pacman::p_load(dplyr, RSA,rio,ggplot2,psych, nlme,GGally,tidyr,sjPlot,osfr,car,msm) Dyadic Analysis workshop, Zurich Nov. 24: RSA Model
RSA Model
Libraries
Simulating data
data <- data.frame(x = rnorm(500),
y = rnorm(500))
data <- data %>% mutate(z=x*.25+rnorm(500,mean=0,sd=.94),
diff=x-y,
abs_diff=abs(x-y),
sqrt_diff=(x-y)^2)
ggpairs(data)
Loading the data from OSF
file <- osf_retrieve_file("6718dd0a2295d821b91b3a1f")
setwd("C:\\Users\\97254\\Downloads")
osf_download(file, progress = T,conflicts = "overwrigt")
temp1 <- import("RSAData.csv")Descriptive stats
Number of completed diaries
descriptive <- temp1 %>% group_by(Id,Female) %>%
summarise(n=n())
describeBy(descriptive$n,descriptive$Female)
Descriptive statistics by group
group: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 82 18.07 2.96 19 18.32 2.97 11 22 11 -0.71 -0.5 0.33
------------------------------------------------------------
group: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 82 18.84 2.52 19 19.09 2.97 12 22 10 -0.76 -0.13 0.28Correlation matrix
ggpairs(temp1 %>% select(Pro_Emo:zSPIN), title="Corr Matrix")
Ploting Terciles of difference scores
# Step 1: Create the difference between the two variables
temp <- temp1%>%
mutate(difference = Pro_Emo - Partner_Pro_Emo )
# Step 2: Calculate quantiles (terciles)
tercile_cutoffs <- quantile(temp$difference, probs = c(1/3, 2/3), na.rm = TRUE)
# Step 3: Assign tercile based on the cutoffs
temp <- temp %>%
mutate(tercile = case_when(
difference <= tercile_cutoffs[1] ~ "Lower",
difference > tercile_cutoffs[1] & difference <= tercile_cutoffs[2] ~ "Medium",
difference > tercile_cutoffs[2] ~ "High"
))
# Step 4: Plot histogram of the difference scores by tercile
ggplot(temp, aes(x = difference, fill = tercile)) +
geom_histogram(bins = 30, position = "identity", alpha = 0.7) +
labs(title = "Histogram of Difference Scores by Tercile",
x = "Difference (Providing vs. Receiving)",
y = "Count",
fill = "Tercile") +
theme_minimal()
Preparing variables
temp1 <- temp1 %>% group_by(Id) %>%
mutate(
#centering variables
x_dyadic=Pro_Emo-mean(Pro_Emo,na.rm=T),
y_dyadic=Partner_Pro_Emo-mean(Pro_Emo,na.rm=T),
x2_dyadic=x_dyadic^2,
xy_dyadic=x_dyadic*y_dyadic,
y2_dyadic=y_dyadic^2,
x_monadic=Pro_Emo-mean(Pro_Emo,na.rm=T),
y_monadic=Rec_Emo-mean(Rec_Emo,na.rm=T),
x2_monadic=x_monadic^2,
xy_monadic=x_monadic*y_monadic,
y2_monadic=y_monadic^2) %>% ungroup() %>%
#standardizing level2 moderator
mutate(zMod = scale(SPIN),
#creating gender_specfic variables
Male=1-Female,
Gender=0.5-Female,
#creating terms manually, so i can then use them for computing SE
M_x_dyadic=Male*x_dyadic,
M_y_dyadic=Male*y_dyadic,
M_x2_dyadic=Male*x2_dyadic,
M_xy_dyadic=Male*xy_dyadic,
M_y2_dyadic=Male*y2_dyadic,
F_x_dyadic=Female*x_dyadic,
F_y_dyadic=Female*y_dyadic,
F_x2_dyadic=Female*x2_dyadic,
F_xy_dyadic=Female*xy_dyadic,
F_y2_dyadic=Female*y2_dyadic,
M_x_monadic=Male*x_monadic,
M_y_monadic=Male*y_monadic,
M_x2_monadic=Male*x2_monadic,
M_xy_monadic=Male*xy_monadic,
M_y2_monadic=Male*y2_monadic,
F_x_monadic=Female*x_monadic,
F_y_monadic=Female*y_monadic,
F_x2_monadic=Female*x2_monadic,
F_xy_monadic=Female*xy_monadic,
F_y2_monadic=Female*y2_monadic
)RSA models
PPR - Dyadic
Model1.1<- lme(PPR ~ -1 +
Male+ M_x_dyadic+M_y_dyadic+M_x2_dyadic+M_xy_dyadic+M_y2_dyadic+
Female+ F_x_dyadic+F_y_dyadic+F_x2_dyadic+F_xy_dyadic+F_y2_dyadic,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = temp1,na.action = na.exclude,
)
summary(Model1.1)Linear mixed-effects model fit by REML
Data: temp1
AIC BIC logLik
6144.151 6250.601 -3054.075
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 0.8176050 Male
Female 0.7843441 0.528
Residual 0.6974472
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2211914
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.000000 0.930997
Fixed effects: PPR ~ -1 + Male + M_x_dyadic + M_y_dyadic + M_x2_dyadic + M_xy_dyadic + M_y2_dyadic + Female + F_x_dyadic + F_y_dyadic + F_x2_dyadic + F_xy_dyadic + F_y2_dyadic
Value Std.Error DF t-value p-value
Male 5.918616 0.09508869 2654 62.24311 0.0000
M_x_dyadic 0.119239 0.02396410 2654 4.97572 0.0000
M_y_dyadic -0.001789 0.02308741 2654 -0.07748 0.9382
M_x2_dyadic -0.048305 0.01864758 2654 -2.59043 0.0096
M_xy_dyadic 0.056410 0.02060884 2654 2.73717 0.0062
M_y2_dyadic -0.014127 0.01285092 2654 -1.09930 0.2717
Female 6.025943 0.09093398 2654 66.26723 0.0000
F_x_dyadic 0.124555 0.02211020 2654 5.63337 0.0000
F_y_dyadic 0.056784 0.02238367 2654 2.53684 0.0112
F_x2_dyadic -0.046313 0.01944135 2654 -2.38221 0.0173
F_xy_dyadic 0.010029 0.01868324 2654 0.53677 0.5915
F_y2_dyadic -0.023040 0.01167830 2654 -1.97291 0.0486
Correlation:
Male M_x_dy M_y_dy M_x2_d M_xy_d M_y2_d Female F_x_dy F_y_dy
M_x_dyadic -0.005
M_y_dyadic 0.027 -0.163
M_x2_dyadic -0.134 -0.147 0.046
M_xy_dyadic 0.044 0.347 -0.039 -0.347
M_y2_dyadic -0.183 0.001 0.258 0.036 -0.141
Female 0.494 -0.010 -0.002 -0.023 0.023 -0.025
F_x_dyadic 0.013 -0.046 0.209 0.010 -0.038 0.020 0.021
F_y_dyadic 0.003 0.211 -0.058 -0.062 0.062 -0.008 -0.018 -0.188
F_x2_dyadic -0.025 0.000 0.036 0.000 -0.020 0.138 -0.151 -0.089 0.033
F_xy_dyadic 0.027 0.044 -0.039 -0.034 0.147 -0.148 0.047 -0.228 0.085
F_y2_dyadic -0.024 -0.055 0.013 0.151 -0.153 0.044 -0.154 0.068 -0.408
F_x2_d F_xy_d
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic -0.260
F_y2_dyadic 0.013 -0.221
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-7.2177425 -0.3245818 0.1122012 0.4915106 3.6628245
Number of Observations: 2747
Number of Groups: 82 tab_model(Model1.1,show.r2=F)| PPR | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Male | 5.92 | 5.73 – 6.11 | <0.001 |
| M x dyadic | 0.12 | 0.07 – 0.17 | <0.001 |
| M y dyadic | -0.00 | -0.05 – 0.04 | 0.938 |
| M x2 dyadic | -0.05 | -0.08 – -0.01 | 0.010 |
| M xy dyadic | 0.06 | 0.02 – 0.10 | 0.006 |
| M y2 dyadic | -0.01 | -0.04 – 0.01 | 0.272 |
| Female | 6.03 | 5.85 – 6.20 | <0.001 |
| F x dyadic | 0.12 | 0.08 – 0.17 | <0.001 |
| F y dyadic | 0.06 | 0.01 – 0.10 | 0.011 |
| F x2 dyadic | -0.05 | -0.08 – -0.01 | 0.017 |
| F xy dyadic | 0.01 | -0.03 – 0.05 | 0.591 |
| F y2 dyadic | -0.02 | -0.05 – -0.00 | 0.049 |
| Random Effects | |||
| σ2 | 0.49 | ||
| τ00 | |||
| τ00 | |||
| τ11 Couple.Female | 0.62 | ||
| ρ01 Couple | 0.53 | ||
| ICC | 0.57 | ||
| N Couple | 82 | ||
| Observations | 2747 | ||
Plotting surface
# Plotting
(fixed_effects <- fixef(Model1.1)) Male M_x_dyadic M_y_dyadic M_x2_dyadic M_xy_dyadic M_y2_dyadic
5.918615945 0.119238583 -0.001788835 -0.048305206 0.056409883 -0.014126993
Female F_x_dyadic F_y_dyadic F_x2_dyadic F_xy_dyadic F_y2_dyadic
6.025942526 0.124554889 0.056783905 -0.046313300 0.010028691 -0.023040264 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
Getting estimates and their SE using the deltaMethod
# Define a function that receives var_names and builds the expression list
build_and_evaluate_expressions <- function(model, vars) {
# Get the fixed effects and variance-covariance matrix from the model
fixed_effects <- fixef(model)
vcov_matrix <- vcov(model)
# Build the expressions dynamically based on the provided variable names
# Male expressions
expressions_list <- list(
"a1" = paste(vars[1], "+", vars[2]), # M_x_monadic + M_y_monadic
"a2" = paste(vars[3], "+", vars[4], "+", vars[5]), # M_x2_monadic + M_xy_monadic + M_y2_monadic
"a3" = paste(vars[1], "-", vars[2]), # M_x_monadic - M_y_monadic
"a4" = paste(vars[3], "-", vars[4], "+", vars[5]), # M_x2_monadic - M_xy_monadic + M_y2_monadic
"X0" = paste("(", vars[2], "*", vars[4], "- 2 *", vars[1], "*", vars[5], ") / (4 *",
vars[3], "*", vars[5], "-", vars[4], "^2)"),
"Y0" = paste("(", vars[1], "*", vars[4], "- 2 *", vars[2], "*", vars[3], ") / (4 *",
vars[3], "*", vars[5], "-", vars[4], "^2)"),
"p10" = paste("((", vars[1], "*", vars[4], "- 2 *", vars[2], "*", vars[3], ") / (4 *",
vars[3], "*", vars[5], "-", vars[4], "^2)) - ((", vars[5], "-",
vars[3], "+ sqrt((", vars[3], "-", vars[5], ")^2 +", vars[4], "^2))/",
vars[4], ") * ((", vars[2], "*", vars[4], "- 2 *", vars[1], "*",
vars[5], ") / (4 *", vars[3], "*", vars[5], "-", vars[4], "^2))"),
"p11" = paste("(", vars[5], "-", vars[3], "+ sqrt((", vars[3], "-", vars[5], ")^2 +",
vars[4], "^2)) /", vars[4]),
"p20" = paste("((", vars[1], "*", vars[4], "- 2 *", vars[2], "*", vars[3], ") / (4 *",
vars[3], "*", vars[5], "-", vars[4], "^2)) - ((", vars[5], "-",
vars[3], "- sqrt((", vars[3], "-", vars[5], ")^2 +", vars[4], "^2))/",
vars[4], ") * ((", vars[2], "*", vars[4], "- 2 *", vars[1], "*",
vars[5], ") / (4 *", vars[3], "*", vars[5], "-", vars[4], "^2))"),
"p21" = paste("(", vars[5], "-", vars[3], "- sqrt((", vars[3], "-", vars[5], ")^2 +",
vars[4], "^2)) /", vars[4])
)
# Initialize an empty dataframe to store results
results_df <- data.frame(Expression = character(), Estimate = numeric(), SE = numeric(),CI_2.5= numeric(),CI_97.5= numeric(), Z = numeric(), p_value = numeric(), stringsAsFactors = FALSE)
# Iterate over the expressions and apply deltaMethod for each
for (expr_name in names(expressions_list)) {
expr <- expressions_list[[expr_name]]
# Apply deltaMethod using the current expression
result <- deltaMethod(object = fixed_effects,
g = expr,
vcov. = vcov_matrix)
# Calculate Z-score and p-value
z_score <- result$Estimate / result$SE
p_value <- round(2 * (1 - pnorm(abs(z_score))),4)
# Append result to the dataframe
results_df <- results_df %>%
add_row(Expression = expr_name, Estimate = result$Estimate, SE = result$SE,CI_2.5=result$`2.5 %`,CI_97.5=result$`97.5 %`, Z = z_score, p_value = p_value)
}
# Return the results dataframe
return(results_df)
}Use the function
# Define the male and female variable names
male_vars <- c("M_x_dyadic", "M_y_dyadic", "M_x2_dyadic", "M_xy_dyadic", "M_y2_dyadic")
female_vars <- c("F_x_dyadic", "F_y_dyadic", "F_x2_dyadic", "F_xy_dyadic", "F_y2_dyadic")
# Apply the function to the model (in this case Model1.1)
results <- build_and_evaluate_expressions(Model1.1, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.117449748 0.03044755 0.05777364 0.17712586 3.8574444
2 a2 -0.006022316 0.02477884 -0.05458795 0.04254332 -0.2430427
3 a3 0.121027419 0.03588259 0.05069884 0.19135600 3.3728731
4 a4 -0.118842082 0.03599637 -0.18939368 -0.04829049 -3.3015016
5 X0 -7.223094168 53.58059320 -112.23912712 97.79293878 -0.1348080
6 Y0 -14.484424524 94.73936398 -200.17016584 171.20131679 -0.1528871
7 p10 -1.662547884 0.69832334 -3.03123649 -0.29385928 -2.3807709
8 p11 1.775122453 0.62265958 0.55473211 2.99551280 2.8508715
9 p20 -18.553492636 124.97838919 -263.50663429 226.39964902 -0.1484536
10 p21 -0.563341418 0.19760323 -0.95063663 -0.17604621 -2.8508715
p_value
1 0.0001
2 0.8080
3 0.0007
4 0.0010
5 0.8928
6 0.8785
7 0.0173
8 0.0044
9 0.8820
10 0.0044# Apply the function to the model (in this case Model1.1)
results <- build_and_evaluate_expressions(Model1.1, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.18133879 0.02834302 1.257875e-01 0.23689009 6.3980062
2 a2 -0.05932487 0.02416042 -1.066784e-01 -0.01197132 -2.4554571
3 a3 0.06777098 0.03429946 5.452841e-04 0.13499668 1.9758617
4 a4 -0.07938226 0.03397742 -1.459768e-01 -0.01278774 -2.3363240
5 X0 1.51378728 0.65465362 2.306898e-01 2.79688479 2.3123484
6 Y0 1.56172713 0.91031939 -2.224661e-01 3.34592035 1.7155815
7 p10 -5.77647672 12.39579125 -3.007178e+01 18.51882769 -0.4660031
8 p11 4.84757929 8.91974648 -1.263480e+01 22.32996114 0.5434660
9 p20 1.87400408 1.32404546 -7.210773e-01 4.46908549 1.4153623
10 p21 -0.20628853 0.37957943 -9.502505e-01 0.53767348 -0.5434660
p_value
1 0.0000
2 0.0141
3 0.0482
4 0.0195
5 0.0208
6 0.0862
7 0.6412
8 0.5868
9 0.1570
10 0.5868PPR - Monadic
Model1.2<- lme(PPR ~ -1 +
Male+ M_x_monadic+M_y_monadic+M_x2_monadic+M_xy_monadic+M_y2_monadic+
Female+ F_x_monadic+F_y_monadic+F_x2_monadic+F_xy_monadic+F_y2_monadic,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = temp1,na.action = na.exclude)
summary(Model1.1)Linear mixed-effects model fit by REML
Data: temp1
AIC BIC logLik
6144.151 6250.601 -3054.075
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 0.8176050 Male
Female 0.7843441 0.528
Residual 0.6974472
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2211914
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.000000 0.930997
Fixed effects: PPR ~ -1 + Male + M_x_dyadic + M_y_dyadic + M_x2_dyadic + M_xy_dyadic + M_y2_dyadic + Female + F_x_dyadic + F_y_dyadic + F_x2_dyadic + F_xy_dyadic + F_y2_dyadic
Value Std.Error DF t-value p-value
Male 5.918616 0.09508869 2654 62.24311 0.0000
M_x_dyadic 0.119239 0.02396410 2654 4.97572 0.0000
M_y_dyadic -0.001789 0.02308741 2654 -0.07748 0.9382
M_x2_dyadic -0.048305 0.01864758 2654 -2.59043 0.0096
M_xy_dyadic 0.056410 0.02060884 2654 2.73717 0.0062
M_y2_dyadic -0.014127 0.01285092 2654 -1.09930 0.2717
Female 6.025943 0.09093398 2654 66.26723 0.0000
F_x_dyadic 0.124555 0.02211020 2654 5.63337 0.0000
F_y_dyadic 0.056784 0.02238367 2654 2.53684 0.0112
F_x2_dyadic -0.046313 0.01944135 2654 -2.38221 0.0173
F_xy_dyadic 0.010029 0.01868324 2654 0.53677 0.5915
F_y2_dyadic -0.023040 0.01167830 2654 -1.97291 0.0486
Correlation:
Male M_x_dy M_y_dy M_x2_d M_xy_d M_y2_d Female F_x_dy F_y_dy
M_x_dyadic -0.005
M_y_dyadic 0.027 -0.163
M_x2_dyadic -0.134 -0.147 0.046
M_xy_dyadic 0.044 0.347 -0.039 -0.347
M_y2_dyadic -0.183 0.001 0.258 0.036 -0.141
Female 0.494 -0.010 -0.002 -0.023 0.023 -0.025
F_x_dyadic 0.013 -0.046 0.209 0.010 -0.038 0.020 0.021
F_y_dyadic 0.003 0.211 -0.058 -0.062 0.062 -0.008 -0.018 -0.188
F_x2_dyadic -0.025 0.000 0.036 0.000 -0.020 0.138 -0.151 -0.089 0.033
F_xy_dyadic 0.027 0.044 -0.039 -0.034 0.147 -0.148 0.047 -0.228 0.085
F_y2_dyadic -0.024 -0.055 0.013 0.151 -0.153 0.044 -0.154 0.068 -0.408
F_x2_d F_xy_d
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic -0.260
F_y2_dyadic 0.013 -0.221
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-7.2177425 -0.3245818 0.1122012 0.4915106 3.6628245
Number of Observations: 2747
Number of Groups: 82 tab_model(Model1.1,Model1.2,show.r2=F)| PPR | PPR | |||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p |
| Male | 5.92 | 5.73 – 6.11 | <0.001 | 5.90 | 5.72 – 6.09 | <0.001 |
| M x dyadic | 0.12 | 0.07 – 0.17 | <0.001 | |||
| M y dyadic | -0.00 | -0.05 – 0.04 | 0.938 | |||
| M x2 dyadic | -0.05 | -0.08 – -0.01 | 0.010 | |||
| M xy dyadic | 0.06 | 0.02 – 0.10 | 0.006 | |||
| M y2 dyadic | -0.01 | -0.04 – 0.01 | 0.272 | |||
| Female | 6.03 | 5.85 – 6.20 | <0.001 | 6.06 | 5.89 – 6.23 | <0.001 |
| F x dyadic | 0.12 | 0.08 – 0.17 | <0.001 | |||
| F y dyadic | 0.06 | 0.01 – 0.10 | 0.011 | |||
| F x2 dyadic | -0.05 | -0.08 – -0.01 | 0.017 | |||
| F xy dyadic | 0.01 | -0.03 – 0.05 | 0.591 | |||
| F y2 dyadic | -0.02 | -0.05 – -0.00 | 0.049 | |||
| M x monadic | 0.02 | -0.03 – 0.07 | 0.406 | |||
| M y monadic | 0.16 | 0.12 – 0.20 | <0.001 | |||
| M x2 monadic | -0.02 | -0.06 – 0.02 | 0.236 | |||
| M xy monadic | 0.08 | 0.03 – 0.12 | 0.001 | |||
| M y2 monadic | -0.05 | -0.09 – -0.02 | 0.005 | |||
| F x monadic | 0.05 | 0.00 – 0.09 | 0.031 | |||
| F y monadic | 0.19 | 0.15 – 0.23 | <0.001 | |||
| F x2 monadic | -0.05 | -0.09 – -0.01 | 0.007 | |||
| F xy monadic | 0.02 | -0.02 – 0.07 | 0.337 | |||
| F y2 monadic | -0.04 | -0.08 – -0.01 | 0.008 | |||
| Random Effects | ||||||
| σ2 | 0.49 | 0.46 | ||||
| τ00 | ||||||
| τ00 | ||||||
| τ11 | 0.62 Couple.Female | 0.60 Couple.Female | ||||
| ρ01 | 0.53 Couple | 0.53 Couple | ||||
| ICC | 0.57 | 0.58 | ||||
| N | 82 Couple | 82 Couple | ||||
| Observations | 2747 | 3020 | ||||
# Getting estimates and their SE using the deltaMethod
# Define the expression for the new parameter
(fixed_effects <- fixef(Model1.2)) Male M_x_monadic M_y_monadic M_x2_monadic M_xy_monadic M_y2_monadic
5.90393305 0.01986546 0.15984800 -0.02430408 0.07739961 -0.05209454
Female F_x_monadic F_y_monadic F_x2_monadic F_xy_monadic F_y2_monadic
6.05867952 0.04690093 0.19001384 -0.05328445 0.02257610 -0.04360192 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
# Define the male and female variable names
male_vars <- c("M_x_monadic", "M_y_monadic", "M_x2_monadic", "M_xy_monadic", "M_y2_monadic")
female_vars <- c("F_x_monadic", "F_y_monadic", "F_x2_monadic", "F_xy_monadic", "F_y2_monadic")
# Apply the function to the model (in this case Model1.2)
results <- build_and_evaluate_expressions(Model1.2, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.179713467 0.02311233 0.13441412 0.22501281 7.77565176
2 a2 0.001000984 0.02098565 -0.04013014 0.04213211 0.04769849
3 a3 -0.139982541 0.04065633 -0.21966748 -0.06029761 -3.44306916
4 a4 -0.153798235 0.04457618 -0.24116595 -0.06643052 -3.45023325
5 X0 -15.591679582 59.60382236 -132.41302475 101.22966558 -0.26158859
6 Y0 -10.048479623 43.75998662 -95.81647737 75.71951812 -0.22962712
7 p10 0.919548123 0.35600948 0.22178237 1.61731388 2.58293158
8 p11 0.703453896 0.27330684 0.16778234 1.23912545 2.57386134
9 p20 -32.212945086 125.16606687 -277.53392823 213.10803806 -0.25736165
10 p21 -1.421557270 0.55230530 -2.50405578 -0.33905876 -2.57386134
p_value
1 0.0000
2 0.9620
3 0.0006
4 0.0006
5 0.7936
6 0.8184
7 0.0098
8 0.0101
9 0.7969
10 0.0101results <- build_and_evaluate_expressions(Model1.2, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z p_value
1 a1 0.23691477 0.02130531 0.19515712 0.27867241 11.1199859 0.0000
2 a2 -0.07431027 0.01951389 -0.11255680 -0.03606374 -3.8080695 0.0001
3 a3 -0.14311291 0.03609114 -0.21385025 -0.07237557 -3.9653196 0.0001
4 a4 -0.11946246 0.04455942 -0.20679732 -0.03212761 -2.6809701 0.0073
5 X0 0.95402485 0.49711697 -0.02030651 1.92835620 1.9191154 0.0550
6 Y0 2.42594842 0.85048647 0.75902558 4.09287126 2.8524245 0.0043
7 p10 0.97871636 0.92231417 -0.82898620 2.78641893 1.0611529 0.2886
8 p11 1.51697523 1.67930037 -1.77439301 4.80834346 0.9033376 0.3663
9 p20 3.05484784 0.91332135 1.26477090 4.84492479 3.3447678 0.0008
10 p21 -0.65920655 0.72974546 -2.08948137 0.77106828 -0.9033376 0.3663Positive Mood - Dyadic
Model2.1<- lme(PosMood ~ -1 +
Male+ M_x_dyadic+M_y_dyadic+M_x2_dyadic+M_xy_dyadic+M_y2_dyadic+
Female+ F_x_dyadic+F_y_dyadic+F_x2_dyadic+F_xy_dyadic+F_y2_dyadic,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = temp1,na.action = na.exclude)
summary(Model2.1)Linear mixed-effects model fit by REML
Data: temp1
AIC BIC logLik
23482.92 23589.37 -11723.46
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 15.13628 Male
Female 13.83860 0.478
Residual 15.19309
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2798224
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.000000 1.159945
Fixed effects: PosMood ~ -1 + Male + M_x_dyadic + M_y_dyadic + M_x2_dyadic + M_xy_dyadic + M_y2_dyadic + Female + F_x_dyadic + F_y_dyadic + F_x2_dyadic + F_xy_dyadic + F_y2_dyadic
Value Std.Error DF t-value p-value
Male 62.78209 1.7918912 2655 35.03677 0.0000
M_x_dyadic 2.66960 0.5207106 2655 5.12684 0.0000
M_y_dyadic -0.90357 0.4997481 2655 -1.80806 0.0707
M_x2_dyadic -0.84872 0.4019108 2655 -2.11171 0.0348
M_xy_dyadic 0.71340 0.4467348 2655 1.59693 0.1104
M_y2_dyadic 0.08308 0.2767632 2655 0.30018 0.7641
Female 59.94347 1.6986030 2655 35.28986 0.0000
F_x_dyadic 2.76044 0.5987163 2655 4.61059 0.0000
F_y_dyadic 0.07564 0.5937280 2655 0.12740 0.8986
F_x2_dyadic -0.52182 0.5188657 2655 -1.00569 0.3147
F_xy_dyadic -0.24909 0.5041970 2655 -0.49403 0.6213
F_y2_dyadic -1.31770 0.3089958 2655 -4.26445 0.0000
Correlation:
Male M_x_dy M_y_dy M_x2_d M_xy_d M_y2_d Female F_x_dy F_y_dy
M_x_dyadic -0.005
M_y_dyadic 0.031 -0.166
M_x2_dyadic -0.154 -0.149 0.045
M_xy_dyadic 0.054 0.346 -0.042 -0.354
M_y2_dyadic -0.209 -0.003 0.261 0.041 -0.151
Female 0.435 -0.016 -0.004 -0.041 0.041 -0.046
F_x_dyadic 0.019 -0.057 0.263 0.013 -0.048 0.026 0.029
F_y_dyadic 0.005 0.262 -0.076 -0.078 0.078 -0.011 -0.025 -0.189
F_x2_dyadic -0.036 0.000 0.046 0.002 -0.028 0.175 -0.217 -0.085 0.032
F_xy_dyadic 0.040 0.056 -0.049 -0.043 0.187 -0.188 0.069 -0.227 0.086
F_y2_dyadic -0.036 -0.070 0.018 0.188 -0.191 0.060 -0.219 0.069 -0.412
F_x2_d F_xy_d
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic -0.269
F_y2_dyadic 0.017 -0.228
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-5.0835974 -0.5780277 0.1015159 0.6407224 3.6087976
Number of Observations: 2748
Number of Groups: 82 tab_model(Model2.1,show.r2=F)| Pos Mood | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Male | 62.78 | 59.27 – 66.30 | <0.001 |
| M x dyadic | 2.67 | 1.65 – 3.69 | <0.001 |
| M y dyadic | -0.90 | -1.88 – 0.08 | 0.071 |
| M x2 dyadic | -0.85 | -1.64 – -0.06 | 0.035 |
| M xy dyadic | 0.71 | -0.16 – 1.59 | 0.110 |
| M y2 dyadic | 0.08 | -0.46 – 0.63 | 0.764 |
| Female | 59.94 | 56.61 – 63.27 | <0.001 |
| F x dyadic | 2.76 | 1.59 – 3.93 | <0.001 |
| F y dyadic | 0.08 | -1.09 – 1.24 | 0.899 |
| F x2 dyadic | -0.52 | -1.54 – 0.50 | 0.315 |
| F xy dyadic | -0.25 | -1.24 – 0.74 | 0.621 |
| F y2 dyadic | -1.32 | -1.92 – -0.71 | <0.001 |
| Random Effects | |||
| σ2 | 230.83 | ||
| τ00 | |||
| τ00 | |||
| τ11 Couple.Female | 191.51 | ||
| ρ01 Couple | 0.48 | ||
| ICC | 0.48 | ||
| N Couple | 82 | ||
| Observations | 2748 | ||
# Getting estimates and their SE using the deltaMethod
# Define the expression for the new parameter
(fixed_effects <- fixef(Model2.1)) Male M_x_dyadic M_y_dyadic M_x2_dyadic M_xy_dyadic M_y2_dyadic
62.78208848 2.66959726 -0.90357453 -0.84871908 0.71340221 0.08307784
Female F_x_dyadic F_y_dyadic F_x2_dyadic F_xy_dyadic F_y2_dyadic
59.94346844 2.76043702 0.07564339 -0.52181758 -0.24908946 -1.31769864 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
# Define the male and female variable names
male_vars <- c("M_x_dyadic", "M_y_dyadic", "M_x2_dyadic", "M_xy_dyadic", "M_y2_dyadic")
female_vars <- c("F_x_dyadic", "F_y_dyadic", "F_x2_dyadic", "F_xy_dyadic", "F_y2_dyadic")
results <- build_and_evaluate_expressions(Model2.1, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z p_value
1 a1 1.76602274 0.6591319 0.4741480 3.05789745 2.679316276 0.0074
2 a2 -0.05223903 0.5312400 -1.0934503 0.98897221 -0.098334147 0.9217
3 a3 3.57317179 0.7793078 2.0457567 5.10058691 4.585058708 0.0000
4 a4 -1.47904345 0.7819433 -3.0116242 0.05353726 -1.891497053 0.0586
5 X0 1.37573450 0.8384339 -0.2675657 3.01903468 1.640838409 0.1008
6 Y0 -0.46870197 1.7442424 -3.8873542 2.94995025 -0.268713787 0.7881
7 p10 -4.52865577 2.9651099 -10.3401644 1.28285290 -1.527314629 0.1267
8 p11 2.95111725 1.6909251 -0.3630351 6.26526960 1.745267843 0.0809
9 p20 -0.00252785 1.7301699 -3.3935985 3.38854283 -0.001461041 0.9988
10 p21 -0.33885472 0.1941563 -0.7193940 0.04168460 -1.745267843 0.0809results <- build_and_evaluate_expressions(Model2.1, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z p_value
1 a1 2.8360804 0.7593657 1.3477510 4.3244098 3.7348019 0.0002
2 a2 -2.0886057 0.6424947 -3.3478722 -0.8293392 -3.2507749 0.0012
3 a3 2.6847936 0.9194086 0.8827858 4.4868014 2.9201310 0.0035
4 a4 -1.5904268 0.9144499 -3.3827156 0.2018620 -1.7392170 0.0820
5 X0 2.6990579 2.8446562 -2.8763658 8.2744816 0.9488169 0.3427
6 Y0 -0.2264036 0.7416986 -1.6801062 1.2272989 -0.3052502 0.7602
7 p10 0.1860974 0.3551387 -0.5099617 0.8821565 0.5240132 0.6003
8 p11 -0.1528315 0.3029263 -0.7465560 0.4408931 -0.5045171 0.6139
9 p20 -17.8867569 37.8614233 -92.0937830 56.3202693 -0.4724270 0.6366
10 p21 6.5431546 12.9691433 -18.8758991 31.9622083 0.5045171 0.6139Positive Mood - Monadic
Model2.2<- lme(PosMood ~ -1 +
Male+ M_x_monadic+M_y_monadic+M_x2_monadic+M_xy_monadic+M_y2_monadic+
Female+ F_x_monadic+F_y_monadic+F_x2_monadic+F_xy_monadic+F_y2_monadic,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = temp1,na.action = na.exclude)
summary(Model1.2)Linear mixed-effects model fit by REML
Data: temp1
AIC BIC logLik
6566.815 6674.978 -3265.408
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 0.8202994 Male
Female 0.7749915 0.533
Residual 0.6747535
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2201971
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.0000000 0.9451634
Fixed effects: PPR ~ -1 + Male + M_x_monadic + M_y_monadic + M_x2_monadic + M_xy_monadic + M_y2_monadic + Female + F_x_monadic + F_y_monadic + F_x2_monadic + F_xy_monadic + F_y2_monadic
Value Std.Error DF t-value p-value
Male 5.903933 0.09351989 2927 63.13024 0.0000
M_x_monadic 0.019865 0.02391115 2927 0.83080 0.4062
M_y_monadic 0.159848 0.02284328 2927 6.99759 0.0000
M_x2_monadic -0.024304 0.02048779 2927 -1.18627 0.2356
M_xy_monadic 0.077400 0.02382414 2927 3.24879 0.0012
M_y2_monadic -0.052095 0.01869336 2927 -2.78679 0.0054
Female 6.058680 0.08847011 2927 68.48278 0.0000
F_x_monadic 0.046901 0.02175687 2927 2.15568 0.0312
F_y_monadic 0.190014 0.02012168 2927 9.44324 0.0000
F_x2_monadic -0.053284 0.01983221 2927 -2.68676 0.0073
F_xy_monadic 0.022576 0.02348669 2927 0.96123 0.3365
F_y2_monadic -0.043602 0.01643096 2927 -2.65364 0.0080
Correlation:
Male M_x_mn M_y_mn M_x2_m M_xy_m M_y2_m Female F_x_mn F_y_mn
M_x_monadic 0.006
M_y_monadic -0.016 -0.512
M_x2_monadic -0.085 -0.174 0.152
M_xy_monadic 0.032 0.149 -0.052 -0.475
M_y2_monadic -0.098 0.045 -0.019 -0.161 -0.347
Female 0.507 0.000 0.000 -0.004 0.002 -0.001
F_x_monadic -0.001 -0.032 0.065 0.012 -0.006 -0.003 -0.005
F_y_monadic 0.001 0.065 -0.025 -0.008 0.002 0.005 0.013 -0.485
F_x2_monadic -0.002 -0.001 0.001 -0.007 -0.012 0.024 -0.107 -0.153 0.021
F_xy_monadic 0.001 0.005 -0.004 -0.018 0.028 -0.005 0.052 -0.045 0.189
F_y2_monadic -0.004 -0.003 0.002 0.044 -0.018 -0.011 -0.109 0.214 -0.221
F_x2_m F_xy_m
M_x_monadic
M_y_monadic
M_x2_monadic
M_xy_monadic
M_y2_monadic
Female
F_x_monadic
F_y_monadic
F_x2_monadic
F_xy_monadic -0.502
F_y2_monadic -0.049 -0.434
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-7.5485127 -0.3572585 0.1099496 0.5018733 3.8163289
Number of Observations: 3020
Number of Groups: 82 tab_model(Model2.1,Model2.2,show.r2=F)| Pos Mood | Pos Mood | |||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p |
| Male | 62.78 | 59.27 – 66.30 | <0.001 | 63.14 | 59.64 – 66.65 | <0.001 |
| M x dyadic | 2.67 | 1.65 – 3.69 | <0.001 | |||
| M y dyadic | -0.90 | -1.88 – 0.08 | 0.071 | |||
| M x2 dyadic | -0.85 | -1.64 – -0.06 | 0.035 | |||
| M xy dyadic | 0.71 | -0.16 – 1.59 | 0.110 | |||
| M y2 dyadic | 0.08 | -0.46 – 0.63 | 0.764 | |||
| Female | 59.94 | 56.61 – 63.27 | <0.001 | 57.88 | 54.72 – 61.05 | <0.001 |
| F x dyadic | 2.76 | 1.59 – 3.93 | <0.001 | |||
| F y dyadic | 0.08 | -1.09 – 1.24 | 0.899 | |||
| F x2 dyadic | -0.52 | -1.54 – 0.50 | 0.315 | |||
| F xy dyadic | -0.25 | -1.24 – 0.74 | 0.621 | |||
| F y2 dyadic | -1.32 | -1.92 – -0.71 | <0.001 | |||
| M x monadic | 2.44 | 1.39 – 3.49 | <0.001 | |||
| M y monadic | 0.39 | -0.61 – 1.38 | 0.449 | |||
| M x2 monadic | -0.85 | -1.75 – 0.04 | 0.061 | |||
| M xy monadic | 1.74 | 0.70 – 2.78 | 0.001 | |||
| M y2 monadic | -0.86 | -1.67 – -0.04 | 0.039 | |||
| F x monadic | 3.44 | 2.26 – 4.62 | <0.001 | |||
| F y monadic | -0.65 | -1.74 – 0.45 | 0.247 | |||
| F x2 monadic | -0.86 | -1.93 – 0.21 | 0.115 | |||
| F xy monadic | -0.03 | -1.31 – 1.24 | 0.958 | |||
| F y2 monadic | 0.30 | -0.59 – 1.19 | 0.507 | |||
| Random Effects | ||||||
| σ2 | 230.83 | 232.98 | ||||
| τ00 | ||||||
| τ00 | ||||||
| τ11 | 191.51 Couple.Female | 181.90 Couple.Female | ||||
| ρ01 | 0.48 Couple | 0.47 Couple | ||||
| ICC | 0.48 | 0.47 | ||||
| N | 82 Couple | 82 Couple | ||||
| Observations | 2748 | 3021 | ||||
# Define the expression for the new parameter
(fixed_effects <- fixef(Model2.2)) Male M_x_monadic M_y_monadic M_x2_monadic M_xy_monadic M_y2_monadic
63.14385867 2.44028729 0.38535943 -0.85483590 1.74315294 -0.85855261
Female F_x_monadic F_y_monadic F_x2_monadic F_xy_monadic F_y2_monadic
57.88132688 3.44237684 -0.64568724 -0.86156511 -0.03449452 0.30090845 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
# Define the male and female variable names
male_vars <- c("M_x_monadic", "M_y_monadic", "M_x2_monadic", "M_xy_monadic", "M_y2_monadic")
female_vars <- c("F_x_monadic", "F_y_monadic", "F_x2_monadic", "F_xy_monadic", "F_y2_monadic")
results <- build_and_evaluate_expressions(Model2.2, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 2.82564671 0.5159059 1.8144896 3.8368038 5.47705783
2 a2 0.02976443 0.4670672 -0.8856704 0.9451992 0.06372624
3 a3 2.05492786 0.9063387 0.2785366 3.8313191 2.26728469
4 a4 -3.45654145 0.9919078 -5.4006450 -1.5124379 -3.48474062
5 X0 -47.25139129 745.3929126 -1508.1946542 1413.6918717 -0.06339125
6 Y0 -47.74375012 743.4792072 -1504.9362194 1409.4487192 -0.06421666
7 p10 -0.59299967 0.4112249 -1.3989857 0.2129864 -1.44203235
8 p11 0.99787010 0.3808083 0.2514995 1.7442407 2.62039985
9 p20 -95.09599707 1490.7734670 -3016.9583015 2826.7663074 -0.06378970
10 p21 -1.00213445 0.3824357 -1.7516946 -0.2525743 -2.62039985
p_value
1 0.0000
2 0.9492
3 0.0234
4 0.0005
5 0.9495
6 0.9488
7 0.1493
8 0.0088
9 0.9491
10 0.0088results <- build_and_evaluate_expressions(Model2.2, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 2.79668960 0.5907927 1.6387572 3.9546220 4.73379166
2 a2 -0.59515117 0.5383999 -1.6503956 0.4600933 -1.10540726
3 a3 4.08806408 0.9996535 2.1287792 6.0473489 4.08948113
4 a4 -0.52616213 1.2273802 -2.9317831 1.8794588 -0.42868717
5 X0 1.97400341 1.0716268 -0.1263465 4.0743534 1.84206236
6 Y0 1.18604103 2.3196192 -3.3603290 5.7324111 0.51130851
7 p10 134.26409871 2496.2521689 -4758.3002487 5026.8284461 0.05378627
8 p11 -67.41531280 1271.4091694 -2559.3314945 2424.5008689 -0.05302409
9 p20 1.15675979 1.9813331 -2.7265817 5.0401013 0.58382904
10 p21 0.01483343 0.2797488 -0.5334641 0.5631310 0.05302409
p_value
1 0.0000
2 0.2690
3 0.0000
4 0.6682
5 0.0655
6 0.6091
7 0.9571
8 0.9577
9 0.5593
10 0.9577Moderated RSA
Average Sx
Model3.1<- lme(PPR ~ -1 +
Male+ M_x_dyadic+M_y_dyadic+M_x2_dyadic+M_xy_dyadic+M_y2_dyadic+
Male:zSPIN+ M_x_dyadic:zSPIN+M_y_dyadic:zSPIN+M_x2_dyadic:zSPIN+M_xy_dyadic:zSPIN+M_y2_dyadic:zSPIN+
Female+ F_x_dyadic+F_y_dyadic+F_x2_dyadic+F_xy_dyadic+F_y2_dyadic+
Female:zSPIN+ F_x_dyadic:zSPIN+F_y_dyadic:zSPIN+F_x2_dyadic:zSPIN+F_xy_dyadic:zSPIN+F_y2_dyadic:zSPIN,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = temp1,na.action = na.exclude)
summary(Model3.1)Linear mixed-effects model fit by REML
Data: temp1
AIC BIC logLik
6214.184 6391.469 -3077.092
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 0.8261990 Male
Female 0.7914660 0.529
Residual 0.6950983
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2229805
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.0000000 0.9338685
Fixed effects: PPR ~ -1 + Male + M_x_dyadic + M_y_dyadic + M_x2_dyadic + M_xy_dyadic + M_y2_dyadic + Male:zSPIN + M_x_dyadic:zSPIN + M_y_dyadic:zSPIN + M_x2_dyadic:zSPIN + M_xy_dyadic:zSPIN + M_y2_dyadic:zSPIN + Female + F_x_dyadic + F_y_dyadic + F_x2_dyadic + F_xy_dyadic + F_y2_dyadic + Female:zSPIN + F_x_dyadic:zSPIN + F_y_dyadic:zSPIN + F_x2_dyadic:zSPIN + F_xy_dyadic:zSPIN + F_y2_dyadic:zSPIN
Value Std.Error DF t-value p-value
Male 5.909116 0.09606896 2642 61.50911 0.0000
M_x_dyadic 0.117202 0.02405648 2642 4.87197 0.0000
M_y_dyadic 0.000240 0.02307963 2642 0.01039 0.9917
M_x2_dyadic -0.039631 0.01884549 2642 -2.10292 0.0356
M_xy_dyadic 0.055807 0.02065558 2642 2.70179 0.0069
M_y2_dyadic -0.013954 0.01282746 2642 -1.08783 0.2768
Female 6.029026 0.09173292 2642 65.72368 0.0000
F_x_dyadic 0.120741 0.02228197 2642 5.41877 0.0000
F_y_dyadic 0.055820 0.02246626 2642 2.48464 0.0130
F_x2_dyadic -0.047125 0.01965932 2642 -2.39708 0.0166
F_xy_dyadic 0.013566 0.01898793 2642 0.71446 0.4750
F_y2_dyadic -0.022593 0.01171524 2642 -1.92847 0.0539
Male:zSPIN -0.176240 0.10267471 2642 -1.71649 0.0862
M_x_dyadic:zSPIN -0.043367 0.02966131 2642 -1.46206 0.1438
M_y_dyadic:zSPIN 0.059491 0.02679760 2642 2.22002 0.0265
M_x2_dyadic:zSPIN 0.067206 0.02247696 2642 2.99001 0.0028
M_xy_dyadic:zSPIN -0.011152 0.02484928 2642 -0.44877 0.6536
M_y2_dyadic:zSPIN 0.015948 0.01498931 2642 1.06393 0.2875
zSPIN:Female -0.043571 0.07376976 2642 -0.59064 0.5548
zSPIN:F_x_dyadic -0.019129 0.02157070 2642 -0.88680 0.3753
zSPIN:F_y_dyadic -0.021583 0.02131870 2642 -1.01240 0.3114
zSPIN:F_x2_dyadic 0.005749 0.01931386 2642 0.29765 0.7660
zSPIN:F_xy_dyadic 0.008043 0.02083664 2642 0.38598 0.6995
zSPIN:F_y2_dyadic 0.015790 0.01268737 2642 1.24457 0.2134
Correlation:
Male M_x_dy M_y_dy M_x2_d M_xy_d M_y2_d Female F_x_dy
M_x_dyadic -0.005
M_y_dyadic 0.028 -0.156
M_x2_dyadic -0.133 -0.151 0.044
M_xy_dyadic 0.044 0.354 -0.034 -0.348
M_y2_dyadic -0.181 0.005 0.259 0.033 -0.136
Female 0.494 -0.009 -0.002 -0.023 0.023 -0.024
F_x_dyadic 0.014 -0.046 0.209 0.010 -0.039 0.018 0.020
F_y_dyadic 0.004 0.209 -0.058 -0.061 0.060 -0.009 -0.017 -0.181
F_x2_dyadic -0.024 -0.001 0.035 0.001 -0.022 0.136 -0.150 -0.076
F_xy_dyadic 0.026 0.045 -0.038 -0.033 0.146 -0.145 0.045 -0.229
F_y2_dyadic -0.024 -0.054 0.013 0.149 -0.151 0.045 -0.153 0.061
Male:zSPIN 0.045 0.002 0.012 -0.021 0.005 -0.009 -0.001 0.003
M_x_dyadic:zSPIN 0.002 0.104 0.012 -0.048 0.082 0.026 -0.001 -0.002
M_y_dyadic:zSPIN 0.011 0.015 0.046 -0.010 0.047 0.012 0.002 0.006
M_x2_dyadic:zSPIN -0.019 -0.038 -0.010 0.163 -0.044 -0.018 0.000 -0.002
M_xy_dyadic:zSPIN 0.002 0.073 0.055 -0.030 0.044 0.033 0.000 -0.001
M_y2_dyadic:zSPIN -0.012 0.026 0.002 -0.013 0.023 0.002 0.000 0.001
zSPIN:Female 0.001 -0.002 -0.002 0.000 -0.003 -0.011 -0.027 -0.003
zSPIN:F_x_dyadic 0.002 -0.003 -0.003 -0.006 -0.007 -0.008 -0.004 0.086
zSPIN:F_y_dyadic 0.001 0.000 0.003 0.004 0.000 -0.002 -0.011 0.046
zSPIN:F_x2_dyadic 0.001 -0.003 -0.001 0.006 -0.006 -0.009 -0.003 0.045
zSPIN:F_xy_dyadic -0.002 0.004 0.003 -0.003 0.007 0.011 -0.012 -0.013
zSPIN:F_y2_dyadic -0.003 0.001 -0.004 0.003 0.001 0.011 0.008 -0.069
F_y_dy F_x2_d F_xy_d F_y2_d M:SPIN M_x_d:SPIN M_y_:SPIN
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic 0.035
F_xy_dyadic 0.074 -0.243
F_y2_dyadic -0.411 0.011 -0.210
Male:zSPIN 0.004 0.000 -0.003 0.001
M_x_dyadic:zSPIN -0.007 -0.010 0.008 0.014 0.022
M_y_dyadic:zSPIN 0.001 -0.003 -0.009 -0.005 0.064 -0.131
M_x2_dyadic:zSPIN 0.000 0.000 0.005 0.001 -0.150 -0.247 0.010
M_xy_dyadic:zSPIN -0.004 -0.005 0.003 0.006 0.039 0.264 0.068
M_y2_dyadic:zSPIN -0.002 0.004 0.000 -0.004 -0.236 0.041 0.042
zSPIN:Female -0.012 -0.006 -0.016 0.012 -0.006 -0.001 -0.014
zSPIN:F_x_dyadic 0.046 0.051 -0.014 -0.063 -0.002 0.004 0.005
zSPIN:F_y_dyadic 0.005 0.037 -0.071 0.008 0.014 0.005 0.012
zSPIN:F_x2_dyadic 0.032 0.116 0.027 -0.013 0.004 -0.015 0.032
zSPIN:F_xy_dyadic -0.074 0.039 0.145 0.056 -0.001 0.003 -0.004
zSPIN:F_y2_dyadic 0.006 0.000 0.068 0.002 -0.004 0.017 -0.008
M_x2_:SPIN M_xy_:SPIN M_y2_:SPIN zSPIN:Fm zSPIN:F_x_d
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic
F_y2_dyadic
Male:zSPIN
M_x_dyadic:zSPIN
M_y_dyadic:zSPIN
M_x2_dyadic:zSPIN
M_xy_dyadic:zSPIN -0.315
M_y2_dyadic:zSPIN 0.027 -0.076
zSPIN:Female -0.001 0.002 0.001
zSPIN:F_x_dyadic -0.003 -0.009 0.007 0.007
zSPIN:F_y_dyadic -0.006 -0.014 -0.002 -0.023 -0.105
zSPIN:F_x2_dyadic -0.002 -0.001 0.018 -0.164 -0.019
zSPIN:F_xy_dyadic 0.012 0.005 0.003 0.015 -0.178
zSPIN:F_y2_dyadic 0.040 -0.002 -0.009 -0.174 -0.002
zSPIN:F_y_ zSPIN:F_x2_ zSPIN:F_xy_
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic
F_y2_dyadic
Male:zSPIN
M_x_dyadic:zSPIN
M_y_dyadic:zSPIN
M_x2_dyadic:zSPIN
M_xy_dyadic:zSPIN
M_y2_dyadic:zSPIN
zSPIN:Female
zSPIN:F_x_dyadic
zSPIN:F_y_dyadic
zSPIN:F_x2_dyadic 0.043
zSPIN:F_xy_dyadic 0.007 -0.144
zSPIN:F_y2_dyadic -0.269 0.015 -0.068
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-7.2103389 -0.3185911 0.1146734 0.4997438 3.6658387
Number of Observations: 2747
Number of Groups: 82 tab_model(Model3.1,show.r2=F)| PPR | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Male | 5.91 | 5.72 – 6.10 | <0.001 |
| M x dyadic | 0.12 | 0.07 – 0.16 | <0.001 |
| M y dyadic | 0.00 | -0.05 – 0.05 | 0.992 |
| M x2 dyadic | -0.04 | -0.08 – -0.00 | 0.036 |
| M xy dyadic | 0.06 | 0.02 – 0.10 | 0.007 |
| M y2 dyadic | -0.01 | -0.04 – 0.01 | 0.277 |
| Female | 6.03 | 5.85 – 6.21 | <0.001 |
| F x dyadic | 0.12 | 0.08 – 0.16 | <0.001 |
| F y dyadic | 0.06 | 0.01 – 0.10 | 0.013 |
| F x2 dyadic | -0.05 | -0.09 – -0.01 | 0.017 |
| F xy dyadic | 0.01 | -0.02 – 0.05 | 0.475 |
| F y2 dyadic | -0.02 | -0.05 – 0.00 | 0.054 |
| Male × zSPIN | -0.18 | -0.38 – 0.03 | 0.086 |
| M x dyadic × zSPIN | -0.04 | -0.10 – 0.01 | 0.144 |
| M y dyadic × zSPIN | 0.06 | 0.01 – 0.11 | 0.027 |
| M x2 dyadic × zSPIN | 0.07 | 0.02 – 0.11 | 0.003 |
| M xy dyadic × zSPIN | -0.01 | -0.06 – 0.04 | 0.654 |
| M y2 dyadic × zSPIN | 0.02 | -0.01 – 0.05 | 0.287 |
| zSPIN × Female | -0.04 | -0.19 – 0.10 | 0.555 |
| zSPIN × F x dyadic | -0.02 | -0.06 – 0.02 | 0.375 |
| zSPIN × F y dyadic | -0.02 | -0.06 – 0.02 | 0.311 |
| zSPIN × F x2 dyadic | 0.01 | -0.03 – 0.04 | 0.766 |
| zSPIN × F xy dyadic | 0.01 | -0.03 – 0.05 | 0.700 |
| zSPIN × F y2 dyadic | 0.02 | -0.01 – 0.04 | 0.213 |
| Random Effects | |||
| σ2 | 0.48 | ||
| τ00 | |||
| τ00 | |||
| τ11 Couple.Female | 0.63 | ||
| ρ01 Couple | 0.53 | ||
| ICC | 0.58 | ||
| N Couple | 82 | ||
| Observations | 2747 | ||
# Plotting
(fixed_effects <- fixef(Model3.1)) Male M_x_dyadic M_y_dyadic M_x2_dyadic
5.9091158986 0.1172024934 0.0002397662 -0.0396306512
M_xy_dyadic M_y2_dyadic Female F_x_dyadic
0.0558069953 -0.0139540994 6.0290256198 0.1207409264
F_y_dyadic F_x2_dyadic F_xy_dyadic F_y2_dyadic
0.0558204717 -0.0471248705 0.0135661683 -0.0225925192
Male:zSPIN M_x_dyadic:zSPIN M_y_dyadic:zSPIN M_x2_dyadic:zSPIN
-0.1762402029 -0.0433666970 0.0594913221 0.0672063859
M_xy_dyadic:zSPIN M_y2_dyadic:zSPIN zSPIN:Female zSPIN:F_x_dyadic
-0.0111516286 0.0159475411 -0.0435710382 -0.0191288185
zSPIN:F_y_dyadic zSPIN:F_x2_dyadic zSPIN:F_xy_dyadic zSPIN:F_y2_dyadic
-0.0215830422 0.0057488166 0.0080425059 0.0157903132 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
# Define the male and female variable names
male_vars <- c("M_x_dyadic", "M_y_dyadic", "M_x2_dyadic", "M_xy_dyadic", "M_y2_dyadic")
female_vars <- c("F_x_dyadic", "F_y_dyadic", "F_x2_dyadic", "F_xy_dyadic", "F_y2_dyadic")
results <- build_and_evaluate_expressions(Model3.1, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.117442260 0.03062036 0.05742745 0.17745707 3.83542985
2 a2 0.002222245 0.02487428 -0.04653045 0.05097494 0.08933905
3 a3 0.116962727 0.03584913 0.04669972 0.18722573 3.26263786
4 a4 -0.109391746 0.03613229 -0.18020974 -0.03857375 -3.02753392
5 X0 -3.639585460 13.19114540 -29.49375536 22.21458444 -0.27591125
6 Y0 -7.269353501 20.31629852 -47.08856690 32.54985990 -0.35780895
7 p10 -1.588460835 0.69778352 -2.95609140 -0.22083027 -2.27643788
8 p11 1.560862557 0.56962738 0.44441341 2.67731171 2.74014664
9 p20 -9.601131815 28.78962939 -66.02776854 46.82550491 -0.33349272
10 p21 -0.640671400 0.23380917 -1.09892894 -0.18241386 -2.74014664
p_value
1 0.0001
2 0.9288
3 0.0011
4 0.0025
5 0.7826
6 0.7205
7 0.0228
8 0.0061
9 0.7388
10 0.0061results <- build_and_evaluate_expressions(Model3.1, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.17656140 0.02863259 0.120442545 0.232680251 6.1664479
2 a2 -0.05615122 0.02479484 -0.104748211 -0.007554232 -2.2646335
3 a3 0.06492045 0.03438914 -0.002481028 0.132321937 1.8878183
4 a4 -0.08328356 0.03411095 -0.150139794 -0.016427322 -2.4415490
5 X0 1.52478685 0.69535248 0.161921032 2.887652676 2.1928258
6 Y0 1.69317078 1.01514225 -0.296471472 3.682813028 1.6679148
7 p10 -4.21503550 7.14469296 -18.218376378 9.788305369 -0.5899533
8 p11 3.87477520 5.42278692 -6.753691870 14.503242265 0.7145358
9 p20 2.08668699 1.42674514 -0.709682103 4.883056081 1.4625506
10 p21 -0.25807949 0.36118484 -0.965988774 0.449829797 -0.7145358
p_value
1 0.0000
2 0.0235
3 0.0591
4 0.0146
5 0.0283
6 0.0953
7 0.5552
8 0.4749
9 0.1436
10 0.4749High Sx
#High Sx
new_data <- temp1 %>% mutate(zSPIN=zSPIN-1)
Model3.2<- lme(PPR ~ -1 +
Male+ M_x_dyadic+M_y_dyadic+M_x2_dyadic+M_xy_dyadic+M_y2_dyadic+
Male:zSPIN+ M_x_dyadic:zSPIN+M_y_dyadic:zSPIN+M_x2_dyadic:zSPIN+M_xy_dyadic:zSPIN+M_y2_dyadic:zSPIN+
Female+ F_x_dyadic+F_y_dyadic+F_x2_dyadic+F_xy_dyadic+F_y2_dyadic+
Female:zSPIN+ F_x_dyadic:zSPIN+F_y_dyadic:zSPIN+F_x2_dyadic:zSPIN+F_xy_dyadic:zSPIN+F_y2_dyadic:zSPIN,
random = ~ -1 +
Male+
Female| Couple,
# Male+ Male:x_dyadic+Male:y_dyadic+
# Female+ Female:x_dyadic+Female:y_dyadic| Couple,
weights = varIdent(form=~1|Gender),
# corr=corAR1(form = ~1 | Couple/DiaryDay),
correlation = corCompSymm(form = ~1|Couple/DiaryDay),
data = new_data,na.action = na.exclude)
summary(Model3.2)Linear mixed-effects model fit by REML
Data: new_data
AIC BIC logLik
6214.184 6391.469 -3077.092
Random effects:
Formula: ~-1 + Male + Female | Couple
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
Male 0.8261989 Male
Female 0.7914661 0.529
Residual 0.6950983
Correlation Structure: Compound symmetry
Formula: ~1 | Couple/DiaryDay
Parameter estimate(s):
Rho
0.2229805
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | Gender
Parameter estimates:
0.5 -0.5
1.0000000 0.9338685
Fixed effects: PPR ~ -1 + Male + M_x_dyadic + M_y_dyadic + M_x2_dyadic + M_xy_dyadic + M_y2_dyadic + Male:zSPIN + M_x_dyadic:zSPIN + M_y_dyadic:zSPIN + M_x2_dyadic:zSPIN + M_xy_dyadic:zSPIN + M_y2_dyadic:zSPIN + Female + F_x_dyadic + F_y_dyadic + F_x2_dyadic + F_xy_dyadic + F_y2_dyadic + Female:zSPIN + F_x_dyadic:zSPIN + F_y_dyadic:zSPIN + F_x2_dyadic:zSPIN + F_xy_dyadic:zSPIN + F_y2_dyadic:zSPIN
Value Std.Error DF t-value p-value
Male 5.732876 0.14369872 2642 39.89511 0.0000
M_x_dyadic 0.073836 0.04009177 2642 1.84167 0.0656
M_y_dyadic 0.059731 0.03615559 2642 1.65206 0.0986
M_x2_dyadic 0.027576 0.03159581 2642 0.87277 0.3829
M_xy_dyadic 0.044655 0.03300811 2642 1.35286 0.1762
M_y2_dyadic 0.001993 0.01974998 2642 0.10093 0.9196
Female 5.985455 0.11616648 2642 51.52480 0.0000
F_x_dyadic 0.101612 0.03232423 2642 3.14353 0.0017
F_y_dyadic 0.034237 0.03104321 2642 1.10290 0.2702
F_x2_dyadic -0.041376 0.02911263 2642 -1.42124 0.1554
F_xy_dyadic 0.021609 0.03015209 2642 0.71666 0.4736
F_y2_dyadic -0.006802 0.01728294 2642 -0.39358 0.6939
Male:zSPIN -0.176240 0.10267472 2642 -1.71649 0.0862
M_x_dyadic:zSPIN -0.043367 0.02966131 2642 -1.46206 0.1438
M_y_dyadic:zSPIN 0.059491 0.02679760 2642 2.22002 0.0265
M_x2_dyadic:zSPIN 0.067206 0.02247696 2642 2.99001 0.0028
M_xy_dyadic:zSPIN -0.011152 0.02484928 2642 -0.44877 0.6536
M_y2_dyadic:zSPIN 0.015948 0.01498931 2642 1.06393 0.2875
zSPIN:Female -0.043571 0.07376978 2642 -0.59064 0.5548
zSPIN:F_x_dyadic -0.019129 0.02157070 2642 -0.88680 0.3753
zSPIN:F_y_dyadic -0.021583 0.02131870 2642 -1.01240 0.3114
zSPIN:F_x2_dyadic 0.005749 0.01931386 2642 0.29765 0.7660
zSPIN:F_xy_dyadic 0.008043 0.02083664 2642 0.38598 0.6995
zSPIN:F_y2_dyadic 0.015790 0.01268737 2642 1.24457 0.2134
Correlation:
Male M_x_dy M_y_dy M_x2_d M_xy_d M_y2_d Female F_x_dy
M_x_dyadic 0.012
M_y_dyadic 0.057 -0.119
M_x2_dyadic -0.148 -0.221 0.013
M_xy_dyadic 0.043 0.351 0.072 -0.332
M_y2_dyadic -0.217 0.049 0.137 0.013 -0.072
Female 0.259 -0.006 -0.007 -0.011 0.011 -0.016
F_x_dyadic 0.008 -0.019 0.096 -0.001 -0.025 0.008 0.010
F_y_dyadic 0.011 0.090 -0.019 -0.028 0.018 -0.008 -0.031 -0.094
F_x2_dyadic -0.008 -0.014 0.029 0.002 -0.014 0.067 -0.153 0.000
F_xy_dyadic 0.009 0.024 -0.021 -0.005 0.065 -0.053 0.016 -0.193
F_y2_dyadic -0.014 -0.005 -0.003 0.083 -0.062 0.018 -0.154 -0.036
Male:zSPIN 0.744 0.018 0.055 -0.119 0.033 -0.185 -0.004 0.001
M_x_dyadic:zSPIN 0.017 0.802 -0.089 -0.204 0.250 0.048 -0.001 0.001
M_y_dyadic:zSPIN 0.053 -0.087 0.770 0.001 0.081 0.039 -0.007 0.008
M_x2_dyadic:zSPIN -0.120 -0.206 0.001 0.808 -0.265 0.009 -0.001 -0.004
M_xy_dyadic:zSPIN 0.029 0.239 0.085 -0.242 0.780 -0.037 0.001 -0.007
M_y2_dyadic:zSPIN -0.177 0.046 0.032 0.012 -0.043 0.760 0.001 0.005
zSPIN:Female -0.003 -0.002 -0.012 -0.001 0.000 -0.006 0.614 0.002
zSPIN:F_x_dyadic 0.000 0.001 0.002 -0.006 -0.011 0.000 0.001 0.727
zSPIN:F_y_dyadic 0.010 0.004 0.011 -0.002 -0.011 -0.003 -0.024 -0.038
zSPIN:F_x2_dyadic 0.004 -0.013 0.023 0.002 -0.004 0.008 -0.106 0.019
zSPIN:F_xy_dyadic -0.002 0.005 -0.001 0.007 0.008 0.009 0.000 -0.127
zSPIN:F_y2_dyadic -0.005 0.013 -0.008 0.030 -0.001 0.000 -0.104 -0.049
F_y_dy F_x2_d F_xy_d F_y2_d M:SPIN M_x_d:SPIN M_y_:SPIN
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic 0.069
F_xy_dyadic -0.031 -0.140
F_y2_dyadic -0.330 0.007 -0.067
Male:zSPIN 0.012 0.003 -0.002 -0.002
M_x_dyadic:zSPIN -0.001 -0.016 0.007 0.022 0.022
M_y_dyadic:zSPIN 0.009 0.019 -0.009 -0.009 0.064 -0.131
M_x2_dyadic:zSPIN -0.004 -0.001 0.012 0.030 -0.150 -0.247 0.010
M_xy_dyadic:zSPIN -0.013 -0.004 0.005 0.002 0.039 0.264 0.068
M_y2_dyadic:zSPIN -0.003 0.015 0.002 -0.009 -0.236 0.041 0.042
zSPIN:Female -0.024 -0.113 0.000 -0.120 -0.006 -0.001 -0.014
zSPIN:F_x_dyadic -0.039 0.022 -0.132 -0.044 -0.002 0.004 0.005
zSPIN:F_y_dyadic 0.690 0.053 -0.040 -0.192 0.014 0.005 0.012
zSPIN:F_x2_dyadic 0.052 0.742 -0.083 0.002 0.004 -0.015 0.032
zSPIN:F_xy_dyadic -0.049 -0.069 0.782 -0.012 -0.001 0.003 -0.004
zSPIN:F_y2_dyadic -0.180 0.010 -0.004 0.735 -0.004 0.017 -0.008
M_x2_:SPIN M_xy_:SPIN M_y2_:SPIN zSPIN:Fm zSPIN:F_x_d
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic
F_y2_dyadic
Male:zSPIN
M_x_dyadic:zSPIN
M_y_dyadic:zSPIN
M_x2_dyadic:zSPIN
M_xy_dyadic:zSPIN -0.315
M_y2_dyadic:zSPIN 0.027 -0.076
zSPIN:Female -0.001 0.002 0.001
zSPIN:F_x_dyadic -0.003 -0.009 0.007 0.007
zSPIN:F_y_dyadic -0.006 -0.014 -0.002 -0.023 -0.105
zSPIN:F_x2_dyadic -0.002 -0.001 0.018 -0.164 -0.019
zSPIN:F_xy_dyadic 0.012 0.005 0.003 0.015 -0.178
zSPIN:F_y2_dyadic 0.040 -0.002 -0.009 -0.174 -0.002
zSPIN:F_y_ zSPIN:F_x2_ zSPIN:F_xy_
M_x_dyadic
M_y_dyadic
M_x2_dyadic
M_xy_dyadic
M_y2_dyadic
Female
F_x_dyadic
F_y_dyadic
F_x2_dyadic
F_xy_dyadic
F_y2_dyadic
Male:zSPIN
M_x_dyadic:zSPIN
M_y_dyadic:zSPIN
M_x2_dyadic:zSPIN
M_xy_dyadic:zSPIN
M_y2_dyadic:zSPIN
zSPIN:Female
zSPIN:F_x_dyadic
zSPIN:F_y_dyadic
zSPIN:F_x2_dyadic 0.043
zSPIN:F_xy_dyadic 0.007 -0.144
zSPIN:F_y2_dyadic -0.269 0.015 -0.068
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-7.2103389 -0.3185911 0.1146734 0.4997438 3.6658387
Number of Observations: 2747
Number of Groups: 82 tab_model(Model3.1,Model3.2,show.r2=F)| PPR | PPR | |||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p |
| Male | 5.91 | 5.72 – 6.10 | <0.001 | 5.73 | 5.45 – 6.01 | <0.001 |
| M x dyadic | 0.12 | 0.07 – 0.16 | <0.001 | 0.07 | -0.00 – 0.15 | 0.066 |
| M y dyadic | 0.00 | -0.05 – 0.05 | 0.992 | 0.06 | -0.01 – 0.13 | 0.099 |
| M x2 dyadic | -0.04 | -0.08 – -0.00 | 0.036 | 0.03 | -0.03 – 0.09 | 0.383 |
| M xy dyadic | 0.06 | 0.02 – 0.10 | 0.007 | 0.04 | -0.02 – 0.11 | 0.176 |
| M y2 dyadic | -0.01 | -0.04 – 0.01 | 0.277 | 0.00 | -0.04 – 0.04 | 0.920 |
| Female | 6.03 | 5.85 – 6.21 | <0.001 | 5.99 | 5.76 – 6.21 | <0.001 |
| F x dyadic | 0.12 | 0.08 – 0.16 | <0.001 | 0.10 | 0.04 – 0.16 | 0.002 |
| F y dyadic | 0.06 | 0.01 – 0.10 | 0.013 | 0.03 | -0.03 – 0.10 | 0.270 |
| F x2 dyadic | -0.05 | -0.09 – -0.01 | 0.017 | -0.04 | -0.10 – 0.02 | 0.155 |
| F xy dyadic | 0.01 | -0.02 – 0.05 | 0.475 | 0.02 | -0.04 – 0.08 | 0.474 |
| F y2 dyadic | -0.02 | -0.05 – 0.00 | 0.054 | -0.01 | -0.04 – 0.03 | 0.694 |
| Male × zSPIN | -0.18 | -0.38 – 0.03 | 0.086 | -0.18 | -0.38 – 0.03 | 0.086 |
| M x dyadic × zSPIN | -0.04 | -0.10 – 0.01 | 0.144 | -0.04 | -0.10 – 0.01 | 0.144 |
| M y dyadic × zSPIN | 0.06 | 0.01 – 0.11 | 0.027 | 0.06 | 0.01 – 0.11 | 0.027 |
| M x2 dyadic × zSPIN | 0.07 | 0.02 – 0.11 | 0.003 | 0.07 | 0.02 – 0.11 | 0.003 |
| M xy dyadic × zSPIN | -0.01 | -0.06 – 0.04 | 0.654 | -0.01 | -0.06 – 0.04 | 0.654 |
| M y2 dyadic × zSPIN | 0.02 | -0.01 – 0.05 | 0.287 | 0.02 | -0.01 – 0.05 | 0.287 |
| zSPIN × Female | -0.04 | -0.19 – 0.10 | 0.555 | -0.04 | -0.19 – 0.10 | 0.555 |
| zSPIN × F x dyadic | -0.02 | -0.06 – 0.02 | 0.375 | -0.02 | -0.06 – 0.02 | 0.375 |
| zSPIN × F y dyadic | -0.02 | -0.06 – 0.02 | 0.311 | -0.02 | -0.06 – 0.02 | 0.311 |
| zSPIN × F x2 dyadic | 0.01 | -0.03 – 0.04 | 0.766 | 0.01 | -0.03 – 0.04 | 0.766 |
| zSPIN × F xy dyadic | 0.01 | -0.03 – 0.05 | 0.700 | 0.01 | -0.03 – 0.05 | 0.700 |
| zSPIN × F y2 dyadic | 0.02 | -0.01 – 0.04 | 0.213 | 0.02 | -0.01 – 0.04 | 0.213 |
| Random Effects | ||||||
| σ2 | 0.48 | 0.48 | ||||
| τ00 | ||||||
| τ00 | ||||||
| τ11 | 0.63 Couple.Female | 0.63 Couple.Female | ||||
| ρ01 | 0.53 Couple | 0.53 Couple | ||||
| ICC | 0.58 | 0.58 | ||||
| N | 82 Couple | 82 Couple | ||||
| Observations | 2747 | 2747 | ||||
# Plotting
(fixed_effects <- fixef(Model3.2)) Male M_x_dyadic M_y_dyadic M_x2_dyadic
5.732875711 0.073835798 0.059731093 0.027575733
M_xy_dyadic M_y2_dyadic Female F_x_dyadic
0.044655367 0.001993444 5.985454583 0.101612107
F_y_dyadic F_x2_dyadic F_xy_dyadic F_y2_dyadic
0.034237431 -0.041376054 0.021608676 -0.006802207
Male:zSPIN M_x_dyadic:zSPIN M_y_dyadic:zSPIN M_x2_dyadic:zSPIN
-0.176240189 -0.043366697 0.059491323 0.067206385
M_xy_dyadic:zSPIN M_y2_dyadic:zSPIN zSPIN:Female zSPIN:F_x_dyadic
-0.011151628 0.015947543 -0.043571037 -0.019128818
zSPIN:F_y_dyadic zSPIN:F_x2_dyadic zSPIN:F_xy_dyadic zSPIN:F_y2_dyadic
-0.021583047 0.005748817 0.008042506 0.015790313 # Men's plot
plotRSA(x=fixed_effects[2],
y=fixed_effects[3],
x2=fixed_effects[4],
xy=fixed_effects[5],
y2=fixed_effects[6]
)
# Women's plot
plotRSA(x=fixed_effects[8],
y=fixed_effects[9],
x2=fixed_effects[10],
xy=fixed_effects[11],
y2=fixed_effects[12]
)
# Define the male and female variable names
male_vars <- c("M_x_dyadic", "M_y_dyadic", "M_x2_dyadic", "M_xy_dyadic", "M_y2_dyadic")
female_vars <- c("F_x_dyadic", "F_y_dyadic", "F_x2_dyadic", "F_xy_dyadic", "F_y2_dyadic")
results <- build_and_evaluate_expressions(Model3.2, male_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.133566891 0.05068406 0.034227950 0.23290583 2.6352837515
2 a2 0.074224544 0.04133212 -0.006784923 0.15523401 1.7958078025
3 a3 0.014104704 0.05709886 -0.097807002 0.12601641 0.2470225298
4 a4 -0.015086190 0.05727694 -0.127346922 0.09717454 -0.2633903109
5 X0 -1.337455361 1.48381020 -4.245669919 1.57075920 -0.9013655241
6 Y0 -0.001638622 3.55693493 -6.973102986 6.96982574 -0.0004606837
7 p10 0.773537836 3.80067174 -6.675641892 8.22271756 0.2035266102
8 p11 0.579590527 0.51268499 -0.425253595 1.58443465 1.1305002775
9 p20 -2.309225270 1.49005075 -5.229671071 0.61122053 -1.5497628336
10 p21 -1.725356012 1.52618805 -4.716629619 1.26591760 -1.1305002775
p_value
1 0.0084
2 0.0725
3 0.8049
4 0.7922
5 0.3674
6 0.9996
7 0.8387
8 0.2583
9 0.1212
10 0.2583results <- build_and_evaluate_expressions(Model3.2, female_vars)
# View the results
print(results) Expression Estimate SE CI_2.5 CI_97.5 Z
1 a1 0.13584954 0.04265645 0.05224444 0.21945464 3.1847363
2 a2 -0.02656959 0.04179868 -0.10849349 0.05535432 -0.6356561
3 a3 0.06737468 0.04687750 -0.02450353 0.15925288 1.4372498
4 a4 -0.06978694 0.04875206 -0.16533921 0.02576534 -1.4314665
5 X0 3.22102053 11.34410716 -19.01302095 25.45506201 0.2839378
6 Y0 7.63277366 36.40793692 -63.72547145 78.99101877 0.2096459
7 p10 -3.59825901 6.10396925 -15.56181890 8.36530088 -0.5894949
8 p11 3.48679326 4.82682992 -5.97361954 12.94720605 0.7223775
9 p20 8.55655098 39.83631189 -69.52118560 86.63428756 0.2147927
10 p21 -0.28679647 0.39701745 -1.06493638 0.49134344 -0.7223775
p_value
1 0.0014
2 0.5250
3 0.1506
4 0.1523
5 0.7765
6 0.8339
7 0.5555
8 0.4701
9 0.8299
10 0.4701