CERT Behavioral & Brain analysis

Load libraries

Create/Load plotting functions

Load Data

Demographic Data

## NULL

## Length  Class   Mode 
##      0   NULL   NULL

Behavioral Data

Create wideform summary dfs

Behavioral Data Analysis

Trait and Symptom Correlations & Overlap

traitsx <- cbind(MNA$N_z,MNA$dep_composite,MNA$anx_composite)
traitsx_viz <- list(MNA$N_z,MNA$dep_composite,MNA$anx_composite)
cor(traitsx, method="pearson")

##           [,1]      [,2]      [,3]
## [1,] 1.0000000 0.8379880 0.7640284
## [2,] 0.8379880 1.0000000 0.8190111
## [3,] 0.7640284 0.8190111 1.0000000

ss <- function(x) {
  sum((x - mean(x))^2)
}

y_total <- ss(MNA$N_z)    # A + D + E + G
x1_total <- ss(MNA$dep_composite)  # B + D + F + G
x2_total <- ss(MNA$anx_composite)  # C + E + F + G

# A
y_alone <- aov(N_z ~ anx_composite + dep_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "Residuals") %>%
  pull(sumsq)

# B
x1_alone <- aov(dep_composite ~ N_z + anx_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "Residuals") %>%
  pull(sumsq)

# C
x2_alone <- aov(anx_composite ~ N_z + dep_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "Residuals") %>%
  pull(sumsq)

# D + G
y_plus_x1 <- aov(N_z ~ dep_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "dep_composite") %>%
  pull(sumsq)

# E + G
y_plus_x2 <- aov(N_z ~ anx_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "anx_composite") %>%
  pull(sumsq)

# F + G
x1_plus_x2 <- aov(dep_composite ~ anx_composite, data = MNA) %>%
  tidy() %>%
  filter(term == "anx_composite") %>%
  pull(sumsq)

# D = (A + D + E + G) − A − (E + G)
y_x1_alone <- y_total - y_alone - y_plus_x2

# E = (A + D + E + G) − A − (D + G)
y_x2_alone <- y_total - y_alone - y_plus_x1

# G = (D + G) − D
y_x1_x2_alone <- y_plus_x1 - y_x1_alone

# F = (F + G) - G
x1_x2_alone <- abs(x1_plus_x2 - y_x1_x2_alone)

all_pieces <- c("Y" = y_alone,
                "X1" = x1_alone,
                "X2" = x2_alone,
                "X1&Y" = y_x1_alone,
                "X2&Y" = y_x2_alone,
                "X1&X2" = x1_x2_alone,
                "Y&X1&X2" = y_x1_x2_alone)
mylabels <- c("Neuroticism","Depression","Anxiety","N&D","N&A","D&A","N&D&A")


#plot(euler(all_pieces),
#     colors = 
#     quantities = c("Neuroticism","Depression","Anxiety","N&D","N&A","D&A","N&D&A"))

nice_plot <- plot(euler(all_pieces),
                  quantities = (c("Neuroticism","Depression","Anxiety","N&D","N&A","D&A","N&D&A")),
                  fills = list(fill = c("#7FDBFF", "gold2", "violetred3",
                                        "green4", "darkorchid", "coral1", "tan3"),
                               alpha = c(1, .75, .75, 0.65, 0.5, 0.65, 0.5)),
                  labels = list(fontface = "bold", fontsize = 20))
nice_plot 

Descriptive Tables:

Emotion Ratings

summary_allobs_emo <- describeBy(emo_resp ~ PicValence + Procedure, data=cert_mna, mat=T)
htmlTable::htmlTable(format(summary_allobs_emo, 
                            digits = 2)) 

	item	group1	group2	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
emo_resp1	1	neg	reg	1	1798	3.1	1.20	3	3.1	1.5	1	5	4	-0.013	-0.95	0.028
emo_resp2	2	neut	reg	1	1797	1.3	0.57	1	1.1	0.0	1	5	4	2.612	7.80	0.014
emo_resp3	3	neg	watch	1	1801	3.7	1.16	4	3.8	1.5	1	5	4	-0.563	-0.61	0.027
emo_resp4	4	neut	watch	1	1808	1.3	0.65	1	1.1	0.0	1	5	4	2.635	7.72	0.015

Thinking Change Ratings

summary_allobs_er <- describeBy(er_resp ~ PicValence + Procedure, data=cert_mna, mat=T)
htmlTable::htmlTable(format(summary_allobs_er, 
                            digits = 2)) 

	item	group1	group2	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
er_resp1	1	neg	reg	1	1804	3.1	1.15	3	3.1	1.5	1	5	4	-0.09	-0.80	0.027
er_resp2	2	neut	reg	1	1794	2.7	1.12	3	2.7	1.5	1	5	4	0.16	-0.68	0.027
er_resp3	3	neg	watch	1	1776	1.7	1.05	1	1.5	0.0	1	5	4	1.57	1.76	0.025
er_resp4	4	neut	watch	1	1797	1.3	0.75	1	1.1	0.0	1	5	4	2.71	7.79	0.018

On average, people found negative images more negative and they changed their thinking more during the reappraise trials

  ggplot(data = sumdf_l, aes(x = Condition, y = Rating, color = Valence)) +
  geom_boxplot(aes(fill=Valence), alpha = .7, outlier.shape = NA) +      
  geom_point(aes(fill=Valence), shape=21,color="black", position=position_jitterdodge(),alpha=.5,size=1) +
  scale_colour_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
    scale_fill_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
  facet_wrap(~Rating_Type) +
  labs(y = "Rating", x = "Condition") +
  #ggtitle("Figure 1: Ratings across Valence and Conditions") +
  theme_minimal() 

Distributions of trial by trial ratings

tbt_valuedis<-ggplot(data = longest_emo_cov_ss, aes(x = Rating)) +
  geom_density(aes(group = Subject,fill=Subject),size=.3,alpha=.1) + 
  geom_density(aes(group = 1),size=1.5) + 
  #scale_colour_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
  #  scale_fill_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
  #facet_wrap(~Valence) +
  labs(x = "Negative Emotion Ratings") +
  theme_minimal() 

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

tbt_valuedis

## Warning: Removed 156 rows containing non-finite values (`stat_density()`).

## Warning: Removed 156 rows containing non-finite values (`stat_density()`).

tbt_thinkingchange_valuedis<-ggplot(data = longest_reg_cov_ss, aes(x = Rating)) +
  geom_density(aes(group = Subject,fill=Subject),size=.3,alpha=.1) + 
  geom_density(aes(group = 1),size=1.5) + 
  #scale_colour_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
  #  scale_fill_manual(values = c("Negative" = "darkred", "Neutral" = "blue4")) +
  #facet_wrap(~Condition) +
  labs(x = "Thinking Change Ratings") +
  theme_minimal() 
tbt_thinkingchange_valuedis

## Warning: Removed 189 rows containing non-finite values (`stat_density()`).

## Warning: Removed 189 rows containing non-finite values (`stat_density()`).

Statistical Models

1. Test Guassian vs Gamma vs Neg. binomial

A. Basic Task Models of Thinking Change Ratings

# Behav_task_regrate_gaus <- lmer(Rating ~ Valence*Condition + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_regrate_gam <- glmer(Rating ~ Valence*Condition + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# #Behav_task_regrate_nb <- glmer.nb(Rating ~ Valence*Condition + (1 | Subject),
# #                            contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
# #                            data=longest_reg_cov_ss, 
# #                            na.action = na.exclude)
# summary(Behav_task_regrate_gaus)
# summary(Behav_task_regrate_gam)
# #summary(Behav_task_regrate_nb)

Gamma distribution fits best

#anova(Behav_task_regrate_gaus,Behav_task_regrate_gam)
#anova(Behav_task_regrate_nb,Behav_task_regrate_gam)

B. Basic Task Models of Negative Emotion Ratings

# Behav_task_emorate_gaus <- lmer(Rating ~ Valence*Condition + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_emorate_gam <- glmer(Rating ~ Valence*Condition + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# #Behav_task_emorate_nb <- glmer.nb(Rating ~ Valence*Condition + (1 | Subject),
# #                            contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
# #                            data=longest_emo_cov_ss, 
# #                            na.action = na.exclude)
# summary(Behav_task_emorate_gaus)
# summary(Behav_task_emorate_gam)
# #summary(Behav_task_emorate_nb)

Gamma distribution fits best

#anova(Behav_task_emorate_gaus,Behav_task_emorate_gam)
#anova(Behav_task_emorate_nb,Behav_task_emorate_gam)

2. Include Neuroticism (and then covariates) in the Gamma model

A. See the Valence * Neuroticism interaction with Thinking Change as in the gaussian model

B. No longer see the Valence * Neuroticism interaction with Negative Emotion as in the gaussian model

# Behav_task_regrate_gam_Nint <- glmer(Rating ~ Valence*Condition*N_z + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             #control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# summary(Behav_task_regrate_gam_Nint)
# plot_model(Behav_task_regrate_gam_Nint,type = "pred", terms=c("N_z","Valence"))
# 
# Behav_task_emorate_gam_Nint <- glmer(Rating ~ Valence*Condition*N_z + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             #control=glmerControl(optimizer="bobyqa"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# summary(Behav_task_emorate_gam_Nint)
# plot_model(Behav_task_emorate_gam_Nint,type = "pred", terms=c("N_z","Valence"))

C. Add Covariates to Thinking Change Model … these will not converge/throw errors

# Behav_task_regrate_gam_Nint <- glmer(Rating ~ Valence*Condition*N_z + dep_composite + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             #control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_regrate_gam_Nint <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite +(1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# summary(Behav_task_regrate_gam_Nint)

# Behav_task_regrate_gam_Nint2 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + SexAtBirth + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_regrate_gam_Nint3 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + Age_c + SexAtBirth + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "log"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)

#does not converge when adding Site as the last covariate
#Behav_task_regrate_gam_Nint4 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + Age_c + SexAtBirth + Site + (1 | Subject),
#                            contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                            family = Gamma(link = "identity"),
#                            control=glmerControl(optimizer="bobyqa"),
#                            data=longest_reg_cov_ss, 
#                            na.action = na.exclude)

#summary(Behav_task_regrate_gam_Nint3)

#plot_model(Behav_task_regrate_gam_Nint3,type = "pred", terms=c("N_z","Valence"),show.data=T,jitter=.05)

D. Add Covariates to Emotion Model … “Error in pwrssUpdate(pp, resp, tol = tolPwrss, GQmat = GQmat, compDev = compDev, : Downdated VtV is not positive definite”

# Behav_task_emorate_gam_Nint2 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_emorate_gam_Nint3 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + Age_c + SexAtBirth + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# Behav_task_emorate_gam_Nint4 <- glmer(Rating ~ Valence*Condition*N_z + anx_composite + dep_composite + Age_c + SexAtBirth + Site + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)

3. Old full gaussian models

Regulate and Emotion ratings are predicted by task conditions

4. Incremental Effect of Neuroticism on Ratings above and beyond D and A

It appears that including N and its interactions does not significantly improve model fit for either thinking change or emotion ratings…

# Behav_task_regrate_gam_noN <- glmer(Rating ~ Valence*Condition + anx_composite + dep_composite + Age_c + SexAtBirth + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_reg_cov_ss, 
#                             na.action = na.exclude)
# anova(Behav_task_regrate_gam_noN,Behav_task_regrate_gam_Nint3)

# Behav_task_emorate_gam_noN <- glmer(Rating ~ Valence*Condition + anx_composite + dep_composite + Age_c + SexAtBirth + Site + (1 | Subject),
#                             contrasts = list(Valence = "contr.sum", Condition = "contr.sum"), 
#                             family = Gamma(link = "identity"),
#                             control=glmerControl(optimizer="bobyqa"),
#                             data=longest_emo_cov_ss, 
#                             na.action = na.exclude)
# anova(Behav_task_emorate_gam_noN ,Behav_task_emorate_gam_Nint4)

Brain Data Analysis

Loading data and format for univariate analysis

Formatting the Apriori ROI data

Formatting the WB ROI data

1. Testing the Apriori ROIs for task-N interactions (& for cov/var structure of Subject and Site)

uniroi_unharm_df$site <-ifelse(uniroi_unharm_df$sub < 3150,-1,1)
uniroi_unharm_df$val <- as.factor(uniroi_unharm_df$val)
contrasts(uniroi_unharm_df$val) <- contr.sum(2)
uniroi_unharm_df$proc <- as.factor(uniroi_unharm_df$proc)
contrasts(uniroi_unharm_df$proc) <- contr.sum(2)
uniroi_unharm_df$Age_c <- uniroi_unharm_df$Age - mean(uniroi_unharm_df$Age, na.rm=T)
uniroi_unharm_df$SexAtBirth <- as.numeric(uniroi_unharm_df$SexAtBirth)
uniroi_unharm_df$SexAtBirth <- uniroi_unharm_df$SexAtBirth - 1

dACC model(s): checking cor structure and site variance

# Test 1: repeated measure subject correlation structure
#corsymm
nositemodel1dacc <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(nositemodel1dacc)
#compsymm
nositemodel2dacc <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(nositemodel2dacc)

# Testing difference of fit between models
anova(nositemodel1dacc,nositemodel2dacc)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1dacc     1 20 -193.7674 -115.6058 116.8837                        
## nositemodel2dacc     2 15 -184.3722 -125.7509 107.1861 1 vs 2 19.39526  0.0016

# Test 2: site var/cov structure
# Different var/cov by Site
sitemodel1dacc <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
#summary(sitemodel1dacc)
# Common var/cov by Site
sitemodel2dacc <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(sitemodel2dacc)
#anova(sitemodel2dacc)

# Testing difference of fit between models
anova(sitemodel1dacc,sitemodel2dacc)

##                Model df       AIC       BIC   logLik   Test   L.Ratio p-value
## sitemodel1dacc     1 21 -192.2215 -110.1518 117.1108                         
## sitemodel2dacc     2 20 -193.7674 -115.6058 116.8837 1 vs 2 0.4541063  0.5004

dACC model(s): comparing more complex N, dpression, and anxiety models

dacc_N <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_N)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -193.7674 -115.6058 116.8837
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.692            
## 3 0.633 0.718      
## 4 0.661 0.793 0.823
## 
## Coefficients:
##                     Value  Std.Error   t-value p-value
## (Intercept)     0.3183443 0.20400255  1.560492  0.1195
## val1           -0.0034291 0.00735352 -0.466326  0.6413
## proc1           0.0170966 0.00724236  2.360641  0.0188
## N_z            -0.0124206 0.02831053 -0.438726  0.6611
## dep_composite   0.0576635 0.05915113  0.974851  0.3303
## anx_composite  -0.0410562 0.05235420 -0.784201  0.4334
## Age            -0.0203510 0.00862274 -2.360156  0.0188
## SexAtBirth      0.0306140 0.05503597  0.556255  0.5784
## site           -0.0276116 0.02580592 -1.069970  0.2854
## val1:proc1      0.0210895 0.00707412  2.981212  0.0031
## val1:N_z       -0.0045431 0.00410192 -1.107560  0.2688
## proc1:N_z      -0.0045812 0.00414219 -1.105977  0.2695
## val1:proc1:N_z -0.0133538 0.00402631 -3.316639  0.0010
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.003                                                        
## proc1           0.004  0.008                                                 
## N_z            -0.251 -0.004 -0.005                                          
## dep_composite   0.082 -0.007  0.006 -0.567                                   
## anx_composite   0.165  0.007 -0.007 -0.242 -0.520                            
## Age            -0.975 -0.001 -0.004  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.000  0.006  0.021  0.131 -0.320  0.110              
## site            0.225  0.009  0.022 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.007 -0.024 -0.078 -0.006  0.013 -0.002 -0.006 -0.006  0.003
## val1:N_z       -0.004 -0.426 -0.027  0.002  0.000  0.000  0.003 -0.001 -0.006
## proc1:N_z      -0.008 -0.028 -0.393 -0.003  0.003 -0.002  0.009 -0.010 -0.009
## val1:proc1:N_z  0.001  0.014  0.035 -0.001  0.001 -0.006 -0.001  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.017              
## proc1:N_z       0.036  0.027       
## val1:proc1:N_z -0.398  0.024 -0.007
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -2.8876631 -0.6341294 -0.0043954  0.6621811  2.6032766 
## 
## Residual standard error: 0.2531437 
## Degrees of freedom: 368 total; 355 residual

#depression with N and A as main effects only
dacc_D <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*dep_composite + N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_D)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * dep_composite +      N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -191.3124 -113.1508 115.6562
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.699            
## 3 0.631 0.707      
## 4 0.659 0.793 0.818
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3175220 0.20399013  1.5565556  0.1205
## val1                     -0.0055425 0.00673927 -0.8224260  0.4114
## proc1                     0.0140180 0.00671522  2.0875017  0.0376
## dep_composite             0.0551072 0.05915346  0.9315968  0.3522
## N_z                      -0.0113352 0.02831108 -0.4003789  0.6891
## anx_composite            -0.0407328 0.05235173 -0.7780601  0.4371
## Age                      -0.0203435 0.00862213 -2.3594461  0.0188
## SexAtBirth                0.0297296 0.05503216  0.5402232  0.5894
## site                     -0.0275245 0.02580497 -1.0666340  0.2869
## val1:proc1                0.0126014 0.00650825  1.9362217  0.0536
## val1:dep_composite       -0.0086753 0.00763050 -1.1369223  0.2563
## proc1:dep_composite      -0.0042165 0.00771321 -0.5466563  0.5850
## val1:proc1:dep_composite -0.0224372 0.00742597 -3.0214502  0.0027
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.002                                          
## proc1                     0.002 -0.005                                   
## dep_composite             0.082 -0.007  0.008                            
## N_z                      -0.251 -0.004 -0.008 -0.568                     
## anx_composite             0.165  0.008 -0.008 -0.520 -0.242              
## Age                      -0.975  0.000 -0.001 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.000  0.003  0.131  0.021 -0.320  0.110
## site                      0.225  0.008  0.020 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.008 -0.011 -0.061  0.014 -0.006 -0.005 -0.006
## val1:dep_composite       -0.004 -0.134 -0.045  0.002 -0.002  0.002  0.002
## proc1:dep_composite      -0.006 -0.048 -0.066 -0.002  0.001 -0.002  0.007
## val1:proc1:dep_composite  0.003  0.003  0.036  0.014 -0.012 -0.007  0.000
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_
## val1                                                       
## proc1                                                      
## dep_composite                                              
## N_z                                                        
## anx_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.007  0.005                     
## val1:dep_composite        0.004 -0.004  0.009              
## proc1:dep_composite      -0.006 -0.011  0.038  0.011       
## val1:proc1:dep_composite  0.001 -0.002 -0.049  0.025 -0.004
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.803215252 -0.625195960 -0.002566357  0.664085600  2.608132960 
## 
## Residual standard error: 0.2533421 
## Degrees of freedom: 368 total; 355 residual

#Anxiety with N and D as main effects only
dacc_A <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*anx_composite + N_z + dep_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_A)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * anx_composite +      N_z + dep_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -188.5546 -110.3929 114.2773
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.691            
## 3 0.640 0.714      
## 4 0.666 0.796 0.809
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3168085 0.20432198  1.5505357  0.1219
## val1                     -0.0061266 0.00675704 -0.9066958  0.3652
## proc1                     0.0143301 0.00676821  2.1172718  0.0349
## anx_composite            -0.0420012 0.05243841 -0.8009622  0.4237
## N_z                      -0.0116373 0.02835676 -0.4103892  0.6818
## dep_composite             0.0565398 0.05924508  0.9543368  0.3406
## Age                      -0.0202961 0.00863617 -2.3501285  0.0193
## SexAtBirth                0.0299359 0.05512129  0.5430916  0.5874
## site                     -0.0273646 0.02584636 -1.0587401  0.2904
## val1:proc1                0.0122433 0.00659357  1.8568600  0.0642
## val1:anx_composite       -0.0083781 0.00769770 -1.0883951  0.2772
## proc1:anx_composite       0.0004776 0.00780727  0.0611763  0.9513
## val1:proc1:anx_composite -0.0189557 0.00756653 -2.5052041  0.0127
## 
##  Correlation: 
##                          (Intr) val1   proc1  anx_cm N_z    dp_cmp Age   
## val1                      0.002                                          
## proc1                     0.002 -0.011                                   
## anx_composite             0.165  0.007 -0.007                            
## N_z                      -0.251 -0.003 -0.007 -0.242                     
## dep_composite             0.082 -0.007  0.007 -0.520 -0.567              
## Age                      -0.975  0.001 -0.001 -0.089  0.173 -0.070       
## SexAtBirth               -0.275  0.000  0.003 -0.320  0.021  0.131  0.110
## site                      0.225  0.008  0.019  0.085 -0.019 -0.084 -0.237
## val1:proc1                0.008 -0.011 -0.054 -0.005 -0.007  0.014 -0.006
## val1:anx_composite       -0.002 -0.084 -0.018  0.007 -0.005  0.001  0.001
## proc1:anx_composite      -0.006 -0.020 -0.051 -0.006 -0.001  0.003  0.005
## val1:proc1:anx_composite  0.002  0.031  0.014 -0.002 -0.009  0.005 -0.001
##                          SxAtBr site   vl1:p1 vl1:n_ prc1:_
## val1                                                       
## proc1                                                      
## anx_composite                                              
## N_z                                                        
## dep_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.006  0.004                     
## val1:anx_composite        0.001 -0.005  0.035              
## proc1:anx_composite      -0.004 -0.009  0.017  0.033       
## val1:proc1:anx_composite  0.003 -0.001 -0.021  0.035 -0.029
## 
## Standardized residuals:
##           Min            Q1           Med            Q3           Max 
## -2.7700767887 -0.6355214000 -0.0006613285  0.6680691051  2.6450197463 
## 
## Residual standard error: 0.2535008 
## Degrees of freedom: 368 total; 355 residual

#which fits better?
anova(dacc_N,dacc_D)

##        Model df       AIC       BIC   logLik
## dacc_N     1 20 -193.7674 -115.6058 116.8837
## dacc_D     2 20 -191.3124 -113.1508 115.6562

anova(dacc_N,dacc_A)

##        Model df       AIC       BIC   logLik
## dacc_N     1 20 -193.7674 -115.6058 116.8837
## dacc_A     2 20 -188.5546 -110.3929 114.2773

#including multiple 3 way interactions:
## all three constructs get their own 3 way interaction
dacc_NDA <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*dep_composite + val*proc*N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_NDA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * dep_composite +      val * proc * N_z + val * proc * anx_composite + Age + SexAtBirth +      site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -183.7848 -82.17467 117.8924
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.695            
## 3 0.644 0.714      
## 4 0.667 0.792 0.821
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3182900 0.20565430  1.5476942  0.1226
## val1                     -0.0055460 0.00821456 -0.6751384  0.5000
## proc1                     0.0204954 0.00822049  2.4932075  0.0131
## dep_composite             0.0562993 0.05964625  0.9438866  0.3459
## N_z                      -0.0119583 0.02854821 -0.4188810  0.6756
## anx_composite            -0.0412544 0.05278471 -0.7815592  0.4350
## Age                      -0.0203990 0.00869249 -2.3467361  0.0195
## SexAtBirth                0.0313212 0.05548400  0.5645084  0.5728
## site                     -0.0270870 0.02601500 -1.0412068  0.2985
## val1:proc1                0.0189780 0.00824875  2.3007120  0.0220
## val1:dep_composite       -0.0026453 0.01652450 -0.1600835  0.8729
## proc1:dep_composite       0.0014132 0.01603473  0.0881368  0.9298
## val1:N_z                 -0.0007133 0.00779147 -0.0915551  0.9271
## proc1:N_z                -0.0104702 0.00781677 -1.3394535  0.1813
## val1:anx_composite       -0.0059167 0.01382969 -0.4278245  0.6690
## proc1:anx_composite       0.0128125 0.01334624  0.9600096  0.3377
## val1:proc1:dep_composite -0.0097077 0.01618753 -0.5997023  0.5491
## val1:proc1:N_z           -0.0102016 0.00783965 -1.3012827  0.1940
## val1:proc1:anx_composite  0.0032793 0.01408259  0.2328618  0.8160
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.002                                          
## proc1                     0.004 -0.045                                   
## dep_composite             0.082 -0.005  0.003                            
## N_z                      -0.251 -0.008 -0.001 -0.568                     
## anx_composite             0.165  0.010 -0.007 -0.520 -0.242              
## Age                      -0.975 -0.001 -0.004 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.003  0.006  0.131  0.021 -0.320  0.110
## site                      0.225  0.009  0.015 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.021 -0.074  0.020 -0.016 -0.001 -0.005
## val1:dep_composite       -0.003  0.259 -0.074  0.004 -0.002 -0.004  0.001
## proc1:dep_composite       0.001 -0.074  0.311 -0.009  0.007  0.004 -0.001
## val1:N_z                 -0.001 -0.566  0.056 -0.004  0.009 -0.006  0.001
## proc1:N_z                -0.004  0.054 -0.572  0.006 -0.008  0.001  0.006
## val1:anx_composite        0.003  0.155  0.012 -0.001 -0.009  0.013 -0.001
## proc1:anx_composite      -0.001  0.009  0.143  0.005 -0.001 -0.008 -0.001
## val1:proc1:dep_composite  0.002 -0.024  0.008  0.021 -0.015 -0.005  0.001
## val1:proc1:N_z           -0.002  0.004  0.049 -0.016  0.018 -0.003  0.000
## val1:proc1:anx_composite  0.000  0.032 -0.050 -0.004 -0.005  0.007 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:N_ pr1:N_
## val1                                                                      
## proc1                                                                     
## dep_composite                                                             
## N_z                                                                       
## anx_composite                                                             
## Age                                                                       
## SexAtBirth                                                                
## site                      0.153                                           
## val1:proc1               -0.004  0.001                                    
## val1:dep_composite        0.008  0.002 -0.017                             
## proc1:dep_composite       0.000 -0.004  0.007 -0.068                      
## val1:N_z                 -0.007 -0.003  0.003 -0.578  0.055               
## proc1:N_z                -0.008  0.001  0.045  0.053 -0.592  -0.062       
## val1:anx_composite       -0.003 -0.003  0.031 -0.523  0.017  -0.226  0.011
## proc1:anx_composite       0.004 -0.001 -0.046  0.015 -0.483   0.012 -0.242
## val1:proc1:dep_composite -0.001 -0.004  0.293  0.032 -0.019  -0.031  0.029
## val1:proc1:N_z           -0.002  0.002 -0.606 -0.034  0.031   0.037 -0.062
## val1:proc1:anx_composite  0.004  0.001  0.200 -0.005 -0.007   0.000  0.035
##                          vl1:n_ prc1:n_ vl1:prc1:d_ v1:1:N
## val1                                                      
## proc1                                                     
## dep_composite                                             
## N_z                                                       
## anx_composite                                             
## Age                                                       
## SexAtBirth                                                
## site                                                      
## val1:proc1                                                
## val1:dep_composite                                        
## proc1:dep_composite                                       
## val1:N_z                                                  
## proc1:N_z                                                 
## val1:anx_composite                                        
## proc1:anx_composite      -0.018                           
## val1:proc1:dep_composite -0.006 -0.003                    
## val1:proc1:N_z            0.002  0.035  -0.553            
## val1:proc1:anx_composite  0.017 -0.045  -0.515      -0.275
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.878417008 -0.631511461 -0.004698701  0.669658468  2.565376518 
## 
## Residual standard error: 0.2526127 
## Degrees of freedom: 368 total; 349 residual

## just symptoms get 3 ways
dacc_DA <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*dep_composite + N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_DA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * dep_composite +      N_z + val * proc * anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -185.9025 -96.01663 115.9513
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.696            
## 3 0.638 0.710      
## 4 0.662 0.792 0.817
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3168652 0.20482907  1.5469735  0.1228
## val1                     -0.0054498 0.00678592 -0.8031085  0.4225
## proc1                     0.0141377 0.00675771  2.0920866  0.0371
## dep_composite             0.0554067 0.05939930  0.9327833  0.3516
## N_z                      -0.0114247 0.02842776 -0.4018840  0.6880
## anx_composite            -0.0413564 0.05257243 -0.7866559  0.4320
## Age                      -0.0203397 0.00865755 -2.3493634  0.0194
## SexAtBirth                0.0302951 0.05525884  0.5482394  0.5839
## site                     -0.0271780 0.02591053 -1.0489178  0.2949
## val1:proc1                0.0124221 0.00656699  1.8916025  0.0594
## val1:dep_composite       -0.0036210 0.01350557 -0.2681150  0.7888
## proc1:dep_composite      -0.0111287 0.01294180 -0.8599046  0.3904
## val1:anx_composite       -0.0057769 0.01349514 -0.4280691  0.6689
## proc1:anx_composite       0.0085902 0.01297409  0.6621019  0.5083
## val1:proc1:dep_composite -0.0216448 0.01352885 -1.5999000  0.1105
## val1:proc1:anx_composite -0.0010771 0.01358337 -0.0792973  0.9368
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.002                                          
## proc1                     0.002 -0.005                                   
## dep_composite             0.082 -0.007  0.008                            
## N_z                      -0.251 -0.004 -0.007 -0.567                     
## anx_composite             0.165  0.008 -0.008 -0.520 -0.242              
## Age                      -0.975  0.000 -0.001 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.000  0.003  0.131  0.021 -0.320  0.110
## site                      0.225  0.008  0.019 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.008 -0.008 -0.061  0.013 -0.006 -0.004 -0.006
## val1:dep_composite       -0.005 -0.102 -0.050  0.002  0.005 -0.008  0.003
## proc1:dep_composite      -0.002 -0.048 -0.042 -0.007  0.003  0.006  0.003
## val1:anx_composite        0.003  0.034  0.030 -0.001 -0.007  0.011 -0.001
## proc1:anx_composite      -0.002  0.026  0.004  0.008 -0.003 -0.009  0.001
## val1:proc1:dep_composite  0.002 -0.041  0.047  0.015 -0.006 -0.009  0.001
## val1:proc1:anx_composite -0.001  0.050 -0.032 -0.010  0.000  0.007 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:n_ prc1:n_
## val1                                                                       
## proc1                                                                      
## dep_composite                                                              
## N_z                                                                        
## anx_composite                                                              
## Age                                                                        
## SexAtBirth                                                                 
## site                      0.153                                            
## val1:proc1               -0.006  0.004                                     
## val1:dep_composite        0.005  0.000 -0.036                              
## proc1:dep_composite      -0.005 -0.005  0.046 -0.043                       
## val1:anx_composite       -0.004 -0.003  0.049 -0.823  0.038                
## proc1:anx_composite       0.002 -0.002 -0.028  0.035 -0.800  -0.020        
## val1:proc1:dep_composite -0.003 -0.003 -0.064  0.012 -0.006  -0.011  0.018 
## val1:proc1:anx_composite  0.004  0.002  0.044 -0.014  0.015   0.023 -0.033 
##                          vl1:prc1:d_
## val1                                
## proc1                               
## dep_composite                       
## N_z                                 
## anx_composite                       
## Age                                 
## SexAtBirth                          
## site                                
## val1:proc1                          
## val1:dep_composite                  
## proc1:dep_composite                 
## val1:anx_composite                  
## proc1:anx_composite                 
## val1:proc1:dep_composite            
## val1:proc1:anx_composite -0.833     
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.801672566 -0.617741216 -0.002729149  0.668574023  2.581488639 
## 
## Residual standard error: 0.2530667 
## Degrees of freedom: 368 total; 352 residual

#does including the N 3way improve fit?
anova(dacc_NDA,dacc_DA)

##          Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## dacc_NDA     1 26 -183.7848 -82.17467 117.8924                        
## dacc_DA      2 23 -185.9025 -96.01663 115.9513 1 vs 2 3.882288  0.2745

## Neuroticism and one of t he two symptom measures gets 3ways
dacc_ND <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*dep_composite + val*proc*N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_ND)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * dep_composite +      val * proc * N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -188.6092 -98.72327 117.3046
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.696            
## 3 0.635 0.712      
## 4 0.663 0.794 0.822
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3186787 0.20488162  1.5554285  0.1207
## val1                     -0.0053776 0.00807258 -0.6661520  0.5058
## proc1                     0.0192977 0.00808739  2.3861451  0.0176
## dep_composite             0.0562960 0.05942445  0.9473545  0.3441
## N_z                      -0.0119171 0.02844064 -0.4190177  0.6755
## anx_composite            -0.0406757 0.05258004 -0.7735957  0.4397
## Age                      -0.0203770 0.00865987 -2.3530378  0.0192
## SexAtBirth                0.0305523 0.05527521  0.5527306  0.5808
## site                     -0.0275338 0.02591796 -1.0623462  0.2888
## val1:proc1                0.0189361 0.00802912  2.3584325  0.0189
## val1:dep_composite       -0.0069471 0.01401771 -0.4955951  0.6205
## proc1:dep_composite       0.0088947 0.01396545  0.6369039  0.5246
## val1:N_z                 -0.0013537 0.00755460 -0.1791912  0.8579
## proc1:N_z                -0.0086438 0.00754190 -1.1461079  0.2525
## val1:proc1:dep_composite -0.0075439 0.01379297 -0.5469381  0.5848
## val1:proc1:N_z           -0.0097782 0.00749345 -1.3049003  0.1928
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.002                                          
## proc1                     0.005 -0.051                                   
## dep_composite             0.082 -0.005  0.002                            
## N_z                      -0.251 -0.006 -0.001 -0.568                     
## anx_composite             0.165  0.008 -0.006 -0.520 -0.242              
## Age                      -0.975 -0.001 -0.005 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.004  0.007  0.131  0.021 -0.320  0.110
## site                      0.225  0.010  0.017 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.034 -0.063  0.023 -0.016 -0.004 -0.004
## val1:dep_composite       -0.002  0.404 -0.086  0.004 -0.007  0.003  0.001
## proc1:dep_composite       0.001 -0.085  0.438 -0.008  0.008  0.000 -0.001
## val1:N_z                 -0.001 -0.552  0.063 -0.003  0.007 -0.003  0.001
## proc1:N_z                -0.005  0.060 -0.560  0.008 -0.009 -0.001  0.006
## val1:proc1:dep_composite  0.002 -0.009 -0.016  0.024 -0.022 -0.003  0.001
## val1:proc1:N_z           -0.001  0.013  0.034 -0.020  0.019 -0.001 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_ vl1:N_ pr1:N_
## val1                                                                     
## proc1                                                                    
## dep_composite                                                            
## N_z                                                                      
## anx_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.005  0.002                                   
## val1:dep_composite        0.008  0.001 -0.001                            
## proc1:dep_composite       0.003 -0.006 -0.010 -0.081                     
## val1:N_z                 -0.008 -0.004  0.010 -0.839  0.079              
## proc1:N_z                -0.008  0.000  0.030  0.077 -0.834 -0.065       
## val1:proc1:dep_composite  0.002 -0.004  0.472  0.044 -0.046 -0.038  0.049
## val1:proc1:N_z           -0.002  0.003 -0.586 -0.041  0.047  0.043 -0.050
##                          v1:1:_
## val1                           
## proc1                          
## dep_composite                  
## N_z                            
## anx_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:dep_composite             
## proc1:dep_composite            
## val1:N_z                       
## proc1:N_z                      
## val1:proc1:dep_composite       
## val1:proc1:N_z           -0.843
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.882576772 -0.635342101 -0.003713457  0.663754007  2.608787177 
## 
## Residual standard error: 0.2530799 
## Degrees of freedom: 368 total; 352 residual

#does D model improve by adding N ineeractions
anova(dacc_ND,dacc_D)

##         Model df       AIC        BIC   logLik   Test  L.Ratio p-value
## dacc_ND     1 23 -188.6092  -98.72327 117.3046                        
## dacc_D      2 20 -191.3124 -113.15075 115.6562 1 vs 2 3.296769  0.3481

dacc_NA <- gls(brant_extract_BN_Atlas_179_180_dACC ~ val*proc*N_z + N_z + val*proc*anx_composite +dep_composite+ Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(dacc_NA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_179_180_dACC ~ val * proc * N_z + N_z +      val * proc * anx_composite + dep_composite + Age + SexAtBirth +      site 
##   Data: uniroi_unharm_df 
##         AIC      BIC   logLik
##   -189.4188 -99.5329 117.7094
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.691            
## 3 0.645 0.719      
## 4 0.668 0.793 0.820
## 
## Coefficients:
##                               Value  Std.Error    t-value p-value
## (Intercept)               0.3181596 0.20483575  1.5532425  0.1213
## val1                     -0.0052471 0.00789261 -0.6648120  0.5066
## proc1                     0.0204427 0.00778689  2.6252662  0.0090
## N_z                      -0.0122470 0.02843062 -0.4307669  0.6669
## anx_composite            -0.0416797 0.05257324 -0.7927924  0.4284
## dep_composite             0.0572382 0.05939370  0.9637083  0.3359
## Age                      -0.0203809 0.00865796 -2.3540068  0.0191
## SexAtBirth                0.0313544 0.05526158  0.5673808  0.5708
## site                     -0.0271576 0.02591114 -1.0481065  0.2953
## val1:proc1                0.0205250 0.00786431  2.6098880  0.0094
## val1:N_z                 -0.0014068 0.00634292 -0.2217829  0.8246
## proc1:N_z                -0.0100236 0.00628881 -1.5938711  0.1119
## val1:anx_composite       -0.0070134 0.01175739 -0.5965071  0.5512
## proc1:anx_composite       0.0135506 0.01166872  1.1612791  0.2463
## val1:proc1:N_z           -0.0127717 0.00650274 -1.9640539  0.0503
## val1:proc1:anx_composite -0.0012177 0.01202694 -0.1012486  0.9194
## 
##  Correlation: 
##                          (Intr) val1   proc1  N_z    anx_cm dp_cmp Age   
## val1                      0.003                                          
## proc1                     0.004 -0.009                                   
## N_z                      -0.251 -0.008 -0.003                            
## anx_composite             0.165  0.011 -0.008 -0.242                     
## dep_composite             0.082 -0.006  0.006 -0.567 -0.520              
## Age                      -0.975 -0.001 -0.004  0.173 -0.089 -0.070       
## SexAtBirth               -0.275  0.001  0.007  0.021 -0.320  0.131  0.110
## site                      0.225  0.009  0.018 -0.019  0.085 -0.084 -0.237
## val1:proc1                0.007 -0.007 -0.090 -0.012  0.000  0.015 -0.006
## val1:N_z                 -0.004 -0.528  0.011  0.010 -0.009 -0.001  0.002
## proc1:N_z                -0.004  0.011 -0.509 -0.004  0.004  0.001  0.007
## val1:anx_composite        0.002  0.355 -0.026 -0.011  0.012  0.002 -0.001
## proc1:anx_composite      -0.001 -0.027  0.353  0.003 -0.008  0.001 -0.001
## val1:proc1:N_z           -0.001 -0.005  0.057  0.011 -0.007 -0.005  0.001
## val1:proc1:anx_composite  0.002  0.020 -0.048 -0.014  0.004  0.007 -0.001
##                          SxAtBr site   vl1:p1 vl1:N_ pr1:N_ vl1:n_ prc1:_
## val1                                                                     
## proc1                                                                    
## N_z                                                                      
## anx_composite                                                            
## dep_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.004  0.002                                   
## val1:N_z                 -0.002 -0.002 -0.005                            
## proc1:N_z                -0.009 -0.002  0.057 -0.033                     
## val1:anx_composite        0.002 -0.002  0.023 -0.760  0.040              
## proc1:anx_composite       0.005 -0.004 -0.045  0.039 -0.749 -0.026       
## val1:proc1:N_z           -0.003  0.001 -0.557  0.014 -0.049 -0.014  0.057
## val1:proc1:anx_composite  0.004 -0.001  0.426 -0.013  0.057  0.027 -0.070
##                          v1:1:N
## val1                           
## proc1                          
## N_z                            
## anx_composite                  
## dep_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:N_z                       
## proc1:N_z                      
## val1:anx_composite             
## proc1:anx_composite            
## val1:proc1:N_z                 
## val1:proc1:anx_composite -0.783
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.883771311 -0.629524269  0.005534385  0.671692836  2.572956561 
## 
## Residual standard error: 0.2526455 
## Degrees of freedom: 368 total; 352 residual

#does A model improve by adding N ineeractions
anova(dacc_NA,dacc_A)

##         Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## dacc_NA     1 23 -189.4188  -99.5329 117.7094                        
## dacc_A      2 20 -188.5546 -110.3929 114.2773 1 vs 2 6.864198  0.0764

spgACC model(s):

# Test 1: repeated measure subject correlation structure
nositemodel1spgacc <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(nositemodel1dacc)

# Common var/cov by Site
nositemodel2spgacc <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(nositemodel2dacc)

# Testing difference of fit 
anova(nositemodel1spgacc,nositemodel2spgacc)

##                    Model df       AIC       BIC   logLik   Test  L.Ratio
## nositemodel1spgacc     1 20 -200.8972 -122.7356 120.4486                
## nositemodel2spgacc     2 15 -202.8584 -144.2372 116.4292 1 vs 2 8.038811
##                    p-value
## nositemodel1spgacc        
## nositemodel2spgacc  0.1541

# Different var/cov by Site
sitemodel1spgacc <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
#summary(sitemodel1dacc)
#anova(sitemodel1dacc)

# Common var/cov by Site
sitemodel2spgacc <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(sitemodel2dacc)

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1spgacc,sitemodel2spgacc)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## sitemodel1spgacc     1 16 -203.9243 -141.3950 117.9622                        
## sitemodel2spgacc     2 15 -202.8584 -144.2372 116.4292 1 vs 2 3.065931  0.0799

# rename winning model
spgacc_N <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(spgacc_N)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC      BIC   logLik
##   -203.9243 -141.395 117.9622
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##      Rho 
## 0.605738 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 1.143994 
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)    -0.03180246 0.17031585 -0.186726  0.8520
## val1           -0.02401834 0.00791055 -3.036242  0.0026
## proc1           0.00681182 0.00791055  0.861106  0.3898
## N_z            -0.00421636 0.02301105 -0.183232  0.8547
## dep_composite   0.05335265 0.04839467  1.102449  0.2710
## anx_composite  -0.04076312 0.04319781 -0.943639  0.3460
## Age            -0.01341902 0.00723504 -1.854727  0.0645
## SexAtBirth      0.00451682 0.04560940  0.099033  0.9212
## site           -0.03622011 0.02183748 -1.658622  0.0981
## val1:proc1      0.02802514 0.00791055  3.542755  0.0004
## val1:N_z        0.00307407 0.00441818  0.695777  0.4870
## proc1:N_z      -0.00160927 0.00441818 -0.364239  0.7159
## val1:proc1:N_z -0.01193725 0.00441818 -2.701845  0.0072
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.216  0.000  0.000                                          
## dep_composite   0.059  0.000  0.000 -0.559                                   
## anx_composite   0.145  0.000  0.000 -0.247 -0.530                            
## Age            -0.975  0.000  0.000  0.141 -0.051 -0.067                     
## SexAtBirth     -0.265  0.000  0.000  0.004  0.151 -0.328  0.101              
## site            0.241  0.000  0.000 -0.016 -0.082  0.076 -0.237  0.149       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.398  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.398  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.398  0.000  0.000
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.5391731 -0.6608344  0.1268850  0.6417937  3.3334229 
## 
## Residual standard error: 0.2075112 
## Degrees of freedom: 368 total; 355 residual

#anova(spgacc_N)

spgACC model(s): comparing more complex N, dpression, and anxiety models

spgacc_N <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_N)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -200.8972 -122.7356 120.4486
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.586            
## 3 0.538 0.641      
## 4 0.578 0.706 0.692
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)    -0.02082816 0.17148940 -0.121455  0.9034
## val1           -0.02322572 0.00777432 -2.987494  0.0030
## proc1           0.00816640 0.00774872  1.053903  0.2926
## N_z            -0.00298570 0.02379870 -0.125456  0.9002
## dep_composite   0.06059413 0.04972367  1.218617  0.2238
## anx_composite  -0.04651595 0.04401020 -1.056936  0.2913
## Age            -0.01370373 0.00724848 -1.890566  0.0595
## SexAtBirth     -0.00262568 0.04626482 -0.056753  0.9548
## site           -0.03899821 0.02169165 -1.797845  0.0731
## val1:proc1      0.03218298 0.00768087  4.190020  0.0000
## val1:N_z        0.00172099 0.00438633  0.392354  0.6950
## proc1:N_z      -0.00258549 0.00440791 -0.586556  0.5579
## val1:proc1:N_z -0.01428206 0.00436116 -3.274830  0.0012
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.002                                                        
## proc1           0.003  0.009                                                 
## N_z            -0.251 -0.004 -0.003                                          
## dep_composite   0.082 -0.006  0.006 -0.567                                   
## anx_composite   0.165  0.005 -0.007 -0.242 -0.520                            
## Age            -0.975  0.000 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.000  0.005  0.021  0.131 -0.320  0.110              
## site            0.225  0.005  0.020 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.007 -0.016 -0.047 -0.006  0.013 -0.002 -0.005 -0.007  0.000
## val1:N_z       -0.004 -0.412 -0.024  0.002 -0.001  0.001  0.002 -0.001 -0.004
## proc1:N_z      -0.006 -0.025 -0.402 -0.004  0.003 -0.002  0.008 -0.008 -0.010
## val1:proc1:N_z  0.002  0.008  0.032 -0.002  0.001 -0.005 -0.001  0.000  0.002
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.009              
## proc1:N_z       0.033  0.029       
## val1:proc1:N_z -0.403  0.024 -0.008
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.3067037 -0.6788581  0.1074699  0.6207046  3.5791800 
## 
## Residual standard error: 0.2230409 
## Degrees of freedom: 368 total; 355 residual

#depression with N and A as main effects only
spgacc_D <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*dep_composite + N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_D)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * dep_composite +      N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -196.9864 -118.8247 118.4932
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.595            
## 3 0.539 0.637      
## 4 0.582 0.698 0.676
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.02296578 0.17153406 -0.133885  0.8936
## val1                     -0.02205009 0.00718572 -3.068600  0.0023
## proc1                     0.00635830 0.00718264  0.885232  0.3766
## dep_composite             0.05892405 0.04973920  1.184660  0.2369
## N_z                      -0.00228897 0.02380624 -0.096150  0.9235
## anx_composite            -0.04644574 0.04402225 -1.055051  0.2921
## Age                      -0.01364411 0.00725032 -1.881865  0.0607
## SexAtBirth               -0.00292863 0.04627680 -0.063285  0.9496
## site                     -0.03907419 0.02169707 -1.800897  0.0726
## val1:proc1                0.02325136 0.00712461  3.263528  0.0012
## val1:dep_composite        0.00546768 0.00818916  0.667672  0.5048
## proc1:dep_composite       0.00075952 0.00821392  0.092468  0.9264
## val1:proc1:dep_composite -0.02069740 0.00813638 -2.543808  0.0114
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.001                                          
## proc1                     0.001 -0.002                                   
## dep_composite             0.082 -0.005  0.008                            
## N_z                      -0.251 -0.004 -0.005 -0.567                     
## anx_composite             0.165  0.005 -0.009 -0.520 -0.242              
## Age                      -0.975  0.001  0.000 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.000  0.003  0.131  0.021 -0.320  0.110
## site                      0.225  0.003  0.017 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.007 -0.024  0.012 -0.006 -0.004 -0.006
## val1:dep_composite       -0.003 -0.094 -0.030 -0.002  0.002  0.002  0.002
## proc1:dep_composite      -0.004 -0.031 -0.078 -0.002  0.001 -0.002  0.005
## val1:proc1:dep_composite  0.003  0.005  0.029  0.011 -0.011 -0.006 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_
## val1                                                       
## proc1                                                      
## dep_composite                                              
## N_z                                                        
## anx_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.007  0.001                     
## val1:dep_composite        0.002 -0.003  0.007              
## proc1:dep_composite      -0.005 -0.011  0.030  0.010       
## val1:proc1:dep_composite  0.001  0.000 -0.073  0.019 -0.002
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.4048569 -0.6414943  0.1036747  0.6162077  3.5376134 
## 
## Residual standard error: 0.223225 
## Degrees of freedom: 368 total; 355 residual

#Anxiety with N and D as main effects only
spgacc_A <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*anx_composite + N_z + dep_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_A)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * anx_composite +      N_z + dep_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC       BIC  logLik
##   -195.544 -117.3823 117.772
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.588            
## 3 0.545 0.641      
## 4 0.572 0.694 0.673
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.02237127 0.17126350 -0.130625  0.8961
## val1                     -0.02173470 0.00718848 -3.023545  0.0027
## proc1                     0.00634468 0.00719578  0.881723  0.3785
## anx_composite            -0.04749692 0.04395286 -1.080633  0.2806
## N_z                      -0.00273732 0.02376810 -0.115168  0.9084
## dep_composite             0.06029404 0.04965753  1.214197  0.2255
## Age                      -0.01363371 0.00723892 -1.883390  0.0605
## SexAtBirth               -0.00334557 0.04620403 -0.072409  0.9423
## site                     -0.03931530 0.02166247 -1.814904  0.0704
## val1:proc1                0.02265094 0.00714147  3.171747  0.0016
## val1:anx_composite       -0.00222289 0.00823003 -0.270095  0.7872
## proc1:anx_composite       0.00902351 0.00826786  1.091396  0.2758
## val1:proc1:anx_composite -0.01705555 0.00819499 -2.081215  0.0381
## 
##  Correlation: 
##                          (Intr) val1   proc1  anx_cm N_z    dp_cmp Age   
## val1                      0.000                                          
## proc1                     0.000 -0.003                                   
## anx_composite             0.165  0.005 -0.009                            
## N_z                      -0.251 -0.004 -0.003 -0.242                     
## dep_composite             0.082 -0.006  0.007 -0.520 -0.567              
## Age                      -0.975  0.001  0.001 -0.090  0.173 -0.070       
## SexAtBirth               -0.275  0.000  0.002 -0.320  0.021  0.131  0.110
## site                      0.225  0.002  0.015  0.085 -0.019 -0.084 -0.237
## val1:proc1                0.007 -0.008 -0.018 -0.003 -0.006  0.012 -0.006
## val1:anx_composite       -0.001 -0.060 -0.011  0.003  0.001 -0.001 -0.001
## proc1:anx_composite      -0.003 -0.012 -0.052 -0.005 -0.001  0.002  0.002
## val1:proc1:anx_composite  0.002  0.023  0.018 -0.001 -0.007  0.002 -0.001
##                          SxAtBr site   vl1:p1 vl1:n_ prc1:_
## val1                                                       
## proc1                                                      
## anx_composite                                              
## N_z                                                        
## dep_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.008 -0.001                     
## val1:anx_composite        0.001 -0.003  0.024              
## proc1:anx_composite      -0.002 -0.010  0.019  0.024       
## val1:proc1:anx_composite  0.003  0.003 -0.042  0.025 -0.013
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.36252272 -0.63152494  0.09815633  0.62995347  3.59608847 
## 
## Residual standard error: 0.2230972 
## Degrees of freedom: 368 total; 355 residual

#which fits better?
anova(spgacc_N,spgacc_D)

##          Model df       AIC       BIC   logLik
## spgacc_N     1 20 -200.8972 -122.7356 120.4486
## spgacc_D     2 20 -196.9864 -118.8247 118.4932

anova(spgacc_N,spgacc_A)

##          Model df       AIC       BIC   logLik
## spgacc_N     1 20 -200.8972 -122.7356 120.4486
## spgacc_A     2 20 -195.5440 -117.3823 117.7720

#including multiple 3 way interactions:
## all three constructs get their own 3 way interaction
spgacc_NDA <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*dep_composite + val*proc*N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_NDA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * dep_composite +      val * proc * N_z + val * proc * anx_composite + Age + SexAtBirth +      site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -197.8463 -96.23616 124.9232
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.591            
## 3 0.564 0.643      
## 4 0.595 0.686 0.695
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.01862426 0.17177512 -0.108422  0.9137
## val1                     -0.02388586 0.00872680 -2.737070  0.0065
## proc1                     0.01513108 0.00870478  1.738249  0.0830
## dep_composite             0.05958643 0.04981158  1.196237  0.2324
## N_z                      -0.00321028 0.02384148 -0.134651  0.8930
## anx_composite            -0.04638566 0.04408450 -1.052199  0.2934
## Age                      -0.01385096 0.00726054 -1.907703  0.0573
## SexAtBirth               -0.00152792 0.04634286 -0.032970  0.9737
## site                     -0.03840622 0.02172691 -1.767680  0.0780
## val1:proc1                0.03270315 0.00871872  3.750910  0.0002
## val1:dep_composite        0.02151591 0.01734504  1.240464  0.2156
## proc1:dep_composite      -0.00346701 0.01715919 -0.202050  0.8400
## val1:N_z                  0.00196981 0.00829756  0.237396  0.8125
## proc1:N_z                -0.01421247 0.00827111 -1.718327  0.0866
## val1:anx_composite       -0.02360915 0.01464652 -1.611930  0.1079
## proc1:anx_composite       0.03163976 0.01446063  2.187992  0.0293
## val1:proc1:dep_composite -0.00251394 0.01717929 -0.146336  0.8837
## val1:proc1:N_z           -0.01621124 0.00830044 -1.953057  0.0516
## val1:proc1:anx_composite  0.00764120 0.01467313  0.520762  0.6029
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.001                                          
## proc1                     0.003 -0.024                                   
## dep_composite             0.082 -0.004  0.002                            
## N_z                      -0.251 -0.006  0.001 -0.568                     
## anx_composite             0.165  0.007 -0.006 -0.520 -0.242              
## Age                      -0.975  0.000 -0.003 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.003  0.004  0.131  0.021 -0.320  0.110
## site                      0.225  0.005  0.012 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.007 -0.036  0.015 -0.011 -0.001 -0.005
## val1:dep_composite       -0.004  0.282 -0.042  0.001 -0.002 -0.001  0.002
## proc1:dep_composite       0.001 -0.042  0.294 -0.007  0.007  0.001 -0.001
## val1:N_z                  0.000 -0.578  0.029 -0.003  0.007 -0.002  0.000
## proc1:N_z                -0.003  0.029 -0.578  0.006 -0.008  0.001  0.005
## val1:anx_composite        0.003  0.162  0.007  0.001 -0.005  0.006 -0.002
## proc1:anx_composite       0.000  0.007  0.158  0.002  0.000 -0.005 -0.002
## val1:proc1:dep_composite  0.002 -0.011  0.009  0.015 -0.010 -0.005  0.000
## val1:proc1:N_z           -0.002 -0.004  0.022 -0.011  0.011 -0.001  0.001
## val1:proc1:anx_composite  0.000  0.022 -0.022 -0.004 -0.002  0.005 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:N_ pr1:N_
## val1                                                                      
## proc1                                                                     
## dep_composite                                                             
## N_z                                                                       
## anx_composite                                                             
## Age                                                                       
## SexAtBirth                                                                
## site                      0.153                                           
## val1:proc1               -0.004 -0.002                                    
## val1:dep_composite        0.006  0.000 -0.010                             
## proc1:dep_composite      -0.001 -0.002  0.009 -0.041                      
## val1:N_z                 -0.005 -0.001 -0.004 -0.575  0.029               
## proc1:N_z                -0.005  0.000  0.020  0.028 -0.578  -0.030       
## val1:anx_composite       -0.002 -0.002  0.022 -0.513  0.015  -0.241  0.005
## proc1:anx_composite       0.004 -0.002 -0.021  0.014 -0.502   0.005 -0.245
## val1:proc1:dep_composite -0.001 -0.003  0.288  0.019 -0.001  -0.022  0.010
## val1:proc1:N_z           -0.002  0.003 -0.587 -0.023  0.010   0.028 -0.024
## val1:proc1:anx_composite  0.004  0.001  0.175  0.000 -0.006  -0.001  0.013
##                          vl1:n_ prc1:n_ vl1:prc1:d_ v1:1:N
## val1                                                      
## proc1                                                     
## dep_composite                                             
## N_z                                                       
## anx_composite                                             
## Age                                                       
## SexAtBirth                                                
## site                                                      
## val1:proc1                                                
## val1:dep_composite                                        
## proc1:dep_composite                                       
## val1:N_z                                                  
## proc1:N_z                                                 
## val1:anx_composite                                        
## proc1:anx_composite      -0.015                           
## val1:proc1:dep_composite  0.000 -0.005                    
## val1:proc1:N_z            0.000  0.012  -0.568            
## val1:proc1:anx_composite  0.007 -0.014  -0.509      -0.257
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.4107210 -0.6819256  0.1029704  0.6571508  3.6320231 
## 
## Residual standard error: 0.2205722 
## Degrees of freedom: 368 total; 349 residual

## just symptoms get 3 ways
spgacc_DA <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*dep_composite + N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_DA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * dep_composite +      N_z + val * proc * anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC      BIC  logLik
##   -196.7319 -106.846 121.366
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.591            
## 3 0.555 0.647      
## 4 0.585 0.682 0.679
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.02262243 0.17117837 -0.132157  0.8949
## val1                     -0.02207280 0.00718174 -3.073460  0.0023
## proc1                     0.00654827 0.00716320  0.914155  0.3613
## dep_composite             0.05898706 0.04963554  1.188404  0.2355
## N_z                      -0.00278078 0.02375622 -0.117055  0.9069
## anx_composite            -0.04658522 0.04393136 -1.060409  0.2897
## Age                      -0.01368179 0.00723529 -1.890980  0.0594
## SexAtBirth               -0.00237966 0.04618099 -0.051529  0.9589
## site                     -0.03880293 0.02165105 -1.792196  0.0740
## val1:proc1                0.02266117 0.00713303  3.176933  0.0016
## val1:dep_composite        0.02410090 0.01430641  1.684622  0.0929
## proc1:dep_composite      -0.02110987 0.01415944 -1.490869  0.1369
## val1:anx_composite       -0.02235703 0.01433906 -1.559170  0.1199
## proc1:anx_composite       0.02639065 0.01418140  1.860934  0.0636
## val1:proc1:dep_composite -0.02120873 0.01426237 -1.487041  0.1379
## val1:proc1:anx_composite  0.00052277 0.01429833  0.036561  0.9709
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.001                                          
## proc1                     0.000  0.001                                   
## dep_composite             0.082 -0.006  0.007                            
## N_z                      -0.251 -0.003 -0.004 -0.567                     
## anx_composite             0.165  0.005 -0.008 -0.520 -0.242              
## Age                      -0.975  0.000  0.001 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.000  0.002  0.131  0.021 -0.320  0.110
## site                      0.225  0.003  0.014 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.004 -0.022  0.010 -0.005 -0.002 -0.006
## val1:dep_composite       -0.005 -0.074 -0.027 -0.004  0.003 -0.001  0.003
## proc1:dep_composite      -0.001 -0.027 -0.062 -0.004  0.003  0.002  0.003
## val1:anx_composite        0.004  0.028  0.016  0.002 -0.002  0.002 -0.003
## proc1:anx_composite      -0.001  0.015  0.021  0.004 -0.003 -0.004 -0.001
## val1:proc1:dep_composite  0.002 -0.026  0.026  0.012 -0.005 -0.008  0.000
## val1:proc1:anx_composite -0.001  0.031 -0.013 -0.009  0.001  0.006  0.000
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:n_ prc1:n_
## val1                                                                       
## proc1                                                                      
## dep_composite                                                              
## N_z                                                                        
## anx_composite                                                              
## Age                                                                        
## SexAtBirth                                                                 
## site                      0.153                                            
## val1:proc1               -0.007 -0.002                                     
## val1:dep_composite        0.003 -0.001 -0.026                              
## proc1:dep_composite      -0.005 -0.003  0.027 -0.031                       
## val1:anx_composite       -0.002 -0.001  0.032 -0.820  0.030                
## proc1:anx_composite       0.003 -0.003 -0.013  0.029 -0.816  -0.020        
## val1:proc1:dep_composite -0.003 -0.002 -0.067  0.004  0.011  -0.002 -0.005 
## val1:proc1:anx_composite  0.004  0.003  0.029 -0.003 -0.005   0.009 -0.003 
##                          vl1:prc1:d_
## val1                                
## proc1                               
## dep_composite                       
## N_z                                 
## anx_composite                       
## Age                                 
## SexAtBirth                          
## site                                
## val1:proc1                          
## val1:dep_composite                  
## proc1:dep_composite                 
## val1:anx_composite                  
## proc1:anx_composite                 
## val1:proc1:dep_composite            
## val1:proc1:anx_composite -0.821     
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.4185573 -0.6334061  0.1055899  0.6774633  3.5310998 
## 
## Residual standard error: 0.2214074 
## Degrees of freedom: 368 total; 352 residual

#does including the N 3way improve fit?
anova(spgacc_NDA,spgacc_DA)

##            Model df       AIC        BIC   logLik   Test  L.Ratio p-value
## spgacc_NDA     1 26 -197.8463  -96.23616 124.9232                        
## spgacc_DA      2 23 -196.7319 -106.84600 121.3660 1 vs 2 7.114407  0.0683

## Neuroticism and one of t he two symptom measures gets 3ways
spgacc_ND <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*dep_composite + val*proc*N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_ND)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * dep_composite +      val * proc * N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -196.2534 -106.3675 121.1267
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.589            
## 3 0.543 0.636      
## 4 0.586 0.706 0.692
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.01956465 0.17223744 -0.113591  0.9096
## val1                     -0.02215472 0.00862593 -2.568386  0.0106
## proc1                     0.01199411 0.00864691  1.387098  0.1663
## dep_composite             0.06016861 0.04995153  1.204540  0.2292
## N_z                      -0.00299737 0.02390713 -0.125376  0.9003
## anx_composite            -0.04612252 0.04420225 -1.043443  0.2975
## Age                      -0.01376655 0.00728008 -1.890989  0.0594
## SexAtBirth               -0.00220078 0.04646758 -0.047362  0.9623
## site                     -0.03887408 0.02178665 -1.784308  0.0752
## val1:proc1                0.03302303 0.00861681  3.832396  0.0002
## val1:dep_composite        0.00633689 0.01492218  0.424663  0.6713
## proc1:dep_composite       0.01534948 0.01492296  1.028581  0.3044
## val1:N_z                 -0.00104035 0.00806659 -0.128971  0.8975
## proc1:N_z                -0.00954702 0.00807491 -1.182306  0.2379
## val1:proc1:dep_composite  0.00291019 0.01485214  0.195944  0.8448
## val1:proc1:N_z           -0.01560222 0.00804742 -1.938786  0.0533
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.001                                          
## proc1                     0.004 -0.035                                   
## dep_composite             0.082 -0.005  0.002                            
## N_z                      -0.251 -0.005  0.001 -0.568                     
## anx_composite             0.165  0.006 -0.007 -0.520 -0.242              
## Age                      -0.975  0.000 -0.004 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.003  0.006  0.131  0.021 -0.320  0.110
## site                      0.225  0.006  0.016 -0.084 -0.019  0.085 -0.237
## val1:proc1                0.007 -0.019 -0.033  0.020 -0.013 -0.003 -0.005
## val1:dep_composite       -0.002  0.427 -0.060  0.001 -0.004  0.002  0.001
## proc1:dep_composite       0.001 -0.061  0.438 -0.008  0.009 -0.001 -0.002
## val1:N_z                  0.000 -0.561  0.042 -0.001  0.004 -0.002  0.001
## proc1:N_z                -0.005  0.041 -0.564  0.008 -0.009 -0.001  0.006
## val1:proc1:dep_composite  0.002  0.000  0.001  0.020 -0.018 -0.003  0.000
## val1:proc1:N_z           -0.001  0.002  0.015 -0.016  0.014  0.000 -0.001
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_ vl1:N_ pr1:N_
## val1                                                                     
## proc1                                                                    
## dep_composite                                                            
## N_z                                                                      
## anx_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.005  0.000                                   
## val1:dep_composite        0.007 -0.001  0.003                            
## proc1:dep_composite       0.002 -0.005  0.004 -0.059                     
## val1:N_z                 -0.006 -0.002  0.002 -0.838  0.054              
## proc1:N_z                -0.006 -0.001  0.013  0.054 -0.837 -0.041       
## val1:proc1:dep_composite  0.002 -0.003  0.449  0.033 -0.009 -0.030  0.011
## val1:proc1:N_z           -0.002  0.003 -0.572 -0.031  0.011  0.036 -0.014
##                          v1:1:_
## val1                           
## proc1                          
## dep_composite                  
## N_z                            
## anx_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:dep_composite             
## proc1:dep_composite            
## val1:N_z                       
## proc1:N_z                      
## val1:proc1:dep_composite       
## val1:proc1:N_z           -0.840
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.3793304 -0.6792302  0.1059741  0.6209562  3.6047498 
## 
## Residual standard error: 0.2228033 
## Degrees of freedom: 368 total; 352 residual

#does D model improve by adding N ineeractions
anova(spgacc_ND,spgacc_D)

##           Model df       AIC       BIC   logLik   Test L.Ratio p-value
## spgacc_ND     1 23 -196.2534 -106.3675 121.1267                       
## spgacc_D      2 20 -196.9864 -118.8247 118.4932 1 vs 2 5.26703  0.1533

spgacc_NA <- gls(brant_extract_BN_Atlas_187_188_spgACC ~ val*proc*N_z + N_z + val*proc*anx_composite +dep_composite+ Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(spgacc_NA)

## Generalized least squares fit by maximum likelihood
##   Model: brant_extract_BN_Atlas_187_188_spgACC ~ val * proc * N_z + N_z +      val * proc * anx_composite + dep_composite + Age + SexAtBirth +      site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -202.2166 -112.3306 124.1083
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.594            
## 3 0.559 0.639      
## 4 0.594 0.694 0.694
## 
## Coefficients:
##                                Value  Std.Error   t-value p-value
## (Intercept)              -0.01854588 0.17148510 -0.108149  0.9139
## val1                     -0.02696943 0.00833884 -3.234192  0.0013
## proc1                     0.01581079 0.00829373  1.906354  0.0574
## N_z                      -0.00306481 0.02379986 -0.128774  0.8976
## anx_composite            -0.04655120 0.04401061 -1.057727  0.2909
## dep_composite             0.05988604 0.04972160  1.204427  0.2292
## Age                      -0.01384092 0.00724833 -1.909533  0.0570
## SexAtBirth               -0.00190238 0.04626418 -0.041120  0.9672
## site                     -0.03855171 0.02169058 -1.777348  0.0764
## val1:proc1                0.03333556 0.00832167  4.005873  0.0001
## val1:N_z                  0.00781761 0.00676066  1.156339  0.2483
## proc1:N_z                -0.01507004 0.00673115 -2.238850  0.0258
## val1:anx_composite       -0.01404507 0.01252383 -1.121467  0.2629
## proc1:anx_composite       0.03001442 0.01247163  2.406615  0.0166
## val1:proc1:N_z           -0.01683033 0.00681094 -2.471074  0.0139
## val1:proc1:anx_composite  0.00639369 0.01259943  0.507459  0.6122
## 
##  Correlation: 
##                          (Intr) val1   proc1  N_z    anx_cm dp_cmp Age   
## val1                      0.003                                          
## proc1                     0.003 -0.004                                   
## N_z                      -0.251 -0.006 -0.002                            
## anx_composite             0.165  0.008 -0.008 -0.242                     
## dep_composite             0.082 -0.004  0.005 -0.567 -0.520              
## Age                      -0.975 -0.001 -0.003  0.173 -0.089 -0.070       
## SexAtBirth               -0.275  0.001  0.005  0.021 -0.320  0.131  0.110
## site                      0.225  0.006  0.015 -0.019  0.085 -0.084 -0.237
## val1:proc1                0.006  0.000 -0.048 -0.009  0.000  0.012 -0.005
## val1:N_z                 -0.003 -0.530  0.005  0.007 -0.005 -0.002  0.002
## proc1:N_z                -0.004  0.005 -0.523 -0.005  0.003  0.002  0.006
## val1:anx_composite        0.002  0.371 -0.016 -0.007  0.007  0.001 -0.001
## proc1:anx_composite       0.000 -0.016  0.369  0.003 -0.006  0.000 -0.002
## val1:proc1:N_z            0.000 -0.008  0.031  0.007 -0.006 -0.003  0.000
## val1:proc1:anx_composite  0.001  0.018 -0.021 -0.010  0.003  0.004 -0.001
##                          SxAtBr site   vl1:p1 vl1:N_ pr1:N_ vl1:n_ prc1:_
## val1                                                                     
## proc1                                                                    
## N_z                                                                      
## anx_composite                                                            
## dep_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.004  0.000                                   
## val1:N_z                 -0.002 -0.001 -0.008                            
## proc1:N_z                -0.007 -0.002  0.031 -0.019                     
## val1:anx_composite        0.002 -0.002  0.018 -0.763  0.026              
## proc1:anx_composite       0.004 -0.003 -0.020  0.025 -0.759 -0.019       
## val1:proc1:N_z           -0.003  0.001 -0.538  0.013 -0.020 -0.010  0.022
## val1:proc1:anx_composite  0.004  0.000  0.392 -0.010  0.022  0.018 -0.029
##                          v1:1:N
## val1                           
## proc1                          
## N_z                            
## anx_composite                  
## dep_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:N_z                       
## proc1:N_z                      
## val1:anx_composite             
## proc1:anx_composite            
## val1:proc1:N_z                 
## val1:proc1:anx_composite -0.770
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -3.3446493 -0.6662116  0.1048877  0.6469078  3.6596894 
## 
## Residual standard error: 0.221208 
## Degrees of freedom: 368 total; 352 residual

#does A model improve by adding N ineeractions
anova(spgacc_NA,spgacc_A)

##           Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## spgacc_NA     1 23 -202.2166 -112.3307 124.1083                        
## spgacc_A      2 20 -195.5440 -117.3823 117.7720 1 vs 2 12.67256  0.0054

Rins models:

# Test 1: subject cor struct
# Different var/cov by Site
nositemodel1rins <- gls(mag1_mask_Rins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub), method = "ML",na.action = "na.omit")
summary(nositemodel1rins)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_Rins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC logLik
##   -223.2799 -145.1183 131.64
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.714            
## 3 0.669 0.679      
## 4 0.673 0.773 0.711
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.08872067 0.18735847  0.4735343  0.6361
## val1           -0.00548021 0.00725855 -0.7550006  0.4507
## proc1           0.00749453 0.00728819  1.0283113  0.3045
## N_z            -0.00181708 0.02600113 -0.0698845  0.9443
## dep_composite   0.05443283 0.05432067  1.0020648  0.3170
## anx_composite  -0.06965838 0.04808194 -1.4487433  0.1483
## Age            -0.00363591 0.00791930 -0.4591200  0.6464
## SexAtBirth      0.11275228 0.05054662  2.2306591  0.0263
## site           -0.01123602 0.02369662 -0.4741611  0.6357
## val1:proc1      0.00099514 0.00712481  0.1396728  0.8890
## val1:N_z       -0.00521953 0.00407756 -1.2800617  0.2014
## proc1:N_z      -0.00191715 0.00417024 -0.4597216  0.6460
## val1:proc1:N_z -0.00885850 0.00404603 -2.1894284  0.0292
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.004                                                 
## N_z            -0.251 -0.002 -0.001                                          
## dep_composite   0.082 -0.002  0.002 -0.567                                   
## anx_composite   0.165  0.001 -0.005 -0.242 -0.520                            
## Age            -0.975  0.000 -0.002  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.005  0.021  0.131 -0.320  0.110              
## site            0.225 -0.002  0.010 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.001 -0.016 -0.021 -0.001  0.007 -0.003 -0.001 -0.003  0.004
## val1:N_z       -0.001 -0.418 -0.022 -0.001  0.002  0.001  0.001  0.000  0.001
## proc1:N_z      -0.004 -0.023 -0.401 -0.003  0.002  0.000  0.005 -0.006 -0.007
## val1:proc1:N_z  0.003  0.012  0.023 -0.003  0.001 -0.002 -0.003 -0.002  0.001
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.014              
## proc1:N_z       0.024  0.027       
## val1:proc1:N_z -0.397  0.021 -0.009
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.09711351 -0.60176263  0.02755367  0.63820147  3.32516188 
## 
## Residual standard error: 0.2334598 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2rins <- gls(mag1_mask_Rins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2rins)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_Rins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -227.3517 -168.7305 128.6759
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7080195 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.09039021 0.18906337  0.4780948  0.6329
## val1           -0.00646584 0.00736273 -0.8781853  0.3804
## proc1           0.01131339 0.00736273  1.5365755  0.1253
## N_z            -0.00066040 0.02623769 -0.0251699  0.9799
## dep_composite   0.05141995 0.05481379  0.9380842  0.3488
## anx_composite  -0.07022871 0.04851909 -1.4474450  0.1487
## Age            -0.00387090 0.00799138 -0.4843849  0.6284
## SexAtBirth      0.11784442 0.05100602  2.3104022  0.0214
## site           -0.00889103 0.02391101 -0.3718381  0.7102
## val1:proc1      0.00100412 0.00736273  0.1363786  0.8916
## val1:N_z       -0.00455709 0.00417487 -1.0915537  0.2758
## proc1:N_z      -0.00172887 0.00417487 -0.4141137  0.6790
## val1:proc1:N_z -0.00982720 0.00417487 -2.3538947  0.0191
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.07498614 -0.59787848  0.02452095  0.62227780  3.30100201 
## 
## Residual standard error: 0.2347246 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1rins,nositemodel2rins)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1rins     1 20 -223.2799 -145.1182 131.6399                        
## nositemodel2rins     2 15 -227.3517 -168.7305 128.6759 1 vs 2 5.928171  0.3133

# Different var/cov by Site
sitemodel1rins <- gls(mag1_mask_Rins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1rins)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_Rins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -226.4534 -163.9241 129.2267
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7128503 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.9217494 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.08457655 0.18767210  0.4506613  0.6525
## val1           -0.00596249 0.00733821 -0.8125269  0.4170
## proc1           0.01153898 0.00733821  1.5724528  0.1167
## N_z            -0.00236296 0.02646274 -0.0892939  0.9289
## dep_composite   0.05287454 0.05503801  0.9606913  0.3374
## anx_composite  -0.06980745 0.04846643 -1.4403257  0.1507
## Age            -0.00350972 0.00790720 -0.4438645  0.6574
## SexAtBirth      0.11603614 0.05080314  2.2840348  0.0230
## site           -0.00937313 0.02368922 -0.3956707  0.6926
## val1:proc1      0.00110293 0.00733821  0.1503002  0.8806
## val1:N_z       -0.00425847 0.00420225 -1.0133774  0.3116
## proc1:N_z      -0.00183459 0.00420225 -0.4365738  0.6627
## val1:proc1:N_z -0.00968366 0.00420225 -2.3043969  0.0218
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.272  0.000  0.000                                          
## dep_composite   0.096  0.000  0.000 -0.573                                   
## anx_composite   0.177  0.000  0.000 -0.239 -0.513                            
## Age            -0.974  0.000  0.000  0.193 -0.082 -0.103                     
## SexAtBirth     -0.281  0.000  0.000  0.032  0.118 -0.314  0.115              
## site            0.214  0.000  0.000 -0.020 -0.085  0.090 -0.236  0.154       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.410  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.410  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.410  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.21389730 -0.60206864  0.02466245  0.62445840  3.46155451 
## 
## Residual standard error: 0.2433445 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2rins <- gls(mag1_mask_Rins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2rins)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_Rins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -227.3517 -168.7305 128.6759
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7080195 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.09039021 0.18906337  0.4780948  0.6329
## val1           -0.00646584 0.00736273 -0.8781853  0.3804
## proc1           0.01131339 0.00736273  1.5365755  0.1253
## N_z            -0.00066040 0.02623769 -0.0251699  0.9799
## dep_composite   0.05141995 0.05481379  0.9380842  0.3488
## anx_composite  -0.07022871 0.04851909 -1.4474450  0.1487
## Age            -0.00387090 0.00799138 -0.4843849  0.6284
## SexAtBirth      0.11784442 0.05100602  2.3104022  0.0214
## site           -0.00889103 0.02391101 -0.3718381  0.7102
## val1:proc1      0.00100412 0.00736273  0.1363786  0.8916
## val1:N_z       -0.00455709 0.00417487 -1.0915537  0.2758
## proc1:N_z      -0.00172887 0.00417487 -0.4141137  0.6790
## val1:proc1:N_z -0.00982720 0.00417487 -2.3538947  0.0191
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.07498614 -0.59787848  0.02452095  0.62227780  3.30100201 
## 
## Residual standard error: 0.2347246 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1rins,sitemodel2rins)

##                Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## sitemodel1rins     1 16 -226.4534 -163.9241 129.2267                        
## sitemodel2rins     2 15 -227.3517 -168.7305 128.6759 1 vs 2 1.101663  0.2939

# rename winning model
rins_N <- gls(mag1_mask_Rins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(rins_N)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_Rins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -227.3517 -168.7305 128.6759
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7080195 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.09039021 0.18906337  0.4780948  0.6329
## val1           -0.00646584 0.00736273 -0.8781853  0.3804
## proc1           0.01131339 0.00736273  1.5365755  0.1253
## N_z            -0.00066040 0.02623769 -0.0251699  0.9799
## dep_composite   0.05141995 0.05481379  0.9380842  0.3488
## anx_composite  -0.07022871 0.04851909 -1.4474450  0.1487
## Age            -0.00387090 0.00799138 -0.4843849  0.6284
## SexAtBirth      0.11784442 0.05100602  2.3104022  0.0214
## site           -0.00889103 0.02391101 -0.3718381  0.7102
## val1:proc1      0.00100412 0.00736273  0.1363786  0.8916
## val1:N_z       -0.00455709 0.00417487 -1.0915537  0.2758
## proc1:N_z      -0.00172887 0.00417487 -0.4141137  0.6790
## val1:proc1:N_z -0.00982720 0.00417487 -2.3538947  0.0191
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.07498614 -0.59787848  0.02452095  0.62227780  3.30100201 
## 
## Residual standard error: 0.2347246 
## Degrees of freedom: 368 total; 355 residual

dlPFC models:

# Different var/cov by Site
nositemodel1dlpfc <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1dlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -455.1545 -376.9928 247.5772
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.640            
## 3 0.734 0.663      
## 4 0.646 0.720 0.731
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.10929219 0.13394260  0.8159629  0.4151
## val1           -0.00347342 0.00530535 -0.6547006  0.5131
## proc1           0.01027751 0.00536909  1.9142006  0.0564
## N_z            -0.02043576 0.01858824 -1.0993919  0.2723
## dep_composite   0.00516798 0.03883372  0.1330798  0.8942
## anx_composite   0.02120959 0.03437342  0.6170346  0.5376
## Age            -0.00069986 0.00566152 -0.1236169  0.9017
## SexAtBirth      0.05136743 0.03613540  1.4215266  0.1560
## site           -0.01613920 0.01694111 -0.9526648  0.3414
## val1:proc1      0.00713673 0.00521987  1.3672248  0.1724
## val1:N_z       -0.00179023 0.00297757 -0.6012378  0.5481
## proc1:N_z       0.00456625 0.00307651  1.4842311  0.1386
## val1:proc1:N_z -0.00320628 0.00295929 -1.0834629  0.2793
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.002                                                        
## proc1          -0.001  0.003                                                 
## N_z            -0.251 -0.001 -0.001                                          
## dep_composite   0.082 -0.003  0.003 -0.567                                   
## anx_composite   0.166  0.004  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.002  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000 -0.004  0.021  0.131 -0.320  0.110              
## site            0.225  0.009  0.002 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.005  0.004 -0.022 -0.004  0.001  0.003 -0.005 -0.003 -0.009
## val1:N_z       -0.002 -0.419  0.004  0.005 -0.005  0.000  0.001 -0.001 -0.005
## proc1:N_z       0.001  0.004 -0.401  0.001  0.000 -0.002 -0.001  0.003  0.002
## val1:proc1:N_z -0.003 -0.007  0.002  0.003 -0.001 -0.002  0.003  0.003  0.001
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z       -0.007              
## proc1:N_z       0.003 -0.005       
## val1:proc1:N_z -0.395 -0.003  0.003
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.874878248 -0.584112659 -0.003565874  0.664503714  2.722587139 
## 
## Residual standard error: 0.168034 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2dlpfc <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2dlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC  logLik
##   -457.9361 -399.3148 243.968
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6942579 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.11475005 0.13516944  0.8489349  0.3965
## val1           -0.00453453 0.00542250 -0.8362443  0.4036
## proc1           0.01132490 0.00542250  2.0885024  0.0375
## N_z            -0.01930145 0.01875844 -1.0289476  0.3042
## dep_composite   0.00139448 0.03918871  0.0355838  0.9716
## anx_composite   0.02347200 0.03468836  0.6766537  0.4991
## Age            -0.00088822 0.00571338 -0.1554626  0.8765
## SexAtBirth      0.04919528 0.03646637  1.3490589  0.1782
## site           -0.01616353 0.01709500 -0.9455122  0.3450
## val1:proc1      0.00630465 0.00542250  1.1626844  0.2457
## val1:N_z       -0.00072833 0.00307470 -0.2368775  0.8129
## proc1:N_z       0.00455360 0.00307470  1.4809889  0.1395
## val1:proc1:N_z -0.00312825 0.00307470 -1.0174163  0.3096
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.885714782 -0.585214020 -0.009491253  0.649897037  2.700670417 
## 
## Residual standard error: 0.1689346 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1dlpfc,nositemodel2dlpfc)

##                   Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1dlpfc     1 20 -455.1545 -376.9928 247.5772                        
## nositemodel2dlpfc     2 15 -457.9361 -399.3148 243.9680 1 vs 2 7.218381  0.2049

# Different var/cov by Site
sitemodel1dlpfc <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1dlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -462.1789 -399.6495 247.0894
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6892239 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 0.823023 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.12508305 0.12814651  0.9760941  0.3297
## val1           -0.00526664 0.00537395 -0.9800312  0.3277
## proc1           0.01218662 0.00537395  2.2677208  0.0239
## N_z            -0.02115514 0.01845960 -1.1460242  0.2526
## dep_composite   0.00900886 0.03814076  0.2362004  0.8134
## anx_composite   0.02315242 0.03334726  0.6942825  0.4880
## Age            -0.00128835 0.00537452 -0.2397139  0.8107
## SexAtBirth      0.04809073 0.03480490  1.3817229  0.1679
## site           -0.01629654 0.01626242 -1.0020984  0.3170
## val1:proc1      0.00728794 0.00537395  1.3561604  0.1759
## val1:N_z       -0.00062635 0.00312212 -0.2006165  0.8411
## proc1:N_z       0.00449957 0.00312212  1.4411922  0.1504
## val1:proc1:N_z -0.00306683 0.00312212 -0.9822929  0.3266
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.299  0.000  0.000                                          
## dep_composite   0.116  0.000  0.000 -0.582                                   
## anx_composite   0.193  0.000  0.000 -0.235 -0.502                            
## Age            -0.974  0.000  0.000  0.219 -0.098 -0.122                     
## SexAtBirth     -0.290  0.000  0.000  0.047  0.101 -0.307  0.122              
## site            0.196  0.000  0.000 -0.022 -0.085  0.095 -0.231  0.154       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.417  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.417  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.417  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.63383176 -0.58630122 -0.01241893  0.66136728  2.53805142 
## 
## Residual standard error: 0.17978 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2dlpfc <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2dlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC  logLik
##   -457.9361 -399.3148 243.968
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6942579 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.11475005 0.13516944  0.8489349  0.3965
## val1           -0.00453453 0.00542250 -0.8362443  0.4036
## proc1           0.01132490 0.00542250  2.0885024  0.0375
## N_z            -0.01930145 0.01875844 -1.0289476  0.3042
## dep_composite   0.00139448 0.03918871  0.0355838  0.9716
## anx_composite   0.02347200 0.03468836  0.6766537  0.4991
## Age            -0.00088822 0.00571338 -0.1554626  0.8765
## SexAtBirth      0.04919528 0.03646637  1.3490589  0.1782
## site           -0.01616353 0.01709500 -0.9455122  0.3450
## val1:proc1      0.00630465 0.00542250  1.1626844  0.2457
## val1:N_z       -0.00072833 0.00307470 -0.2368775  0.8129
## proc1:N_z       0.00455360 0.00307470  1.4809889  0.1395
## val1:proc1:N_z -0.00312825 0.00307470 -1.0174163  0.3096
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.885714782 -0.585214020 -0.009491253  0.649897037  2.700670417 
## 
## Residual standard error: 0.1689346 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1dlpfc,sitemodel2dlpfc)

##                 Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## sitemodel1dlpfc     1 16 -462.1789 -399.6495 247.0894                        
## sitemodel2dlpfc     2 15 -457.9361 -399.3148 243.9680 1 vs 2 6.242792  0.0125

#rename winning model
dlpfc_N <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(dlpfc_N)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -462.1789 -399.6495 247.0894
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6892239 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 0.823023 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)     0.12508305 0.12814651  0.9760941  0.3297
## val1           -0.00526664 0.00537395 -0.9800312  0.3277
## proc1           0.01218662 0.00537395  2.2677208  0.0239
## N_z            -0.02115514 0.01845960 -1.1460242  0.2526
## dep_composite   0.00900886 0.03814076  0.2362004  0.8134
## anx_composite   0.02315242 0.03334726  0.6942825  0.4880
## Age            -0.00128835 0.00537452 -0.2397139  0.8107
## SexAtBirth      0.04809073 0.03480490  1.3817229  0.1679
## site           -0.01629654 0.01626242 -1.0020984  0.3170
## val1:proc1      0.00728794 0.00537395  1.3561604  0.1759
## val1:N_z       -0.00062635 0.00312212 -0.2006165  0.8411
## proc1:N_z       0.00449957 0.00312212  1.4411922  0.1504
## val1:proc1:N_z -0.00306683 0.00312212 -0.9822929  0.3266
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.299  0.000  0.000                                          
## dep_composite   0.116  0.000  0.000 -0.582                                   
## anx_composite   0.193  0.000  0.000 -0.235 -0.502                            
## Age            -0.974  0.000  0.000  0.219 -0.098 -0.122                     
## SexAtBirth     -0.290  0.000  0.000  0.047  0.101 -0.307  0.122              
## site            0.196  0.000  0.000 -0.022 -0.085  0.095 -0.231  0.154       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.417  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.417  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.417  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.63383176 -0.58630122 -0.01241893  0.66136728  2.53805142 
## 
## Residual standard error: 0.17978 
## Degrees of freedom: 368 total; 355 residual

#rename winning model
dlpfc_D <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*dep_composite + N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(dlpfc_D)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * dep_composite + N_z + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC  logLik
##   -462.2379 -399.7086 247.119
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6892449 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.8221445 
## 
## Coefficients:
##                                Value  Std.Error    t-value p-value
## (Intercept)               0.12514593 0.12811006  0.9768626  0.3293
## val1                     -0.00542744 0.00490418 -1.1066964  0.2692
## proc1                     0.01468131 0.00490418  2.9936314  0.0029
## dep_composite             0.00905191 0.03813495  0.2373653  0.8125
## N_z                      -0.02116585 0.01845796 -1.1467055  0.2523
## anx_composite             0.02315226 0.03333995  0.6944299  0.4879
## Age                      -0.00129079 0.00537276 -0.2402478  0.8103
## SexAtBirth                0.04808406 0.03479583  1.3818917  0.1679
## site                     -0.01629713 0.01625934 -1.0023246  0.3169
## val1:proc1                0.00524851 0.00490418  1.0702120  0.2853
## val1:dep_composite       -0.00366212 0.00574167 -0.6378144  0.5240
## proc1:dep_composite       0.00928253 0.00574167  1.6166947  0.1068
## val1:proc1:dep_composite -0.00197526 0.00574167 -0.3440214  0.7310
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.000                                          
## proc1                     0.000  0.000                                   
## dep_composite             0.116  0.000  0.000                            
## N_z                      -0.300  0.000  0.000 -0.582                     
## anx_composite             0.193  0.000  0.000 -0.502 -0.235              
## Age                      -0.974  0.000  0.000 -0.099  0.219 -0.122       
## SexAtBirth               -0.290  0.000  0.000  0.100  0.048 -0.307  0.123
## site                      0.196  0.000  0.000 -0.085 -0.022  0.095 -0.231
## val1:proc1                0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:dep_composite        0.000 -0.094  0.000  0.000  0.000  0.000  0.000
## proc1:dep_composite       0.000  0.000 -0.094  0.000  0.000  0.000  0.000
## val1:proc1:dep_composite  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_
## val1                                                       
## proc1                                                      
## dep_composite                                              
## N_z                                                        
## anx_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.154                            
## val1:proc1                0.000  0.000                     
## val1:dep_composite        0.000  0.000  0.000              
## proc1:dep_composite       0.000  0.000  0.000  0.000       
## val1:proc1:dep_composite  0.000  0.000 -0.094  0.000  0.000
## 
## Standardized residuals:
##           Min            Q1           Med            Q3           Max 
## -3.6587448600 -0.5906296363  0.0007177856  0.6713808813  2.5622586556 
## 
## Residual standard error: 0.1798448 
## Degrees of freedom: 368 total; 355 residual

dlpfc_DN <- gls(mag1_mask_bilatdlpfc_b ~ val*proc*dep_composite + val*proc*N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(dlpfc_DN)

## Generalized least squares fit by maximum likelihood
##   Model: mag1_mask_bilatdlpfc_b ~ val * proc * dep_composite + val * proc *      N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -458.2628 -384.0092 248.1314
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6907341 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.8142329 
## 
## Coefficients:
##                                Value  Std.Error    t-value p-value
## (Intercept)               0.12571806 0.12832468  0.9796874  0.3279
## val1                     -0.00743144 0.00592625 -1.2539875  0.2107
## proc1                     0.01412300 0.00592625  2.3831253  0.0177
## dep_composite             0.00944209 0.03824436  0.2468885  0.8051
## N_z                      -0.02126296 0.01852162 -1.1480077  0.2517
## anx_composite             0.02315170 0.03341542  0.6928448  0.4889
## Age                      -0.00131306 0.00537963 -0.2440790  0.8073
## SexAtBirth                0.04802342 0.03486135  1.3775548  0.1692
## site                     -0.01630241 0.01630123 -1.0000727  0.3180
## val1:proc1                0.00947413 0.00592625  1.5986718  0.1108
## val1:dep_composite       -0.00872344 0.01026700 -0.8496589  0.3961
## proc1:dep_composite       0.00775563 0.01026700  0.7553943  0.4505
## val1:N_z                  0.00331107 0.00558402  0.5929547  0.5536
## proc1:N_z                 0.00100115 0.00558402  0.1792892  0.8578
## val1:proc1:dep_composite  0.00875524 0.01026700  0.8527555  0.3944
## val1:proc1:N_z           -0.00700884 0.00558402 -1.2551598  0.2103
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.000                                          
## proc1                     0.000  0.000                                   
## dep_composite             0.118  0.000  0.000                            
## N_z                      -0.302  0.000  0.000 -0.582                     
## anx_composite             0.194  0.000  0.000 -0.501 -0.234              
## Age                      -0.974  0.000  0.000 -0.100  0.221 -0.123       
## SexAtBirth               -0.291  0.000  0.000  0.099  0.049 -0.306  0.123
## site                      0.194  0.000  0.000 -0.084 -0.022  0.096 -0.231
## val1:proc1                0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:dep_composite        0.000  0.422  0.000  0.000  0.000  0.000  0.000
## proc1:dep_composite       0.000  0.000  0.422  0.000  0.000  0.000  0.000
## val1:N_z                  0.000 -0.562  0.000  0.000  0.000  0.000  0.000
## proc1:N_z                 0.000  0.000 -0.562  0.000  0.000  0.000  0.000
## val1:proc1:dep_composite  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z            0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_ vl1:N_ pr1:N_
## val1                                                                     
## proc1                                                                    
## dep_composite                                                            
## N_z                                                                      
## anx_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1                0.000  0.000                                   
## val1:dep_composite        0.000  0.000  0.000                            
## proc1:dep_composite       0.000  0.000  0.000  0.000                     
## val1:N_z                  0.000  0.000  0.000 -0.829  0.000              
## proc1:N_z                 0.000  0.000  0.000  0.000 -0.829  0.000       
## val1:proc1:dep_composite  0.000  0.000  0.422  0.000  0.000  0.000  0.000
## val1:proc1:N_z            0.000  0.000 -0.562  0.000  0.000  0.000  0.000
##                          v1:1:_
## val1                           
## proc1                          
## dep_composite                  
## N_z                            
## anx_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:dep_composite             
## proc1:dep_composite            
## val1:N_z                       
## proc1:N_z                      
## val1:proc1:dep_composite       
## val1:proc1:N_z           -0.829
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.623345783 -0.589331843 -0.001839483  0.657642700  2.564367577 
## 
## Residual standard error: 0.1803224 
## Degrees of freedom: 368 total; 352 residual

vlPFC models:

# Different var/cov by Site
nositemodel1vlpfc <- gls(mag2_mask_bilatvlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1vlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag2_mask_bilatvlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -273.5209 -195.3593 156.7605
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.735            
## 3 0.719 0.764      
## 4 0.670 0.788 0.732
## 
## Coefficients:
##                     Value  Std.Error    t-value p-value
## (Intercept)     0.4874019 0.18458387  2.6405445  0.0086
## val1            0.0078976 0.00661909  1.1931570  0.2336
## proc1           0.0092588 0.00668549  1.3849049  0.1670
## N_z            -0.0070949 0.02561730 -0.2769579  0.7820
## dep_composite   0.0609469 0.05351546  1.1388659  0.2555
## anx_composite  -0.0529896 0.04737057 -1.1186182  0.2641
## Age            -0.0154395 0.00780196 -1.9789310  0.0486
## SexAtBirth      0.0223344 0.04979763  0.4485035  0.6541
## site           -0.0699717 0.02334849 -2.9968391  0.0029
## val1:proc1     -0.0039555 0.00658105 -0.6010437  0.5482
## val1:N_z       -0.0052460 0.00373837 -1.4032947  0.1614
## proc1:N_z      -0.0021111 0.00381437 -0.5534594  0.5803
## val1:proc1:N_z -0.0074833 0.00373079 -2.0058255  0.0456
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1           -0.002                                                        
## proc1          -0.003  0.016                                                 
## N_z            -0.251 -0.004  0.007                                          
## dep_composite   0.082  0.000  0.002 -0.567                                   
## anx_composite   0.166 -0.003 -0.008 -0.242 -0.520                            
## Age            -0.975  0.001  0.003  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.001  0.001  0.021  0.131 -0.320  0.110              
## site            0.225 -0.013  0.006 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.001  0.000 -0.011  0.000  0.005  0.000 -0.001 -0.007 -0.012
## val1:N_z        0.000 -0.412 -0.023  0.001 -0.004  0.005 -0.001 -0.001  0.005
## proc1:N_z       0.000 -0.024 -0.404 -0.008  0.005 -0.001  0.001 -0.001 -0.009
## val1:proc1:N_z  0.006 -0.009  0.037 -0.007  0.001  0.000 -0.005 -0.001  0.008
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z       -0.008              
## proc1:N_z       0.038  0.041       
## val1:proc1:N_z -0.399  0.028 -0.010
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.65532773 -0.57802302 -0.03731492  0.60087397  2.75541900 
## 
## Residual standard error: 0.2267857 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2vlpfc <- gls(mag2_mask_bilatvlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2vlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag2_mask_bilatvlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -276.9589 -218.3376 153.4794
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7313777 
## 
## Coefficients:
##                     Value  Std.Error    t-value p-value
## (Intercept)     0.4776923 0.18387710  2.5978890  0.0098
## val1            0.0065930 0.00679260  0.9706156  0.3324
## proc1           0.0111269 0.00679260  1.6380874  0.1023
## N_z            -0.0063749 0.02551796 -0.2498198  0.8029
## dep_composite   0.0566780 0.05331017  1.0631742  0.2884
## anx_composite  -0.0482875 0.04718814 -1.0232971  0.3069
## Age            -0.0150337 0.00777216 -1.9343065  0.0539
## SexAtBirth      0.0232562 0.04960685  0.4688100  0.6395
## site           -0.0672972 0.02325510 -2.8938686  0.0040
## val1:proc1     -0.0037427 0.00679260 -0.5510023  0.5820
## val1:N_z       -0.0049242 0.00385159 -1.2784878  0.2019
## proc1:N_z      -0.0021841 0.00385159 -0.5670737  0.5710
## val1:proc1:N_z -0.0068901 0.00385159 -1.7889005  0.0745
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.67448205 -0.57553465 -0.02964829  0.61808301  2.78797859 
## 
## Residual standard error: 0.2257678 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1vlpfc,nositemodel2vlpfc)

##                   Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1vlpfc     1 20 -273.5209 -195.3593 156.7605                        
## nositemodel2vlpfc     2 15 -276.9589 -218.3376 153.4794 1 vs 2 6.562035  0.2553

# Different var/cov by Site
sitemodel1vlpfc <- gls(mag2_mask_bilatvlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1vlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag2_mask_bilatvlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -283.1387 -220.6094 157.5694
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7389704 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.7986848 
## 
## Coefficients:
##                     Value  Std.Error    t-value p-value
## (Intercept)     0.4976954 0.17641733  2.8211254  0.0051
## val1            0.0082070 0.00665256  1.2336632  0.2181
## proc1           0.0121522 0.00665256  1.8266948  0.0686
## N_z            -0.0127879 0.02555225 -0.5004624  0.6171
## dep_composite   0.0639787 0.05270050  1.2140053  0.2256
## anx_composite  -0.0437539 0.04599106 -0.9513556  0.3421
## Age            -0.0155357 0.00738994 -2.1022764  0.0362
## SexAtBirth      0.0153585 0.04794464  0.3203372  0.7489
## site           -0.0677691 0.02245529 -3.0179562  0.0027
## val1:proc1     -0.0030192 0.00665256 -0.4538445  0.6502
## val1:N_z       -0.0049127 0.00388014 -1.2661068  0.2063
## proc1:N_z      -0.0016350 0.00388014 -0.4213706  0.6737
## val1:proc1:N_z -0.0063607 0.00388014 -1.6392986  0.1020
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.307  0.000  0.000                                          
## dep_composite   0.121  0.000  0.000 -0.584                                   
## anx_composite   0.197  0.000  0.000 -0.233 -0.499                            
## Age            -0.974  0.000  0.000  0.225 -0.103 -0.126                     
## SexAtBirth     -0.292  0.000  0.000  0.052  0.096 -0.305  0.124              
## site            0.191  0.000  0.000 -0.022 -0.084  0.097 -0.230  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.419  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.419  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.419  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.39368280 -0.59360236 -0.04407989  0.61852101  2.99007379 
## 
## Residual standard error: 0.2462182 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2vlpfc <- gls(mag2_mask_bilatvlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2vlpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag2_mask_bilatvlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -276.9589 -218.3376 153.4794
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7313777 
## 
## Coefficients:
##                     Value  Std.Error    t-value p-value
## (Intercept)     0.4776923 0.18387710  2.5978890  0.0098
## val1            0.0065930 0.00679260  0.9706156  0.3324
## proc1           0.0111269 0.00679260  1.6380874  0.1023
## N_z            -0.0063749 0.02551796 -0.2498198  0.8029
## dep_composite   0.0566780 0.05331017  1.0631742  0.2884
## anx_composite  -0.0482875 0.04718814 -1.0232971  0.3069
## Age            -0.0150337 0.00777216 -1.9343065  0.0539
## SexAtBirth      0.0232562 0.04960685  0.4688100  0.6395
## site           -0.0672972 0.02325510 -2.8938686  0.0040
## val1:proc1     -0.0037427 0.00679260 -0.5510023  0.5820
## val1:N_z       -0.0049242 0.00385159 -1.2784878  0.2019
## proc1:N_z      -0.0021841 0.00385159 -0.5670737  0.5710
## val1:proc1:N_z -0.0068901 0.00385159 -1.7889005  0.0745
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.67448205 -0.57553465 -0.02964829  0.61808301  2.78797859 
## 
## Residual standard error: 0.2257678 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1vlpfc,sitemodel2vlpfc)

##                 Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## sitemodel1vlpfc     1 16 -283.1387 -220.6094 157.5693                        
## sitemodel2vlpfc     2 15 -276.9589 -218.3376 153.4794 1 vs 2 8.179827  0.0042

#rename winning model
vlpfc_N <- gls(mag2_mask_bilatvlpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(vlpfc_N)

## Generalized least squares fit by maximum likelihood
##   Model: mag2_mask_bilatvlpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -283.1387 -220.6094 157.5694
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7389704 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.7986848 
## 
## Coefficients:
##                     Value  Std.Error    t-value p-value
## (Intercept)     0.4976954 0.17641733  2.8211254  0.0051
## val1            0.0082070 0.00665256  1.2336632  0.2181
## proc1           0.0121522 0.00665256  1.8266948  0.0686
## N_z            -0.0127879 0.02555225 -0.5004624  0.6171
## dep_composite   0.0639787 0.05270050  1.2140053  0.2256
## anx_composite  -0.0437539 0.04599106 -0.9513556  0.3421
## Age            -0.0155357 0.00738994 -2.1022764  0.0362
## SexAtBirth      0.0153585 0.04794464  0.3203372  0.7489
## site           -0.0677691 0.02245529 -3.0179562  0.0027
## val1:proc1     -0.0030192 0.00665256 -0.4538445  0.6502
## val1:N_z       -0.0049127 0.00388014 -1.2661068  0.2063
## proc1:N_z      -0.0016350 0.00388014 -0.4213706  0.6737
## val1:proc1:N_z -0.0063607 0.00388014 -1.6392986  0.1020
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.307  0.000  0.000                                          
## dep_composite   0.121  0.000  0.000 -0.584                                   
## anx_composite   0.197  0.000  0.000 -0.233 -0.499                            
## Age            -0.974  0.000  0.000  0.225 -0.103 -0.126                     
## SexAtBirth     -0.292  0.000  0.000  0.052  0.096 -0.305  0.124              
## site            0.191  0.000  0.000 -0.022 -0.084  0.097 -0.230  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.419  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.419  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.419  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.39368280 -0.59360236 -0.04407989  0.61852101  2.99007379 
## 
## Residual standard error: 0.2462182 
## Degrees of freedom: 368 total; 355 residual

amyg models:

# Different var/cov by Site
nositemodel1amyg <- gls(mag3_mask_bilatamyg_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1amyg)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_bilatamyg_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC       BIC   logLik
##   -454.191 -376.0293 247.0955
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.767            
## 3 0.764 0.754      
## 4 0.729 0.711 0.749
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.03897720 0.14658970 -0.2658932  0.7905
## val1           -0.00987989 0.00523760 -1.8863396  0.0601
## proc1          -0.00901620 0.00518549 -1.7387380  0.0829
## N_z             0.04626689 0.02034328  2.2743081  0.0235
## dep_composite  -0.05596947 0.04250009 -1.3169257  0.1887
## anx_composite  -0.00770881 0.03761920 -0.2049170  0.8378
## Age            -0.00326918 0.00619609 -0.5276205  0.5981
## SexAtBirth     -0.04160059 0.03954746 -1.0519157  0.2936
## site           -0.01046014 0.01854007 -0.5641909  0.5730
## val1:proc1     -0.00602284 0.00518249 -1.1621516  0.2460
## val1:N_z       -0.00389243 0.00296027 -1.3148890  0.1894
## proc1:N_z       0.00033614 0.00294275  0.1142256  0.9091
## val1:proc1:N_z -0.00537910 0.00294488 -1.8265963  0.0686
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1           -0.001                                                        
## proc1          -0.003  0.003                                                 
## N_z            -0.251  0.001  0.003                                          
## dep_composite   0.082  0.002 -0.002 -0.567                                   
## anx_composite   0.165 -0.002  0.002 -0.242 -0.520                            
## Age            -0.975  0.000  0.003  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000 -0.003  0.021  0.131 -0.320  0.110              
## site            0.225 -0.003 -0.007 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1     -0.001  0.012  0.016  0.002 -0.005  0.002  0.001  0.001 -0.006
## val1:N_z        0.001 -0.409  0.008  0.001 -0.002  0.000 -0.001  0.000  0.001
## proc1:N_z       0.003  0.008 -0.401  0.000  0.000  0.000 -0.004  0.004  0.003
## val1:proc1:N_z -0.001 -0.012 -0.005  0.000 -0.001  0.002  0.000  0.001  0.002
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z       -0.012              
## proc1:N_z      -0.005 -0.005       
## val1:proc1:N_z -0.405 -0.006  0.003
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.68551018 -0.73655517  0.06829994  0.74065574  3.15403165 
## 
## Residual standard error: 0.178883 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2amyg <- gls(mag3_mask_bilatamyg_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2amyg)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_bilatamyg_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -462.3293 -403.7081 246.1647
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7479641 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.03392126 0.14724575 -0.2303718  0.8179
## val1           -0.00938418 0.00522823 -1.7949050  0.0735
## proc1          -0.00873229 0.00522823 -1.6702180  0.0958
## N_z             0.04455260 0.02043436  2.1802788  0.0299
## dep_composite  -0.05428521 0.04268991 -1.2716171  0.2043
## anx_composite  -0.00703722 0.03778749 -0.1862316  0.8524
## Age            -0.00345910 0.00622382 -0.5557845  0.5787
## SexAtBirth     -0.04039507 0.03972435 -1.0168845  0.3099
## site           -0.01059010 0.01862230 -0.5686785  0.5699
## val1:proc1     -0.00572695 0.00522823 -1.0953888  0.2741
## val1:N_z       -0.00400945 0.00296455 -1.3524641  0.1771
## proc1:N_z       0.00040748 0.00296455  0.1374508  0.8908
## val1:proc1:N_z -0.00479760 0.00296455 -1.6183244  0.1065
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.69031793 -0.74263900  0.05125709  0.73490157  3.13828338 
## 
## Residual standard error: 0.1793991 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1amyg,nositemodel2amyg)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1amyg     1 20 -454.1910 -376.0293 247.0955                        
## nositemodel2amyg     2 15 -462.3293 -403.7081 246.1646 1 vs 2 1.861704  0.8679

# Different var/cov by Site
sitemodel1amyg <- gls(mag3_mask_bilatamyg_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1amyg)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_bilatamyg_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -460.5069 -397.9776 246.2534
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7465092 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 1.033046 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.03923000 0.14753961 -0.2658947  0.7905
## val1           -0.00930146 0.00522551 -1.7800080  0.0759
## proc1          -0.00884531 0.00522551 -1.6927158  0.0914
## N_z             0.04447763 0.02034370  2.1863096  0.0294
## dep_composite  -0.05433687 0.04257270 -1.2763313  0.2027
## anx_composite  -0.00701158 0.03776108 -0.1856827  0.8528
## Age            -0.00328215 0.00624402 -0.5256468  0.5995
## SexAtBirth     -0.03813264 0.03974039 -0.9595436  0.3379
## site           -0.01049078 0.01869902 -0.5610335  0.5751
## val1:proc1     -0.00589625 0.00522551 -1.1283570  0.2599
## val1:N_z       -0.00401828 0.00295178 -1.3613079  0.1743
## proc1:N_z       0.00037300 0.00295178  0.1263631  0.8995
## val1:proc1:N_z -0.00473862 0.00295178 -1.6053443  0.1093
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.242  0.000  0.000                                          
## dep_composite   0.076  0.000  0.000 -0.565                                   
## anx_composite   0.161  0.000  0.000 -0.244 -0.522                            
## Age            -0.975  0.000  0.000  0.166 -0.065 -0.084                     
## SexAtBirth     -0.273  0.000  0.000  0.017  0.136 -0.322  0.108              
## site            0.229  0.000  0.000 -0.018 -0.084  0.083 -0.237  0.152       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.403  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.403  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.403  0.000  0.000
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -2.6450345 -0.7445877  0.0464514  0.7371171  3.0760830 
## 
## Residual standard error: 0.1767365 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2amyg <- gls(mag3_mask_bilatamyg_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2amyg)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_bilatamyg_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -462.3293 -403.7081 246.1647
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.7479641 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.03392126 0.14724575 -0.2303718  0.8179
## val1           -0.00938418 0.00522823 -1.7949050  0.0735
## proc1          -0.00873229 0.00522823 -1.6702180  0.0958
## N_z             0.04455260 0.02043436  2.1802788  0.0299
## dep_composite  -0.05428521 0.04268991 -1.2716171  0.2043
## anx_composite  -0.00703722 0.03778749 -0.1862316  0.8524
## Age            -0.00345910 0.00622382 -0.5557845  0.5787
## SexAtBirth     -0.04039507 0.03972435 -1.0168845  0.3099
## site           -0.01059010 0.01862230 -0.5686785  0.5699
## val1:proc1     -0.00572695 0.00522823 -1.0953888  0.2741
## val1:N_z       -0.00400945 0.00296455 -1.3524641  0.1771
## proc1:N_z       0.00040748 0.00296455  0.1374508  0.8908
## val1:proc1:N_z -0.00479760 0.00296455 -1.6183244  0.1065
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.69031793 -0.74263900  0.05125709  0.73490157  3.13828338 
## 
## Residual standard error: 0.1793991 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1amyg,sitemodel2amyg)

##                Model df       AIC       BIC   logLik   Test   L.Ratio p-value
## sitemodel1amyg     1 16 -460.5069 -397.9776 246.2534                         
## sitemodel2amyg     2 15 -462.3293 -403.7081 246.1646 1 vs 2 0.1775891  0.6735

#rename winning model:
amyg_N <- gls(mag3_mask_bilatamyg_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
anova(amyg_N)

## Denom. DF: 355 
##               numDF  F-value p-value
## (Intercept)       1 38.75008  <.0001
## val               1  6.56575  0.0108
## proc              1  3.11841  0.0783
## N_z               1  2.08230  0.1499
## dep_composite     1  3.14889  0.0768
## anx_composite     1  0.24781  0.6189
## Age               1  0.33014  0.5659
## SexAtBirth        1  0.88559  0.3473
## site              1  0.32340  0.5699
## val:proc          1  3.66747  0.0563
## val:N_z           1  1.82916  0.1771
## proc:N_z          1  0.01889  0.8908
## val:proc:N_z      1  2.61897  0.1065

mpfc models:

# Different var/cov by Site
nositemodel1mpfc <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub), method = "ML",na.action = "na.omit")
summary(nositemodel1mpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   178.7755 256.9372 -69.38776
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.682            
## 3 0.576 0.742      
## 4 0.627 0.792 0.757
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.11509149 0.3231043  0.356205  0.7219
## val1           -0.03913614 0.0123720 -3.163281  0.0017
## proc1           0.00309380 0.0122856  0.251824  0.8013
## N_z             0.03148977 0.0448418  0.702241  0.4830
## dep_composite   0.00304918 0.0936883  0.032546  0.9741
## anx_composite   0.02054551 0.0829234  0.247765  0.8045
## Age            -0.02190188 0.0136568 -1.603739  0.1097
## SexAtBirth     -0.18324830 0.0871724 -2.102137  0.0362
## site           -0.03486447 0.0408744 -0.852967  0.3943
## val1:proc1      0.03351636 0.0121871  2.750158  0.0063
## val1:N_z        0.00261383 0.0069858  0.374162  0.7085
## proc1:N_z      -0.00554389 0.0069962 -0.792415  0.4286
## val1:proc1:N_z -0.02210865 0.0069328 -3.188982  0.0016
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.019                                                 
## N_z            -0.251 -0.006  0.002                                          
## dep_composite   0.082 -0.004  0.006 -0.567                                   
## anx_composite   0.165  0.001 -0.014 -0.242 -0.520                            
## Age            -0.975  0.001 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.001  0.009  0.021  0.131 -0.320  0.110              
## site            0.225 -0.008  0.024 -0.019 -0.084  0.084 -0.237  0.153       
## val1:proc1      0.004 -0.018 -0.048 -0.004  0.016 -0.004 -0.003 -0.011 -0.003
## val1:N_z       -0.003 -0.414 -0.038  0.000 -0.001  0.005  0.001 -0.002  0.003
## proc1:N_z      -0.007 -0.041 -0.402 -0.011  0.007 -0.002  0.009 -0.011 -0.018
## val1:proc1:N_z  0.009  0.004  0.055 -0.009  0.003 -0.005 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.007              
## proc1:N_z       0.057  0.057       
## val1:proc1:N_z -0.403  0.042 -0.014
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.87259611 -0.56629998  0.01541648  0.65999634  3.38633117 
## 
## Residual standard error: 0.4066082 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2mpfc <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2mpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   187.8397 246.4609 -78.91984
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6974995 
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.12458410 0.3267254  0.381311  0.7032
## val1           -0.04141097 0.0130168 -3.181340  0.0016
## proc1           0.00155973 0.0130168  0.119824  0.9047
## N_z             0.02523743 0.0453420  0.556601  0.5782
## dep_composite   0.00319005 0.0947251  0.033677  0.9732
## anx_composite   0.03019730 0.0838471  0.360147  0.7190
## Age            -0.02221410 0.0138101 -1.608539  0.1086
## SexAtBirth     -0.18367901 0.0881448 -2.083832  0.0379
## site           -0.02991036 0.0413213 -0.723849  0.4696
## val1:proc1      0.03041047 0.0130168  2.336242  0.0200
## val1:N_z        0.00488951 0.0073809  0.662454  0.5081
## proc1:N_z      -0.00563648 0.0073809 -0.763658  0.4456
## val1:proc1:N_z -0.01968956 0.0073809 -2.667639  0.0080
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.84718479 -0.55756746  0.02434608  0.65312712  3.37644291 
## 
## Residual standard error: 0.4076983 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1mpfc,nositemodel2mpfc)

##                  Model df      AIC      BIC    logLik   Test  L.Ratio p-value
## nositemodel1mpfc     1 20 178.7755 256.9372 -69.38776                        
## nositemodel2mpfc     2 15 187.8397 246.4609 -78.91984 1 vs 2 19.06416  0.0019

# Different var/cov by Site
sitemodel1mpfc <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1mpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   180.7146 262.7843 -69.35729
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.680            
## 3 0.577 0.742      
## 4 0.625 0.791 0.758
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 1.019812 
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.11327811 0.3239023  0.349729  0.7267
## val1           -0.03929878 0.0123619 -3.179022  0.0016
## proc1           0.00305873 0.0122821  0.249041  0.8035
## N_z             0.03092127 0.0447784  0.690540  0.4903
## dep_composite   0.00248982 0.0936521  0.026586  0.9788
## anx_composite   0.02090932 0.0829941  0.251937  0.8012
## Age            -0.02182187 0.0137008 -1.592740  0.1121
## SexAtBirth     -0.18252462 0.0873048 -2.090659  0.0373
## site           -0.03479281 0.0410235 -0.848120  0.3969
## val1:proc1      0.03337712 0.0121789  2.740562  0.0064
## val1:N_z        0.00260327 0.0069640  0.373817  0.7088
## proc1:N_z      -0.00549069 0.0069789 -0.786760  0.4319
## val1:proc1:N_z -0.02194840 0.0069119 -3.175458  0.0016
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.018                                                 
## N_z            -0.246 -0.007  0.002                                          
## dep_composite   0.078 -0.004  0.006 -0.566                                   
## anx_composite   0.162  0.001 -0.014 -0.243 -0.522                            
## Age            -0.975  0.001 -0.003  0.169 -0.067 -0.086                     
## SexAtBirth     -0.274  0.001  0.009  0.019  0.134 -0.321  0.108              
## site            0.227 -0.008  0.023 -0.018 -0.084  0.083 -0.237  0.153       
## val1:proc1      0.005 -0.016 -0.048 -0.004  0.016 -0.004 -0.003 -0.011 -0.003
## val1:N_z       -0.003 -0.413 -0.038  0.000 -0.001  0.004  0.001 -0.002  0.002
## proc1:N_z      -0.007 -0.041 -0.401 -0.011  0.007 -0.002  0.009 -0.011 -0.017
## val1:proc1:N_z  0.008  0.003  0.054 -0.009  0.002 -0.005 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.006              
## proc1:N_z       0.056  0.057       
## val1:proc1:N_z -0.402  0.044 -0.014
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.89498953 -0.57519758  0.01303851  0.65455912  3.34977425 
## 
## Residual standard error: 0.4033167 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2mpfc <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2mpfc)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   178.7755 256.9372 -69.38776
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.682            
## 3 0.576 0.742      
## 4 0.627 0.792 0.757
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.11509149 0.3231043  0.356205  0.7219
## val1           -0.03913614 0.0123720 -3.163281  0.0017
## proc1           0.00309380 0.0122856  0.251824  0.8013
## N_z             0.03148977 0.0448418  0.702241  0.4830
## dep_composite   0.00304918 0.0936883  0.032546  0.9741
## anx_composite   0.02054551 0.0829234  0.247765  0.8045
## Age            -0.02190188 0.0136568 -1.603739  0.1097
## SexAtBirth     -0.18324830 0.0871724 -2.102137  0.0362
## site           -0.03486447 0.0408744 -0.852967  0.3943
## val1:proc1      0.03351636 0.0121871  2.750158  0.0063
## val1:N_z        0.00261383 0.0069858  0.374162  0.7085
## proc1:N_z      -0.00554389 0.0069962 -0.792415  0.4286
## val1:proc1:N_z -0.02210865 0.0069328 -3.188982  0.0016
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.019                                                 
## N_z            -0.251 -0.006  0.002                                          
## dep_composite   0.082 -0.004  0.006 -0.567                                   
## anx_composite   0.165  0.001 -0.014 -0.242 -0.520                            
## Age            -0.975  0.001 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.001  0.009  0.021  0.131 -0.320  0.110              
## site            0.225 -0.008  0.024 -0.019 -0.084  0.084 -0.237  0.153       
## val1:proc1      0.004 -0.018 -0.048 -0.004  0.016 -0.004 -0.003 -0.011 -0.003
## val1:N_z       -0.003 -0.414 -0.038  0.000 -0.001  0.005  0.001 -0.002  0.003
## proc1:N_z      -0.007 -0.041 -0.402 -0.011  0.007 -0.002  0.009 -0.011 -0.018
## val1:proc1:N_z  0.009  0.004  0.055 -0.009  0.003 -0.005 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.007              
## proc1:N_z       0.057  0.057       
## val1:proc1:N_z -0.403  0.042 -0.014
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.87259611 -0.56629998  0.01541648  0.65999634  3.38633117 
## 
## Residual standard error: 0.4066082 
## Degrees of freedom: 368 total; 355 residual

anova(sitemodel2mpfc)

## Denom. DF: 355 
##               numDF   F-value p-value
## (Intercept)       1 147.51065  <.0001
## val               1  10.33914  0.0014
## proc              1   0.02293  0.8797
## N_z               1   1.96908  0.1614
## dep_composite     1   0.03393  0.8540
## anx_composite     1   0.17702  0.6742
## Age               1   2.48747  0.1156
## SexAtBirth        1   3.97559  0.0469
## site              1   0.71880  0.3971
## val:proc          1   2.68020  0.1025
## val:N_z           1   0.31245  0.5765
## proc:N_z          1   0.70329  0.4022
## val:proc:N_z      1  10.16960  0.0016

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1mpfc,sitemodel2mpfc)

##                Model df      AIC      BIC    logLik   Test    L.Ratio p-value
## sitemodel1mpfc     1 21 180.7146 262.7843 -69.35729                          
## sitemodel2mpfc     2 20 178.7755 256.9372 -69.38776 1 vs 2 0.06093169   0.805

#rename winning model:
mpfc_N <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub), method = "ML",na.action = "na.omit")
summary(mpfc_N)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   178.7755 256.9372 -69.38776
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.682            
## 3 0.576 0.742      
## 4 0.627 0.792 0.757
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.11509149 0.3231043  0.356205  0.7219
## val1           -0.03913614 0.0123720 -3.163281  0.0017
## proc1           0.00309380 0.0122856  0.251824  0.8013
## N_z             0.03148977 0.0448418  0.702241  0.4830
## dep_composite   0.00304918 0.0936883  0.032546  0.9741
## anx_composite   0.02054551 0.0829234  0.247765  0.8045
## Age            -0.02190188 0.0136568 -1.603739  0.1097
## SexAtBirth     -0.18324830 0.0871724 -2.102137  0.0362
## site           -0.03486447 0.0408744 -0.852967  0.3943
## val1:proc1      0.03351636 0.0121871  2.750158  0.0063
## val1:N_z        0.00261383 0.0069858  0.374162  0.7085
## proc1:N_z      -0.00554389 0.0069962 -0.792415  0.4286
## val1:proc1:N_z -0.02210865 0.0069328 -3.188982  0.0016
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.019                                                 
## N_z            -0.251 -0.006  0.002                                          
## dep_composite   0.082 -0.004  0.006 -0.567                                   
## anx_composite   0.165  0.001 -0.014 -0.242 -0.520                            
## Age            -0.975  0.001 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.001  0.009  0.021  0.131 -0.320  0.110              
## site            0.225 -0.008  0.024 -0.019 -0.084  0.084 -0.237  0.153       
## val1:proc1      0.004 -0.018 -0.048 -0.004  0.016 -0.004 -0.003 -0.011 -0.003
## val1:N_z       -0.003 -0.414 -0.038  0.000 -0.001  0.005  0.001 -0.002  0.003
## proc1:N_z      -0.007 -0.041 -0.402 -0.011  0.007 -0.002  0.009 -0.011 -0.018
## val1:proc1:N_z  0.009  0.004  0.055 -0.009  0.003 -0.005 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.007              
## proc1:N_z       0.057  0.057       
## val1:proc1:N_z -0.403  0.042 -0.014
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.87259611 -0.56629998  0.01541648  0.65999634  3.38633117 
## 
## Residual standard error: 0.4066082 
## Degrees of freedom: 368 total; 355 residual

anova(mpfc_N)

## Denom. DF: 355 
##               numDF   F-value p-value
## (Intercept)       1 147.51065  <.0001
## val               1  10.33914  0.0014
## proc              1   0.02293  0.8797
## N_z               1   1.96908  0.1614
## dep_composite     1   0.03393  0.8540
## anx_composite     1   0.17702  0.6742
## Age               1   2.48747  0.1156
## SexAtBirth        1   3.97559  0.0469
## site              1   0.71880  0.3971
## val:proc          1   2.68020  0.1025
## val:N_z           1   0.31245  0.5765
## proc:N_z          1   0.70329  0.4022
## val:proc:N_z      1  10.16960  0.0016

mpfc models:comparing more complex N, dpression, and anxiety models

mpfc_N <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_N)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   178.7755 256.9372 -69.38776
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.682            
## 3 0.576 0.742      
## 4 0.627 0.792 0.757
## 
## Coefficients:
##                      Value Std.Error   t-value p-value
## (Intercept)     0.11509149 0.3231043  0.356205  0.7219
## val1           -0.03913614 0.0123720 -3.163281  0.0017
## proc1           0.00309380 0.0122856  0.251824  0.8013
## N_z             0.03148977 0.0448418  0.702241  0.4830
## dep_composite   0.00304918 0.0936883  0.032546  0.9741
## anx_composite   0.02054551 0.0829234  0.247765  0.8045
## Age            -0.02190188 0.0136568 -1.603739  0.1097
## SexAtBirth     -0.18324830 0.0871724 -2.102137  0.0362
## site           -0.03486447 0.0408744 -0.852967  0.3943
## val1:proc1      0.03351636 0.0121871  2.750158  0.0063
## val1:N_z        0.00261383 0.0069858  0.374162  0.7085
## proc1:N_z      -0.00554389 0.0069962 -0.792415  0.4286
## val1:proc1:N_z -0.02210865 0.0069328 -3.188982  0.0016
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.002  0.019                                                 
## N_z            -0.251 -0.006  0.002                                          
## dep_composite   0.082 -0.004  0.006 -0.567                                   
## anx_composite   0.165  0.001 -0.014 -0.242 -0.520                            
## Age            -0.975  0.001 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.001  0.009  0.021  0.131 -0.320  0.110              
## site            0.225 -0.008  0.024 -0.019 -0.084  0.084 -0.237  0.153       
## val1:proc1      0.004 -0.018 -0.048 -0.004  0.016 -0.004 -0.003 -0.011 -0.003
## val1:N_z       -0.003 -0.414 -0.038  0.000 -0.001  0.005  0.001 -0.002  0.003
## proc1:N_z      -0.007 -0.041 -0.402 -0.011  0.007 -0.002  0.009 -0.011 -0.018
## val1:proc1:N_z  0.009  0.004  0.055 -0.009  0.003 -0.005 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.007              
## proc1:N_z       0.057  0.057       
## val1:proc1:N_z -0.403  0.042 -0.014
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.87259611 -0.56629998  0.01541648  0.65999634  3.38633117 
## 
## Residual standard error: 0.4066082 
## Degrees of freedom: 368 total; 355 residual

#depression with N and A as main effects only
mpfc_D <- gls(mag3_mask_mpfc_b ~ val*proc*dep_composite + N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_D)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * dep_composite + N_z + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC     BIC    logLik
##   178.3654 256.527 -69.18269
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.678            
## 3 0.572 0.748      
## 4 0.623 0.792 0.757
## 
## Coefficients:
##                                Value Std.Error   t-value p-value
## (Intercept)               0.11453616 0.3225534  0.355092  0.7227
## val1                     -0.03662046 0.0113053 -3.239234  0.0013
## proc1                    -0.00008228 0.0112672 -0.007303  0.9942
## dep_composite            -0.00096036 0.0935470 -0.010266  0.9918
## N_z                       0.03283343 0.0447719  0.733349  0.4638
## anx_composite             0.02129206 0.0827867  0.257192  0.7972
## Age                      -0.02193823 0.0136332 -1.609174  0.1085
## SexAtBirth               -0.18375218 0.0870239 -2.111515  0.0354
## site                     -0.03484197 0.0408067 -0.853830  0.3938
## val1:proc1                0.01997931 0.0111760  1.787704  0.0747
## val1:dep_composite        0.00578481 0.0128853  0.448945  0.6537
## proc1:dep_composite      -0.01467410 0.0129084 -1.136786  0.2564
## val1:proc1:dep_composite -0.04040368 0.0127624 -3.165845  0.0017
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                     -0.001                                          
## proc1                    -0.001  0.003                                   
## dep_composite             0.082 -0.003  0.011                            
## N_z                      -0.251 -0.009 -0.003 -0.568                     
## anx_composite             0.165  0.003 -0.017 -0.520 -0.242              
## Age                      -0.975  0.001  0.001 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.001  0.006  0.131  0.021 -0.320  0.110
## site                      0.225 -0.009  0.020 -0.084 -0.019  0.084 -0.237
## val1:proc1                0.008 -0.011 -0.012  0.017 -0.007 -0.006 -0.006
## val1:dep_composite       -0.003 -0.096 -0.045 -0.011  0.010  0.004  0.000
## proc1:dep_composite      -0.004 -0.048 -0.076 -0.002  0.000 -0.002  0.005
## val1:proc1:dep_composite  0.008  0.006  0.054  0.016 -0.018 -0.009 -0.005
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_
## val1                                                       
## proc1                                                      
## dep_composite                                              
## N_z                                                        
## anx_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.013 -0.001                     
## val1:dep_composite        0.000  0.000  0.009              
## proc1:dep_composite      -0.009 -0.019  0.056  0.026       
## val1:proc1:dep_composite  0.000  0.007 -0.072  0.033  0.006
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -2.8251948 -0.5827237  0.0275136  0.6546350  3.3750026 
## 
## Residual standard error: 0.4063602 
## Degrees of freedom: 368 total; 355 residual

#Anxiety with N and D as main effects only
mpfc_A <- gls(mag3_mask_mpfc_b ~ val*proc*anx_composite + N_z + dep_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_A)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * anx_composite + N_z + dep_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   185.6852 263.8469 -72.84262
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.683            
## 3 0.579 0.743      
## 4 0.619 0.785 0.743
## 
## Coefficients:
##                                Value Std.Error   t-value p-value
## (Intercept)               0.11638158 0.3233170  0.359961  0.7191
## val1                     -0.03737878 0.0114432 -3.266454  0.0012
## proc1                    -0.00049133 0.0114151 -0.043042  0.9657
## anx_composite             0.01820854 0.0829834  0.219424  0.8264
## N_z                       0.03136204 0.0448752  0.698872  0.4851
## dep_composite             0.00345076 0.0937518  0.036807  0.9707
## Age                      -0.02192409 0.0136659 -1.604294  0.1095
## SexAtBirth               -0.18504171 0.0872308 -2.121289  0.0346
## site                     -0.03573897 0.0409057 -0.873691  0.3829
## val1:proc1                0.01903146 0.0113316  1.679503  0.0939
## val1:anx_composite        0.00618515 0.0131144  0.471632  0.6375
## proc1:anx_composite       0.00856232 0.0131321  0.652016  0.5148
## val1:proc1:anx_composite -0.02428841 0.0130042 -1.867735  0.0626
## 
##  Correlation: 
##                          (Intr) val1   proc1  anx_cm N_z    dp_cmp Age   
## val1                     -0.002                                          
## proc1                    -0.002 -0.001                                   
## anx_composite             0.165  0.002 -0.016                            
## N_z                      -0.251 -0.008 -0.001 -0.242                     
## dep_composite             0.082 -0.004  0.009 -0.520 -0.567              
## Age                      -0.975  0.002  0.002 -0.090  0.173 -0.070       
## SexAtBirth               -0.275  0.001  0.005 -0.320  0.021  0.131  0.110
## site                      0.225 -0.011  0.017  0.085 -0.019 -0.084 -0.237
## val1:proc1                0.008 -0.011  0.000 -0.006 -0.007  0.017 -0.006
## val1:anx_composite        0.002 -0.061 -0.014 -0.001  0.009 -0.004 -0.005
## proc1:anx_composite      -0.002 -0.016 -0.051 -0.007 -0.003  0.004  0.001
## val1:proc1:anx_composite  0.003  0.039  0.038  0.003 -0.013  0.000 -0.001
##                          SxAtBr site   vl1:p1 vl1:n_ prc1:_
## val1                                                       
## proc1                                                      
## anx_composite                                              
## N_z                                                        
## dep_composite                                              
## Age                                                        
## SexAtBirth                                                 
## site                      0.153                            
## val1:proc1               -0.013 -0.002                     
## val1:anx_composite        0.002  0.003  0.041              
## proc1:anx_composite      -0.005 -0.019  0.040  0.040       
## val1:proc1:anx_composite  0.002  0.011 -0.041  0.039 -0.011
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.90115309 -0.56737007  0.03943237  0.65293357  3.44481297 
## 
## Residual standard error: 0.4076334 
## Degrees of freedom: 368 total; 355 residual

#which fits better?
anova(mpfc_N,mpfc_D)

##        Model df      AIC      BIC    logLik
## mpfc_N     1 20 178.7755 256.9372 -69.38776
## mpfc_D     2 20 178.3654 256.5270 -69.18269

anova(mpfc_N,mpfc_A)

##        Model df      AIC      BIC    logLik
## mpfc_N     1 20 178.7755 256.9372 -69.38776
## mpfc_A     2 20 185.6852 263.8469 -72.84262

#including multiple 3 way interactions:
## all three constructs get their own 3 way interaction
mpfc_NDA <- gls(mag3_mask_mpfc_b ~ val*proc*dep_composite + val*proc*N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_NDA)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * dep_composite + val * proc *      N_z + val * proc * anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##       AIC      BIC    logLik
##   178.369 279.9792 -63.18451
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.675            
## 3 0.575 0.764      
## 4 0.648 0.795 0.767
## 
## Coefficients:
##                                Value Std.Error    t-value p-value
## (Intercept)               0.11637436 0.3249004  0.3581848  0.7204
## val1                     -0.03452220 0.0137798 -2.5052795  0.0127
## proc1                     0.00407534 0.0136213  0.2991882  0.7650
## dep_composite             0.00185202 0.0942740  0.0196451  0.9843
## N_z                       0.03126382 0.0451074  0.6930980  0.4887
## anx_composite             0.02035152 0.0833989  0.2440261  0.8074
## Age                      -0.02214617 0.0137327 -1.6126543  0.1077
## SexAtBirth               -0.18156571 0.0876570 -2.0713213  0.0391
## site                     -0.03492488 0.0411021 -0.8497112  0.3961
## val1:proc1                0.03077513 0.0136609  2.2527868  0.0249
## val1:dep_composite        0.01349566 0.0274688  0.4913092  0.6235
## proc1:dep_composite      -0.06050803 0.0270084 -2.2403404  0.0257
## val1:N_z                 -0.00118013 0.0131037 -0.0900609  0.9283
## proc1:N_z                -0.00603093 0.0128756 -0.4683996  0.6398
## val1:anx_composite       -0.00478856 0.0231222 -0.2070984  0.8361
## proc1:anx_composite       0.06586078 0.0226841  2.9033937  0.0039
## val1:proc1:dep_composite -0.04392165 0.0268187 -1.6377221  0.1024
## val1:proc1:N_z           -0.01805765 0.0130457 -1.3841847  0.1672
## val1:proc1:anx_composite  0.03776746 0.0228829  1.6504702  0.0997
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.001                                          
## proc1                     0.004 -0.033                                   
## dep_composite             0.082 -0.010  0.003                            
## N_z                      -0.251 -0.002  0.005 -0.568                     
## anx_composite             0.165  0.004 -0.015 -0.520 -0.242              
## Age                      -0.975 -0.001 -0.006 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.003  0.010  0.131  0.021 -0.320  0.110
## site                      0.225 -0.005  0.020 -0.084 -0.019  0.084 -0.237
## val1:proc1                0.004  0.003 -0.029  0.025 -0.014 -0.004 -0.001
## val1:dep_composite       -0.005  0.294 -0.072 -0.012  0.011  0.001  0.003
## proc1:dep_composite       0.002 -0.074  0.294 -0.013  0.013  0.005 -0.001
## val1:N_z                 -0.003 -0.584  0.041  0.009 -0.009  0.001  0.004
## proc1:N_z                -0.006  0.040 -0.580  0.011 -0.016  0.002  0.009
## val1:anx_composite        0.008  0.157  0.018  0.003 -0.002  0.001 -0.008
## proc1:anx_composite       0.000  0.019  0.154  0.007 -0.001 -0.011 -0.004
## val1:proc1:dep_composite  0.004 -0.012  0.029  0.031 -0.017 -0.015 -0.001
## val1:proc1:N_z            0.005 -0.027  0.013 -0.017  0.014 -0.001 -0.006
## val1:proc1:anx_composite -0.007  0.048 -0.018 -0.015 -0.003  0.017  0.005
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:N_ pr1:N_
## val1                                                                      
## proc1                                                                     
## dep_composite                                                             
## N_z                                                                       
## anx_composite                                                             
## Age                                                                       
## SexAtBirth                                                                
## site                      0.153                                           
## val1:proc1               -0.007  0.001                                    
## val1:dep_composite        0.002 -0.004 -0.010                             
## proc1:dep_composite      -0.001 -0.004  0.032 -0.086                      
## val1:N_z                 -0.005  0.003 -0.025 -0.579  0.046               
## proc1:N_z                -0.009 -0.003  0.010  0.045 -0.576  -0.037       
## val1:anx_composite        0.002  0.002  0.048 -0.515  0.046  -0.235  0.005
## proc1:anx_composite       0.005 -0.004 -0.018  0.045 -0.510   0.004 -0.240
## val1:proc1:dep_composite  0.001 -0.002  0.285  0.040  0.037  -0.047 -0.020
## val1:proc1:N_z           -0.007  0.001 -0.582 -0.050 -0.017   0.065  0.012
## val1:proc1:anx_composite  0.006  0.005  0.171 -0.001 -0.015  -0.005  0.001
##                          vl1:n_ prc1:n_ vl1:prc1:d_ v1:1:N
## val1                                                      
## proc1                                                     
## dep_composite                                             
## N_z                                                       
## anx_composite                                             
## Age                                                       
## SexAtBirth                                                
## site                                                      
## val1:proc1                                                
## val1:dep_composite                                        
## proc1:dep_composite                                       
## val1:N_z                                                  
## proc1:N_z                                                 
## val1:anx_composite                                        
## proc1:anx_composite      -0.049                           
## val1:proc1:dep_composite -0.002 -0.013                    
## val1:proc1:N_z           -0.002 -0.001  -0.569            
## val1:proc1:anx_composite  0.016  0.008  -0.503      -0.259
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.858777834 -0.572453536  0.002749666  0.664234885  3.250981142 
## 
## Residual standard error: 0.4040964 
## Degrees of freedom: 368 total; 349 residual

## just symptoms get 3 ways
mpfc_DA <- gls(mag3_mask_mpfc_b ~ val*proc*dep_composite + N_z + val*proc*anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_DA)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * dep_composite + N_z + val * proc *      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   174.5515 264.4374 -64.27575
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.672            
## 3 0.571 0.765      
## 4 0.643 0.796 0.761
## 
## Coefficients:
##                                Value Std.Error    t-value p-value
## (Intercept)               0.11574915 0.3232013  0.3581333  0.7205
## val1                     -0.03458684 0.0111671 -3.0972025  0.0021
## proc1                     0.00065153 0.0110885  0.0587572  0.9532
## dep_composite            -0.00022907 0.0937629 -0.0024431  0.9981
## N_z                       0.03205159 0.0448610  0.7144642  0.4754
## anx_composite             0.02017806 0.0829673  0.2432051  0.8080
## Age                      -0.02213106 0.0136605 -1.6200830  0.1061
## SexAtBirth               -0.18265733 0.0871959 -2.0947919  0.0369
## site                     -0.03506063 0.0408890 -0.8574582  0.3918
## val1:proc1                0.01985250 0.0111067  1.7874380  0.0747
## val1:dep_composite        0.01148906 0.0223355  0.5143861  0.6073
## proc1:dep_composite      -0.06846999 0.0220786 -3.1011924  0.0021
## val1:anx_composite       -0.00423016 0.0224191 -0.1886858  0.8504
## proc1:anx_composite       0.06374638 0.0220438  2.8918053  0.0041
## val1:proc1:dep_composite -0.06499691 0.0220420 -2.9487738  0.0034
## val1:proc1:anx_composite  0.02935452 0.0220937  1.3286386  0.1848
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                     -0.001                                          
## proc1                     0.001  0.004                                   
## dep_composite             0.082 -0.005  0.012                            
## N_z                      -0.251 -0.008 -0.005 -0.568                     
## anx_composite             0.165  0.005 -0.017 -0.520 -0.242              
## Age                      -0.975  0.001  0.000 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.001  0.006  0.131  0.021 -0.320  0.110
## site                      0.225 -0.006  0.022 -0.084 -0.019  0.084 -0.237
## val1:proc1                0.009 -0.009 -0.014  0.018 -0.007 -0.006 -0.007
## val1:dep_composite       -0.008 -0.066 -0.053 -0.010  0.008  0.003  0.006
## proc1:dep_composite      -0.002 -0.055 -0.060 -0.009  0.004  0.007  0.005
## val1:anx_composite        0.007  0.025  0.034  0.007 -0.004 -0.001 -0.007
## proc1:anx_composite      -0.001  0.034  0.020  0.010 -0.005 -0.011 -0.002
## val1:proc1:dep_composite  0.009 -0.049  0.043  0.027 -0.012 -0.019 -0.006
## val1:proc1:anx_composite -0.006  0.063 -0.014 -0.021  0.001  0.017  0.004
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:d_ vl1:n_ prc1:n_
## val1                                                                       
## proc1                                                                      
## dep_composite                                                              
## N_z                                                                        
## anx_composite                                                              
## Age                                                                        
## SexAtBirth                                                                 
## site                      0.153                                            
## val1:proc1               -0.013  0.002                                     
## val1:dep_composite       -0.001 -0.003 -0.050                              
## proc1:dep_composite      -0.008 -0.006  0.048 -0.072                       
## val1:anx_composite        0.002  0.003  0.065 -0.822  0.073                
## proc1:anx_composite       0.003 -0.006 -0.017  0.071 -0.818  -0.055        
## val1:proc1:dep_composite -0.004 -0.002 -0.067  0.011  0.034  -0.007 -0.024 
## val1:proc1:anx_composite  0.005  0.006  0.026 -0.009 -0.025   0.020  0.013 
##                          vl1:prc1:d_
## val1                                
## proc1                               
## dep_composite                       
## N_z                                 
## anx_composite                       
## Age                                 
## SexAtBirth                          
## site                                
## val1:proc1                          
## val1:dep_composite                  
## proc1:dep_composite                 
## val1:anx_composite                  
## proc1:anx_composite                 
## val1:proc1:dep_composite            
## val1:proc1:anx_composite -0.819     
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.868490980 -0.584120878  0.006025221  0.664172601  3.266239743 
## 
## Residual standard error: 0.404356 
## Degrees of freedom: 368 total; 352 residual

#does including the N 3way improve fit?
anova(mpfc_NDA,mpfc_DA)

##          Model df      AIC      BIC    logLik   Test L.Ratio p-value
## mpfc_NDA     1 26 178.3690 279.9792 -63.18451                       
## mpfc_DA      2 23 174.5515 264.4374 -64.27575 1 vs 2 2.18249  0.5354

## Neuroticism and one of t he two symptom measures gets 3ways
mpfc_ND <- gls(mag3_mask_mpfc_b ~ val*proc*dep_composite + val*proc*N_z + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_ND)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * dep_composite + val * proc *      N_z + anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC    logLik
##   183.2953 273.1812 -68.64763
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.678            
## 3 0.572 0.749      
## 4 0.623 0.794 0.759
## 
## Coefficients:
##                                Value Std.Error    t-value p-value
## (Intercept)               0.11280994 0.3239332  0.3482506  0.7279
## val1                     -0.03666614 0.0137321 -2.6701028  0.0079
## proc1                    -0.00211396 0.0136971 -0.1543355  0.8774
## dep_composite             0.00123425 0.0939819  0.0131329  0.9895
## N_z                       0.03217750 0.0449775  0.7154138  0.4748
## anx_composite             0.02092293 0.0831395  0.2516604  0.8015
## Age                      -0.02183975 0.0136919 -1.5950872  0.1116
## SexAtBirth               -0.18349088 0.0873969 -2.0995130  0.0365
## site                     -0.03502249 0.0409814 -0.8545947  0.3934
## val1:proc1                0.02781141 0.0136498  2.0374996  0.0423
## val1:dep_composite        0.00637726 0.0237919  0.2680436  0.7888
## proc1:dep_composite      -0.01896866 0.0236419 -0.8023321  0.4229
## val1:N_z                 -0.00032716 0.0128666 -0.0254270  0.9797
## proc1:N_z                 0.00299701 0.0127576  0.2349195  0.8144
## val1:proc1:dep_composite -0.02123328 0.0235312 -0.9023446  0.3675
## val1:proc1:N_z           -0.01252596 0.0127767 -0.9803787  0.3276
## 
##  Correlation: 
##                          (Intr) val1   proc1  dp_cmp N_z    anx_cm Age   
## val1                      0.000                                          
## proc1                     0.003 -0.039                                   
## dep_composite             0.082 -0.010  0.001                            
## N_z                      -0.251  0.000  0.008 -0.568                     
## anx_composite             0.165  0.001 -0.014 -0.520 -0.242              
## Age                      -0.975  0.000 -0.004 -0.070  0.173 -0.089       
## SexAtBirth               -0.275  0.002  0.009  0.131  0.021 -0.320  0.110
## site                      0.225 -0.010  0.020 -0.084 -0.019  0.084 -0.237
## val1:proc1                0.004 -0.011 -0.020  0.025 -0.011 -0.007 -0.002
## val1:dep_composite       -0.001  0.432 -0.079 -0.016  0.016  0.000 -0.001
## proc1:dep_composite       0.003 -0.079  0.439 -0.012  0.016 -0.001 -0.004
## val1:N_z                 -0.001 -0.565  0.048  0.013 -0.014  0.003  0.002
## proc1:N_z                -0.006  0.046 -0.565  0.014 -0.019  0.000  0.008
## val1:proc1:dep_composite  0.001  0.015  0.028  0.025 -0.017 -0.009  0.002
## val1:proc1:N_z            0.004 -0.016  0.005 -0.019  0.009  0.005 -0.006
##                          SxAtBr site   vl1:p1 vl1:d_ prc1:_ vl1:N_ pr1:N_
## val1                                                                     
## proc1                                                                    
## dep_composite                                                            
## N_z                                                                      
## anx_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.008 -0.003                                   
## val1:dep_composite        0.002 -0.006  0.019                            
## proc1:dep_composite       0.001 -0.006  0.033 -0.072                     
## val1:N_z                 -0.002  0.006 -0.017 -0.839  0.064              
## proc1:N_z                -0.007 -0.005  0.002  0.064 -0.836 -0.039       
## val1:proc1:dep_composite  0.004  0.002  0.448  0.058  0.043 -0.057 -0.034
## val1:proc1:N_z           -0.005  0.003 -0.572 -0.060 -0.034  0.070  0.022
##                          v1:1:_
## val1                           
## proc1                          
## dep_composite                  
## N_z                            
## anx_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:dep_composite             
## proc1:dep_composite            
## val1:N_z                       
## proc1:N_z                      
## val1:proc1:dep_composite       
## val1:proc1:N_z           -0.839
## 
## Standardized residuals:
##        Min         Q1        Med         Q3        Max 
## -2.8389790 -0.5862404  0.0263696  0.6594723  3.3744613 
## 
## Residual standard error: 0.4062711 
## Degrees of freedom: 368 total; 352 residual

#does D model improve by adding N ineeractions
anova(mpfc_ND,mpfc_D)

##         Model df      AIC      BIC    logLik   Test L.Ratio p-value
## mpfc_ND     1 23 183.2953 273.1812 -68.64763                       
## mpfc_D      2 20 178.3654 256.5270 -69.18269 1 vs 2 1.07011  0.7843

mpfc_NA <- gls(mag3_mask_mpfc_b ~ val*proc*N_z + N_z + val*proc*anx_composite +dep_composite+ Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(mpfc_NA)

## Generalized least squares fit by maximum likelihood
##   Model: mag3_mask_mpfc_b ~ val * proc * N_z + N_z + val * proc * anx_composite +      dep_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC      BIC   logLik
##   179.7862 269.6721 -66.8931
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.688            
## 3 0.583 0.744      
## 4 0.646 0.790 0.759
## 
## Coefficients:
##                                Value Std.Error    t-value p-value
## (Intercept)               0.11873519 0.3248342  0.3655255  0.7149
## val1                     -0.03918906 0.0133024 -2.9460038  0.0034
## proc1                     0.01320747 0.0131508  1.0043080  0.3159
## N_z                       0.03090065 0.0450845  0.6853947  0.4935
## anx_composite             0.02006519 0.0833716  0.2406716  0.8099
## dep_composite             0.00386255 0.0941877  0.0410091  0.9673
## Age                      -0.02213106 0.0137302 -1.6118515  0.1079
## SexAtBirth               -0.18199018 0.0876397 -2.0765726  0.0386
## site                     -0.03494650 0.0410925 -0.8504354  0.3957
## val1:proc1                0.03808108 0.0132368  2.8769028  0.0043
## val1:N_z                  0.00284934 0.0107818  0.2642724  0.7917
## proc1:N_z                -0.02204132 0.0106519 -2.0692302  0.0393
## val1:anx_composite        0.00046694 0.0199908  0.0233576  0.9814
## proc1:anx_composite       0.03965369 0.0197414  2.0086523  0.0453
## val1:proc1:N_z           -0.03008003 0.0108632 -2.7689733  0.0059
## val1:proc1:anx_composite  0.01925721 0.0200491  0.9605014  0.3375
## 
##  Correlation: 
##                          (Intr) val1   proc1  N_z    anx_cm dp_cmp Age   
## val1                      0.003                                          
## proc1                     0.003  0.003                                   
## N_z                      -0.251 -0.004  0.003                            
## anx_composite             0.165  0.002 -0.016 -0.242                     
## dep_composite             0.082 -0.006  0.006 -0.567 -0.520              
## Age                      -0.975 -0.002 -0.005  0.173 -0.089 -0.070       
## SexAtBirth               -0.275  0.003  0.010  0.021 -0.320  0.131  0.110
## site                      0.225 -0.005  0.020 -0.019  0.084 -0.084 -0.237
## val1:proc1                0.002  0.012 -0.045 -0.009  0.000  0.015 -0.001
## val1:N_z                 -0.007 -0.531  0.000 -0.004  0.002  0.003  0.006
## proc1:N_z                -0.006 -0.002 -0.522 -0.011  0.006  0.004  0.010
## val1:anx_composite        0.006  0.373 -0.022  0.005  0.000 -0.004 -0.007
## proc1:anx_composite       0.001 -0.022  0.367  0.006 -0.009  0.000 -0.005
## val1:proc1:N_z            0.009 -0.028  0.033  0.004 -0.011  0.001 -0.008
## val1:proc1:anx_composite -0.005  0.042 -0.006 -0.012  0.010  0.001  0.006
##                          SxAtBr site   vl1:p1 vl1:N_ pr1:N_ vl1:n_ prc1:_
## val1                                                                     
## proc1                                                                    
## N_z                                                                      
## anx_composite                                                            
## dep_composite                                                            
## Age                                                                      
## SexAtBirth                                                               
## site                      0.153                                          
## val1:proc1               -0.006  0.002                                   
## val1:N_z                 -0.004  0.001 -0.027                            
## proc1:N_z                -0.011 -0.006  0.034 -0.021                     
## val1:anx_composite        0.004  0.000  0.043 -0.762  0.043              
## proc1:anx_composite       0.005 -0.007 -0.005  0.042 -0.757 -0.036       
## val1:proc1:N_z           -0.007  0.000 -0.538  0.033  0.003 -0.023 -0.006
## val1:proc1:anx_composite  0.007  0.005  0.390 -0.023 -0.006  0.032  0.001
##                          v1:1:N
## val1                           
## proc1                          
## N_z                            
## anx_composite                  
## dep_composite                  
## Age                            
## SexAtBirth                     
## site                           
## val1:proc1                     
## val1:N_z                       
## proc1:N_z                      
## val1:anx_composite             
## proc1:anx_composite            
## val1:proc1:N_z                 
## val1:proc1:anx_composite -0.770
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -2.904118860 -0.566969455  0.008952133  0.659659407  3.331986408 
## 
## Residual standard error: 0.4056199 
## Degrees of freedom: 368 total; 352 residual

#does A model improve by adding N ineeractions
anova(mpfc_NA,mpfc_A)

##         Model df      AIC      BIC    logLik   Test  L.Ratio p-value
## mpfc_NA     1 23 179.7862 269.6721 -66.89310                        
## mpfc_A      2 20 185.6852 263.8469 -72.84262 1 vs 2 11.89903  0.0077

Lins

# Different var/cov by Site
nositemodel1Lins <- gls(mag4_mask_Lins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1Lins)

## Generalized least squares fit by maximum likelihood
##   Model: mag4_mask_Lins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -324.3308 -246.1692 182.1654
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.595            
## 3 0.599 0.700      
## 4 0.572 0.716 0.715
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)     0.04129644 0.15057516  0.274258  0.7840
## val1           -0.02890990 0.00647000 -4.468302  0.0000
## proc1          -0.02149860 0.00647994 -3.317714  0.0010
## N_z             0.01844966 0.02089684  0.882892  0.3779
## dep_composite  -0.00440548 0.04365868 -0.100907  0.9197
## anx_composite  -0.00557637 0.03864256 -0.144306  0.8853
## Age            -0.00347690 0.00636451 -0.546296  0.5852
## SexAtBirth      0.10543884 0.04062254  2.595575  0.0098
## site           -0.00229633 0.01904589 -0.120568  0.9041
## val1:proc1     -0.00728523 0.00642539 -1.133820  0.2576
## val1:N_z       -0.00214796 0.00365627 -0.587473  0.5573
## proc1:N_z       0.00492006 0.00368616  1.334737  0.1828
## val1:proc1:N_z -0.00518898 0.00364494 -1.423613  0.1554
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.001                                                        
## proc1          -0.002  0.016                                                 
## N_z            -0.251 -0.005  0.003                                          
## dep_composite   0.082 -0.004  0.005 -0.567                                   
## anx_composite   0.165  0.003 -0.007 -0.242 -0.520                            
## Age            -0.975  0.000  0.003  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000 -0.001  0.021  0.131 -0.320  0.110              
## site            0.225  0.001  0.012 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.007 -0.002 -0.041 -0.006  0.008  0.002 -0.006 -0.009 -0.015
## val1:N_z       -0.003 -0.411 -0.019  0.006 -0.007  0.004  0.001 -0.002 -0.002
## proc1:N_z      -0.001 -0.021 -0.404 -0.006  0.004 -0.003  0.002 -0.001 -0.008
## val1:proc1:N_z  0.001 -0.010  0.038 -0.003 -0.001 -0.003 -0.001  0.002  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z       -0.008              
## proc1:N_z       0.038  0.033       
## val1:proc1:N_z -0.403  0.025 -0.007
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.29011196 -0.63351703  0.02050172  0.68512437  2.85727997 
## 
## Residual standard error: 0.193207 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2Lins <- gls(mag4_mask_Lins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2Lins)

## Generalized least squares fit by maximum likelihood
##   Model: mag4_mask_Lins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -325.8269 -267.2056 177.9134
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6399303 
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)     0.05370083 0.14905611  0.360273  0.7189
## val1           -0.02866677 0.00666778 -4.299299  0.0000
## proc1          -0.02162074 0.00666778 -3.242570  0.0013
## N_z             0.02009212 0.02068559  0.971310  0.3321
## dep_composite  -0.00907204 0.04321477 -0.209929  0.8338
## anx_composite  -0.00277471 0.03825208 -0.072538  0.9422
## Age            -0.00406193 0.00630034 -0.644715  0.5195
## SexAtBirth      0.10740876 0.04021275  2.671012  0.0079
## site           -0.00187062 0.01885126 -0.099231  0.9210
## val1:proc1     -0.00903041 0.00666778 -1.354336  0.1765
## val1:N_z       -0.00211944 0.00378081 -0.560578  0.5754
## proc1:N_z       0.00463856 0.00378081  1.226869  0.2207
## val1:proc1:N_z -0.00457849 0.00378081 -1.210982  0.2267
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.285732688 -0.648989476  0.005293426  0.687574160  2.885075517 
## 
## Residual standard error: 0.1914189 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1Lins,nositemodel2Lins)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1Lins     1 20 -324.3308 -246.1692 182.1654                        
## nositemodel2Lins     2 15 -325.8269 -267.2056 177.9134 1 vs 2 8.503979  0.1306

# Different var/cov by Site
sitemodel1Lins <- gls(mag4_mask_Lins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1Lins)

## Generalized least squares fit by maximum likelihood
##   Model: mag4_mask_Lins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -334.3326 -271.8033 183.1663
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6553883 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.7720986 
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)     0.07711553 0.14268829  0.540448  0.5892
## val1           -0.02925943 0.00647176 -4.521091  0.0000
## proc1          -0.01994550 0.00647176 -3.081927  0.0022
## N_z             0.02346722 0.02079222  1.128654  0.2598
## dep_composite  -0.01781952 0.04279541 -0.416389  0.6774
## anx_composite  -0.00400148 0.03726913 -0.107367  0.9146
## Age            -0.00464447 0.00596879 -0.778125  0.4370
## SexAtBirth      0.08818039 0.03879978  2.272704  0.0236
## site           -0.00308784 0.01823786 -0.169309  0.8656
## val1:proc1     -0.00891837 0.00647176 -1.378043  0.1691
## val1:N_z       -0.00186726 0.00379156 -0.492478  0.6227
## proc1:N_z       0.00348230 0.00379156  0.918434  0.3590
## val1:proc1:N_z -0.00533662 0.00379156 -1.407500  0.1602
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.315  0.000  0.000                                          
## dep_composite   0.127  0.000  0.000 -0.586                                   
## anx_composite   0.201  0.000  0.000 -0.232 -0.496                            
## Age            -0.974  0.000  0.000  0.233 -0.108 -0.132                     
## SexAtBirth     -0.295  0.000  0.000  0.056  0.090 -0.302  0.127              
## site            0.186  0.000  0.000 -0.022 -0.084  0.098 -0.228  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.422  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.422  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.422  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.16663431 -0.61369235  0.02411984  0.72812618  2.52079728 
## 
## Residual standard error: 0.211851 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2Lins <- gls(mag4_mask_Lins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2Lins)

## Generalized least squares fit by maximum likelihood
##   Model: mag4_mask_Lins_b ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -325.8269 -267.2056 177.9134
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6399303 
## 
## Coefficients:
##                      Value  Std.Error   t-value p-value
## (Intercept)     0.05370083 0.14905611  0.360273  0.7189
## val1           -0.02866677 0.00666778 -4.299299  0.0000
## proc1          -0.02162074 0.00666778 -3.242570  0.0013
## N_z             0.02009212 0.02068559  0.971310  0.3321
## dep_composite  -0.00907204 0.04321477 -0.209929  0.8338
## anx_composite  -0.00277471 0.03825208 -0.072538  0.9422
## Age            -0.00406193 0.00630034 -0.644715  0.5195
## SexAtBirth      0.10740876 0.04021275  2.671012  0.0079
## site           -0.00187062 0.01885126 -0.099231  0.9210
## val1:proc1     -0.00903041 0.00666778 -1.354336  0.1765
## val1:N_z       -0.00211944 0.00378081 -0.560578  0.5754
## proc1:N_z       0.00463856 0.00378081  1.226869  0.2207
## val1:proc1:N_z -0.00457849 0.00378081 -1.210982  0.2267
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##          Min           Q1          Med           Q3          Max 
## -3.285732688 -0.648989476  0.005293426  0.687574160  2.885075517 
## 
## Residual standard error: 0.1914189 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1Lins,sitemodel2Lins)

##                Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## sitemodel1Lins     1 16 -334.3326 -271.8033 183.1663                        
## sitemodel2Lins     2 15 -325.8269 -267.2056 177.9134 1 vs 2 10.50573  0.0012

#Rename winning model:
lins_N <- gls(mag4_mask_Lins_b ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
anova(lins_N)

## Denom. DF: 355 
##               numDF   F-value p-value
## (Intercept)       1  6.505924  0.0112
## val               1 27.195422  <.0001
## proc              1  8.831286  0.0032
## N_z               1  2.717081  0.1002
## dep_composite     1  0.431511  0.5117
## anx_composite     1  0.383602  0.5361
## Age               1  1.555844  0.2131
## SexAtBirth        1  5.409650  0.0206
## site              1  0.028666  0.8656
## val:proc          1  4.727071  0.0304
## val:N_z           1  0.242535  0.6227
## proc:N_z          1  0.843521  0.3590
## val:proc:N_z      1  1.981057  0.1602

PCC

# Different var/cov by Site
nositemodel1pcc <- gls(r3Default_PCC_Foxcoord ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1pcc)

## Generalized least squares fit by maximum likelihood
##   Model: r3Default_PCC_Foxcoord ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##        AIC       BIC   logLik
##   -145.557 -67.39538 92.77852
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.608            
## 3 0.615 0.685      
## 4 0.654 0.755 0.717
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.10349017 0.19855316 -0.5212215  0.6025
## val1           -0.00014788 0.00809588 -0.0182662  0.9854
## proc1          -0.00193651 0.00818732 -0.2365252  0.8132
## N_z             0.01046685 0.02755439  0.3798614  0.7043
## dep_composite  -0.06646884 0.05756990 -1.1545764  0.2490
## anx_composite   0.03009198 0.05095519  0.5905577  0.5552
## Age            -0.01269921 0.00839240 -1.5131797  0.1311
## SexAtBirth      0.07011052 0.05356489  1.3088895  0.1914
## site           -0.04081593 0.02511600 -1.6250964  0.1050
## val1:proc1      0.00406319 0.00813505  0.4994671  0.6178
## val1:N_z       -0.00175459 0.00459157 -0.3821319  0.7026
## proc1:N_z       0.00105587 0.00464628  0.2272510  0.8204
## val1:proc1:N_z -0.00358343 0.00460219 -0.7786350  0.4367
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.003                                                        
## proc1           0.004  0.006                                                 
## N_z            -0.251 -0.003 -0.006                                          
## dep_composite   0.082 -0.007  0.006 -0.567                                   
## anx_composite   0.165  0.008 -0.005 -0.242 -0.520                            
## Age            -0.975 -0.001 -0.003  0.173 -0.070 -0.089                     
## SexAtBirth     -0.275  0.000  0.004  0.021  0.131 -0.320  0.110              
## site            0.225  0.012  0.019 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.008 -0.020 -0.052 -0.007  0.012 -0.001 -0.006 -0.006  0.002
## val1:N_z       -0.004 -0.406 -0.024  0.003 -0.001  0.000  0.003 -0.001 -0.007
## proc1:N_z      -0.007 -0.024 -0.408 -0.001  0.002 -0.002  0.007 -0.007 -0.006
## val1:proc1:N_z -0.001  0.013  0.029  0.001  0.000 -0.006  0.001  0.001 -0.001
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.015              
## proc1:N_z       0.030  0.025       
## val1:proc1:N_z -0.403  0.022 -0.008
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.39318440 -0.63284074 -0.02601134  0.78486898  2.51796444 
## 
## Residual standard error: 0.2517569 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2pcc <- gls(r3Default_PCC_Foxcoord ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2pcc)

## Generalized least squares fit by maximum likelihood
##   Model: r3Default_PCC_Foxcoord ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -147.3914 -88.77013 88.69569
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6756626 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.10952655 0.20023182 -0.5469988  0.5847
## val1           -0.00237967 0.00834911 -0.2850210  0.7758
## proc1          -0.00173444 0.00834911 -0.2077396  0.8356
## N_z             0.01170906 0.02778762  0.4213769  0.6737
## dep_composite  -0.06923967 0.05805178 -1.1927227  0.2338
## anx_composite   0.02823280 0.05138523  0.5494341  0.5831
## Age            -0.01261148 0.00846345 -1.4901108  0.1371
## SexAtBirth      0.07591246 0.05401907  1.4052900  0.1608
## site           -0.03528069 0.02532350 -1.3931997  0.1644
## val1:proc1      0.00185951 0.00834911  0.2227198  0.8239
## val1:N_z        0.00002725 0.00473417  0.0057569  0.9954
## proc1:N_z       0.00101928 0.00473417  0.2153016  0.8297
## val1:proc1:N_z -0.00173659 0.00473417 -0.3668195  0.7140
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.38471584 -0.60780362 -0.02333471  0.77331642  2.50400510 
## 
## Residual standard error: 0.2525449 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1pcc,nositemodel2pcc)

##                 Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1pcc     1 20 -145.5570 -67.39538 92.77852                        
## nositemodel2pcc     2 15 -147.3914 -88.77013 88.69569 1 vs 2 8.165671  0.1473

# Different var/cov by Site
sitemodel1pcc <- gls(r3Default_PCC_Foxcoord ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1pcc)

## Generalized least squares fit by maximum likelihood
##   Model: r3Default_PCC_Foxcoord ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -145.5598 -83.03049 88.77991
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6747904 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##       -1        1 
## 1.000000 1.032146 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.11288546 0.20087594 -0.5619661  0.5745
## val1           -0.00218715 0.00834148 -0.2622020  0.7933
## proc1          -0.00215106 0.00834148 -0.2578749  0.7967
## N_z             0.01145593 0.02770285  0.4135290  0.6795
## dep_composite  -0.06938345 0.05797039 -1.1968775  0.2322
## anx_composite   0.02939985 0.05141568  0.5718071  0.5678
## Age            -0.01247806 0.00850098 -1.4678382  0.1430
## SexAtBirth      0.07676693 0.05410915  1.4187423  0.1569
## site           -0.03524426 0.02545712 -1.3844559  0.1671
## val1:proc1      0.00162760 0.00834148  0.1951210  0.8454
## val1:N_z        0.00010922 0.00471239  0.0231773  0.9815
## proc1:N_z       0.00120674 0.00471239  0.2560785  0.7980
## val1:proc1:N_z -0.00160650 0.00471239 -0.3409107  0.7334
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.243  0.000  0.000                                          
## dep_composite   0.076  0.000  0.000 -0.565                                   
## anx_composite   0.161  0.000  0.000 -0.244 -0.522                            
## Age            -0.975  0.000  0.000  0.166 -0.065 -0.084                     
## SexAtBirth     -0.273  0.000  0.000  0.017  0.136 -0.322  0.108              
## site            0.229  0.000  0.000 -0.018 -0.084  0.083 -0.237  0.152       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.403  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.403  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.403  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.32376673 -0.61572088 -0.02442418  0.77492315  2.51427373 
## 
## Residual standard error: 0.2491562 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2pcc <- gls(r3Default_PCC_Foxcoord ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2pcc)

## Generalized least squares fit by maximum likelihood
##   Model: r3Default_PCC_Foxcoord ~ val * proc * N_z + dep_composite + anx_composite +      Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -147.3914 -88.77013 88.69569
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6756626 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.10952655 0.20023182 -0.5469988  0.5847
## val1           -0.00237967 0.00834911 -0.2850210  0.7758
## proc1          -0.00173444 0.00834911 -0.2077396  0.8356
## N_z             0.01170906 0.02778762  0.4213769  0.6737
## dep_composite  -0.06923967 0.05805178 -1.1927227  0.2338
## anx_composite   0.02823280 0.05138523  0.5494341  0.5831
## Age            -0.01261148 0.00846345 -1.4901108  0.1371
## SexAtBirth      0.07591246 0.05401907  1.4052900  0.1608
## site           -0.03528069 0.02532350 -1.3931997  0.1644
## val1:proc1      0.00185951 0.00834911  0.2227198  0.8239
## val1:N_z        0.00002725 0.00473417  0.0057569  0.9954
## proc1:N_z       0.00101928 0.00473417  0.2153016  0.8297
## val1:proc1:N_z -0.00173659 0.00473417 -0.3668195  0.7140
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -3.38471584 -0.60780362 -0.02333471  0.77331642  2.50400510 
## 
## Residual standard error: 0.2525449 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1pcc,sitemodel2pcc)

##               Model df       AIC       BIC   logLik   Test   L.Ratio p-value
## sitemodel1pcc     1 16 -145.5598 -83.03049 88.77991                         
## sitemodel2pcc     2 15 -147.3914 -88.77013 88.69569 1 vs 2 0.1684432  0.6815

#rename winning model
pcc_N <- gls(r3Default_PCC_Foxcoord ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
anova(pcc_N)

## Denom. DF: 355 
##               numDF   F-value p-value
## (Intercept)       1 195.86090  <.0001
## val               1   0.09560  0.7574
## proc              1   0.01738  0.8952
## N_z               1   0.02492  0.8747
## dep_composite     1   1.94045  0.1645
## anx_composite     1   1.56903  0.2112
## Age               1   4.61102  0.0324
## SexAtBirth        1   2.68088  0.1024
## site              1   1.94101  0.1644
## val:proc          1   0.00657  0.9354
## val:N_z           1   0.00003  0.9954
## proc:N_z          1   0.04635  0.8297
## val:proc:N_z      1   0.13456  0.7140

Prec

# Different var/cov by Site
nositemodel1prec <- gls(r3_Yeo7DMN_Precuneus_LR ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel1prec)

## Generalized least squares fit by maximum likelihood
##   Model: r3_Yeo7DMN_Precuneus_LR ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -210.2791 -132.1174 125.1395
## 
## Correlation Structure: General
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##  Correlation: 
##   1     2     3    
## 2 0.707            
## 3 0.682 0.716      
## 4 0.648 0.778 0.658
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.19378655 0.18948244 -1.0227151  0.3071
## val1           -0.00241362 0.00727812 -0.3316269  0.7404
## proc1          -0.00494419 0.00742899 -0.6655260  0.5061
## N_z             0.03941142 0.02629734  1.4986847  0.1348
## dep_composite  -0.08247277 0.05493489 -1.5012822  0.1342
## anx_composite   0.02578391 0.04862776  0.5302301  0.5963
## Age            -0.00221972 0.00800898 -0.2771539  0.7818
## SexAtBirth     -0.01039816 0.05111925 -0.2034099  0.8389
## site           -0.03812179 0.02396884 -1.5904727  0.1126
## val1:proc1      0.00730263 0.00731115  0.9988345  0.3186
## val1:N_z        0.00066098 0.00412406  0.1602731  0.8728
## proc1:N_z       0.00105511 0.00423558  0.2491056  0.8034
## val1:proc1:N_z -0.00326517 0.00413606 -0.7894397  0.4304
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1           -0.002                                                        
## proc1          -0.002  0.013                                                 
## N_z            -0.251 -0.003  0.007                                          
## dep_composite   0.082  0.002  0.001 -0.567                                   
## anx_composite   0.166 -0.005 -0.009 -0.242 -0.520                            
## Age            -0.975  0.001  0.001  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.001  0.005  0.021  0.131 -0.320  0.110              
## site            0.225 -0.017  0.006 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1     -0.002 -0.008 -0.006  0.002  0.005 -0.002  0.002 -0.005 -0.005
## val1:N_z        0.000 -0.408 -0.027 -0.002  0.000  0.004 -0.001  0.000  0.008
## proc1:N_z      -0.001 -0.029 -0.408 -0.009  0.005  0.000  0.002 -0.004 -0.011
## val1:proc1:N_z  0.008  0.000  0.037 -0.009  0.002  0.001 -0.007 -0.003  0.007
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.002              
## proc1:N_z       0.038  0.044       
## val1:proc1:N_z -0.399  0.031 -0.013
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.75535833 -0.74814507 -0.04655522  0.69177537  2.63395702 
## 
## Residual standard error: 0.2369884 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
nositemodel2prec <- gls(r3_Yeo7DMN_Precuneus_LR ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(nositemodel2prec)

## Generalized least squares fit by maximum likelihood
##   Model: r3_Yeo7DMN_Precuneus_LR ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -212.0641 -153.4429 121.0321
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6945489 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.17865462 0.18886973 -0.9459145  0.3448
## val1           -0.00120829 0.00757208 -0.1595716  0.8733
## proc1          -0.00168354 0.00757208 -0.2223354  0.8242
## N_z             0.03799470 0.02621082  1.4495807  0.1481
## dep_composite  -0.08297186 0.05475765 -1.5152561  0.1306
## anx_composite   0.02663550 0.04846939  0.5495324  0.5830
## Age            -0.00276239 0.00798319 -0.3460252  0.7295
## SexAtBirth     -0.01315367 0.05095378 -0.2581490  0.7964
## site           -0.03522583 0.02388653 -1.4747157  0.1412
## val1:proc1      0.00637688 0.00757208  0.8421571  0.4003
## val1:N_z        0.00027235 0.00429357  0.0634327  0.9495
## proc1:N_z       0.00002878 0.00429357  0.0067032  0.9947
## val1:proc1:N_z -0.00270561 0.00429357 -0.6301548  0.5290
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.79346322 -0.72423921 -0.03851973  0.67666294  2.64586735 
## 
## Residual standard error: 0.2360157 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(nositemodel1prec,nositemodel2prec)

##                  Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## nositemodel1prec     1 20 -210.2791 -132.1174 125.1395                        
## nositemodel2prec     2 15 -212.0641 -153.4429 121.0321 1 vs 2 8.214922  0.1448

# Different var/cov by Site
sitemodel1prec <- gls(r3_Yeo7DMN_Precuneus_LR ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub), weights = varIdent(form = ~ 1 | site),method = "ML",na.action = "na.omit")
summary(sitemodel1prec)

## Generalized least squares fit by maximum likelihood
##   Model: r3_Yeo7DMN_Precuneus_LR ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -211.0302 -148.5009 121.5151
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##      Rho 
## 0.689893 
## Variance function:
##  Structure: Different standard deviations per stratum
##  Formula: ~1 | site 
##  Parameter estimates:
##        -1         1 
## 1.0000000 0.9260137 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.17479904 0.18481133 -0.9458243  0.3449
## val1           -0.00152127 0.00758727 -0.2005023  0.8412
## proc1          -0.00086511 0.00758727 -0.1140209  0.9093
## N_z             0.03740314 0.02603613  1.4365862  0.1517
## dep_composite  -0.08373848 0.05416479 -1.5459949  0.1230
## anx_composite   0.02788027 0.04771145  0.5843516  0.5594
## Age            -0.00286755 0.00778810 -0.3681971  0.7129
## SexAtBirth     -0.01485582 0.05002022 -0.2969963  0.7666
## site           -0.03522741 0.02332765 -1.5101140  0.1319
## val1:proc1      0.00672646 0.00758727  0.8865450  0.3759
## val1:N_z       -0.00000886 0.00434240 -0.0020399  0.9984
## proc1:N_z      -0.00047664 0.00434240 -0.1097642  0.9127
## val1:proc1:N_z -0.00317005 0.00434240 -0.7300224  0.4659
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.270  0.000  0.000                                          
## dep_composite   0.095  0.000  0.000 -0.573                                   
## anx_composite   0.177  0.000  0.000 -0.239 -0.513                            
## Age            -0.974  0.000  0.000  0.192 -0.081 -0.102                     
## SexAtBirth     -0.281  0.000  0.000  0.031  0.119 -0.315  0.115              
## site            0.214  0.000  0.000 -0.020 -0.085  0.089 -0.236  0.154       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.410  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.410  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.410  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.73810377 -0.70777069 -0.03334756  0.69690208  2.57699958 
## 
## Residual standard error: 0.2416664 
## Degrees of freedom: 368 total; 355 residual

# Common var/cov by Site
sitemodel2prec <- gls(r3_Yeo7DMN_Precuneus_LR ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
summary(sitemodel2prec)

## Generalized least squares fit by maximum likelihood
##   Model: r3_Yeo7DMN_Precuneus_LR ~ val * proc * N_z + dep_composite +      anx_composite + Age + SexAtBirth + site 
##   Data: uniroi_unharm_df 
##         AIC       BIC   logLik
##   -212.0641 -153.4429 121.0321
## 
## Correlation Structure: Compound symmetry
##  Formula: ~1 | sub 
##  Parameter estimate(s):
##       Rho 
## 0.6945489 
## 
## Coefficients:
##                      Value  Std.Error    t-value p-value
## (Intercept)    -0.17865462 0.18886973 -0.9459145  0.3448
## val1           -0.00120829 0.00757208 -0.1595716  0.8733
## proc1          -0.00168354 0.00757208 -0.2223354  0.8242
## N_z             0.03799470 0.02621082  1.4495807  0.1481
## dep_composite  -0.08297186 0.05475765 -1.5152561  0.1306
## anx_composite   0.02663550 0.04846939  0.5495324  0.5830
## Age            -0.00276239 0.00798319 -0.3460252  0.7295
## SexAtBirth     -0.01315367 0.05095378 -0.2581490  0.7964
## site           -0.03522583 0.02388653 -1.4747157  0.1412
## val1:proc1      0.00637688 0.00757208  0.8421571  0.4003
## val1:N_z        0.00027235 0.00429357  0.0634327  0.9495
## proc1:N_z       0.00002878 0.00429357  0.0067032  0.9947
## val1:proc1:N_z -0.00270561 0.00429357 -0.6301548  0.5290
## 
##  Correlation: 
##                (Intr) val1   proc1  N_z    dp_cmp anx_cm Age    SxAtBr site  
## val1            0.000                                                        
## proc1           0.000  0.000                                                 
## N_z            -0.251  0.000  0.000                                          
## dep_composite   0.082  0.000  0.000 -0.567                                   
## anx_composite   0.165  0.000  0.000 -0.242 -0.520                            
## Age            -0.975  0.000  0.000  0.173 -0.070 -0.090                     
## SexAtBirth     -0.275  0.000  0.000  0.021  0.131 -0.320  0.110              
## site            0.225  0.000  0.000 -0.019 -0.084  0.085 -0.237  0.153       
## val1:proc1      0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## val1:N_z        0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000  0.000
## proc1:N_z       0.000  0.000 -0.405  0.000  0.000  0.000  0.000  0.000  0.000
## val1:proc1:N_z  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
##                vl1:p1 vl1:N_ pr1:N_
## val1                               
## proc1                              
## N_z                                
## dep_composite                      
## anx_composite                      
## Age                                
## SexAtBirth                         
## site                               
## val1:proc1                         
## val1:N_z        0.000              
## proc1:N_z       0.000  0.000       
## val1:proc1:N_z -0.405  0.000  0.000
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.79346322 -0.72423921 -0.03851973  0.67666294  2.64586735 
## 
## Residual standard error: 0.2360157 
## Degrees of freedom: 368 total; 355 residual

# Testing difference of fit between Diff/Common Site var/cov
anova(sitemodel1prec,sitemodel2prec)

##                Model df       AIC       BIC   logLik   Test   L.Ratio p-value
## sitemodel1prec     1 16 -211.0302 -148.5009 121.5151                         
## sitemodel2prec     2 15 -212.0641 -153.4429 121.0321 1 vs 2 0.9660844  0.3257

#rename winning model
prec_N <- gls(r3_Yeo7DMN_Precuneus_LR ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_unharm_df, correlation = corCompSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
anova(prec_N)

## Denom. DF: 355 
##               numDF  F-value p-value
## (Intercept)       1 98.38466  <.0001
## val               1  0.02144  0.8837
## proc              1  0.05770  0.8103
## N_z               1  0.62883  0.4283
## dep_composite     1  2.65537  0.1041
## anx_composite     1  0.47826  0.4897
## Age               1  0.51737  0.4724
## SexAtBirth        1  0.00111  0.9735
## site              1  2.17479  0.1412
## val:proc          1  0.41207  0.5213
## val:N_z           1  0.00402  0.9495
## proc:N_z          1  0.00004  0.9947
## val:proc:N_z      1  0.39710  0.5290

FDR correction for multiple comparison in Apriori ROI models

models <- c("dlpfc_N", "rins_N", "dacc_N", "spgacc_N", "vlpfc_N", "amyg_N", "mpfc_N", "lins_N", "pcc_N", "prec_N")
n_coefficients_list <- list()
n_p_list <- list()

#which terms do we actually care about? Anything w/ N
## Sub model: val,proc,N,dep,anx,age,sex,site,val*proc,val*N,proc*N,val*proc*N
termstokeep <- c(4,11:13)
for (m in 1:length(models)){
  thisMod <- models[m]
  thisMod_sum <- summary(get(thisMod))
  n_coefficients_list[[m]] <- coef(thisMod_sum)[termstokeep]
  n_p_list[[m]] <- thisMod_sum$tTable[termstokeep,4] 
}  

n_allps_uncorrected <- unlist(n_p_list)

getnull<-get.pi0(
  n_allps_uncorrected,
  set.pi0 = 1,
  zvalues = "two.sided",
  estim.method = "storey",
  threshold = 0.05
)

n_allps_uncorrected <- unlist(n_p_list)
n_allps_FDRcorrected <- p.adjust(n_allps_uncorrected, method = "fdr")
n_allps_FDRcorrected_newnew <- p.fdr(n_allps_uncorrected, set.pi0=getnull)

##see what survives:
n_allps_uncorrected[which(n_allps_uncorrected < .051)] #10 values

## val1:proc1:N_z val1:proc1:N_z val1:proc1:N_z            N_z val1:proc1:N_z 
##    0.019122301    0.001005234    0.001161329    0.029893060    0.001554740

n_allps_FDRcorrected_newnew

## $fdrs
##  [1] 0.77709863 0.98954510 0.66849717 0.72583226 1.00000000 0.64886958
##  [7] 0.93660820 0.19122301 1.00000000 0.71680456 0.67371374 0.04020937
## [13] 1.00000000 0.92670931 0.97021711 0.02322658 1.00000000 0.68768348
## [19] 0.96248497 0.68024613 0.23914448 0.64395539 1.00000000 0.60846164
## [25] 0.91997893 0.91420255 0.85729598 0.02072987 0.74230316 0.99629691
## [31] 0.75582184 0.64061648 0.99812581 0.99540990 1.00000000 0.89246530
## [37] 0.74029154 1.00000000 1.00000000 0.96181586
## 
## $`Results Matrix`
##                     BH FDRs Adjusted p-values Raw p-values
## N_z              0.77709863        0.64886958  0.252557056
## val1.N_z         0.98954510        0.98954510  0.841113335
## proc1.N_z        0.66849717        0.64061648  0.150411862
## val1.proc1.N_z   0.72583226        0.72583226  0.326624516
## N_z.1            1.00000000        0.99540990  0.979933554
## val1.N_z.1       0.64886958        0.64886958  0.275769573
## proc1.N_z.1      0.93660820        0.89246530  0.679040948
## val1.proc1.N_z.1 0.19122301        0.19122301  0.019122301
## N_z.2            1.00000000        0.89246530  0.661126684
## val1.N_z.2       0.71680456        0.64886958  0.268801711
## proc1.N_z.2      0.67371374        0.64886958  0.269485496
## val1.proc1.N_z.2 0.04020937        0.02072987  0.001005234
## N_z.3            1.00000000        0.99540990  0.900233215
## val1.N_z.3       0.92670931        0.89246530  0.695031980
## proc1.N_z.3      0.97021711        0.89246530  0.557874837
## val1.proc1.N_z.3 0.02322658        0.02072987  0.001161329
## N_z.4            1.00000000        0.89246530  0.617059639
## val1.N_z.4       0.68768348        0.64886958  0.206305043
## proc1.N_z.4      0.96248497        0.89246530  0.673739476
## val1.proc1.N_z.4 0.68024613        0.60846164  0.102036920
## N_z.5            0.23914448        0.23914448  0.029893060
## val1.N_z.5       0.64395539        0.64395539  0.177087731
## proc1.N_z.5      1.00000000        0.99540990  0.890752367
## val1.proc1.N_z.5 0.60846164        0.60846164  0.106480787
## N_z.6            0.91997893        0.89246530  0.482988936
## val1.N_z.6       0.91420255        0.89246530  0.708506974
## proc1.N_z.6      0.85729598        0.85729598  0.428647990
## val1.proc1.N_z.6 0.02072987        0.02072987  0.001554740
## N_z.7            0.74230316        0.64886958  0.259806106
## val1.N_z.7       0.99629691        0.89246530  0.622685568
## proc1.N_z.7      0.75582184        0.75582184  0.359015375
## val1.proc1.N_z.7 0.64061648        0.64061648  0.160154119
## N_z.8            0.99812581        0.89246530  0.673734920
## val1.N_z.8       0.99540990        0.99540990  0.995409900
## proc1.N_z.8      1.00000000        0.98954510  0.829655806
## val1.proc1.N_z.8 0.89246530        0.89246530  0.713972237
## N_z.9            0.74029154        0.64061648  0.148058308
## val1.N_z.9       1.00000000        0.99540990  0.949457641
## proc1.N_z.9      1.00000000        0.99540990  0.994655386
## val1.proc1.N_z.9 0.96181586        0.89246530  0.528998723
## 
## $`Reject Vector`
##  [1] "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"   
##  [7] "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "Reject.H0"
## [13] "FTR.H0"    "FTR.H0"    "FTR.H0"    "Reject.H0" "FTR.H0"    "FTR.H0"   
## [19] "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"   
## [25] "FTR.H0"    "FTR.H0"    "FTR.H0"    "Reject.H0" "FTR.H0"    "FTR.H0"   
## [31] "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"   
## [37] "FTR.H0"    "FTR.H0"    "FTR.H0"    "FTR.H0"   
## 
## $pi0
## [1] 1
## 
## $threshold
## [1] 0.05
## 
## $`Adjustment Method`
## [1] "BH"
## 
## $Call
## p.fdr(pvalues = n_allps_uncorrected, set.pi0 = getnull)
## 
## attr(,"class")
## [1] "p.fdr"

which(n_allps_FDRcorrected< .051)

## val1:proc1:N_z val1:proc1:N_z val1:proc1:N_z 
##             12             16             28

#3rd model:  all ints w N are significant
#4th model:proc*N and 3way int are significant still

Loading in and formatting data from gPPI anaylsis

###The gppi file (actually has all three seeds included)
uniroi_ppi <- read.csv("/Users/nikki/OneDrive - The Ohio State University Wexner Medical Center/FournierLab/Datafiles/CERT/gppi/Extracted_gppirois_2025_6_17.csv")

#create useful labels from large string labels
uniroi_ppi$sub <- substring(uniroi_ppi$Conss,109,112)
uniroi_ppi$con <- substring(uniroi_ppi$Conss,121,121)

uniroi_ppi$con <- factor(uniroi_ppi$con,
                     levels <- c(5,6,7,8),
                     labels <- c("Neutral_Watch", "Neutral_Regulate", "Negative_Watch", "Negative_Regulate"))

#assign addition within-sub/repeated measures
uniroi_ppi$con2 <- as.character(uniroi_ppi$con)
uniroi_ppi$val <- NA
uniroi_ppi$proc <- NA
for (r in 1:length(uniroi_ppi$Conss)){
  parts <- strsplit(uniroi_ppi$con2[r], "_")[[1]]
  uniroi_ppi$val[r] <- parts[1]
  uniroi_ppi$proc[r] <- parts[2]
}

#grab the cols needed
uniroi_ppi <- uniroi_ppi[,c(1,11,12,14,15,2:9)]
uniroi_ppi$sub <- as.numeric(uniroi_ppi$sub)

Visualize Brain by Task interactions:

Apriori ROIs

Primary Plots of predicted three way interactions:

Raw data for the supplemnent: Supp. Figure 1.

uniroi_viz<- uniroi_unharm_df[,c(1,2,5,19)]
uniroi_viz_w <- spread(uniroi_viz, con, brant_extract_BN_Atlas_179_180_dACC)
uniroi_viz_w <- merge(uniroi_viz_w,
                          unique(uniroi_unharm_df[,c(1,19)]),
                          by = "sub",all.x = T)
uniroi_viz_w$negRminusW <- uniroi_viz_w$Negative_Regulate-uniroi_viz_w$Negative_Watch
uniroi_viz_w$neutRminusW <- uniroi_viz_w$Neutral_Regulate-uniroi_viz_w$Neutral_Watch

uniroi_viz_w$negRminusneuR <- uniroi_viz_w$Negative_Regulate -uniroi_viz_w$Neutral_Regulate
uniroi_viz_w$negWminusneuW <- uniroi_viz_w$Negative_Watch-uniroi_viz_w$Neutral_Watch

###############
daccraw<-ggplot(data = uniroi_unharm_df, aes(x = N_z, y = brant_extract_BN_Atlas_179_180_dACC, color=proc)) + 
      geom_smooth(method = "lm", se=F,size=.75) + 
      #geom_smooth(group=1, color="black",method = "lm", se=F) + 
      geom_point(aes(color=proc),alpha=.25) +
  facet_wrap(~val) +
   # ylim(-.5,1)+
        labs(
        title = " ",
        x = "z-scored N",
        y = "dacc activity") +
  theme_minimal()

mpfcraw<-ggplot(data = uniroi_unharm_df, aes(x = N_z, y = mag3_mask_mpfc_b, color=proc)) + 
      geom_smooth(method = "lm", se=F,size=.75) + 
      #geom_smooth(group=1, color="black",method = "lm", se=F) + 
      geom_point(aes(color=proc),alpha=.25) +
  facet_wrap(~val) +
   # ylim(-.5,1)+
        labs(
        title = " ",
        x = "z-scored N",
        y = "mpfc activity") +
  theme_minimal()

###################
# apriori_dacc_uni_raw
#   ggplot(data = uniroi_viz_w, aes(x = N_z.x)) + 
#       geom_point(aes(y = negRminusW),alpha=.5,size=3,shape=21, color="gray30", fill="deepskyblue2") + 
#       geom_smooth(aes(y = negRminusW), color="deepskyblue4", method = "lm") +
#         labs(
#         title = "",
#         x = "Neuroticism",
#         y = "dACC: Negative Reg - Negative Watch")+
#   theme_minimal()

Create predicted Univariate DF

uniroi_unharm_df$Age_c <- uniroi_unharm_df$Age - mean(uniroi_unharm_df$Age, na.rm=T)
uniroi_unharm_df$site <- as.numeric(uniroi_unharm_df$site)
uniroi_unharm_df$site <- uniroi_unharm_df$site - 1
uniroi_unharm_df$SexAtBirth <- as.numeric(uniroi_unharm_df$SexAtBirth)
uniroi_unharm_df$SexAtBirth <- uniroi_unharm_df$SexAtBirth - 1

# Create a grid of predictor values
predicted_uni <- expand.grid(
  N_z = seq(min(uniroi_unharm_df$N_z),
                          max(uniroi_unharm_df$N_z), length.out = 92),
  val = factor(c("Negative", "Neutral"), levels=c("Negative", "Neutral")),
  proc = factor(c("Regulate", "Watch"), levels=c("Regulate", "Watch")),
  dep_composite = mean(uniroi_unharm_df$dep_composite),
  anx_composite = mean(uniroi_unharm_df$anx_composite),
  SexAtBirth = mean(uniroi_unharm_df$SexAtBirth),
  site = mean(uniroi_unharm_df$site,na.rm=T),
  Age = mean(uniroi_unharm_df$Age_c)
  )

Plot: dacc

# Call the function:
# dacc_pred_contr_val <- generate_model_plot_contr_val(
#   model = dacc_N, 
#   predicted_data = predicted_uni, 
#   plot_title = "dACC", 
#   color_Regulate = "mediumpurple2", 
#   color_Watch = "mediumpurple4"
# )
# 
# dacc_pred_nocontr_val <- generate_model_plot_nocontr_wrapval(
#   model = dacc_N, 
#   predicted_data = predicted_uni, 
#   plot_title = "difference in activity between regulate and watch is signifacntly larger for negative (than neutral) in dACC", 
#   color_Regulate = "mediumpurple2", 
#   color_Watch = "mediumpurple4"
# )
# 
# dacc_pred_contr_proc <- generate_model_plot_contr_proc(
#   model = dacc_N, 
#   predicted_data = predicted_uni, 
#   plot_title = "dACC", 
#   color_Negative = "mediumpurple2", 
#   color_Neutral = "mediumpurple4"
# )
#grid.arrange(dacc_pred_contr_val, dacc_pred_contr_proc, nrow=1)
#dacc_pred_nocontr_val


p<-plot_model(dacc_N, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("orange3", "springgreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p + theme_bw()

mpfc

mpfc_pred_contr_val <- generate_model_plot_contr_val(
  model = mpfc_N, 
  predicted_data = predicted_uni, 
  plot_title = "mPFC ", 
  color_Regulate = "steelblue2", 
  color_Watch = "steelblue4"
)

## Warning: contrasts dropped from factor val

## Warning: contrasts dropped from factor proc

mpfc_pred_nocontr_val <- generate_model_plot_nocontr_wrapval(
  model = mpfc_N, 
  predicted_data = predicted_uni, 
  plot_title = "mPFC ", 
  color_Regulate = "steelblue2", 
  color_Watch = "steelblue4"
)

mpfc_pred_contr_proc <- generate_model_plot_contr_proc(
  model = mpfc_N, 
  predicted_data = predicted_uni, 
  plot_title = "mPFC", 
  color_Negative = "steelblue2", 
  color_Neutral = "steelblue4"
)

## Warning: contrasts dropped from factor val

## Warning: contrasts dropped from factor proc

grid.arrange(mpfc_pred_contr_val, mpfc_pred_contr_proc, nrow=1)

mpfc_pred_nocontr_val

p<-plot_model(mpfc_N, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("orange3", "springgreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p + theme_bw()

Whole Brain Clusters:

raw data for the supplemnent: Supp. Figure 1.

#uniroi_viz<- uniroi_wb[,c(1,2,5,19)]
#uniroi_viz_w <- spread(uniroi_viz, con, brant_extract_BN_Atlas_179_180_dACC)
#uniroi_viz_w <- merge(uniroi_viz_w,
 #                         unique(uniroi_unharm_df[,c(1,19)]),
#                          by = "sub",all.x = T)

###############
wb1raw<-ggplot(data = uniroi_wb, aes(x = N_z, y = VxCxN_33voxel_p001__mask_0001, color=proc)) + 
      geom_smooth(method = "lm", se=F,size=.75) + 
      #geom_smooth(group=1, color="black",method = "lm", se=F) + 
      geom_point(aes(color=proc),alpha=.25) +
  facet_wrap(~val) +
   # ylim(-.5,1)+
        labs(
        title = " ",
        x = "z-scored N",
        y = "spgACC") +
  theme_minimal()
wb2raw<-ggplot(data = uniroi_wb, aes(x = N_z, y = VxCxN_33voxel_p001__mask_0002, color=proc)) + 
      geom_smooth(method = "lm", se=F,size=.75) + 
      #geom_smooth(group=1, color="black",method = "lm", se=F) + 
      geom_point(aes(color=proc),alpha=.25) +
  facet_wrap(~val) +
   # ylim(-.5,1)+
        labs(
        title = " ",
        x = "z-scored N",
        y = "OFC") +
  theme_minimal()
wb3raw<-ggplot(data = uniroi_wb, aes(x = N_z, y = VxCxN_33voxel_p001__mask_0003, color=proc)) + 
      geom_smooth(method = "lm", se=F,size=.75) + 
      #geom_smooth(group=1, color="black",method = "lm", se=F) + 
      geom_point(aes(color=proc),alpha=.25) +
  facet_wrap(~val) +
   # ylim(-.5,1)+
        labs(
        title = " ",
        x = "z-scored N",
        y = "dlPFC") +
  theme_minimal()

Format dataframe and recreate the model ran in Neuropointilist for plotting purposes

uniroi_wb$site <-ifelse(uniroi_wb$sub < 3150,0,1)
uniroi_wb$site <-as.factor(uniroi_wb$site)

uniroi_wb$Age_c <- uniroi_wb$Age - mean(uniroi_wb$Age, na.rm=T)
uniroi_wb$site <- as.numeric(uniroi_wb$site)
uniroi_wb$site <- uniroi_wb$site - 1
uniroi_wb$SexAtBirth <- as.numeric(uniroi_wb$SexAtBirth)
uniroi_wb$SexAtBirth <- uniroi_wb$SexAtBirth - 1

uniroi_wb$val <- as.factor(uniroi_wb$val)
contrasts(uniroi_wb$val) <- contr.sum(2)
uniroi_wb$proc <- as.factor(uniroi_wb$proc)
contrasts(uniroi_wb$proc) <- contr.sum(2)
# Create a grid of predictor values
predicted <- expand.grid(
  N_z = seq(min(uniroi_wb$N_z),
                          max(uniroi_wb$N_z), length.out = 100),
  val = c("Negative", "Neutral"),
  proc = c("Regulate", "Watch"),
  dep_composite = mean(uniroi_wb$dep_composite),
  anx_composite = mean(uniroi_wb$anx_composite),
  SexAtBirth = mean(uniroi_wb$SexAtBirth),
  site = mean(uniroi_wb$site,na.rm=T),
  Age = mean(uniroi_wb$Age_c)
  )
#recreated models:
wb_3w_cl1 <- gls(VxCxN_33voxel_p001__mask_0001 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_wb, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(wb_3w_cl1)

wb_3w_cl2 <- gls(VxCxN_33voxel_p001__mask_0002 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_wb, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(wb_3w_cl2)

wb_3w_cl3 <- gls(VxCxN_33voxel_p001__mask_0003 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_wb, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(wb_3w_cl3)

#recreated models:
wb_2w_cl1 <- gls(VxN_33voxel_p001__mask_0001 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_wb, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(wb_3w_cl1)

wb_w_cl2 <- gls(VxN_33voxel_p001__mask_0002 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_wb, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")
#summary(wb_3w_cl2)

Three-way Interaction Plots

# wb_3w_cl1_pred_cont <- generate_model_plot(
#   model = wb_3w_cl1, 
#   predicted_data = predicted, 
#   plot_title = "Difference between Regulate and Watch in the  ", 
#   color_negative = "coral2", 
#   color_neutral = "coral4"
# )
# 
# grid.arrange(mpfc_pred_contr_val, mpfc_pred_contr_proc, nrow=1)
# mpfc_pred_nocontr_val

wbp<-plot_model(wb_3w_cl1, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("orange3", "springgreen4"),
              dot.size = 2,
              line.size = 1,
              axis.lim = c(-1.2,1.2))

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

wbp + theme_bw()

wbp2<-plot_model(wb_3w_cl2, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("orange3", "springgreen4"),
              dot.size = 2,
              line.size = 1,
              axis.lim = c(-1.2,1.2))

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

wbp2 + theme_bw()

wbp3<-plot_model(wb_3w_cl3, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("orange3", "springgreen4"),
              dot.size = 2,
              line.size = 1,
              axis.lim = c(-1.2,1.2))

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

wbp3 + theme_bw()

## Warning: Removed 1 rows containing missing values (`geom_point()`).

GPPI Clusters:

Format dataframe and recreate the model ran in Neuropointilist for plotting purposes

uniroi_ppi <- merge(uniroi_ppi, MNA, by.x = "sub", by.y="SubjectID")
uniroi_ppi$site <-ifelse(uniroi_ppi$sub < 3150,0,1)
uniroi_ppi$site <-as.factor(uniroi_ppi$site)
uniroi_ppi$Age_c <- uniroi_ppi$Age - mean(uniroi_ppi$Age, na.rm=T)
uniroi_ppi$site <- as.numeric(uniroi_ppi$site)
uniroi_ppi$site <- uniroi_ppi$site - 1
uniroi_ppi$SexAtBirth <- as.numeric(uniroi_ppi$SexAtBirth)
uniroi_ppi$SexAtBirth <- uniroi_ppi$SexAtBirth - 1

uniroi_ppi$val <- as.factor(uniroi_ppi$val)
contrasts(uniroi_ppi$val) <- contr.sum(2)
uniroi_ppi$proc <- as.factor(uniroi_ppi$proc)
contrasts(uniroi_ppi$proc) <- contr.sum(2)

# Create a grid of predictor values
predicted <- expand.grid(
  N_z = seq(min(uniroi_ppi$N_z),
                          max(uniroi_ppi$N_z), length.out = 100),
val = factor(c("Negative", "Neutral"), levels=c("Negative", "Neutral")),
  proc = factor(c("Regulate", "Watch"), levels=c("Regulate", "Watch")),
  dep_composite = mean(uniroi_ppi$dep_composite),
  anx_composite = mean(uniroi_ppi$anx_composite),
  SexAtBirth = mean(uniroi_ppi$SexAtBirth),
  site = mean(uniroi_ppi$site,na.rm=T),
  Age = mean(uniroi_ppi$Age_c)
  )

#recreated models:
gppi_3w_dacc_cl1 <- gls(dacc_vxcxn_cl1_mask_0001 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_3w_dacc_cl2 <- gls(dacc_vxcxn_cl2_mask_0002 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_3w_dacc_cl3 <- gls(dacc_vxcxn_cl3_mask_0003 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_2w_spgacc_cl1 <- gls(spgacc_fixanx_052325_valntXNz_cluster_num1 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_2w_spgacc_cl2 <- gls(spgacc_fixanx_052325_valntXNz_cluster_num2 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_2w_spgacc_cl3 <- gls(spgacc_fixanx_052325_valntXNz_cluster_num3 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_2w_spgacc_cl4 <- gls(spgacc_fixanx_052325_valntXNz_cluster_num4 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

#gppi_amyg_cl1 <- gls(amyg_Nz_cl1 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, #correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

gppi_2w_dlpfc_cl1 <- gls(dlpfc_cxn_cl1_mask_0001 ~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, data = uniroi_ppi, correlation = corSymm(form = ~ 1 | sub),method = "ML",na.action = "na.omit")

Three-way Interaction Plots

dACC

# gppi_3w_dacc_cl1_pred_cont <- generate_model_plot(
#   model = gppi_3w_dacc_cl1, 
#   predicted_data = predicted, 
#   plot_title = "Difference in connectivity Negative - Neutral in the  ", 
#   color_Regulate = "violetred3", 
#   color_Watch = "violetred4"
# )
# 
# gppi_3w_dacc_cl2_pred_cont <- generate_model_plot(
#   model = gppi_3w_dacc_cl2, 
#   predicted_data = predicted, 
#   plot_title = "Difference in connevtivity Regulate and Watch in the  ", 
#   color_Regulate = "violetred3", 
#   color_Watch = "violetred4"
# )
# 
# gppi_3w_dacc_cl3_pred_cont <- generate_model_plot(
#   model = gppi_3w_dacc_cl3, 
#   predicted_data = predicted, 
#   plot_title = "Difference in connevtivity Regulate and Watch in the  ", 
#   color_Regulate = "violetred3", 
#   color_Watch = "violetred4"
# )
# 
# gppi_3w_dacc_cl1_pred_cont 
# gppi_3w_dacc_cl2_pred_cont 
# gppi_3w_dacc_cl3_pred_cont 

p1<-plot_model(gppi_3w_dacc_cl1, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("darkgoldenrod3", "palegreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p2<-plot_model(gppi_3w_dacc_cl2, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("darkgoldenrod3", "palegreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p3<-plot_model(gppi_3w_dacc_cl3, type = "pred", terms = c("N_z","proc","val"),
              show.data = T,
              colors = c("darkgoldenrod3", "palegreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p1 + theme_bw()

p2 + theme_bw()

p3 + theme_bw()

Two-way Interaction Plots

(create function for 2way plot)

generate_2waymodel_plot <- function(model, predicted_data, plot_title, color, fill) {
  
  # Predict values and standard errors
  pred <- predict(model, 
                  newdata = predicted_data, 
                  level = 0, 
                  se.fit = TRUE)
  
  # Extract variance-covariance matrix and fixed coefficients
  vcov_matrix <- vcov(model)
  fixed_coefs <- coefficients(model)
  
  # Design matrix
  design_matrix <- model.matrix(~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, 
                                data = predicted_data)
  
  # Calculate predicted values and standard errors
  predicted_data$predicted <- design_matrix %*% fixed_coefs
  predicted_data$se <- sqrt(diag(design_matrix %*% vcov_matrix %*% t(design_matrix)))
  
  # Extract unique levels
  composite_levels <- unique(predicted_data$N_z)
  
  # Estimated marginal means and contrasts
  emm_results <- emmeans(model, ~ val * N_z,
                         at = list(N_z = composite_levels), mode = "df.error")
  
  contrast_results <- contrast(emm_results, method = "pairwise", simple = "val")
  df_contrast <- data.frame(summary(contrast_results))
  
  # Generate plot with customizable colors
  plot <- ggplot(df_contrast, aes(x = N_z, 
                                  y = estimate))+
    geom_line(linewidth = .65, color=color) +
    geom_ribbon(aes(ymin = estimate - 1.96 * SE, 
                    ymax = estimate + 1.96 * SE), 
                alpha = 0.3, fill=fill) +
    theme(text = element_text(size = 12, family = "Helvetica"), 
          panel.background = element_blank(),
          panel.border = element_rect(color = "black", fill = NA, linewidth = 1),  
          strip.background = element_rect(fill = "white"),  
          strip.text = element_text(size = 12)) +  
    labs(title = plot_title, x = "N (z-scored)") +
    coord_cartesian(ylim=c(-.4,.4))
  
  return(plot)
}

####################CHANGE TO dLPFC ##### Amyg main effect of N (no fxn, hard code)

# # Predict values and standard errors
#   pred <- predict(gppi_amyg_cl1, 
#                   newdata = predicted_data, 
#                   level = 0, 
#                   se.fit = TRUE)
#   vcov_matrix <- vcov(gppi_amyg_cl1)
#   fixed_coefs <- coefficients(gppi_amyg_cl1)
#   design_matrix <- model.matrix(~ val*proc*N_z + dep_composite + anx_composite + Age + SexAtBirth + site, 
#                                 data = predicted_data)
#   predicted_data$predicted <- design_matrix %*% fixed_coefs
#   predicted_data$se <- sqrt(diag(design_matrix %*% vcov_matrix %*% t(design_matrix)))
#   composite_levels <- unique(predicted_data$N_z)
#   emm_results <- emmeans(gppi_amyg_cl1, ~ N_z,
#                          at = list(N_z = composite_levels), mode = "df.error")
#   df_pred <- data.frame(summary(emm_results))
#   #Plot!!
#   ggplot(df_pred, aes(x = N_z, y = emmean)) +
#     geom_smooth(aes(group=1),method="lm",color="turquoise2",size=1,se=F) +
#     geom_ribbon(aes(ymin = emmean - 1.96 * SE, 
#                     ymax = emmean + 1.96 * SE), 
#                fill="turquoise3",alpha = 0.3) +
#     labs(x = "N (z-scored)", y = "Amygdala connectivity") +
#     coord_cartesian(ylim=c(-.2,.5)) +
#  theme(text = element_text(size = 12, family = "Helvetica"), 
#           panel.background = element_blank(),
#           panel.border = element_rect(color = "black", fill = NA, linewidth = 1),  
#           strip.background = element_rect(fill = "white"),  
#           strip.text = element_text(size = 12)) 

Raw data plots for supplement and interpretation:

dacc

#dACC 3-way-int
dacc_1<-ggplot(data = uniroi_ppi, aes(x = N_z, y = dacc_vxcxn_cl1_mask_0001, color=val, fill=val)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~proc) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "dACC & pre/postcentral gyrus") +
   theme_minimal()

dacc_2<-ggplot(data = uniroi_ppi, aes(x = N_z, y = dacc_vxcxn_cl2_mask_0002, color=val, fill=val)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~proc) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "dACC & Lat. Occipital/TemporalOccip. fusiform") +
   theme_minimal()

dacc_3<-ggplot(data = uniroi_ppi, aes(x = N_z, y = dacc_vxcxn_cl3_mask_0003, color=val, fill=val)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~proc) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "dACC & temporoccipital/cerebellar") +
   theme_minimal()
grid.arrange(dacc_1,dacc_2,dacc_3, nrow=1)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

#spgACC 2-way-int(shown in three way plots?)
spgacc_1<-ggplot(data = uniroi_ppi, aes(x = N_z, y = spgacc_fixanx_052325_valntXNz_cluster_num1, color=proc, fill=proc)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~val) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "spgACC & frontal pole") +
   theme_minimal()

spgacc_2<-ggplot(data = uniroi_ppi, aes(x = N_z, y = spgacc_fixanx_052325_valntXNz_cluster_num2, color=proc, fill=proc)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~val) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "spgACC & L. vlPFC/temp. pole") +
   theme_minimal()

spgacc_3<-ggplot(data = uniroi_ppi, aes(x = N_z, y = spgacc_fixanx_052325_valntXNz_cluster_num3, color=proc, fill=proc)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~val) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "spgACC & L. Superior Frontal Gyrus") +
   theme_minimal()

spgacc_4<-ggplot(data = uniroi_ppi, aes(x = N_z, y = spgacc_fixanx_052325_valntXNz_cluster_num4, color=proc, fill=proc)) + 
      geom_smooth(method = "lm", se=F) + 
      geom_point(alpha=.25,size=1) +
      facet_wrap(~val) + 
        labs(
        title = " ",
        x = "z-scored N",
        y = "spgACC & R. vlPFC/temp. pole ") +
   theme_minimal()
grid.arrange(spgacc_1,spgacc_2,spgacc_3,spgacc_4,nrow=2)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

p1<-plot_model(gppi_2w_spgacc_cl1, type = "pred", terms = c("N_z","val"),
              show.data = T,
              colors = c("darkred", "blue4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p2<-plot_model(gppi_2w_spgacc_cl2, type = "pred", terms = c("N_z","val"),
              show.data = T,
              colors = c("darkred", "blue4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p3<-plot_model(gppi_2w_spgacc_cl3, type = "pred", terms = c("N_z","val"),
              show.data = T,
              colors = c("darkred", "blue4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p4<-plot_model(gppi_2w_spgacc_cl4, type = "pred", terms = c("N_z","val"),
              show.data = T,
              colors = c("darkred", "blue4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

p1 + theme_bw()

p2 + theme_bw()

p3 + theme_bw()

p4 + theme_bw()

dlpfc1<-plot_model(gppi_2w_dlpfc_cl1, type = "pred", terms = c("N_z","proc"),
              show.data = T,
              colors = c("darkgoldenrod3", "palegreen4"),
              dot.size = 2,
              line.size = 1)

## Data points may overlap. Use the `jitter` argument to add some amount of
##   random variation to the location of data points and avoid overplotting.

dlpfc1 + theme_bw()