# Age by Gender
psychopathy_df %>% group_by(GENDER) %>% summarise_at(vars(AGE, RELATIONSHIP_MONTHS), list(mean = mean, sd = sd), na.rm = TRUE)
# Sexual Orien by Gender
#psychopathy_df %>% group_by(GENDER) %>% count(SEXUAL_PREF)
psychopathy_df %>% group_by(GENDER, SEXUAL_PREF) %>% summarise(n = n()) %>% mutate(percentage = n / 85 * 100)`summarise()` has grouped output by 'GENDER'. You can override using the `.groups` argument.# Education by Gender
#psychopathy_df %>% group_by(GENDER) %>% count(EDUCATION)
psychopathy_df %>% group_by(GENDER, EDUCATION) %>% summarise(n = n()) %>% mutate(percentage = n / 85 * 100)`summarise()` has grouped output by 'GENDER'. You can override using the `.groups` argument.# Relationship by Gender
#psychopathy_df %>% group_by(GENDER) %>% count(RELATIONSHIP)
psychopathy_df %>% group_by(GENDER, RELATIONSHIP) %>% summarise(n = n()) %>% mutate(percentage = n / 85 * 100)`summarise()` has grouped output by 'GENDER'. You can override using the `.groups` argument.# Employment by Gender
#psychopathy_df %>% group_by(GENDER) %>% count(EMPLOYMENT)
psychopathy_df %>% group_by(GENDER, EMPLOYMENT) %>% summarise(n = n()) %>% mutate(percentage = n / 85 * 100)`summarise()` has grouped output by 'GENDER'. You can override using the `.groups` argument.# SES by Gender
# psychopathy_df %>% group_by(GENDER) %>% count(SES)
psychopathy_df %>% group_by(GENDER, SES) %>% summarise(n = n()) %>% mutate(percentage = n / 85 * 100)`summarise()` has grouped output by 'GENDER'. You can override using the `.groups` argument.# comparisons
t.test(AGE ~ GENDER, psychopathy_df, var.equal = TRUE) # no difference
Two Sample t-test
data: AGE by GENDER
t = -1.2146, df = 168, p-value = 0.2262
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-3.0269146 0.7210323
sample estimates:
mean in group Female mean in group Male
22.87059 24.02353 cohen.d(psychopathy_df$AGE, psychopathy_df$GENDER)
Cohen's d
d estimate: -0.1863104 (negligible)
95 percent confidence interval:
lower upper
-0.4897929 0.1171720 psychopathy_df_total$GENDER <- factor(psychopathy_df_total$GENDER, ordered = FALSE)
psychopathy_df_total$GENDER <- relevel(psychopathy_df_total$GENDER, ref = "Male")
# Differences in the above
t.test(SRP_SELF_TOTAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, men greater than women
Two Sample t-test
data: SRP_SELF_TOTAL by GENDER
t = 3.6182, df = 168, p-value = 0.0003921
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
2.480382 8.437265
sample estimates:
mean in group Male mean in group Female
42.07059 36.61176 t.test(SRP_PV_TOTAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, women greater than men
Two Sample t-test
data: SRP_PV_TOTAL by GENDER
t = -2.0378, df = 168, p-value = 0.04314
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-6.7401677 -0.1068911
sample estimates:
mean in group Male mean in group Female
33.67059 37.09412 t.test(BDI_TOTAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, women greater than men
Two Sample t-test
data: BDI_TOTAL by GENDER
t = -3.0055, df = 168, p-value = 0.003058
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-1.9492439 -0.4036973
sample estimates:
mean in group Male mean in group Female
2.329412 3.505882 t.test(RAS_TOTAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # no difference
Two Sample t-test
data: RAS_TOTAL by GENDER
t = 0.46889, df = 168, p-value = 0.6398
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-0.8309124 1.3485594
sample estimates:
mean in group Male mean in group Female
31.34118 31.08235 t.test(SRP_SELF_INTERPERSONAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, men greater than women
Two Sample t-test
data: SRP_SELF_INTERPERSONAL by GENDER
t = 2.3218, df = 168, p-value = 0.02144
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
0.2061073 2.5468338
sample estimates:
mean in group Male mean in group Female
12.82353 11.44706 t.test(SRP_SELF_AFFECTIVE ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, men greater than women
Two Sample t-test
data: SRP_SELF_AFFECTIVE by GENDER
t = 5.3239, df = 168, p-value = 3.217e-07
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
1.924562 4.193085
sample estimates:
mean in group Male mean in group Female
14.29412 11.23529 t.test(SRP_SELF_LIFESTYLE ~ GENDER, psychopathy_df_total, var.equal = TRUE) # no difference
Two Sample t-test
data: SRP_SELF_LIFESTYLE by GENDER
t = 1.6918, df = 168, p-value = 0.09254
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-0.1708457 2.2179045
sample estimates:
mean in group Male mean in group Female
14.95294 13.92941 t.test(SRP_PARTNER_INTERPERSONAL ~ GENDER, psychopathy_df_total, var.equal = TRUE) # no difference
Two Sample t-test
data: SRP_PARTNER_INTERPERSONAL by GENDER
t = -1.5791, df = 168, p-value = 0.1162
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-2.3560603 0.2619427
sample estimates:
mean in group Male mean in group Female
10.38824 11.43529 t.test(SRP_PARTNER_AFFECTIVE ~ GENDER, psychopathy_df_total, var.equal = TRUE) # sig difference, women greater than men
Two Sample t-test
data: SRP_PARTNER_AFFECTIVE by GENDER
t = -3.171, df = 168, p-value = 0.001806
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-3.1306120 -0.7282116
sample estimates:
mean in group Male mean in group Female
10.76471 12.69412 t.test(SRP_PARTNER_LIFESTYLE ~ GENDER, psychopathy_df_total, var.equal = TRUE) # no difference
Two Sample t-test
data: SRP_PARTNER_LIFESTYLE by GENDER
t = -0.66882, df = 168, p-value = 0.5045
alternative hypothesis: true difference in means between group Male and group Female is not equal to 0
95 percent confidence interval:
-1.7666583 0.8725407
sample estimates:
mean in group Male mean in group Female
12.51765 12.96471 # Cohen's d
cohen.d(psychopathy_df_total$SRP_SELF_TOTAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: 0.5550134 (medium)
95 percent confidence interval:
lower upper
0.2464121 0.8636146 cohen.d(psychopathy_df_total$SRP_PV_TOTAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: -0.3125859 (small)
95 percent confidence interval:
lower upper
-0.6172558 -0.0079160 cohen.d(psychopathy_df_total$BDI_TOTAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: -0.4610228 (small)
95 percent confidence interval:
lower upper
-0.7678453 -0.1542003 cohen.d(psychopathy_df_total$RAS_TOTAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: 0.07192435 (negligible)
95 percent confidence interval:
lower upper
-0.2309997 0.3748484 cohen.d(psychopathy_df_total$SRP_SELF_INTERPERSONAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: 0.3561555 (small)
95 percent confidence interval:
lower upper
0.050938 0.661373 cohen.d(psychopathy_df_total$SRP_SELF_AFFECTIVE, psychopathy_df_total$GENDER)
Cohen's d
d estimate: 0.8166476 (large)
95 percent confidence interval:
lower upper
0.5014516 1.1318436 cohen.d(psychopathy_df_total$SRP_SELF_LIFESTYLE, psychopathy_df_total$GENDER)
Cohen's d
d estimate: 0.2595093 (small)
95 percent confidence interval:
lower upper
-0.04458878 0.56360747 cohen.d(psychopathy_df_total$SRP_PARTNER_INTERPERSONAL, psychopathy_df_total$GENDER)
Cohen's d
d estimate: -0.242228 (small)
95 percent confidence interval:
lower upper
-0.54616268 0.06170665 cohen.d(psychopathy_df_total$SRP_PARTNER_AFFECTIVE, psychopathy_df_total$GENDER)
Cohen's d
d estimate: -0.4864105 (small)
95 percent confidence interval:
lower upper
-0.793682 -0.179139 cohen.d(psychopathy_df_total$SRP_PARTNER_LIFESTYLE, psychopathy_df_total$GENDER)
Cohen's d
d estimate: -0.1025926 (negligible)
95 percent confidence interval:
lower upper
-0.4056179 0.2004327 print(corr, short=FALSE)
Call:corr.test(x = as.matrix(psychopathy_df_dyad))
Correlation matrix
SRP_SELF_TOTAL SRP_PV_TOTAL SRP_SELF_INTERPERSONAL SRP_SELF_AFFECTIVE SRP_SELF_LIFESTYLE SRP_PARTNER_INTERPERSONAL
SRP_SELF_TOTAL 1.00 0.46 0.85 0.85 0.83 0.41
SRP_PV_TOTAL 0.46 1.00 0.46 0.43 0.27 0.87
SRP_SELF_INTERPERSONAL 0.85 0.46 1.00 0.61 0.56 0.44
SRP_SELF_AFFECTIVE 0.85 0.43 0.61 1.00 0.54 0.42
SRP_SELF_LIFESTYLE 0.83 0.27 0.56 0.54 1.00 0.20
SRP_PARTNER_INTERPERSONAL 0.41 0.87 0.44 0.42 0.20 1.00
SRP_PARTNER_AFFECTIVE 0.40 0.88 0.38 0.42 0.22 0.74
SRP_PARTNER_LIFESTYLE 0.36 0.82 0.37 0.27 0.28 0.54
BDI_TOTAL 0.43 0.08 0.28 0.36 0.45 0.02
RAS_TOTAL -0.29 -0.34 -0.33 -0.22 -0.19 -0.34
SRP_SELF_TOTAL_women 0.20 0.42 0.16 0.21 0.13 0.30
SRP_PV_TOTAL_women 0.40 0.25 0.29 0.29 0.43 0.21
SRP_SELF_INTERPERSONAL_women 0.17 0.30 0.18 0.21 0.05 0.26
SRP_SELF_AFFECTIVE_women 0.19 0.44 0.12 0.21 0.15 0.29
SRP_SELF_LIFESTYLE_women 0.14 0.36 0.10 0.13 0.13 0.23
SRP_PARTNER_INTERPERSONAL_women 0.24 0.18 0.24 0.13 0.23 0.14
SRP_PARTNER_AFFECTIVE_women 0.38 0.24 0.27 0.37 0.33 0.24
SRP_PARTNER_LIFESTYLE_women 0.43 0.23 0.24 0.29 0.56 0.16
BDI_TOTAL_women 0.18 0.28 0.11 0.15 0.20 0.26
RAS_TOTAL_women -0.21 -0.31 -0.23 -0.16 -0.16 -0.31
SRP_PARTNER_AFFECTIVE SRP_PARTNER_LIFESTYLE BDI_TOTAL RAS_TOTAL SRP_SELF_TOTAL_women SRP_PV_TOTAL_women
SRP_SELF_TOTAL 0.40 0.36 0.43 -0.29 0.20 0.40
SRP_PV_TOTAL 0.88 0.82 0.08 -0.34 0.42 0.25
SRP_SELF_INTERPERSONAL 0.38 0.37 0.28 -0.33 0.16 0.29
SRP_SELF_AFFECTIVE 0.42 0.27 0.36 -0.22 0.21 0.29
SRP_SELF_LIFESTYLE 0.22 0.28 0.45 -0.19 0.13 0.43
SRP_PARTNER_INTERPERSONAL 0.74 0.54 0.02 -0.34 0.30 0.21
SRP_PARTNER_AFFECTIVE 1.00 0.55 0.09 -0.34 0.39 0.25
SRP_PARTNER_LIFESTYLE 0.55 1.00 0.09 -0.19 0.38 0.18
BDI_TOTAL 0.09 0.09 1.00 -0.22 0.07 0.22
RAS_TOTAL -0.34 -0.19 -0.22 1.00 -0.18 -0.22
SRP_SELF_TOTAL_women 0.39 0.38 0.07 -0.18 1.00 0.66
SRP_PV_TOTAL_women 0.25 0.18 0.22 -0.22 0.66 1.00
SRP_SELF_INTERPERSONAL_women 0.32 0.20 0.04 -0.28 0.85 0.61
SRP_SELF_AFFECTIVE_women 0.50 0.32 0.12 -0.13 0.87 0.56
SRP_SELF_LIFESTYLE_women 0.22 0.47 0.02 -0.05 0.87 0.53
SRP_PARTNER_INTERPERSONAL_women 0.19 0.12 0.14 -0.19 0.55 0.88
SRP_PARTNER_AFFECTIVE_women 0.24 0.14 0.12 -0.26 0.59 0.87
SRP_PARTNER_LIFESTYLE_women 0.22 0.21 0.31 -0.13 0.59 0.86
BDI_TOTAL_women 0.22 0.24 0.30 -0.21 0.54 0.46
RAS_TOTAL_women -0.34 -0.14 -0.09 0.54 -0.33 -0.31
SRP_SELF_INTERPERSONAL_women SRP_SELF_AFFECTIVE_women SRP_SELF_LIFESTYLE_women SRP_PARTNER_INTERPERSONAL_women
SRP_SELF_TOTAL 0.17 0.19 0.14 0.24
SRP_PV_TOTAL 0.30 0.44 0.36 0.18
SRP_SELF_INTERPERSONAL 0.18 0.12 0.10 0.24
SRP_SELF_AFFECTIVE 0.21 0.21 0.13 0.13
SRP_SELF_LIFESTYLE 0.05 0.15 0.13 0.23
SRP_PARTNER_INTERPERSONAL 0.26 0.29 0.23 0.14
SRP_PARTNER_AFFECTIVE 0.32 0.50 0.22 0.19
SRP_PARTNER_LIFESTYLE 0.20 0.32 0.47 0.12
BDI_TOTAL 0.04 0.12 0.02 0.14
RAS_TOTAL -0.28 -0.13 -0.05 -0.19
SRP_SELF_TOTAL_women 0.85 0.87 0.87 0.55
SRP_PV_TOTAL_women 0.61 0.56 0.53 0.88
SRP_SELF_INTERPERSONAL_women 1.00 0.62 0.58 0.56
SRP_SELF_AFFECTIVE_women 0.62 1.00 0.66 0.46
SRP_SELF_LIFESTYLE_women 0.58 0.66 1.00 0.39
SRP_PARTNER_INTERPERSONAL_women 0.56 0.46 0.39 1.00
SRP_PARTNER_AFFECTIVE_women 0.54 0.54 0.47 0.68
SRP_PARTNER_LIFESTYLE_women 0.51 0.47 0.54 0.61
BDI_TOTAL_women 0.38 0.47 0.55 0.38
RAS_TOTAL_women -0.33 -0.30 -0.22 -0.30
SRP_PARTNER_AFFECTIVE_women SRP_PARTNER_LIFESTYLE_women BDI_TOTAL_women RAS_TOTAL_women
SRP_SELF_TOTAL 0.38 0.43 0.18 -0.21
SRP_PV_TOTAL 0.24 0.23 0.28 -0.31
SRP_SELF_INTERPERSONAL 0.27 0.24 0.11 -0.23
SRP_SELF_AFFECTIVE 0.37 0.29 0.15 -0.16
SRP_SELF_LIFESTYLE 0.33 0.56 0.20 -0.16
SRP_PARTNER_INTERPERSONAL 0.24 0.16 0.26 -0.31
SRP_PARTNER_AFFECTIVE 0.24 0.22 0.22 -0.34
SRP_PARTNER_LIFESTYLE 0.14 0.21 0.24 -0.14
BDI_TOTAL 0.12 0.31 0.30 -0.09
RAS_TOTAL -0.26 -0.13 -0.21 0.54
SRP_SELF_TOTAL_women 0.59 0.59 0.54 -0.33
SRP_PV_TOTAL_women 0.87 0.86 0.46 -0.31
SRP_SELF_INTERPERSONAL_women 0.54 0.51 0.38 -0.33
SRP_SELF_AFFECTIVE_women 0.54 0.47 0.47 -0.30
SRP_SELF_LIFESTYLE_women 0.47 0.54 0.55 -0.22
SRP_PARTNER_INTERPERSONAL_women 0.68 0.61 0.38 -0.30
SRP_PARTNER_AFFECTIVE_women 1.00 0.64 0.38 -0.25
SRP_PARTNER_LIFESTYLE_women 0.64 1.00 0.43 -0.27
BDI_TOTAL_women 0.38 0.43 1.00 -0.34
RAS_TOTAL_women -0.25 -0.27 -0.34 1.00
Sample Size
[1] 85
Probability values (Entries above the diagonal are adjusted for multiple tests.)
SRP_SELF_TOTAL SRP_PV_TOTAL SRP_SELF_INTERPERSONAL SRP_SELF_AFFECTIVE SRP_SELF_LIFESTYLE SRP_PARTNER_INTERPERSONAL
SRP_SELF_TOTAL 0.00 0.00 0.00 0.00 0.00 0.01
SRP_PV_TOTAL 0.00 0.00 0.00 0.01 0.98 0.00
SRP_SELF_INTERPERSONAL 0.00 0.00 0.00 0.00 0.00 0.00
SRP_SELF_AFFECTIVE 0.00 0.00 0.00 0.00 0.00 0.01
SRP_SELF_LIFESTYLE 0.00 0.01 0.00 0.00 0.00 1.00
SRP_PARTNER_INTERPERSONAL 0.00 0.00 0.00 0.00 0.07 0.00
SRP_PARTNER_AFFECTIVE 0.00 0.00 0.00 0.00 0.04 0.00
SRP_PARTNER_LIFESTYLE 0.00 0.00 0.00 0.01 0.01 0.00
BDI_TOTAL 0.00 0.45 0.01 0.00 0.00 0.82
RAS_TOTAL 0.01 0.00 0.00 0.04 0.09 0.00
SRP_SELF_TOTAL_women 0.07 0.00 0.16 0.05 0.25 0.00
SRP_PV_TOTAL_women 0.00 0.02 0.01 0.01 0.00 0.06
SRP_SELF_INTERPERSONAL_women 0.11 0.01 0.10 0.05 0.64 0.02
SRP_SELF_AFFECTIVE_women 0.08 0.00 0.29 0.05 0.16 0.01
SRP_SELF_LIFESTYLE_women 0.19 0.00 0.34 0.25 0.24 0.03
SRP_PARTNER_INTERPERSONAL_women 0.03 0.10 0.02 0.25 0.03 0.20
SRP_PARTNER_AFFECTIVE_women 0.00 0.03 0.01 0.00 0.00 0.02
SRP_PARTNER_LIFESTYLE_women 0.00 0.03 0.03 0.01 0.00 0.14
BDI_TOTAL_women 0.10 0.01 0.31 0.17 0.07 0.02
RAS_TOTAL_women 0.05 0.00 0.04 0.14 0.16 0.00
SRP_PARTNER_AFFECTIVE SRP_PARTNER_LIFESTYLE BDI_TOTAL RAS_TOTAL SRP_SELF_TOTAL_women SRP_PV_TOTAL_women
SRP_SELF_TOTAL 0.02 0.07 0.01 0.64 1.00 0.02
SRP_PV_TOTAL 0.00 0.00 1.00 0.18 0.01 1.00
SRP_SELF_INTERPERSONAL 0.04 0.05 0.85 0.20 1.00 0.68
SRP_SELF_AFFECTIVE 0.01 1.00 0.09 1.00 1.00 0.62
SRP_SELF_LIFESTYLE 1.00 0.81 0.00 1.00 1.00 0.01
SRP_PARTNER_INTERPERSONAL 0.00 0.00 1.00 0.15 0.46 1.00
SRP_PARTNER_AFFECTIVE 0.00 0.00 1.00 0.15 0.03 1.00
SRP_PARTNER_LIFESTYLE 0.00 0.00 1.00 1.00 0.04 1.00
BDI_TOTAL 0.40 0.40 0.00 1.00 1.00 1.00
RAS_TOTAL 0.00 0.09 0.05 0.00 1.00 1.00
SRP_SELF_TOTAL_women 0.00 0.00 0.54 0.10 0.00 0.00
SRP_PV_TOTAL_women 0.02 0.10 0.04 0.04 0.00 0.00
SRP_SELF_INTERPERSONAL_women 0.00 0.07 0.68 0.01 0.00 0.00
SRP_SELF_AFFECTIVE_women 0.00 0.00 0.27 0.23 0.00 0.00
SRP_SELF_LIFESTYLE_women 0.05 0.00 0.89 0.68 0.00 0.00
SRP_PARTNER_INTERPERSONAL_women 0.08 0.25 0.19 0.08 0.00 0.00
SRP_PARTNER_AFFECTIVE_women 0.03 0.21 0.28 0.02 0.00 0.00
SRP_PARTNER_LIFESTYLE_women 0.04 0.05 0.00 0.22 0.00 0.00
BDI_TOTAL_women 0.05 0.03 0.01 0.05 0.00 0.00
RAS_TOTAL_women 0.00 0.19 0.43 0.00 0.00 0.00
SRP_SELF_INTERPERSONAL_women SRP_SELF_AFFECTIVE_women SRP_SELF_LIFESTYLE_women SRP_PARTNER_INTERPERSONAL_women
SRP_SELF_TOTAL 1.00 1.00 1.00 1.00
SRP_PV_TOTAL 0.49 0.00 0.07 1.00
SRP_SELF_INTERPERSONAL 1.00 1.00 1.00 1.00
SRP_SELF_AFFECTIVE 1.00 1.00 1.00 1.00
SRP_SELF_LIFESTYLE 1.00 1.00 1.00 1.00
SRP_PARTNER_INTERPERSONAL 1.00 0.60 1.00 1.00
SRP_PARTNER_AFFECTIVE 0.32 0.00 1.00 1.00
SRP_PARTNER_LIFESTYLE 1.00 0.31 0.00 1.00
BDI_TOTAL 1.00 1.00 1.00 1.00
RAS_TOTAL 0.73 1.00 1.00 1.00
SRP_SELF_TOTAL_women 0.00 0.00 0.00 0.00
SRP_PV_TOTAL_women 0.00 0.00 0.00 0.00
SRP_SELF_INTERPERSONAL_women 0.00 0.00 0.00 0.00
SRP_SELF_AFFECTIVE_women 0.00 0.00 0.00 0.00
SRP_SELF_LIFESTYLE_women 0.00 0.00 0.00 0.03
SRP_PARTNER_INTERPERSONAL_women 0.00 0.00 0.00 0.00
SRP_PARTNER_AFFECTIVE_women 0.00 0.00 0.00 0.00
SRP_PARTNER_LIFESTYLE_women 0.00 0.00 0.00 0.00
BDI_TOTAL_women 0.00 0.00 0.00 0.00
RAS_TOTAL_women 0.00 0.01 0.04 0.01
SRP_PARTNER_AFFECTIVE_women SRP_PARTNER_LIFESTYLE_women BDI_TOTAL_women RAS_TOTAL_women
SRP_SELF_TOTAL 0.04 0.01 1.00 1.00
SRP_PV_TOTAL 1.00 1.00 0.92 0.43
SRP_SELF_INTERPERSONAL 0.98 1.00 1.00 1.00
SRP_SELF_AFFECTIVE 0.06 0.66 1.00 1.00
SRP_SELF_LIFESTYLE 0.23 0.00 1.00 1.00
SRP_PARTNER_INTERPERSONAL 1.00 1.00 1.00 0.35
SRP_PARTNER_AFFECTIVE 1.00 1.00 1.00 0.16
SRP_PARTNER_LIFESTYLE 1.00 1.00 1.00 1.00
BDI_TOTAL 1.00 0.43 0.55 1.00
RAS_TOTAL 1.00 1.00 1.00 0.00
SRP_SELF_TOTAL_women 0.00 0.00 0.00 0.22
SRP_PV_TOTAL_women 0.00 0.00 0.00 0.35
SRP_SELF_INTERPERSONAL_women 0.00 0.00 0.04 0.21
SRP_SELF_AFFECTIVE_women 0.00 0.00 0.00 0.51
SRP_SELF_LIFESTYLE_women 0.00 0.00 0.00 1.00
SRP_PARTNER_INTERPERSONAL_women 0.00 0.00 0.04 0.52
SRP_PARTNER_AFFECTIVE_women 0.00 0.00 0.04 1.00
SRP_PARTNER_LIFESTYLE_women 0.00 0.00 0.00 1.00
BDI_TOTAL_women 0.00 0.00 0.00 0.14
RAS_TOTAL_women 0.02 0.01 0.00 0.00
Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci# Full APIM taking totals
full_apim <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_TOTAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_TOTAL_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_SELF_TOTAL_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_TOTAL # Path b4, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b5*SRP_PV_TOTAL # Path b5, regressing RAS_women onto SRP_PV_men
RAS_TOTAL ~ b6*SRP_PV_TOTAL_women # Path b6, regressing RAS_men onto SRP_PV_women
RAS_TOTAL_women ~ b7*SRP_PV_TOTAL_women # Path b7, regressing RAS_women onto SRP_PV_women
RAS_TOTAL ~ b8*SRP_PV_TOTAL # Path b8, regressing RAS_men onto SRP_PV_men
# Intercepts
SRP_SELF_TOTAL ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_TOTAL_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
SRP_PV_TOTAL ~ a5*1 # Intercept for SRP_PV_men
SRP_PV_TOTAL_women ~ a6*1 # Intercept for SRP_PV_women
# Variances
SRP_SELF_TOTAL ~~ v1*SRP_SELF_TOTAL
SRP_SELF_TOTAL_women ~~ v2*SRP_SELF_TOTAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
SRP_PV_TOTAL ~~ v5*SRP_PV_TOTAL
SRP_PV_TOTAL_women ~~ v6*SRP_PV_TOTAL_women
# Covariances
SRP_SELF_TOTAL_women ~~ c1*SRP_SELF_TOTAL
RAS_TOTAL ~~ c2*RAS_TOTAL_women
SRP_PV_TOTAL_women ~~ c3*SRP_PV_TOTAL
SRP_SELF_TOTAL ~~ c4*SRP_PV_TOTAL
SRP_SELF_TOTAL ~~ c5*SRP_PV_TOTAL_women
SRP_SELF_TOTAL_women ~~ c6*SRP_PV_TOTAL
SRP_SELF_TOTAL_women ~~ c7*SRP_PV_TOTAL_women
# Defined parameters
# Avg effects
sf_actor := (b4 + b3)/2
sf_partner := (b2 + b1)/2
pv_actor := (b8 + b7)/2
pv_partner := (b6 + b5)/2
# Differences
sf_actor_diff := b4 - b3
sf_partner_diff := b2 - b1
pv_actor_diff := b8 - b7
pv_partner_diff := b6 - b5
'
# Fit the above model using MLR
fit_apim <- sem(full_apim, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_apim)
parameterestimates(fit_apim, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Mini models for totals
# Men rating themselves - women rating their partners
APIM_mini1 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_TOTAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_TOTAL # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_PV_TOTAL_women # Path b3, regressing RAS_women onto SRP_PV_women
RAS_TOTAL ~ b4*SRP_PV_TOTAL_women # Path b4, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_SELF_TOTAL ~ a1*1 # Intercept for SRP_SF_men
SRP_PV_TOTAL_women ~ a2*1 # Intercept for SRP_PV_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_TOTAL ~~ v1*SRP_SELF_TOTAL
SRP_PV_TOTAL_women ~~ v2*SRP_PV_TOTAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_TOTAL ~~ c2*SRP_PV_TOTAL_women
'
fit_mini1 <- sem(APIM_mini1, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini1)
parameterestimates(fit_mini1, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Men rating themselves, women rating themselves
APIM_mini2 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_TOTAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_TOTAL # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_SELF_TOTAL_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_TOTAL_women # Path b4, regressing RAS_men onto SRP_SF_women
# Intercepts
SRP_SELF_TOTAL ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_TOTAL_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_TOTAL ~~ v1*SRP_SELF_TOTAL
SRP_SELF_TOTAL_women ~~ v2*SRP_SELF_TOTAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_TOTAL ~~ c2*SRP_SELF_TOTAL_women
'
fit_mini2 <- sem(APIM_mini2, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini2)
parameterestimates(fit_mini2, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating themselves, men rating their partner
APIM_mini3 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_TOTAL_women # Path b1, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b2*SRP_SELF_TOTAL_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_PV_TOTAL # Path b3, regressing RAS_women onto SRP_PV_men
RAS_TOTAL ~ b4*SRP_PV_TOTAL # Path b4, regressing RAS_men onto SRP_PV_men
# Intercepts
SRP_SELF_TOTAL_women ~ a1*1 # Intercept for SRP_SF_women
SRP_PV_TOTAL ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_TOTAL_women ~~ v1*SRP_SELF_TOTAL_women
SRP_PV_TOTAL ~~ v2*SRP_PV_TOTAL
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_TOTAL_women ~~ c2*SRP_PV_TOTAL
'
fit_mini3 <- sem(APIM_mini3, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini3)
parameterestimates(fit_mini3, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating their partner, men rating their partner
APIM_mini4 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_PV_TOTAL # Path b1, regressing RAS women onto SRP_PV_men
RAS_TOTAL ~ b2*SRP_PV_TOTAL # Path b2, regressing RAS_men onto SRP_PV_men
RAS_TOTAL_women ~ b1*SRP_PV_TOTAL_women # Path b1, regressing RAS women onto SRP_PV_women
RAS_TOTAL ~ b2*SRP_PV_TOTAL_women # Path b2, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_PV_TOTAL ~ a1*1 # Intercept for SRP_SF_women
SRP_PV_TOTAL_women ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_PV_TOTAL ~~ v1*SRP_PV_TOTAL
SRP_PV_TOTAL_women ~~ v2*SRP_PV_TOTAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_PV_TOTAL ~~ c2*SRP_PV_TOTAL_women
'
fit_mini4 <- sem(APIM_mini4, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini4)
parameterestimates(fit_mini4, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Full APIM interpersonal
full_apim2 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_INTERPERSONAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_INTERPERSONAL_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_SELF_INTERPERSONAL_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_INTERPERSONAL # Path b4, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b5*SRP_PARTNER_INTERPERSONAL # Path b5, regressing RAS_women onto SRP_PARTNER_men
RAS_TOTAL ~ b6*SRP_PARTNER_INTERPERSONAL_women # Path b6, regressing RAS_men onto SRP_PARTNER_women
RAS_TOTAL_women ~ b7*SRP_PARTNER_INTERPERSONAL_women # Path b7, regressing RAS_women onto SRP_PARTNER_women
RAS_TOTAL ~ b8*SRP_PARTNER_INTERPERSONAL # Path b8, regressing RAS_men onto SRP_PARTNER_men
# Intercepts
SRP_SELF_INTERPERSONAL ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_INTERPERSONAL_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
SRP_PARTNER_INTERPERSONAL ~ a5*1 # Intercept for SRP_PARTNER_men
SRP_PARTNER_INTERPERSONAL_women ~ a6*1 # Intercept for SRP_PARTNER_women
# Variances
SRP_SELF_INTERPERSONAL ~~ v1*SRP_SELF_INTERPERSONAL
SRP_SELF_INTERPERSONAL_women ~~ v2*SRP_SELF_INTERPERSONAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
SRP_PARTNER_INTERPERSONAL ~~ v5*SRP_PARTNER_INTERPERSONAL
SRP_PARTNER_INTERPERSONAL_women ~~ v6*SRP_PARTNER_INTERPERSONAL_women
# Covariances
SRP_SELF_INTERPERSONAL_women ~~ c1*SRP_SELF_INTERPERSONAL
RAS_TOTAL ~~ c2*RAS_TOTAL_women
SRP_PARTNER_INTERPERSONAL_women ~~ c3*SRP_PARTNER_INTERPERSONAL
SRP_SELF_INTERPERSONAL ~~ c4*SRP_PARTNER_INTERPERSONAL
SRP_SELF_INTERPERSONAL ~~ c5*SRP_PARTNER_INTERPERSONAL_women
SRP_SELF_INTERPERSONAL_women ~~ c6*SRP_PARTNER_INTERPERSONAL
SRP_SELF_INTERPERSONAL_women ~~ c7*SRP_PARTNER_INTERPERSONAL_women
# Defined parameters
# Avg effects
sf_actor := (b4 + b3)/2
sf_partner := (b2 + b1)/2
pv_actor := (b8 + b7)/2
pv_partner := (b6 + b5)/2
# Differences
sf_actor_diff := b4 - b3
sf_partner_diff := b2 - b1
pv_actor_diff := b8 - b7
pv_partner_diff := b6 - b5
'
# Fit the above model using MLR
fit_apim2 <- sem(full_apim2, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_apim)
parameterestimates(fit_apim2, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)# Mini models for interpersonal
# Men rating themselves - women rating their partners
APIM_mini_int1 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_INTERPERSONAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_INTERPERSONAL # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_PARTNER_INTERPERSONAL_women # Path b3, regressing RAS_women onto SRP_PV_women
RAS_TOTAL ~ b4*SRP_PARTNER_INTERPERSONAL_women # Path b4, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_SELF_INTERPERSONAL ~ a1*1 # Intercept for SRP_SF_men
SRP_PARTNER_INTERPERSONAL_women ~ a2*1 # Intercept for SRP_PV_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_INTERPERSONAL ~~ v1*SRP_SELF_INTERPERSONAL
SRP_PARTNER_INTERPERSONAL_women ~~ v2*SRP_PARTNER_INTERPERSONAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_INTERPERSONAL ~~ c2*SRP_PARTNER_INTERPERSONAL_women
'
fit_mini_int1 <- sem(APIM_mini_int1, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_int1)
parameterestimates(fit_mini_int1, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Men rating themselves, women rating themselves
APIM_mini_int2 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_INTERPERSONAL # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_INTERPERSONAL # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_SELF_INTERPERSONAL_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_INTERPERSONAL_women # Path b4, regressing RAS_men onto SRP_SF_women
# Intercepts
SRP_SELF_INTERPERSONAL ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_INTERPERSONAL_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_INTERPERSONAL ~~ v1*SRP_SELF_INTERPERSONAL
SRP_SELF_INTERPERSONAL_women ~~ v2*SRP_SELF_INTERPERSONAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_INTERPERSONAL ~~ c2*SRP_SELF_INTERPERSONAL_women
'
fit_mini_int2 <- sem(APIM_mini_int2, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini2)
parameterestimates(fit_mini_int2, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating themselves, men rating their partner
APIM_mini_int3 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_INTERPERSONAL_women # Path b1, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b2*SRP_SELF_INTERPERSONAL_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_PARTNER_INTERPERSONAL # Path b3, regressing RAS_women onto SRP_PV_men
RAS_TOTAL ~ b4*SRP_PARTNER_INTERPERSONAL # Path b4, regressing RAS_men onto SRP_PV_men
# Intercepts
SRP_SELF_INTERPERSONAL_women ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_INTERPERSONAL ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_INTERPERSONAL_women ~~ v1*SRP_SELF_INTERPERSONAL_women
SRP_PARTNER_INTERPERSONAL ~~ v2*SRP_PARTNER_INTERPERSONAL
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_INTERPERSONAL_women ~~ c2*SRP_PARTNER_INTERPERSONAL
'
fit_mini_int3 <- sem(APIM_mini_int3, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini3)
parameterestimates(fit_mini_int3, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating their partner, men rating their partner
APIM_mini_int4 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_PARTNER_INTERPERSONAL # Path b1, regressing RAS women onto SRP_PV_men
RAS_TOTAL ~ b2*SRP_PARTNER_INTERPERSONAL # Path b2, regressing RAS_men onto SRP_PV_men
RAS_TOTAL_women ~ b1*SRP_PARTNER_INTERPERSONAL_women # Path b1, regressing RAS women onto SRP_PV_women
RAS_TOTAL ~ b2*SRP_PARTNER_INTERPERSONAL_women # Path b2, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_PARTNER_INTERPERSONAL ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_INTERPERSONAL_women ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_PARTNER_INTERPERSONAL ~~ v1*SRP_PARTNER_INTERPERSONAL
SRP_PARTNER_INTERPERSONAL_women ~~ v2*SRP_PARTNER_INTERPERSONAL_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_PARTNER_INTERPERSONAL ~~ c2*SRP_PARTNER_INTERPERSONAL_women
'
fit_mini_int4 <- sem(APIM_mini_int4, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_int4)
parameterestimates(fit_mini_int4, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Full APIM Affective
full_apim3 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_AFFECTIVE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_AFFECTIVE_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_SELF_AFFECTIVE_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_AFFECTIVE # Path b4, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b5*SRP_PARTNER_AFFECTIVE # Path b5, regressing RAS_women onto SRP_PARTNER_men
RAS_TOTAL ~ b6*SRP_PARTNER_AFFECTIVE_women # Path b6, regressing RAS_men onto SRP_PARTNER_women
RAS_TOTAL_women ~ b7*SRP_PARTNER_AFFECTIVE_women # Path b7, regressing RAS_women onto SRP_PARTNER_women
RAS_TOTAL ~ b8*SRP_PARTNER_AFFECTIVE # Path b8, regressing RAS_men onto SRP_PARTNER_men
# Intercepts
SRP_SELF_AFFECTIVE ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_AFFECTIVE_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
SRP_PARTNER_AFFECTIVE ~ a5*1 # Intercept for SRP_PARTNER_men
SRP_PARTNER_AFFECTIVE_women ~ a6*1 # Intercept for SRP_PARTNER_women
# Variances
SRP_SELF_AFFECTIVE ~~ v1*SRP_SELF_AFFECTIVE
SRP_SELF_AFFECTIVE_women ~~ v2*SRP_SELF_AFFECTIVE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
SRP_PARTNER_AFFECTIVE ~~ v5*SRP_PARTNER_AFFECTIVE
SRP_PARTNER_AFFECTIVE_women ~~ v6*SRP_PARTNER_AFFECTIVE_women
# Covariances
SRP_SELF_AFFECTIVE_women ~~ c1*SRP_SELF_AFFECTIVE
RAS_TOTAL ~~ c2*RAS_TOTAL_women
SRP_PARTNER_AFFECTIVE_women ~~ c3*SRP_PARTNER_AFFECTIVE
SRP_SELF_AFFECTIVE ~~ c4*SRP_PARTNER_AFFECTIVE
SRP_SELF_AFFECTIVE ~~ c5*SRP_PARTNER_AFFECTIVE_women
SRP_SELF_AFFECTIVE_women ~~ c6*SRP_PARTNER_AFFECTIVE
SRP_SELF_AFFECTIVE_women ~~ c7*SRP_PARTNER_AFFECTIVE_women
# Defined parameters
# Avg effects
sf_actor := (b4 + b3)/2
sf_partner := (b2 + b1)/2
pv_actor := (b8 + b7)/2
pv_partner := (b6 + b5)/2
# Differences
sf_actor_diff := b4 - b3
sf_partner_diff := b2 - b1
pv_actor_diff := b8 - b7
pv_partner_diff := b6 - b5
'
# Fit the above model using MLR
fit_apim3 <- sem(full_apim3, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_apim)
parameterestimates(fit_apim3, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Mini models for Affective
# Men rating themselves - women rating their partners
APIM_mini_aff1 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_AFFECTIVE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_AFFECTIVE # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_PARTNER_AFFECTIVE_women # Path b3, regressing RAS_women onto SRP_PV_women
RAS_TOTAL ~ b4*SRP_PARTNER_AFFECTIVE_women # Path b4, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_SELF_AFFECTIVE ~ a1*1 # Intercept for SRP_SF_men
SRP_PARTNER_AFFECTIVE_women ~ a2*1 # Intercept for SRP_PV_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_AFFECTIVE ~~ v1*SRP_SELF_AFFECTIVE
SRP_PARTNER_AFFECTIVE_women ~~ v2*SRP_PARTNER_AFFECTIVE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_AFFECTIVE ~~ c2*SRP_PARTNER_AFFECTIVE_women
'
fit_mini_aff1 <- sem(APIM_mini_aff1, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_aff1)
parameterestimates(fit_mini_aff1, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Men rating themselves, women rating themselves
APIM_mini_aff2 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_AFFECTIVE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_AFFECTIVE # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_SELF_AFFECTIVE_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_AFFECTIVE_women # Path b4, regressing RAS_men onto SRP_SF_women
# Intercepts
SRP_SELF_AFFECTIVE ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_AFFECTIVE_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_AFFECTIVE ~~ v1*SRP_SELF_AFFECTIVE
SRP_SELF_AFFECTIVE_women ~~ v2*SRP_SELF_AFFECTIVE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_AFFECTIVE ~~ c2*SRP_SELF_AFFECTIVE_women
'
fit_mini_aff2 <- sem(APIM_mini_aff2, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_aff2)
parameterestimates(fit_mini_aff2, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating themselves, men rating their partner
APIM_mini_aff3 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_AFFECTIVE_women # Path b1, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b2*SRP_SELF_AFFECTIVE_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_PARTNER_AFFECTIVE # Path b3, regressing RAS_women onto SRP_PV_men
RAS_TOTAL ~ b4*SRP_PARTNER_AFFECTIVE # Path b4, regressing RAS_men onto SRP_PV_men
# Intercepts
SRP_SELF_AFFECTIVE_women ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_AFFECTIVE ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_AFFECTIVE_women ~~ v1*SRP_SELF_AFFECTIVE_women
SRP_PARTNER_AFFECTIVE ~~ v2*SRP_PARTNER_AFFECTIVE
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_AFFECTIVE_women ~~ c2*SRP_PARTNER_AFFECTIVE
'
fit_mini_aff3 <- sem(APIM_mini_aff3, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_aff3)
parameterestimates(fit_mini_aff3, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating their partner, men rating their partner
APIM_mini_aff4 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_PARTNER_AFFECTIVE # Path b1, regressing RAS women onto SRP_PV_men
RAS_TOTAL ~ b2*SRP_PARTNER_AFFECTIVE # Path b2, regressing RAS_men onto SRP_PV_men
RAS_TOTAL_women ~ b1*SRP_PARTNER_AFFECTIVE_women # Path b1, regressing RAS women onto SRP_PV_women
RAS_TOTAL ~ b2*SRP_PARTNER_AFFECTIVE_women # Path b2, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_PARTNER_AFFECTIVE ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_AFFECTIVE_women ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_PARTNER_AFFECTIVE ~~ v1*SRP_PARTNER_AFFECTIVE
SRP_PARTNER_AFFECTIVE_women ~~ v2*SRP_PARTNER_AFFECTIVE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_PARTNER_AFFECTIVE ~~ c2*SRP_PARTNER_AFFECTIVE_women
'
fit_mini_aff4 <- sem(APIM_mini_aff4, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_aff4)
parameterestimates(fit_mini_aff4, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Full APIM Lifestyle
full_apim4 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_LIFESTYLE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_LIFESTYLE_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_SELF_LIFESTYLE_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_LIFESTYLE # Path b4, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b5*SRP_PARTNER_LIFESTYLE # Path b5, regressing RAS_women onto SRP_PARTNER_men
RAS_TOTAL ~ b6*SRP_PARTNER_LIFESTYLE_women # Path b6, regressing RAS_men onto SRP_PARTNER_women
RAS_TOTAL_women ~ b7*SRP_PARTNER_LIFESTYLE_women # Path b7, regressing RAS_women onto SRP_PARTNER_women
RAS_TOTAL ~ b8*SRP_PARTNER_LIFESTYLE # Path b8, regressing RAS_men onto SRP_PARTNER_men
# Intercepts
SRP_SELF_LIFESTYLE ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_LIFESTYLE_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
SRP_PARTNER_LIFESTYLE ~ a5*1 # Intercept for SRP_PARTNER_men
SRP_PARTNER_LIFESTYLE_women ~ a6*1 # Intercept for SRP_PARTNER_women
# Variances
SRP_SELF_LIFESTYLE ~~ v1*SRP_SELF_LIFESTYLE
SRP_SELF_LIFESTYLE_women ~~ v2*SRP_SELF_LIFESTYLE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
SRP_PARTNER_LIFESTYLE ~~ v5*SRP_PARTNER_LIFESTYLE
SRP_PARTNER_LIFESTYLE_women ~~ v6*SRP_PARTNER_LIFESTYLE_women
# Covariances
SRP_SELF_LIFESTYLE_women ~~ c1*SRP_SELF_LIFESTYLE
RAS_TOTAL ~~ c2*RAS_TOTAL_women
SRP_PARTNER_LIFESTYLE_women ~~ c3*SRP_PARTNER_LIFESTYLE
SRP_SELF_LIFESTYLE ~~ c4*SRP_PARTNER_LIFESTYLE
SRP_SELF_LIFESTYLE ~~ c5*SRP_PARTNER_LIFESTYLE_women
SRP_SELF_LIFESTYLE_women ~~ c6*SRP_PARTNER_LIFESTYLE
SRP_SELF_LIFESTYLE_women ~~ c7*SRP_PARTNER_LIFESTYLE_women
# Defined parameters
# Avg effects
sf_actor := (b4 + b3)/2
sf_partner := (b2 + b1)/2
pv_actor := (b8 + b7)/2
pv_partner := (b6 + b5)/2
# Differences
sf_actor_diff := b4 - b3
sf_partner_diff := b2 - b1
pv_actor_diff := b8 - b7
pv_partner_diff := b6 - b5
'
# Fit the above model using MLR
fit_apim4 <- sem(full_apim4, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_apim)
parameterestimates(fit_apim4, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)# Mini models for Lifestyle
# Men rating themselves - women rating their partners
APIM_mini_lif1 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_LIFESTYLE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_LIFESTYLE # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_PARTNER_LIFESTYLE_women # Path b3, regressing RAS_women onto SRP_PV_women
RAS_TOTAL ~ b4*SRP_PARTNER_LIFESTYLE_women # Path b4, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_SELF_LIFESTYLE ~ a1*1 # Intercept for SRP_SF_men
SRP_PARTNER_LIFESTYLE_women ~ a2*1 # Intercept for SRP_PV_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_LIFESTYLE ~~ v1*SRP_SELF_LIFESTYLE
SRP_PARTNER_LIFESTYLE_women ~~ v2*SRP_PARTNER_LIFESTYLE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_LIFESTYLE ~~ c2*SRP_PARTNER_LIFESTYLE_women
'
fit_mini_lif1 <- sem(APIM_mini_lif1, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_lif1)
parameterestimates(fit_mini_lif1, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Men rating themselves, women rating themselves
APIM_mini_lif2 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_LIFESTYLE # Path b1, regressing RAS_women onto SRP_SF_men
RAS_TOTAL ~ b2*SRP_SELF_LIFESTYLE # Path b2, regressing RAS_men onto SRP_SF_men
RAS_TOTAL_women ~ b3*SRP_SELF_LIFESTYLE_women # Path b3, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b4*SRP_SELF_LIFESTYLE_women # Path b4, regressing RAS_men onto SRP_SF_women
# Intercepts
SRP_SELF_LIFESTYLE ~ a1*1 # Intercept for SRP_SF_men
SRP_SELF_LIFESTYLE_women ~ a2*1 # Intercept for SRP_SF_women
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_LIFESTYLE ~~ v1*SRP_SELF_LIFESTYLE
SRP_SELF_LIFESTYLE_women ~~ v2*SRP_SELF_LIFESTYLE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_LIFESTYLE ~~ c2*SRP_SELF_LIFESTYLE_women
'
fit_mini_lif2 <- sem(APIM_mini_lif2, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_lif2)
parameterestimates(fit_mini_lif2, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating themselves, men rating their partner
APIM_mini_lif3 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_SELF_LIFESTYLE_women # Path b1, regressing RAS_women onto SRP_SF_women
RAS_TOTAL ~ b2*SRP_SELF_LIFESTYLE_women # Path b2, regressing RAS_men onto SRP_SF_women
RAS_TOTAL_women ~ b3*SRP_PARTNER_LIFESTYLE # Path b3, regressing RAS_women onto SRP_PV_men
RAS_TOTAL ~ b4*SRP_PARTNER_LIFESTYLE # Path b4, regressing RAS_men onto SRP_PV_men
# Intercepts
SRP_SELF_LIFESTYLE_women ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_LIFESTYLE ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_SELF_LIFESTYLE_women ~~ v1*SRP_SELF_LIFESTYLE_women
SRP_PARTNER_LIFESTYLE ~~ v2*SRP_PARTNER_LIFESTYLE
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_SELF_LIFESTYLE_women ~~ c2*SRP_PARTNER_LIFESTYLE
'
fit_mini_lif3 <- sem(APIM_mini_lif3, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_lif3)
parameterestimates(fit_mini_lif3, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
# Women rating their partner, men rating their partner
APIM_mini_lif4 <- '
# Regression paths
RAS_TOTAL_women ~ b1*SRP_PARTNER_LIFESTYLE # Path b1, regressing RAS women onto SRP_PV_men
RAS_TOTAL ~ b2*SRP_PARTNER_LIFESTYLE # Path b2, regressing RAS_men onto SRP_PV_men
RAS_TOTAL_women ~ b1*SRP_PARTNER_LIFESTYLE_women # Path b1, regressing RAS women onto SRP_PV_women
RAS_TOTAL ~ b2*SRP_PARTNER_LIFESTYLE_women # Path b2, regressing RAS_men onto SRP_PV_women
# Intercepts
SRP_PARTNER_LIFESTYLE ~ a1*1 # Intercept for SRP_SF_women
SRP_PARTNER_LIFESTYLE_women ~ a2*1 # Intercept for SRP_PV_men
RAS_TOTAL_women ~ a3*1 # Intercept for RAS_women
RAS_TOTAL ~ a4*1 # Intercept for RAS_men
# Variances
SRP_PARTNER_LIFESTYLE ~~ v1*SRP_PARTNER_LIFESTYLE
SRP_PARTNER_LIFESTYLE_women ~~ v2*SRP_PARTNER_LIFESTYLE_women
RAS_TOTAL_women ~~ v3*RAS_TOTAL_women
RAS_TOTAL ~~ v4*RAS_TOTAL
# Covariances
RAS_TOTAL ~~ c1*RAS_TOTAL_women
SRP_PARTNER_LIFESTYLE ~~ c2*SRP_PARTNER_LIFESTYLE_women
'
fit_mini_lif4 <- sem(APIM_mini_lif4, data = psychopathy_df_dyad_stan, estimator = "MLM")
# summary(fit_mini_lif4)
parameterestimates(fit_mini_lif4, boot.ci.type = "bca.simple", standardized = TRUE, rsquare = TRUE)
library(robustbase)
# Men
m0 <- lmrob(RAS_TOTAL ~ 1, psychopathy_df_dyad_stan)
m1 <- lmrob(RAS_TOTAL ~ BDI_TOTAL, psychopathy_df_dyad_stan)
summary(m1)
cbind(coef(m1),confint(m1, level = 0.95))
m2 <- lmrob(RAS_TOTAL ~ BDI_TOTAL + SRP_SELF_TOTAL, psychopathy_df_dyad_stan)
summary(m2)
cbind(coef(m2),confint(m2, level = 0.95))
m3 <- lmrob(RAS_TOTAL ~ BDI_TOTAL + SRP_SELF_TOTAL + SRP_PV_TOTAL, psychopathy_df_dyad_stan)
summary(m3)
cbind(coef(m3),confint(m3, level = 0.95))
anova(m0, m1, test = "Deviance")
anova(m1, m2, test = "Deviance")
anova(m2, m3, test = "Deviance")
# Women
m0 <- lmrob(RAS_TOTAL_women ~ 1, psychopathy_df_dyad_stan)
m1 <- lmrob(RAS_TOTAL_women ~ BDI_TOTAL_women, psychopathy_df_dyad_stan)
summary(m1)
cbind(coef(m1),confint(m1, level = 0.95))
m2 <- lmrob(RAS_TOTAL_women ~ BDI_TOTAL_women + SRP_SELF_TOTAL_women, psychopathy_df_dyad_stan)
summary(m2)
cbind(coef(m2),confint(m2, level = 0.95))
m3 <- lmrob(RAS_TOTAL_women ~ BDI_TOTAL_women + SRP_SELF_TOTAL_women + SRP_PV_TOTAL_women, psychopathy_df_dyad_stan)
summary(m3)
cbind(coef(m3),confint(m3, level = 0.95))
anova(m0, m1, test = "Deviance")
anova(m1, m2, test = "Deviance")
anova(m2, m3, test = "Deviance")