##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## rel.sat sex.sat self.comp love age rel.years
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## N 273 273 274 274 276 276
## Missing 3 3 2 2 0 0
## Mean 4.019231 4.805861 3.033693 6.072802 31.83696 7.097826
## Median 4.250000 5.000000 3.000000 6.190476 32.00000 6.000000
## Standard deviation 0.9346015 1.399298 0.7036088 0.6793942 4.569330 4.139547
## Minimum 0.2500000 1.000000 1.333333 3.333333 20 1
## Maximum 5.250000 7.000000 4.750000 7.000000 46 22
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## vars n mean sd median trimmed mad min max range skew
## rel.sat 1 273 4.02 0.93 4.25 4.10 1.11 0.25 5.25 5.00 -0.80
## sex.sat 2 273 4.81 1.40 5.00 4.87 1.48 1.00 7.00 6.00 -0.39
## self.comp 3 274 3.03 0.70 3.00 3.03 0.74 1.33 4.75 3.42 0.05
## love 4 274 6.07 0.68 6.19 6.14 0.64 3.33 7.00 3.67 -1.00
## age 5 276 31.84 4.57 32.00 31.67 4.45 20.00 46.00 26.00 0.32
## kurtosis se percent_data_present
## rel.sat 0.31 0.06 98.91
## sex.sat -0.32 0.08 98.91
## self.comp -0.54 0.04 99.28
## love 1.22 0.04 99.28
## age 0.05 0.28 100.00
## mydata$role.f n percent
## non-gestational 138 0.5
## gestational 138 0.5
## mydata$sex.f n percent
## male 138 0.5
## female 138 0.5
## mydata$gender.f n percent
## man 133 0.48188406
## woman 136 0.49275362
## other gender 7 0.02536232
## mydata$gestational.partner.f n percent
## non-gestational 138 0.5
## gestational 138 0.5
##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.89 0.72 11 0.0093 4 0.93 0.72
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.91 0.92
## Duhachek 0.89 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## rs1 0.91 0.91 0.87 0.77 10.0 0.0096 0.0010 0.75
## rs2 0.87 0.88 0.85 0.72 7.7 0.0133 0.0056 0.68
## rs3 0.87 0.88 0.83 0.71 7.3 0.0133 0.0016 0.69
## rs4 0.87 0.88 0.83 0.70 7.0 0.0137 0.0017 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## rs1 273 0.87 0.85 0.77 0.74 3.9 1.25
## rs2 273 0.89 0.89 0.85 0.81 4.1 1.02
## rs3 273 0.90 0.90 0.87 0.82 4.0 0.99
## rs4 273 0.90 0.91 0.88 0.83 4.1 0.94
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## rs1 0 0.03 0.11 0.22 0.26 0.30 0.07 0.01
## rs2 0 0.02 0.05 0.19 0.29 0.45 0.00 0.01
## rs3 0 0.01 0.05 0.23 0.31 0.40 0.00 0.01
## rs4 0 0.00 0.05 0.21 0.33 0.40 0.00 0.01
##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.92 0.89 0.73 11 0.0084 4.8 1.4 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.93
## Duhachek 0.9 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## ss1 0.89 0.89 0.85 0.73 8.2 0.012 0.00124 0.74
## ss2 0.88 0.88 0.83 0.71 7.4 0.012 0.00039 0.72
## ss3 0.89 0.89 0.85 0.74 8.4 0.011 0.00047 0.74
## ss4 0.89 0.89 0.85 0.74 8.4 0.011 0.00004 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ss1 273 0.90 0.89 0.84 0.80 4.6 1.5
## ss2 271 0.91 0.91 0.87 0.83 4.9 1.5
## ss3 271 0.89 0.89 0.83 0.79 4.9 1.5
## ss4 272 0.89 0.89 0.83 0.79 4.8 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## ss1 0.04 0.07 0.10 0.23 0.28 0.18 0.11 0.01
## ss2 0.02 0.04 0.09 0.23 0.23 0.24 0.15 0.02
## ss3 0.03 0.04 0.06 0.26 0.21 0.21 0.18 0.02
## ss4 0.03 0.08 0.12 0.17 0.22 0.20 0.17 0.01
##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.87 0.34 6.1 0.012 3 0.7 0.32
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sc1r 0.84 0.84 0.86 0.33 5.3 0.014 0.013 0.31
## sc2 0.85 0.85 0.87 0.34 5.7 0.013 0.014 0.32
## sc3 0.85 0.85 0.86 0.34 5.7 0.013 0.013 0.33
## sc4r 0.85 0.85 0.86 0.34 5.6 0.014 0.014 0.31
## sc5 0.85 0.85 0.86 0.34 5.6 0.013 0.015 0.31
## sc6 0.85 0.85 0.86 0.33 5.5 0.013 0.015 0.31
## sc7 0.85 0.85 0.86 0.34 5.7 0.013 0.013 0.33
## sc8r 0.85 0.85 0.87 0.35 5.9 0.013 0.012 0.32
## sc9r 0.85 0.85 0.86 0.34 5.6 0.014 0.012 0.32
## sc10 0.86 0.86 0.87 0.35 6.0 0.012 0.012 0.35
## sc11r 0.84 0.84 0.85 0.33 5.3 0.014 0.010 0.31
## sc12r 0.84 0.84 0.86 0.33 5.4 0.014 0.012 0.31
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sc1r 274 0.72 0.70 0.68 0.63 2.7 1.25
## sc2 274 0.57 0.59 0.53 0.48 3.3 1.00
## sc3 274 0.57 0.59 0.55 0.49 3.7 0.93
## sc4r 273 0.66 0.64 0.60 0.56 2.8 1.23
## sc5 271 0.62 0.63 0.59 0.54 3.4 1.05
## sc6 274 0.64 0.65 0.61 0.56 3.0 1.11
## sc7 274 0.59 0.61 0.57 0.50 3.7 0.99
## sc8r 274 0.57 0.55 0.49 0.46 2.5 1.16
## sc9r 274 0.66 0.64 0.61 0.56 2.5 1.23
## sc10 273 0.50 0.50 0.43 0.38 2.9 1.14
## sc11r 274 0.73 0.72 0.71 0.66 2.6 1.20
## sc12r 274 0.69 0.68 0.66 0.61 3.2 1.12
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## sc1r 0.21 0.26 0.25 0.18 0.09 0.01
## sc2 0.02 0.23 0.27 0.38 0.09 0.01
## sc3 0.03 0.07 0.28 0.45 0.18 0.01
## sc4r 0.14 0.32 0.22 0.21 0.11 0.01
## sc5 0.04 0.14 0.32 0.33 0.17 0.02
## sc6 0.08 0.30 0.28 0.25 0.09 0.01
## sc7 0.03 0.08 0.27 0.42 0.20 0.01
## sc8r 0.19 0.36 0.22 0.16 0.07 0.01
## sc9r 0.23 0.34 0.19 0.18 0.07 0.01
## sc10 0.13 0.24 0.26 0.30 0.06 0.01
## sc11r 0.18 0.35 0.24 0.14 0.09 0.01
## sc12r 0.06 0.22 0.32 0.25 0.14 0.01
##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.94 0.95 0.41 15 0.0061 6.1 0.68 0.41
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.94
## Duhachek 0.92 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cl1r 0.93 0.93 0.95 0.41 14 0.0063 0.015 0.41
## cl2r 0.93 0.94 0.95 0.43 15 0.0057 0.011 0.42
## cl3r 0.92 0.93 0.95 0.41 14 0.0065 0.014 0.41
## cl4r 0.93 0.93 0.95 0.42 14 0.0062 0.015 0.41
## cl5r 0.92 0.93 0.95 0.40 14 0.0064 0.014 0.41
## cl6r 0.93 0.93 0.95 0.41 14 0.0064 0.015 0.41
## cl7r 0.93 0.93 0.95 0.42 14 0.0063 0.014 0.41
## cl8r 0.93 0.93 0.95 0.41 14 0.0064 0.015 0.41
## cl9r 0.92 0.93 0.95 0.40 14 0.0065 0.014 0.40
## cl10r 0.92 0.93 0.95 0.41 14 0.0065 0.015 0.41
## cl11r 0.93 0.93 0.95 0.41 14 0.0063 0.015 0.41
## cl12r 0.92 0.93 0.95 0.41 14 0.0065 0.015 0.41
## cl13r 0.92 0.93 0.95 0.41 14 0.0065 0.015 0.41
## cl14r 0.93 0.93 0.95 0.42 14 0.0063 0.015 0.42
## cl15r 0.92 0.93 0.95 0.40 13 0.0066 0.015 0.40
## cl16r 0.93 0.93 0.95 0.41 14 0.0064 0.015 0.41
## cl17r 0.93 0.93 0.95 0.41 14 0.0064 0.015 0.41
## cl18r 0.93 0.93 0.95 0.41 14 0.0063 0.015 0.41
## cl19r 0.93 0.93 0.95 0.41 14 0.0063 0.015 0.41
## cl20r 0.92 0.93 0.95 0.41 14 0.0065 0.015 0.41
## cl21r 0.93 0.93 0.95 0.41 14 0.0063 0.015 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cl1r 274 0.63 0.64 0.62 0.58 6.4 0.88
## cl2r 274 0.39 0.37 0.32 0.31 5.5 1.32
## cl3r 274 0.71 0.72 0.71 0.68 6.2 0.93
## cl4r 273 0.61 0.60 0.58 0.55 5.6 1.31
## cl5r 274 0.73 0.75 0.75 0.70 6.5 0.79
## cl6r 273 0.70 0.71 0.70 0.66 6.5 0.80
## cl7r 274 0.62 0.60 0.59 0.56 6.1 1.24
## cl8r 274 0.67 0.65 0.64 0.62 5.9 1.17
## cl9r 274 0.76 0.76 0.75 0.72 6.3 1.00
## cl10r 273 0.72 0.70 0.69 0.67 5.8 1.17
## cl11r 274 0.65 0.63 0.61 0.60 5.2 1.29
## cl12r 273 0.72 0.71 0.70 0.67 6.2 0.97
## cl13r 271 0.72 0.70 0.68 0.66 5.8 1.20
## cl14r 274 0.60 0.59 0.57 0.55 5.8 1.12
## cl15r 274 0.78 0.78 0.77 0.75 5.9 1.11
## cl16r 274 0.66 0.65 0.64 0.61 5.8 1.11
## cl17r 274 0.65 0.64 0.63 0.60 5.7 1.17
## cl18r 274 0.62 0.64 0.62 0.58 6.6 0.69
## cl19r 274 0.66 0.69 0.68 0.63 6.6 0.70
## cl20r 274 0.71 0.72 0.71 0.68 6.3 0.93
## cl21r 274 0.60 0.64 0.62 0.57 6.8 0.62
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## cl1r 0 0.00 0.00 0.04 0.12 0.24 0.59 0.01
## cl2r 0 0.03 0.05 0.12 0.27 0.25 0.27 0.01
## cl3r 0 0.00 0.00 0.04 0.19 0.24 0.53 0.01
## cl4r 0 0.01 0.04 0.15 0.21 0.25 0.33 0.01
## cl5r 0 0.00 0.00 0.03 0.07 0.24 0.66 0.01
## cl6r 0 0.00 0.01 0.02 0.08 0.22 0.67 0.01
## cl7r 0 0.01 0.02 0.10 0.15 0.16 0.54 0.01
## cl8r 0 0.01 0.02 0.12 0.17 0.27 0.41 0.01
## cl9r 0 0.00 0.01 0.04 0.15 0.22 0.58 0.01
## cl10r 0 0.00 0.02 0.16 0.19 0.28 0.35 0.01
## cl11r 0 0.01 0.04 0.32 0.22 0.19 0.23 0.01
## cl12r 0 0.00 0.01 0.02 0.22 0.27 0.48 0.01
## cl13r 0 0.01 0.03 0.11 0.21 0.26 0.38 0.02
## cl14r 0 0.01 0.01 0.11 0.24 0.32 0.31 0.01
## cl15r 0 0.00 0.02 0.11 0.22 0.22 0.43 0.01
## cl16r 0 0.01 0.02 0.09 0.24 0.31 0.34 0.01
## cl17r 0 0.00 0.03 0.12 0.28 0.23 0.33 0.01
## cl18r 0 0.00 0.00 0.01 0.07 0.20 0.72 0.01
## cl19r 0 0.00 0.00 0.02 0.06 0.18 0.74 0.01
## cl20r 0 0.00 0.01 0.04 0.13 0.26 0.56 0.01
## cl21r 0 0.00 0.00 0.01 0.05 0.09 0.85 0.01
#looking at distributions
hist(mydata$age)
hist(mydata$rel.sat) #relationship status is a little left skewed (most people answered strongly positive)
hist(mydata$sex.sat)
hist(mydata$self.comp)
hist(mydata$love) #compassionate love is a little left skewed (most people answered strongly positive)
ggplot(data=subset(mydata, !is.na(role.f)), aes(x=sex.sat, y=role.f)) +
geom_boxplot(fill='#94AEBC', color='black') +
coord_flip() +
theme_classic() +
labs(x = "sexual satisfaction", y = "relationship role", title ='sexual satisfaction by partner')
## Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
ggplot(data=subset(mydata, !is.na(role.f)), aes(x=rel.sat, y=role.f)) +
geom_boxplot(fill='#94AEBC', color='black') +
coord_flip() +
theme_classic() +
labs(x = "relationship satisfaction", y = "relationship role", title ='relationship satisfaction by partner')
## Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
ggplot(data=subset(mydata, !is.na(role.f)), aes(x=love, y=role.f)) +
geom_boxplot(fill='#94AEBC', color='black') +
coord_flip() +
theme_classic() +
labs(x = "compassionate love", y = "relationship role", title ='compassionate love by partner')
## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
ggplot(data=subset(mydata, !is.na(role.f)), aes(x=self.comp, y=role.f)) +
geom_boxplot(fill='#94AEBC', color='black') +
coord_flip() +
theme_classic() +
labs(x = "self compassion", y = "relationship role", title ='self compassion by partner')
## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
#Correlations
comp <- mydata[,c("self.comp", "sex.sat", "rel.sat", "love", "gestational.partner", "age", "income", "education", "loss.weeks", "rel.years")]
#calculating correlations and CIs
cor1 <- cor.mtest(comp, use="pairwise.complete.obs", conf.level = 0.95)
cor1
## $p
## self.comp sex.sat rel.sat love
## self.comp 0.000000e+00 3.834986e-02 5.962195e-02 8.769832e-01
## sex.sat 3.834986e-02 0.000000e+00 2.046658e-14 1.916208e-02
## rel.sat 5.962195e-02 2.046658e-14 0.000000e+00 3.136925e-14
## love 8.769832e-01 1.916208e-02 3.136925e-14 0.000000e+00
## gestational.partner 6.422483e-05 5.988147e-01 1.488873e-01 9.567515e-01
## age 6.035453e-01 2.143584e-03 2.617902e-04 4.258826e-02
## income 9.332211e-01 8.275007e-01 4.684359e-01 1.595956e-02
## education 2.519745e-01 9.889428e-01 8.230529e-01 3.602680e-01
## loss.weeks 3.796762e-01 3.203366e-01 5.508404e-01 8.618932e-01
## rel.years 5.644727e-01 1.543879e-03 1.301954e-03 1.102045e-01
## gestational.partner age income education
## self.comp 6.422483e-05 6.035453e-01 9.332211e-01 2.519745e-01
## sex.sat 5.988147e-01 2.143584e-03 8.275007e-01 9.889428e-01
## rel.sat 1.488873e-01 2.617902e-04 4.684359e-01 8.230529e-01
## love 9.567515e-01 4.258826e-02 1.595956e-02 3.602680e-01
## gestational.partner 0.000000e+00 1.075398e-02 8.664865e-01 2.502990e-03
## age 1.075398e-02 0.000000e+00 1.254813e-04 4.127823e-06
## income 8.664865e-01 1.254813e-04 0.000000e+00 6.571578e-07
## education 2.502990e-03 4.127823e-06 6.571578e-07 0.000000e+00
## loss.weeks 8.917981e-02 7.564722e-01 6.444847e-02 4.330702e-01
## rel.years 9.421988e-01 6.260874e-08 7.601757e-01 3.801821e-02
## loss.weeks rel.years
## self.comp 0.37967622 5.644727e-01
## sex.sat 0.32033656 1.543879e-03
## rel.sat 0.55084044 1.301954e-03
## love 0.86189324 1.102045e-01
## gestational.partner 0.08917981 9.421988e-01
## age 0.75647222 6.260874e-08
## income 0.06444847 7.601757e-01
## education 0.43307022 3.801821e-02
## loss.weeks 0.00000000 1.580312e-01
## rel.years 0.15803119 0.000000e+00
##
## $lowCI
## self.comp sex.sat rel.sat love
## self.comp 1.000000000 0.00682565 -0.004629601 -0.10922731
## sex.sat 0.006825650 1.00000000 0.341247652 0.02342028
## rel.sat -0.004629601 0.34124765 1.000000000 0.33690088
## love -0.109227311 0.02342028 0.336900882 1.00000000
## gestational.partner -0.347685961 -0.15012712 -0.031447271 -0.12174369
## age -0.087317106 -0.29721072 -0.329390317 -0.23764487
## income -0.123737005 -0.13200028 -0.162226813 -0.25995864
## education -0.049650409 -0.11998875 -0.132585893 -0.17347121
## loss.weeks -0.093607061 -0.25146783 -0.218828229 -0.15366686
## rel.years -0.152826184 -0.30261085 -0.305353780 -0.21277222
## gestational.partner age income education
## self.comp -0.34768596 -0.08731711 -0.1237370 -0.049650409
## sex.sat -0.15012712 -0.29721072 -0.1320003 -0.119988747
## rel.sat -0.03144727 -0.32939032 -0.1622268 -0.132585893
## love -0.12174369 -0.23764487 -0.2599586 -0.173471213
## gestational.partner 1.00000000 -0.26655871 -0.1283137 0.064827790
## age -0.26655871 1.00000000 0.1140342 0.160819225
## income -0.12831371 0.11403422 1.0000000 0.183335819
## education 0.06482779 0.16081923 0.1833358 1.000000000
## loss.weeks -0.30601126 -0.14161519 -0.3177025 -0.233441013
## rel.years -0.11374376 0.20840350 -0.1000128 0.007017119
## loss.weeks rel.years
## self.comp -0.09360706 -0.152826184
## sex.sat -0.25146783 -0.302610855
## rel.sat -0.21882823 -0.305353780
## love -0.15366686 -0.212772220
## gestational.partner -0.30601126 -0.113743760
## age -0.14161519 0.208403496
## income -0.31770246 -0.100012765
## education -0.23344101 0.007017119
## loss.weeks 1.00000000 -0.047402384
## rel.years -0.04740238 1.000000000
##
## $uppCI
## self.comp sex.sat rel.sat love
## self.comp 1.00000000 0.24099292 0.22975385 0.127751956
## sex.sat 0.24099292 1.00000000 0.53344754 0.256563987
## rel.sat 0.22975385 0.53344754 1.00000000 0.529285227
## love 0.12775196 0.25656399 0.52928523 1.000000000
## gestational.partner -0.12404821 0.08706771 0.20419057 0.115253667
## age 0.14945079 -0.06779390 -0.10321610 -0.004157844
## income 0.11369116 0.10582940 0.07518721 -0.027497893
## education 0.18708137 0.11832125 0.10568053 0.063672601
## loss.weeks 0.24117278 0.08400836 0.11820566 0.182924757
## rel.years 0.08388812 -0.07369807 -0.07670317 0.022041859
## gestational.partner age income education
## self.comp -0.12404821 0.149450788 0.113691163 0.1870814
## sex.sat 0.08706771 -0.067793897 0.105829400 0.1183213
## rel.sat 0.20419057 -0.103216097 0.075187210 0.1056805
## love 0.11525367 -0.004157844 -0.027497893 0.0636726
## gestational.partner 1.00000000 -0.035895370 0.108230003 0.2940899
## age -0.03589537 1.000000000 0.338336639 0.3802452
## income 0.10823000 0.338336639 1.000000000 0.4002878
## education 0.29408988 0.380245166 0.400287787 1.0000000
## loss.weeks 0.02248781 0.193582763 0.009540358 0.1017221
## rel.years 0.12239005 0.420868169 0.136475397 0.2403409
## loss.weeks rel.years
## self.comp 0.241172785 0.08388812
## sex.sat 0.084008361 -0.07369807
## rel.sat 0.118205655 -0.07670317
## love 0.182924757 0.02204186
## gestational.partner 0.022487812 0.12239005
## age 0.193582763 0.42086817
## income 0.009540358 0.13647540
## education 0.101722084 0.24034092
## loss.weeks 1.000000000 0.28323180
## rel.years 0.283231803 1.00000000
cor1b <- cor(comp, use="pairwise.complete.obs")
rownames(cor1b) <- c("Self-Compassion",
"Sexual Satisfaction",
"Relationship Satisfaction",
"Compassionate Love",
"Gestational Partner",
"Age",
"Income",
"Educational attainment",
"Weeks at loss",
"Years in relationship")
colnames(cor1b) <- c("Self-Compassion",
"Sexual Satisfaction",
"Relationship Satisfaction",
"Compassionate Love",
"Gestational Partner",
"Age",
"Income",
"Educational attainment",
"Weeks at loss",
"Years in relationship")
corrplot(cor1b, method="color", type="upper",
addCoef.col = "black", tl.col="black", tl.srt=40, p.mat = cor1$p, tl.cex = 0.7,
insig = "label_sig",sig.level =.05, pch.cex = .5,
diag=FALSE, number.cex = .8,
col=colorRampPalette(c("hotpink", "white", "#728393"))(50), cl.pos = 'n')
## Warning in corrplot(cor1b, method = "color", type = "upper", addCoef.col =
## "black", : p.mat and corr may be not paired, their rownames and colnames are
## not totally same!
#non-gestational partner is the lower binary value, correlations with being pregnant would be positive
# MODEL INTERPRETATION CHEAT SHEAT #
# a1 = non-gestational partner's actor effect
# a2 = gestational partner's actor effect
# p12 = partner effect: non-gestational partner influences gestational partner
# p21 = partner effect: gestational partner influences non-gestational partner
#########################################################################################################
#MODEL 1: self-compassion and relationship satisfaction
model1 <- '
rel.sat_1 ~ a1*self.comp_1
rel.sat_2 ~ a2*self.comp_2
rel.sat_1 ~ p12*self.comp_2
rel.sat_2 ~ p21*self.comp_1
self.comp_1 ~ mx1*1
self.comp_2 ~ mx2*1
rel.sat_1 ~ my1*1
rel.sat_2 ~ my2*1
self.comp_1 ~~ vx1*self.comp_1
self.comp_2 ~~ vx2*self.comp_2
rel.sat_1 ~~ vy1*rel.sat_1
rel.sat_2 ~~ vy2*rel.sat_2
self.comp_2 ~~ cx*self.comp_1
rel.sat_2 ~~ cy*rel.sat_1'
model1.fit <- lavaan::sem(model1,fixed.x=FALSE, se = "bootstrap", bootstrap = 5000, data = mydata.wide, missing="fiml")
summary(model1.fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,estimates = TRUE, ci = TRUE)
## lavaan 0.6.15 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 139
## Number of missing patterns 4
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 64.887
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -618.797
## Loglikelihood unrestricted model (H1) -618.797
##
## Akaike (AIC) 1265.594
## Bayesian (BIC) 1306.677
## Sample-size adjusted Bayesian (SABIC) 1262.384
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## rel.sat_1 ~
## slf.cm_1 (a1) 0.174 0.089 1.967 0.049 -0.002 0.342
## rel.sat_2 ~
## slf.cm_2 (a2) 0.191 0.116 1.644 0.100 -0.036 0.419
## rel.sat_1 ~
## slf.cm_2 (p12) 0.098 0.103 0.957 0.339 -0.097 0.305
## rel.sat_2 ~
## slf.cm_1 (p21) 0.045 0.112 0.405 0.686 -0.181 0.267
## Std.lv Std.all
##
## 0.174 0.130
##
## 0.191 0.139
##
## 0.098 0.073
##
## 0.045 0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## self.comp_1 ~~
## slf.cmp_2 (cx) 0.058 0.043 1.345 0.179 -0.024 0.141
## .rel.sat_1 ~~
## .rel.sat_2 (cy) 0.492 0.107 4.622 0.000 0.292 0.710
## Std.lv Std.all
##
## 0.058 0.124
##
## 0.492 0.585
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## slf.cm_1 (mx1) 2.864 0.059 48.329 0.000 2.748 2.978
## slf.cm_2 (mx2) 3.199 0.058 55.434 0.000 3.085 3.314
## .rel.st_1 (my1) 3.291 0.415 7.939 0.000 2.482 4.090
## .rel.st_2 (my2) 3.193 0.417 7.651 0.000 2.416 4.052
## Std.lv Std.all
## 2.864 4.197
## 3.199 4.693
## 3.291 3.581
## 3.193 3.405
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## slf.cm_1 (vx1) 0.466 0.048 9.740 0.000 0.369 0.559
## slf.cm_2 (vx2) 0.465 0.046 10.174 0.000 0.374 0.557
## .rel.st_1 (vy1) 0.824 0.111 7.400 0.000 0.603 1.038
## .rel.st_2 (vy2) 0.860 0.117 7.368 0.000 0.639 1.102
## Std.lv Std.all
## 0.466 1.000
## 0.465 1.000
## 0.824 0.976
## 0.860 0.978
##
## R-Square:
## Estimate
## rel.sat_1 0.024
## rel.sat_2 0.022
model1.stand <- standardizedSolution(model1.fit, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
#non-gestational partner actor effect (a1): relationship satisfaction is predicted by self-compassion
semPaths(model1.fit,
# general lay-out for a nice APIM:
fade = F, "est", layout='tree2', rotation = 2, style = "ram",
intercepts = F, residuals = F, optimizeLatRes = T, curve = 3.1,
# labels and their sizes:
nodeLabels=c("Gest. P: Relation. Satisf.", "Non-Gest. P: Relation. Satisf.",
"Gest. P: Self Comp.", "Non-Gest. P: Self Comp."), sizeMan=20, sizeMan2=18,
# position and size of parameter estimates:
edge.label.position = 0.45, edge.label.cex=1.2, label.cex = 1.25)
stargazer(model1.stand, summary=FALSE, type='html', rownames=TRUE, initial.zero=FALSE, digits=3,
title='Standardized Coefficients', out = "model1.html")
##
## <table style="text-align:center"><caption><strong>Standardized Coefficients</strong></caption>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lhs</td><td>op</td><td>rhs</td><td>label</td><td>est.std</td><td>se</td><td>z</td><td>pvalue</td><td>ci.lower</td><td>ci.upper</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>rel.sat_1</td><td>~</td><td>self.comp_1</td><td>a1</td><td>.130</td><td>.067</td><td>1.925</td><td>.054</td><td>-.002</td><td>.261</td></tr>
## <tr><td style="text-align:left">2</td><td>rel.sat_2</td><td>~</td><td>self.comp_2</td><td>a2</td><td>.139</td><td>.085</td><td>1.631</td><td>.103</td><td>-.028</td><td>.306</td></tr>
## <tr><td style="text-align:left">3</td><td>rel.sat_1</td><td>~</td><td>self.comp_2</td><td>p12</td><td>.073</td><td>.075</td><td>.977</td><td>.328</td><td>-.073</td><td>.219</td></tr>
## <tr><td style="text-align:left">4</td><td>rel.sat_2</td><td>~</td><td>self.comp_1</td><td>p21</td><td>.033</td><td>.082</td><td>.403</td><td>.687</td><td>-.127</td><td>.193</td></tr>
## <tr><td style="text-align:left">5</td><td>self.comp_1</td><td></td><td></td><td>mx1</td><td>4.197</td><td>.230</td><td>18.266</td><td>0</td><td>3.747</td><td>4.648</td></tr>
## <tr><td style="text-align:left">6</td><td>self.comp_2</td><td></td><td></td><td>mx2</td><td>4.693</td><td>.244</td><td>19.217</td><td>0</td><td>4.214</td><td>5.172</td></tr>
## <tr><td style="text-align:left">7</td><td>rel.sat_1</td><td></td><td></td><td>my1</td><td>3.581</td><td>.570</td><td>6.282</td><td>0</td><td>2.464</td><td>4.698</td></tr>
## <tr><td style="text-align:left">8</td><td>rel.sat_2</td><td></td><td></td><td>my2</td><td>3.405</td><td>.490</td><td>6.954</td><td>0</td><td>2.446</td><td>4.365</td></tr>
## <tr><td style="text-align:left">9</td><td>self.comp_1</td><td>~~</td><td>self.comp_1</td><td>vx1</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">10</td><td>self.comp_2</td><td>~~</td><td>self.comp_2</td><td>vx2</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">11</td><td>rel.sat_1</td><td>~~</td><td>rel.sat_1</td><td>vy1</td><td>.976</td><td>.022</td><td>45.283</td><td>0</td><td>.933</td><td>1.018</td></tr>
## <tr><td style="text-align:left">12</td><td>rel.sat_2</td><td>~~</td><td>rel.sat_2</td><td>vy2</td><td>.978</td><td>.024</td><td>41.436</td><td>0</td><td>.932</td><td>1.025</td></tr>
## <tr><td style="text-align:left">13</td><td>self.comp_1</td><td>~~</td><td>self.comp_2</td><td>cx</td><td>.124</td><td>.090</td><td>1.376</td><td>.169</td><td>-.053</td><td>.300</td></tr>
## <tr><td style="text-align:left">14</td><td>rel.sat_1</td><td>~~</td><td>rel.sat_2</td><td>cy</td><td>.585</td><td>.070</td><td>8.350</td><td>0</td><td>.447</td><td>.722</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr></table>
#MODEL 2: compassionate love and sexual satisfaction
model2 <- '
sex.sat_1 ~ a1*love_1
sex.sat_2 ~ a2*love_2
sex.sat_1 ~ p12*love_2
sex.sat_2 ~ p21*love_1
love_1 ~ mx1*1
love_2 ~ mx2*1
sex.sat_1 ~ my1*1
sex.sat_2 ~ my2*1
love_1 ~~ vx1*love_1
love_2 ~~ vx2*love_2
sex.sat_1 ~~ vy1*sex.sat_1
sex.sat_2 ~~ vy2*sex.sat_2
love_2 ~~ cx*love_1
sex.sat_2 ~~ cy*sex.sat_1'
model2.fit <- lavaan::sem(model2,fixed.x=FALSE, se = "bootstrap", bootstrap = 5000, data = mydata.wide, missing="fiml")
summary(model2.fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,estimates = TRUE, ci = TRUE)
## lavaan 0.6.15 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 139
## Number of missing patterns 6
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 91.528
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -714.991
## Loglikelihood unrestricted model (H1) -714.991
##
## Akaike (AIC) 1457.982
## Bayesian (BIC) 1499.064
## Sample-size adjusted Bayesian (SABIC) 1454.772
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## sex.sat_1 ~
## love_1 (a1) 0.623 0.179 3.474 0.001 0.256 0.960
## sex.sat_2 ~
## love_2 (a2) -0.091 0.194 -0.472 0.637 -0.438 0.314
## sex.sat_1 ~
## love_2 (p12) -0.181 0.185 -0.983 0.326 -0.538 0.181
## sex.sat_2 ~
## love_1 (p21) 0.323 0.183 1.765 0.078 -0.041 0.680
## Std.lv Std.all
##
## 0.623 0.305
##
## -0.091 -0.044
##
## -0.181 -0.089
##
## 0.323 0.155
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 ~~
## love_2 (cx) 0.214 0.062 3.457 0.001 0.101 0.340
## .sex.sat_1 ~~
## .sex.sat_2 (cy) 1.021 0.190 5.381 0.000 0.657 1.399
## Std.lv Std.all
##
## 0.214 0.465
##
## 1.021 0.547
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 (mx1) 6.074 0.057 107.412 0.000 5.959 6.183
## love_2 (mx2) 6.078 0.058 104.875 0.000 5.962 6.190
## .sex.st_1 (my1) 2.069 1.137 1.820 0.069 -0.150 4.268
## .sex.st_2 (my2) 3.441 1.211 2.843 0.004 0.864 5.607
## Std.lv Std.all
## 6.074 8.968
## 6.078 8.936
## 2.069 1.494
## 3.441 2.431
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 (vx1) 0.459 0.063 7.240 0.000 0.340 0.588
## love_2 (vx2) 0.463 0.077 5.998 0.000 0.321 0.621
## .sex.st_1 (vy1) 1.775 0.189 9.386 0.000 1.393 2.132
## .sex.st_2 (vy2) 1.964 0.240 8.186 0.000 1.474 2.400
## Std.lv Std.all
## 0.459 1.000
## 0.463 1.000
## 1.775 0.924
## 1.964 0.980
##
## R-Square:
## Estimate
## sex.sat_1 0.076
## sex.sat_2 0.020
model2.stand <- standardizedSolution(model2.fit, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
semPaths(model2.fit,
fade = F, "est", layout='tree2', rotation = 2, style = "ram",
intercepts = F, residuals = F, optimizeLatRes = T, curve = 3.1,
# labels and their sizes:
nodeLabels=c("Gest. P: Sex Satisf.", "Non-Gest. P: Sex Satisf.",
"Gest. P: Love", "Non-Gest. P: Love"), sizeMan=20, sizeMan2=18,
# position and size of parameter estimates:
edge.label.position = 0.45, edge.label.cex=1.2, label.cex = 1.25)
stargazer(model2.stand, summary=FALSE, type='html', rownames=TRUE, initial.zero=FALSE, digits=3,
title='Standardized Coefficients', out = "model2.html")
##
## <table style="text-align:center"><caption><strong>Standardized Coefficients</strong></caption>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lhs</td><td>op</td><td>rhs</td><td>label</td><td>est.std</td><td>se</td><td>z</td><td>pvalue</td><td>ci.lower</td><td>ci.upper</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>sex.sat_1</td><td>~</td><td>love_1</td><td>a1</td><td>.305</td><td>.089</td><td>3.439</td><td>.001</td><td>.131</td><td>.479</td></tr>
## <tr><td style="text-align:left">2</td><td>sex.sat_2</td><td>~</td><td>love_2</td><td>a2</td><td>-.044</td><td>.094</td><td>-.467</td><td>.640</td><td>-.228</td><td>.140</td></tr>
## <tr><td style="text-align:left">3</td><td>sex.sat_1</td><td>~</td><td>love_2</td><td>p12</td><td>-.089</td><td>.090</td><td>-.989</td><td>.322</td><td>-.266</td><td>.087</td></tr>
## <tr><td style="text-align:left">4</td><td>sex.sat_2</td><td>~</td><td>love_1</td><td>p21</td><td>.155</td><td>.088</td><td>1.751</td><td>.080</td><td>-.018</td><td>.328</td></tr>
## <tr><td style="text-align:left">5</td><td>love_1</td><td></td><td></td><td>mx1</td><td>8.968</td><td>.669</td><td>13.405</td><td>0</td><td>7.657</td><td>10.279</td></tr>
## <tr><td style="text-align:left">6</td><td>love_2</td><td></td><td></td><td>mx2</td><td>8.936</td><td>.794</td><td>11.248</td><td>0</td><td>7.379</td><td>10.493</td></tr>
## <tr><td style="text-align:left">7</td><td>sex.sat_1</td><td></td><td></td><td>my1</td><td>1.494</td><td>.834</td><td>1.791</td><td>.073</td><td>-.141</td><td>3.128</td></tr>
## <tr><td style="text-align:left">8</td><td>sex.sat_2</td><td></td><td></td><td>my2</td><td>2.431</td><td>.873</td><td>2.784</td><td>.005</td><td>.720</td><td>4.143</td></tr>
## <tr><td style="text-align:left">9</td><td>love_1</td><td>~~</td><td>love_1</td><td>vx1</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">10</td><td>love_2</td><td>~~</td><td>love_2</td><td>vx2</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">11</td><td>sex.sat_1</td><td>~~</td><td>sex.sat_1</td><td>vy1</td><td>.924</td><td>.044</td><td>21.145</td><td>0</td><td>.839</td><td>1.010</td></tr>
## <tr><td style="text-align:left">12</td><td>sex.sat_2</td><td>~~</td><td>sex.sat_2</td><td>vy2</td><td>.980</td><td>.022</td><td>44.507</td><td>0</td><td>.937</td><td>1.024</td></tr>
## <tr><td style="text-align:left">13</td><td>love_1</td><td>~~</td><td>love_2</td><td>cx</td><td>.465</td><td>.088</td><td>5.295</td><td>0.00000</td><td>.293</td><td>.638</td></tr>
## <tr><td style="text-align:left">14</td><td>sex.sat_1</td><td>~~</td><td>sex.sat_2</td><td>cy</td><td>.547</td><td>.061</td><td>9.013</td><td>0</td><td>.428</td><td>.666</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr></table>
#non-gestational partner actor effect (a1): sexual satisfaction is predicted by compassionate love
#MODEL 3: self compassion and sexual satisfaction
model3 <- '
sex.sat_1 ~ a1*self.comp_1
sex.sat_2 ~ a2*self.comp_2
sex.sat_1 ~ p12*self.comp_2
sex.sat_2 ~ p21*self.comp_1
self.comp_1 ~ mx1*1
self.comp_2 ~ mx2*1
sex.sat_1 ~ my1*1
sex.sat_2 ~ my2*1
self.comp_1 ~~ vx1*self.comp_1
self.comp_2 ~~ vx2*self.comp_2
sex.sat_1 ~~ vy1*sex.sat_1
sex.sat_2 ~~ vy2*sex.sat_2
self.comp_2 ~~ cx*self.comp_1
sex.sat_2 ~~ cy*sex.sat_1'
model3.fit <- lavaan::sem(model3,fixed.x=FALSE, se = "bootstrap", bootstrap = 5000, data = mydata.wide, missing="fiml")
summary(model3.fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,estimates = TRUE, ci = TRUE)
## lavaan 0.6.15 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 139
## Number of missing patterns 6
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 58.538
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -733.024
## Loglikelihood unrestricted model (H1) -733.024
##
## Akaike (AIC) 1494.048
## Bayesian (BIC) 1535.131
## Sample-size adjusted Bayesian (SABIC) 1490.838
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## sex.sat_1 ~
## slf.cm_1 (a1) 0.094 0.172 0.546 0.585 -0.258 0.430
## sex.sat_2 ~
## slf.cm_2 (a2) 0.397 0.181 2.193 0.028 0.050 0.766
## sex.sat_1 ~
## slf.cm_2 (p12) -0.015 0.191 -0.078 0.938 -0.390 0.355
## sex.sat_2 ~
## slf.cm_1 (p21) 0.071 0.200 0.355 0.723 -0.326 0.458
## Std.lv Std.all
##
## 0.094 0.046
##
## 0.397 0.191
##
## -0.015 -0.007
##
## 0.071 0.034
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## self.comp_1 ~~
## slf.cmp_2 (cx) 0.057 0.043 1.340 0.180 -0.029 0.140
## .sex.sat_1 ~~
## .sex.sat_2 (cy) 1.089 0.184 5.932 0.000 0.715 1.439
## Std.lv Std.all
##
## 0.057 0.124
##
## 1.089 0.568
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## slf.cm_1 (mx1) 2.864 0.059 48.391 0.000 2.749 2.979
## slf.cm_2 (mx2) 3.200 0.058 55.150 0.000 3.086 3.314
## .sex.st_1 (my1) 4.535 0.826 5.492 0.000 2.913 6.162
## .sex.st_2 (my2) 3.373 0.782 4.314 0.000 1.814 4.881
## Std.lv Std.all
## 2.864 4.197
## 3.200 4.694
## 4.535 3.277
## 3.373 2.384
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## slf.cm_1 (vx1) 0.466 0.048 9.776 0.000 0.373 0.558
## slf.cm_2 (vx2) 0.465 0.046 10.000 0.000 0.370 0.554
## .sex.st_1 (vy1) 1.911 0.203 9.411 0.000 1.481 2.285
## .sex.st_2 (vy2) 1.923 0.223 8.618 0.000 1.450 2.326
## Std.lv Std.all
## 0.466 1.000
## 0.465 1.000
## 1.911 0.998
## 1.923 0.961
##
## R-Square:
## Estimate
## sex.sat_1 0.002
## sex.sat_2 0.039
model3.stand <- standardizedSolution(model3.fit, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
# gestational partner - actor effect (a2): sexual satisfaction is predicted by self-compassion
semPaths(model3.fit,
fade = F, "est", layout='tree2', rotation = 2, style = "ram",
intercepts = F, residuals = F, optimizeLatRes = T, curve = 3.1,
# labels and their sizes:
nodeLabels=c("Gest. P: Sex Satisf.", "Non-Gest. P: Sex Satisf.",
"Gest. P: Self. Comp.", "Non-Gest. P: Self. Comp"), sizeMan=20, sizeMan2=18,
# position and size of parameter estimates:
edge.label.position = 0.45, edge.label.cex=1.2, label.cex = 1.25)
stargazer(model3.stand, summary=FALSE, type='html', rownames=TRUE, initial.zero=FALSE, digits=3,
title='Standardized Coefficients', out = "model3.html")
##
## <table style="text-align:center"><caption><strong>Standardized Coefficients</strong></caption>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lhs</td><td>op</td><td>rhs</td><td>label</td><td>est.std</td><td>se</td><td>z</td><td>pvalue</td><td>ci.lower</td><td>ci.upper</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>sex.sat_1</td><td>~</td><td>self.comp_1</td><td>a1</td><td>.046</td><td>.085</td><td>.546</td><td>.585</td><td>-.120</td><td>.212</td></tr>
## <tr><td style="text-align:left">2</td><td>sex.sat_2</td><td>~</td><td>self.comp_2</td><td>a2</td><td>.191</td><td>.084</td><td>2.266</td><td>.023</td><td>.026</td><td>.356</td></tr>
## <tr><td style="text-align:left">3</td><td>sex.sat_1</td><td>~</td><td>self.comp_2</td><td>p12</td><td>-.007</td><td>.094</td><td>-.078</td><td>.938</td><td>-.192</td><td>.177</td></tr>
## <tr><td style="text-align:left">4</td><td>sex.sat_2</td><td>~</td><td>self.comp_1</td><td>p21</td><td>.034</td><td>.097</td><td>.355</td><td>.723</td><td>-.155</td><td>.224</td></tr>
## <tr><td style="text-align:left">5</td><td>self.comp_1</td><td></td><td></td><td>mx1</td><td>4.197</td><td>.228</td><td>18.423</td><td>0</td><td>3.750</td><td>4.643</td></tr>
## <tr><td style="text-align:left">6</td><td>self.comp_2</td><td></td><td></td><td>mx2</td><td>4.694</td><td>.246</td><td>19.096</td><td>0</td><td>4.212</td><td>5.176</td></tr>
## <tr><td style="text-align:left">7</td><td>sex.sat_1</td><td></td><td></td><td>my1</td><td>3.277</td><td>.632</td><td>5.184</td><td>0.00000</td><td>2.038</td><td>4.516</td></tr>
## <tr><td style="text-align:left">8</td><td>sex.sat_2</td><td></td><td></td><td>my2</td><td>2.384</td><td>.611</td><td>3.902</td><td>.0001</td><td>1.187</td><td>3.581</td></tr>
## <tr><td style="text-align:left">9</td><td>self.comp_1</td><td>~~</td><td>self.comp_1</td><td>vx1</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">10</td><td>self.comp_2</td><td>~~</td><td>self.comp_2</td><td>vx2</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">11</td><td>sex.sat_1</td><td>~~</td><td>sex.sat_1</td><td>vy1</td><td>.998</td><td>.008</td><td>130.027</td><td>0</td><td>.983</td><td>1.013</td></tr>
## <tr><td style="text-align:left">12</td><td>sex.sat_2</td><td>~~</td><td>sex.sat_2</td><td>vy2</td><td>.961</td><td>.034</td><td>28.626</td><td>0</td><td>.895</td><td>1.026</td></tr>
## <tr><td style="text-align:left">13</td><td>self.comp_1</td><td>~~</td><td>self.comp_2</td><td>cx</td><td>.124</td><td>.090</td><td>1.373</td><td>.170</td><td>-.053</td><td>.300</td></tr>
## <tr><td style="text-align:left">14</td><td>sex.sat_1</td><td>~~</td><td>sex.sat_2</td><td>cy</td><td>.568</td><td>.057</td><td>9.982</td><td>0</td><td>.457</td><td>.680</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr></table>
#MODEL 4: compassionate love and relationship satisfaction
model4 <- '
rel.sat_1 ~ a1*love_1
rel.sat_2 ~ a2*love_2
rel.sat_1 ~ p12*love_2
rel.sat_2 ~ p21*love_1
love_1 ~ mx1*1
love_2 ~ mx2*1
rel.sat_1 ~ my1*1
rel.sat_2 ~ my2*1
love_1 ~~ vx1*love_1
love_2 ~~ vx2*love_2
rel.sat_1 ~~ vy1*rel.sat_1
rel.sat_2 ~~ vy2*rel.sat_2
love_2 ~~ cx*love_1
rel.sat_2 ~~ cy*rel.sat_1'
model4.fit <- lavaan::sem(model4,fixed.x=FALSE, se = "bootstrap", bootstrap = 5000, data = mydata.wide, missing="fiml")
summary(model4.fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,estimates = TRUE, ci = TRUE)
## lavaan 0.6.15 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 139
## Number of missing patterns 4
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 140.387
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -579.509
## Loglikelihood unrestricted model (H1) -579.509
##
## Akaike (AIC) 1187.018
## Bayesian (BIC) 1228.101
## Sample-size adjusted Bayesian (SABIC) 1183.808
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## rel.sat_1 ~
## love_1 (a1) 0.558 0.118 4.730 0.000 0.323 0.789
## rel.sat_2 ~
## love_2 (a2) 0.363 0.147 2.462 0.014 0.089 0.669
## rel.sat_1 ~
## love_2 (p12) 0.219 0.139 1.579 0.114 -0.048 0.502
## rel.sat_2 ~
## love_1 (p21) 0.398 0.115 3.463 0.001 0.171 0.620
## Std.lv Std.all
##
## 0.558 0.410
##
## 0.363 0.263
##
## 0.219 0.162
##
## 0.398 0.287
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 ~~
## love_2 (cx) 0.212 0.062 3.423 0.001 0.100 0.340
## .rel.sat_1 ~~
## .rel.sat_2 (cy) 0.306 0.065 4.747 0.000 0.179 0.429
## Std.lv Std.all
##
## 0.212 0.462
##
## 0.306 0.467
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 (mx1) 6.074 0.057 106.280 0.000 5.960 6.184
## love_2 (mx2) 6.076 0.058 105.091 0.000 5.961 6.188
## .rel.st_1 (my1) -0.611 0.951 -0.643 0.520 -2.481 1.230
## .rel.st_2 (my2) -0.686 1.044 -0.657 0.511 -2.793 1.264
## Std.lv Std.all
## 6.074 8.977
## 6.076 8.946
## -0.611 -0.665
## -0.686 -0.733
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## love_1 (vx1) 0.458 0.063 7.217 0.000 0.340 0.589
## love_2 (vx2) 0.461 0.076 6.037 0.000 0.321 0.618
## .rel.st_1 (vy1) 0.630 0.089 7.084 0.000 0.446 0.792
## .rel.st_2 (vy2) 0.683 0.077 8.904 0.000 0.525 0.823
## Std.lv Std.all
## 0.458 1.000
## 0.461 1.000
## 0.630 0.744
## 0.683 0.778
##
## R-Square:
## Estimate
## rel.sat_1 0.256
## rel.sat_2 0.222
model4.stand <- standardizedSolution(model4.fit, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
semPaths(model4.fit,
fade = F, "est", layout='tree2', rotation = 2, style = "ram",
intercepts = F, residuals = F, optimizeLatRes = T, curve = 3.1,
# labels and their sizes:
nodeLabels=c("Gest. P: Rel. Satisf.", "Non-Gest. P: Rel. Satisf.",
"Gest. P: Love", "Non-Gest. P: Love"), sizeMan=20, sizeMan2=18,
# position and size of parameter estimates:
edge.label.position = 0.45, edge.label.cex=1.2, label.cex = 1.25)
#non-gestational partner - actor effect (a1): relationship satisfaction is predicted by compassionate love
#gestational partner - actor effect (a2): relationship satisfaction is predicted by compassionate love
#partner effect (p21): gestational partner's relationship satisfaction is predicted by non-gest partner's love
stargazer(model4.stand, summary=FALSE, type='html', rownames=TRUE, initial.zero=FALSE, digits=3,
title='Standardized Coefficients', out = "model4.html")
##
## <table style="text-align:center"><caption><strong>Standardized Coefficients</strong></caption>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lhs</td><td>op</td><td>rhs</td><td>label</td><td>est.std</td><td>se</td><td>z</td><td>pvalue</td><td>ci.lower</td><td>ci.upper</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>rel.sat_1</td><td>~</td><td>love_1</td><td>a1</td><td>.410</td><td>.085</td><td>4.817</td><td>0.00000</td><td>.243</td><td>.577</td></tr>
## <tr><td style="text-align:left">2</td><td>rel.sat_2</td><td>~</td><td>love_2</td><td>a2</td><td>.263</td><td>.096</td><td>2.755</td><td>.006</td><td>.076</td><td>.451</td></tr>
## <tr><td style="text-align:left">3</td><td>rel.sat_1</td><td>~</td><td>love_2</td><td>p12</td><td>.162</td><td>.099</td><td>1.640</td><td>.101</td><td>-.032</td><td>.356</td></tr>
## <tr><td style="text-align:left">4</td><td>rel.sat_2</td><td>~</td><td>love_1</td><td>p21</td><td>.287</td><td>.082</td><td>3.524</td><td>.0004</td><td>.128</td><td>.447</td></tr>
## <tr><td style="text-align:left">5</td><td>love_1</td><td></td><td></td><td>mx1</td><td>8.977</td><td>.673</td><td>13.346</td><td>0</td><td>7.658</td><td>10.295</td></tr>
## <tr><td style="text-align:left">6</td><td>love_2</td><td></td><td></td><td>mx2</td><td>8.946</td><td>.790</td><td>11.320</td><td>0</td><td>7.397</td><td>10.495</td></tr>
## <tr><td style="text-align:left">7</td><td>rel.sat_1</td><td></td><td></td><td>my1</td><td>-.665</td><td>1.013</td><td>-.656</td><td>.512</td><td>-2.650</td><td>1.320</td></tr>
## <tr><td style="text-align:left">8</td><td>rel.sat_2</td><td></td><td></td><td>my2</td><td>-.733</td><td>1.087</td><td>-.674</td><td>.500</td><td>-2.863</td><td>1.398</td></tr>
## <tr><td style="text-align:left">9</td><td>love_1</td><td>~~</td><td>love_1</td><td>vx1</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">10</td><td>love_2</td><td>~~</td><td>love_2</td><td>vx2</td><td>1</td><td>0</td><td></td><td></td><td>1</td><td>1</td></tr>
## <tr><td style="text-align:left">11</td><td>rel.sat_1</td><td>~~</td><td>rel.sat_1</td><td>vy1</td><td>.744</td><td>.085</td><td>8.713</td><td>0</td><td>.577</td><td>.912</td></tr>
## <tr><td style="text-align:left">12</td><td>rel.sat_2</td><td>~~</td><td>rel.sat_2</td><td>vy2</td><td>.778</td><td>.078</td><td>9.999</td><td>0</td><td>.626</td><td>.931</td></tr>
## <tr><td style="text-align:left">13</td><td>love_1</td><td>~~</td><td>love_2</td><td>cx</td><td>.462</td><td>.088</td><td>5.227</td><td>0.00000</td><td>.289</td><td>.635</td></tr>
## <tr><td style="text-align:left">14</td><td>rel.sat_1</td><td>~~</td><td>rel.sat_2</td><td>cy</td><td>.467</td><td>.072</td><td>6.471</td><td>0</td><td>.326</td><td>.609</td></tr>
## <tr><td colspan="11" style="border-bottom: 1px solid black"></td></tr></table>