models and simple slopes
bootymodel1 <- lm(fear ~ R_image*Dis_Prop, data = nickbooty)
summary(bootymodel1)
##
## Call:
## lm(formula = fear ~ R_image * Dis_Prop, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6009 -0.7937 -0.1725 0.7613 3.2841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43588 0.41664 1.046 0.29614
## R_image2 -0.41909 0.65224 -0.643 0.52090
## R_image3 -0.11986 0.60294 -0.199 0.84253
## Dis_Prop 0.07147 0.02413 2.962 0.00324 **
## R_image2:Dis_Prop 0.05924 0.03727 1.590 0.11277
## R_image3:Dis_Prop 0.07135 0.03483 2.048 0.04121 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 384 degrees of freedom
## Multiple R-squared: 0.2462, Adjusted R-squared: 0.2364
## F-statistic: 25.09 on 5 and 384 DF, p-value: < 2.2e-16
sim_slopes(bootymodel1, pred = Dis_Prop, modx = R_image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Dis_Prop when R_image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.14 0.03 5.68 0.00
##
## Slope of Dis_Prop when R_image = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.13 0.03 4.60 0.00
##
## Slope of Dis_Prop when R_image = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.02 2.96 0.00
bootymodel2 <- lm(disgust ~ R_image*Dis_Prop, data = nickbooty)
summary(bootymodel2)
##
## Call:
## lm(formula = disgust ~ R_image * Dis_Prop, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5478 -0.8037 0.0479 0.7779 3.0479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.67045 0.42780 1.567 0.11789
## R_image2 0.24383 0.66970 0.364 0.71599
## R_image3 0.64672 0.61908 1.045 0.29685
## Dis_Prop 0.07120 0.02477 2.874 0.00427 **
## R_image2:Dis_Prop 0.06455 0.03826 1.687 0.09243 .
## R_image3:Dis_Prop 0.07564 0.03577 2.115 0.03509 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.138 on 384 degrees of freedom
## Multiple R-squared: 0.4032, Adjusted R-squared: 0.3954
## F-statistic: 51.88 on 5 and 384 DF, p-value: < 2.2e-16
sim_slopes(bootymodel2, pred = Dis_Prop, modx = R_image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Dis_Prop when R_image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.15 0.03 5.69 0.00
##
## Slope of Dis_Prop when R_image = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.14 0.03 4.65 0.00
##
## Slope of Dis_Prop when R_image = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.02 2.87 0.00
bootymodel3 <- lm(sadness ~ R_image*NFA, data = nickbooty)
summary(bootymodel3)
##
## Call:
## lm(formula = sadness ~ R_image * NFA, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.98027 -0.96608 -0.02914 0.97207 2.93302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.62193 0.58289 4.498 9.09e-06 ***
## R_image2 0.13101 0.77401 0.169 0.8657
## R_image3 -0.52501 0.82110 -0.639 0.5230
## NFA -0.01261 0.01118 -1.129 0.2598
## R_image2:NFA 0.01846 0.01516 1.218 0.2239
## R_image3:NFA 0.04072 0.01618 2.517 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.248 on 384 degrees of freedom
## Multiple R-squared: 0.2119, Adjusted R-squared: 0.2016
## F-statistic: 20.65 on 5 and 384 DF, p-value: < 2.2e-16
sim_slopes(bootymodel3, pred = NFA, modx = R_image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of NFA when R_image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.01 2.40 0.02
##
## Slope of NFA when R_image = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.01 0.01 0.57 0.57
##
## Slope of NFA when R_image = 1:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.01 -1.13 0.26
bootymodel4 <- lm(anger ~ R_image*Purity, data = nickbooty)
summary(bootymodel4)
##
## Call:
## lm(formula = anger ~ R_image * Purity, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0875 -0.7125 -0.5199 0.7764 3.4508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.666739 0.217195 7.674 1.39e-13 ***
## R_image2 -0.204539 0.312316 -0.655 0.5129
## R_image3 0.188787 0.313067 0.603 0.5468
## Purity -0.004896 0.013015 -0.376 0.7070
## R_image2:Purity 0.046614 0.018281 2.550 0.0112 *
## R_image3:Purity 0.045964 0.018933 2.428 0.0157 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.18 on 384 degrees of freedom
## Multiple R-squared: 0.1231, Adjusted R-squared: 0.1117
## F-statistic: 10.78 on 5 and 384 DF, p-value: 1.022e-09
sim_slopes(bootymodel4, pred = Purity, modx = R_image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Purity when R_image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.04 0.01 2.99 0.00
##
## Slope of Purity when R_image = 2:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.04 0.01 3.25 0.00
##
## Slope of Purity when R_image = 1:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.00 0.01 -0.38 0.71
bootymodel5 <- lm(fear ~ image*Care, data = nickbooty)
summary(bootymodel5)
##
## Call:
## lm(formula = fear ~ image * Care, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9427 -0.7762 -0.3495 1.1405 3.2203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.96605 0.47304 2.042 0.04181 *
## image2 2.12645 0.65388 3.252 0.00125 **
## image3 -0.07375 0.67491 -0.109 0.91305
## Care 0.05887 0.02064 2.852 0.00458 **
## image2:Care -0.07552 0.02890 -2.613 0.00932 **
## image3:Care -0.02600 0.02930 -0.888 0.37536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.177 on 384 degrees of freedom
## Multiple R-squared: 0.1491, Adjusted R-squared: 0.138
## F-statistic: 13.46 on 5 and 384 DF, p-value: 4.201e-12
sim_slopes(bootymodel5, pred = Care, modx = image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Care when image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.02 1.58 0.11
##
## Slope of Care when image = 2:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.02 0.02 -0.82 0.41
##
## Slope of Care when image = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.06 0.02 2.85 0.00
bootymodel6 <- lm(anger ~ image*Care, data = nickbooty)
summary(bootymodel6)
##
## Call:
## lm(formula = anger ~ image * Care, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6765 -0.7877 -0.4103 0.6839 3.5094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.98870 0.48091 2.056 0.04047 *
## image2 1.75438 0.66477 2.639 0.00865 **
## image3 -0.07250 0.68615 -0.106 0.91591
## Care 0.05019 0.02099 2.392 0.01726 *
## image2:Care -0.06350 0.02938 -2.161 0.03128 *
## image3:Care -0.02014 0.02979 -0.676 0.49945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.197 on 384 degrees of freedom
## Multiple R-squared: 0.09749, Adjusted R-squared: 0.08574
## F-statistic: 8.296 on 5 and 384 DF, p-value: 1.848e-07
sim_slopes(bootymodel6, pred = Care, modx = image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Care when image = 3:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.02 1.42 0.16
##
## Slope of Care when image = 2:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.02 -0.65 0.52
##
## Slope of Care when image = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.05 0.02 2.39 0.02
bootymodel7 <- lm(disgust ~ image*Exper, data = nickbooty)
summary(bootymodel7)
##
## Call:
## lm(formula = disgust ~ image * Exper, data = nickbooty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1494 -0.8665 0.1189 0.9101 3.1700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.96786 0.27494 10.795 < 2e-16 ***
## image2 1.57311 0.39423 3.990 7.9e-05 ***
## image3 -1.04305 0.41140 -2.535 0.01163 *
## Exper 0.03756 0.03175 1.183 0.23747
## image2:Exper -0.13546 0.04695 -2.885 0.00413 **
## image3:Exper -0.04486 0.04723 -0.950 0.34279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.212 on 384 degrees of freedom
## Multiple R-squared: 0.323, Adjusted R-squared: 0.3142
## F-statistic: 36.64 on 5 and 384 DF, p-value: < 2.2e-16
sim_slopes(bootymodel7, pred = Exper, modx = image, centered = "all")
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of Exper when image = 3:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.03 -0.21 0.83
##
## Slope of Exper when image = 2:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.10 0.03 -2.83 0.00
##
## Slope of Exper when image = 1:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.04 0.03 1.18 0.24