harm:BE -0.12427 0.13508 -0.920 0.35766
## Norm:BE -0.12682 0.13530 -0.937 0.34866
## Harm:Norm:BE 0.01346 0.19134 0.070 0.94391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 3200 degrees of freedom
## Multiple R-squared: 0.006881, Adjusted R-squared: 0.004709
## F-statistic: 3.168 on 7 and 3200 DF, p-value: 0.002428
S3HA1 <- lm(HA1 ~ Harm*Norm*BE, data = S3)
summary(S3HA1)
##
## Call:
## lm(formula = HA1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5633 -0.4509 0.4367 0.6525 1.6683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.34750 0.06353 68.428 <2e-16 ***
## Harm -0.01582 0.08963 -0.176 0.8599
## Norm 0.00646 0.08963 0.072 0.9425
## BE 0.21577 0.08968 2.406 0.0162 *
## Harm:Norm 0.16436 0.12675 1.297 0.1948
## Harm:BE -0.13950 0.12671 -1.101 0.2710
## Norm:BE -0.11885 0.12691 -0.937 0.3491
## Harm:Norm:BE -0.02776 0.17948 -0.155 0.8771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 3200 degrees of freedom
## Multiple R-squared: 0.003561, Adjusted R-squared: 0.001381
## F-statistic: 1.634 on 7 and 3200 DF, p-value: 0.1212
S3HA2 <- lm(HA2 ~ Harm*Norm*BE, data = S3)
summary(S3HA2)
##
## Call:
## lm(formula = HA2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4541 -0.4129 0.5603 0.6849 1.6849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.357500 0.069728 62.493 <2e-16 ***
## Harm -0.042364 0.098426 -0.430 0.667
## Norm -0.010965 0.098365 -0.111 0.911
## BE 0.096594 0.098426 0.981 0.326
## Harm:Norm 0.068329 0.139152 0.491 0.623
## Harm:BE 0.001205 0.139108 0.009 0.993
## Norm:BE -0.032550 0.139284 -0.234 0.815
## Harm:Norm:BE 0.001949 0.197007 0.010 0.992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.00113
## F-statistic: 0.4832 on 7 and 3199 DF, p-value: 0.8474
S3EB1 <- lm(EB1 ~ Harm*Norm*BE, data = S3)
summary(S3EB1)
##
## Call:
## lm(formula = EB1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05528 -0.87065 0.09068 1.09068 2.31188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.77250 0.05994 46.254 <2e-16 ***
## Harm 0.03691 0.08456 0.436 0.6625
## Norm -0.08438 0.08456 -0.998 0.3184
## BE 0.18532 0.08461 2.190 0.0286 *
## Harm:Norm 0.07248 0.11959 0.606 0.5445
## Harm:BE -0.12408 0.11955 -1.038 0.2994
## Norm:BE 0.03588 0.11974 0.300 0.7644
## Harm:Norm:BE 0.16065 0.16933 0.949 0.3428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.199 on 3200 degrees of freedom
## Multiple R-squared: 0.008112, Adjusted R-squared: 0.005943
## F-statistic: 3.739 on 7 and 3200 DF, p-value: 0.0004862
S3EB2 <- lm(EB2 ~ Harm*Norm*BE, data = S3)
summary(S3EB2)
##
## Call:
## lm(formula = EB2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11911 -0.91089 -0.02267 0.96020 2.19554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.86750 0.05816 49.305 <2e-16 ***
## Harm 0.04339 0.08204 0.529 0.5969
## Norm -0.06304 0.08204 -0.768 0.4423
## BE 0.25161 0.08210 3.065 0.0022 **
## Harm:Norm 0.04215 0.11603 0.363 0.7164
## Harm:BE -0.12270 0.11599 -1.058 0.2902
## Norm:BE -0.03339 0.11617 -0.287 0.7738
## Harm:Norm:BE 0.12001 0.16430 0.730 0.4652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.163 on 3200 degrees of freedom
## Multiple R-squared: 0.008831, Adjusted R-squared: 0.006663
## F-statistic: 4.073 on 7 and 3200 DF, p-value: 0.0001855
S3EB3 <- lm(EB3 ~ Harm*Norm*BE, data = S3)
summary(S3EB3)
##
## Call:
## lm(formula = EB3 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03518 -0.93035 0.06965 1.06965 2.30750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.69250 0.06305 42.707 < 2e-16 ***
## Harm 0.16358 0.08899 1.838 0.06614 .
## Norm 0.03770 0.08894 0.424 0.67169
## BE 0.29013 0.08899 3.260 0.00113 **
## Harm:Norm -0.13628 0.12582 -1.083 0.27883
## Harm:BE -0.21586 0.12578 -1.716 0.08622 .
## Norm:BE -0.09590 0.12594 -0.761 0.44644
## Harm:Norm:BE 0.29930 0.17813 1.680 0.09300 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.008598, Adjusted R-squared: 0.006429
## F-statistic: 3.964 on 7 and 3199 DF, p-value: 0.0002547
S3SR1 <- lm(SR1 ~ Harm*Norm*BE, data = S3)
summary(S3SR1)
##
## Call:
## lm(formula = SR1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3184 -0.3184 -0.1439 0.8200 1.8561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.160000 0.069192 60.123 <2e-16 ***
## Harm -0.016079 0.097670 -0.165 0.869
## Norm 0.030594 0.097609 0.313 0.754
## BE 0.070769 0.097670 0.725 0.469
## Harm:Norm 0.005485 0.138083 0.040 0.968
## Harm:BE 0.103718 0.138039 0.751 0.452
## Norm:BE -0.044739 0.138214 -0.324 0.746
## Harm:Norm:BE -0.035879 0.195493 -0.184 0.854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.00159, Adjusted R-squared: -0.000595
## F-statistic: 0.7276 on 7 and 3199 DF, p-value: 0.6486
S3SR2 <- lm(SR2 ~ Harm*Norm*BE, data = S3)
summary(S3SR2)
##
## Call:
## lm(formula = SR2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3325 -0.3325 0.6675 0.9158 1.9650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.035000 0.071651 56.315 < 2e-16 ***
## Harm 0.049158 0.101078 0.486 0.62676
## Norm 0.096188 0.101078 0.952 0.34136
## BE 0.297506 0.101140 2.942 0.00329 **
## Harm:Norm 0.072153 0.142946 0.505 0.61376
## Harm:BE -0.130421 0.142901 -0.913 0.36149
## Norm:BE -0.146578 0.143126 -1.024 0.30585
## Harm:Norm:BE 0.003375 0.202409 0.017 0.98670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.433 on 3200 degrees of freedom
## Multiple R-squared: 0.004901, Adjusted R-squared: 0.002724
## F-statistic: 2.251 on 7 and 3200 DF, p-value: 0.02767
S3ISM1 <- lm(ISM1 ~ Harm*Norm*BE, data = S3)
summary(S3ISM1)
##
## Call:
## lm(formula = ISM1 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.20101 -1.06203 -0.06203 0.93797 2.12871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.88250 0.06353 45.375 <2e-16 ***
## Harm 0.17953 0.08967 2.002 0.0454 *
## Norm -0.01121 0.08962 -0.125 0.9004
## BE 0.22668 0.08967 2.528 0.0115 *
## Harm:Norm -0.03832 0.12678 -0.302 0.7625
## Harm:BE -0.10464 0.12674 -0.826 0.4091
## Norm:BE -0.02996 0.12690 -0.236 0.8134
## Harm:Norm:BE 0.09642 0.17948 0.537 0.5912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.271 on 3199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.008179, Adjusted R-squared: 0.006009
## F-statistic: 3.769 on 7 and 3199 DF, p-value: 0.0004465
S3ISM2 <- lm(ISM2 ~ Harm*Norm*BE, data = S3)
summary(S3ISM2)
##
## Call:
## lm(formula = ISM2 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6977 -0.6144 0.3856 0.5211 1.5211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57000 0.05796 61.594 <2e-16 ***
## Harm -0.09109 0.08182 -1.113 0.266
## Norm 0.01809 0.08182 0.221 0.825
## BE 0.05283 0.08182 0.646 0.519
## Harm:Norm 0.11050 0.11570 0.955 0.340
## Harm:BE 0.08269 0.11563 0.715 0.475
## Norm:BE 0.05681 0.11581 0.491 0.624
## Harm:Norm:BE -0.20687 0.16378 -1.263 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.159 on 3198 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002417, Adjusted R-squared: 0.0002336
## F-statistic: 1.107 on 7 and 3198 DF, p-value: 0.3556
S3ISM3 <- lm(ISM3 ~ Harm*Norm*BE, data = S3)
summary(S3ISM3)
##
## Call:
## lm(formula = ISM3 ~ Harm * Norm * BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.469 -1.235 -0.235 0.745 1.765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23500 0.06132 52.760 <2e-16 ***
## Harm 0.07193 0.08650 0.832 0.4057
## Norm 0.01995 0.08650 0.231 0.8176
## BE 0.23398 0.08655 2.703 0.0069 **
## Harm:Norm -0.02938 0.12233 -0.240 0.8102
## Harm:BE -0.10559 0.12229 -0.863 0.3880
## Norm:BE -0.09599 0.12248 -0.784 0.4333
## Harm:Norm:BE 0.07964 0.17321 0.460 0.6457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.226 on 3200 degrees of freedom
## Multiple R-squared: 0.004462, Adjusted R-squared: 0.002284
## F-statistic: 2.049 on 7 and 3200 DF, p-value: 0.04577
summary(lm(ISM~HA, data = S3))
##
## Call:
## lm(formula = ISM ~ HA, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.00461 -0.41575 -0.03054 0.58425 2.53237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.62325 0.04585 13.59 <2e-16 ***
## HA 0.61479 0.01002 61.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7041 on 3206 degrees of freedom
## Multiple R-squared: 0.5402, Adjusted R-squared: 0.5401
## F-statistic: 3767 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(ISM~IN, data = S3))
##
## Call:
## lm(formula = ISM ~ IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5874 -0.5758 0.0266 0.5529 2.5646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.89745 0.04012 47.3 <2e-16 ***
## IN 0.53799 0.01394 38.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8578 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3174, Adjusted R-squared: 0.3172
## F-statistic: 1490 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(ISM~EB, data = S3))
##
## Call:
## lm(formula = ISM ~ EB, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40215 -0.41374 0.00016 0.39090 2.57468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.03459 0.03163 32.71 <2e-16 ***
## EB 0.79305 0.01026 77.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6136 on 3206 degrees of freedom
## Multiple R-squared: 0.6509, Adjusted R-squared: 0.6508
## F-statistic: 5977 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(ISM~SR, data = S3))
##
## Call:
## lm(formula = ISM ~ SR, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6783 -0.4962 0.0697 0.5542 3.2015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.949735 0.042274 22.47 <2e-16 ***
## SR 0.565874 0.009579 59.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7185 on 3206 degrees of freedom
## Multiple R-squared: 0.5212, Adjusted R-squared: 0.5211
## F-statistic: 3490 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro~ISM, data = S3))
##
## Call:
## lm(formula = Degro ~ ISM, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9926 -0.4926 0.0489 0.4732 3.4050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01477 0.04532 -0.326 0.744
## ISM 0.96587 0.01298 74.386 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7635 on 3206 degrees of freedom
## Multiple R-squared: 0.6331, Adjusted R-squared: 0.633
## F-statistic: 5533 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro~ISM, data = S3))
##
## Call:
## lm(formula = Degro ~ ISM, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9926 -0.4926 0.0489 0.4732 3.4050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01477 0.04532 -0.326 0.744
## ISM 0.96587 0.01298 74.386 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7635 on 3206 degrees of freedom
## Multiple R-squared: 0.6331, Adjusted R-squared: 0.633
## F-statistic: 5533 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro1~ISM, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ISM, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4642 -0.4891 0.1692 0.5233 4.1567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.14426 0.05048 -2.858 0.00429 **
## ISM 0.98752 0.01446 68.273 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8505 on 3206 degrees of freedom
## Multiple R-squared: 0.5925, Adjusted R-squared: 0.5924
## F-statistic: 4661 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro2~ISM, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ISM, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8358 -0.5769 0.0526 0.4789 3.6263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11472 0.05577 2.057 0.0398 *
## ISM 0.94423 0.01598 59.089 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9396 on 3206 degrees of freedom
## Multiple R-squared: 0.5213, Adjusted R-squared: 0.5212
## F-statistic: 3492 on 1 and 3206 DF, p-value: < 2.2e-16
#main effects
summary(lm(HA~Harm, data = S3))
##
## Call:
## lm(formula = HA ~ Harm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4105 -0.4105 0.0985 0.5985 1.5985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.41054 0.03100 142.283 <2e-16 ***
## Harm -0.00904 0.04384 -0.206 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 3206 degrees of freedom
## Multiple R-squared: 1.326e-05, Adjusted R-squared: -0.0002986
## F-statistic: 0.04252 on 1 and 3206 DF, p-value: 0.8366
summary(lm(IN~ClimNorm, data = S3))
##
## Call:
## lm(formula = IN ~ ClimNorm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8421 -0.8421 -0.1286 0.8714 2.3714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.62857 0.03901 67.377 < 2e-16 ***
## ClimNorm 0.21353 0.05529 3.862 0.000117 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.107 on 1601 degrees of freedom
## (1605 observations deleted due to missingness)
## Multiple R-squared: 0.009229, Adjusted R-squared: 0.008611
## F-statistic: 14.91 on 1 and 1601 DF, p-value: 0.000117
summary(lm(EB~ClimNorm, data = S3))
##
## Call:
## lm(formula = EB ~ ClimNorm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.93985 -0.90343 0.06015 0.76323 2.09657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.90343 0.03817 76.067 <2e-16 ***
## ClimNorm 0.03642 0.05411 0.673 0.501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.084 on 1602 degrees of freedom
## (1604 observations deleted due to missingness)
## Multiple R-squared: 0.0002826, Adjusted R-squared: -0.0003414
## F-statistic: 0.4529 on 1 and 1602 DF, p-value: 0.5011
summary(lm(EB~BE, data = S3))
##
## Call:
## lm(formula = EB ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.99604 -0.79840 0.00396 0.86826 2.20160
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.79840 0.02623 106.691 < 2e-16 ***
## BE 0.19764 0.03714 5.321 1.1e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.052 on 3206 degrees of freedom
## Multiple R-squared: 0.008755, Adjusted R-squared: 0.008446
## F-statistic: 28.32 on 1 and 3206 DF, p-value: 1.1e-07
summary(lm(SR~BE, data = S3))
##
## Call:
## lm(formula = SR ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2728 -0.6474 0.2272 0.8526 1.8526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.14739 0.03300 125.671 < 2e-16 ***
## BE 0.12542 0.04673 2.684 0.00731 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.323 on 3206 degrees of freedom
## Multiple R-squared: 0.002242, Adjusted R-squared: 0.001931
## F-statistic: 7.204 on 1 and 3206 DF, p-value: 0.007312
summary(lm(IN~BE, data = S3))
##
## Call:
## lm(formula = IN ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.73469 -0.73469 -0.09676 0.76531 2.40324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.59676 0.02707 95.945 < 2e-16 ***
## BE 0.13792 0.03832 3.599 0.000324 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.085 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.004026, Adjusted R-squared: 0.003715
## F-statistic: 12.96 on 1 and 3205 DF, p-value: 0.0003237
summary(lm(HA~BE, data = S3))
##
## Call:
## lm(formula = HA ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4466 -0.4466 0.1343 0.6343 1.6343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.36567 0.03094 141.085 <2e-16 ***
## BE 0.08089 0.04382 1.846 0.065 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 3206 degrees of freedom
## Multiple R-squared: 0.001062, Adjusted R-squared: 0.0007504
## F-statistic: 3.408 on 1 and 3206 DF, p-value: 0.06496
summary(lm(ISM~BE, data = S3))
##
## Call:
## lm(formula = ISM ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.39979 -0.73313 0.06872 0.73539 1.73539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.26461 0.02584 126.332 < 2e-16 ***
## BE 0.13518 0.03659 3.694 0.000224 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.036 on 3206 degrees of freedom
## Multiple R-squared: 0.004239, Adjusted R-squared: 0.003928
## F-statistic: 13.65 on 1 and 3206 DF, p-value: 0.0002242
summary(lm(ISM~ClimNorm, data = S3))
##
## Call:
## lm(formula = ISM ~ ClimNorm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35338 -0.68672 -0.01489 0.65178 1.65178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.348222 0.037529 89.216 <2e-16 ***
## ClimNorm 0.005162 0.053207 0.097 0.923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.065 on 1602 degrees of freedom
## (1604 observations deleted due to missingness)
## Multiple R-squared: 5.875e-06, Adjusted R-squared: -0.0006183
## F-statistic: 0.009411 on 1 and 1602 DF, p-value: 0.9227
summary(lm(ISM~Harm, data = S3))
##
## Call:
## lm(formula = ISM ~ Harm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35079 -0.68412 0.02005 0.68672 1.68672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.31328 0.02592 127.805 <2e-16 ***
## Harm 0.03751 0.03666 1.023 0.306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.038 on 3206 degrees of freedom
## Multiple R-squared: 0.0003264, Adjusted R-squared: 1.458e-05
## F-statistic: 1.047 on 1 and 3206 DF, p-value: 0.3063
summary(lm(Degro1~ClimNorm, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ClimNorm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2368 -1.1873 -0.1873 0.8127 1.8127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.18734 0.04759 66.968 <2e-16 ***
## ClimNorm 0.04950 0.06748 0.734 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 1602 degrees of freedom
## (1604 observations deleted due to missingness)
## Multiple R-squared: 0.0003358, Adjusted R-squared: -0.0002882
## F-statistic: 0.5381 on 1 and 1602 DF, p-value: 0.4633
summary(lm(Degro1~Harm, data = S3))
##
## Call:
## lm(formula = Degro1 ~ Harm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.21197 -1.08042 -0.08042 0.91958 1.91958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.08042 0.03322 92.72 < 2e-16 ***
## Harm 0.13155 0.04699 2.80 0.00515 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.331 on 3206 degrees of freedom
## Multiple R-squared: 0.002439, Adjusted R-squared: 0.002128
## F-statistic: 7.838 on 1 and 3206 DF, p-value: 0.005146
summary(lm(Degro1~BE, data = S3))
##
## Call:
## lm(formula = Degro1 ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.23438 -1.05846 -0.05846 0.94154 1.94154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.05846 0.03315 92.258 < 2e-16 ***
## BE 0.17592 0.04694 3.748 0.000182 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.329 on 3206 degrees of freedom
## Multiple R-squared: 0.004362, Adjusted R-squared: 0.004051
## F-statistic: 14.04 on 1 and 3206 DF, p-value: 0.0001817
summary(lm(Degro2~ClimNorm, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ClimNorm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3396 -1.2792 0.6604 0.7208 1.7208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.27916 0.04816 68.085 <2e-16 ***
## ClimNorm 0.06044 0.06828 0.885 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 1602 degrees of freedom
## (1604 observations deleted due to missingness)
## Multiple R-squared: 0.0004889, Adjusted R-squared: -0.000135
## F-statistic: 0.7835 on 1 and 1602 DF, p-value: 0.3762
summary(lm(Degro2~Harm, data = S3))
##
## Call:
## lm(formula = Degro2 ~ Harm, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3092 -1.2126 -0.2126 0.7874 1.7874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.21259 0.03389 94.804 <2e-16 ***
## Harm 0.09663 0.04792 2.016 0.0438 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 3206 degrees of freedom
## Multiple R-squared: 0.001267, Adjusted R-squared: 0.0009551
## F-statistic: 4.066 on 1 and 3206 DF, p-value: 0.04384
summary(lm(Degro2~BE, data = S3))
##
## Call:
## lm(formula = Degro2 ~ BE, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3438 -1.1785 -0.1785 0.8215 1.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.17848 0.03380 94.029 < 2e-16 ***
## BE 0.16527 0.04786 3.453 0.000562 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 3206 degrees of freedom
## Multiple R-squared: 0.003705, Adjusted R-squared: 0.003394
## F-statistic: 11.92 on 1 and 3206 DF, p-value: 0.000562
#DISCRETE TO DEGROWTH
summary(lm(Degro1~HA, data = S3))
##
## Call:
## lm(formula = Degro1 ~ HA, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2751 -0.5669 0.1413 0.7249 4.2660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02579 0.06518 0.396 0.692
## HA 0.70821 0.01424 49.738 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.001 on 3206 degrees of freedom
## Multiple R-squared: 0.4355, Adjusted R-squared: 0.4354
## F-statistic: 2474 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro2~HA, data = S3))
##
## Call:
## lm(formula = Degro2 ~ HA, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3695 -0.6740 0.0215 0.6737 4.1079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19661 0.06826 2.88 0.004 **
## HA 0.69548 0.01491 46.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.048 on 3206 degrees of freedom
## Multiple R-squared: 0.4042, Adjusted R-squared: 0.4041
## F-statistic: 2175 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro1~IN, data = S3))
##
## Call:
## lm(formula = Degro1 ~ IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6345 -0.7215 -0.0403 0.6843 2.9161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.44620 0.05322 27.18 <2e-16 ***
## IN 0.63766 0.01849 34.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.138 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2707, Adjusted R-squared: 0.2705
## F-statistic: 1190 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(Degro2~IN, data = S3))
##
## Call:
## lm(formula = Degro2 ~ IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6944 -0.8519 0.1481 0.8410 2.7623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.62353 0.05532 29.35 <2e-16 ***
## IN 0.61418 0.01922 31.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.183 on 3205 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2417, Adjusted R-squared: 0.2415
## F-statistic: 1022 on 1 and 3205 DF, p-value: < 2.2e-16
summary(lm(Degro1~SR, data = S3))
##
## Call:
## lm(formula = Degro1 ~ SR, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2683 -0.6414 -0.0146 0.7317 3.8660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50717 0.06129 8.275 <2e-16 ***
## SR 0.62686 0.01389 45.140 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.042 on 3206 degrees of freedom
## Multiple R-squared: 0.3886, Adjusted R-squared: 0.3884
## F-statistic: 2038 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro2~SR, data = S3))
##
## Call:
## lm(formula = Degro2 ~ SR, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3306 -0.7330 0.0597 0.6694 3.6573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.74514 0.06491 11.48 <2e-16 ***
## SR 0.59758 0.01471 40.63 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 3206 degrees of freedom
## Multiple R-squared: 0.3399, Adjusted R-squared: 0.3397
## F-statistic: 1651 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro1~EB, data = S3))
##
## Call:
## lm(formula = Degro1 ~ EB, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7959 -0.5530 -0.1067 0.5148 3.6222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44556 0.04625 9.633 <2e-16 ***
## EB 0.93223 0.01500 62.148 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8972 on 3206 degrees of freedom
## Multiple R-squared: 0.5464, Adjusted R-squared: 0.5463
## F-statistic: 3862 on 1 and 3206 DF, p-value: < 2.2e-16
summary(lm(Degro2~EB, data = S3))
##
## Call:
## lm(formula = Degro2 ~ EB, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5164 -0.6423 -0.0596 0.6490 3.3972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.72876 0.05133 14.20 <2e-16 ***
## EB 0.87407 0.01665 52.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9958 on 3206 degrees of freedom
## Multiple R-squared: 0.4623, Adjusted R-squared: 0.4622
## F-statistic: 2757 on 1 and 3206 DF, p-value: < 2.2e-16
#Mediational model of DB to ISM to Degrowth
summary(lm(Degro1~ClimNorm + IN, data = S3))
##
## Call:
## lm(formula = Degro1 ~ ClimNorm + IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6395 -0.7777 -0.0394 0.8723 2.9606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.47770 0.07899 18.706 <2e-16 ***
## ClimNorm -0.08830 0.05743 -1.537 0.124
## IN 0.65003 0.02584 25.156 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 1600 degrees of freedom
## (1605 observations deleted due to missingness)
## Multiple R-squared: 0.2837, Adjusted R-squared: 0.2828
## F-statistic: 316.8 on 2 and 1600 DF, p-value: < 2.2e-16
summary(lm(Degro2~ClimNorm + IN, data = S3))
##
## Call:
## lm(formula = Degro2 ~ ClimNorm + IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4541 -0.8833 0.1167 0.8600 2.8178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.62666 0.08131 20.005 <2e-16 ***
## ClimNorm -0.07283 0.05912 -1.232 0.218
## IN 0.62832 0.02660 23.624 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.178 on 1600 degrees of freedom
## (1605 observations deleted due to missingness)
## Multiple R-squared: 0.259, Adjusted R-squared: 0.258
## F-statistic: 279.6 on 2 and 1600 DF, p-value: < 2.2e-16
summary(lm(Degro1~HA+EB+SR+IN+ISM, data = S3))
##
## Call:
## lm(formula = Degro1 ~ HA + EB + SR + IN + ISM, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8684 -0.4926 -0.0228 0.4658 4.4277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52324 0.05475 -9.557 < 2e-16 ***
## HA 0.16213 0.01806 8.975 < 2e-16 ***
## EB 0.35889 0.02460 14.591 < 2e-16 ***
## SR 0.03966 0.01666 2.380 0.01736 *
## IN 0.05022 0.01682 2.985 0.00285 **
## ISM 0.48461 0.02691 18.008 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7926 on 3201 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6466, Adjusted R-squared: 0.6461
## F-statistic: 1171 on 5 and 3201 DF, p-value: < 2.2e-16
summary(lm(Degro1~HA+EB+SR+IN, data = S3))
##
## Call:
## lm(formula = Degro1 ~ HA + EB + SR + IN, data = S3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7214 -0.5280 -0.0390 0.4948 4.3747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.41822 0.05712 -7.322 3.09e-13 ***
## HA 0.26722 0.01794 14.897 < 2e-16 ***
## EB 0.58002 0.02236 25.939 < 2e-16 ***
## SR 0.11884 0.01686 7.047 2.22e-12 ***
## IN 0.07745 0.01758 4.406 1.09e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8316 on 3202 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6108, Adjusted R-squared: 0.6103
## F-statistic: 1256 on 4 and 3202 DF, p-value: < 2.2e-16