SOCIAL CONNECTION SCALE

Exclusion Criteria (per scale):

Step 1: If Pp has complete or partial data for 15% of EMA timepoints > included

Step 2: If Pp has completed 15% of all item responses > included

Step 4: Series means will be imputed for any remaining missing data

Step 5: Create composite score with included, imputed items

Evaluating missingness for state social connection items

Step 1 - Percentage of participants who are excluded based on total timepoints completed

library(dplyr)

# Extract column for the daily social connection scale
socialconnect_scale <- eq_EMA_data %>%
  dplyr::select(contains("soc_"))

socialconnect_scale$PID <- eq_EMA_data$PID
socialconnect_scale$EMA_timepoint <- eq_EMA_data$EMA_timepoint
socialconnect_scale$Condition <- eq_EMA_data$Condition

#socialconnect_scale

#Count the total number of timepoints completed per participant
socialconnect_NumComplete <- count(socialconnect_scale, vars = PID)
socialconnect_NumComplete$PercentComplete <- socialconnect_NumComplete$n / 14
colnames(socialconnect_NumComplete)[1:2] <- c("PID", "surveyComplete")

# Extract the PID with less than 15% completion rate across all timepoints
socialconnect_exclude1 <- socialconnect_NumComplete$PID[socialconnect_NumComplete$PercentComplete < 0.15]

#socialconnect_exclude1

# Calculate the percentage of participants who are excluded based on total timepoints completed
socialconnect_percent_AllTime <- length(socialconnect_exclude1)/113
socialconnect_percent_AllTime
## [1] 0.01769912
#Remove participants from the scale dataset who have been excluded based on the first criteria
socialconnect_scale_include1 <- socialconnect_scale %>%
  filter(! PID %in% socialconnect_exclude1)

Step 2 - Percentage of remaining participants excluded due to less than 15% of all item responses completed

# Calculate the number of total completed data per row
socialconnect_scale_include1$itemComplete <- rowSums(!is.na(socialconnect_scale_include1[,1:7]))

#socialconnect_scale_include1

# Aggregate within subject
socialconnect_itemComplete <- aggregate(itemComplete ~ PID, data = socialconnect_scale_include1 [,8:11], sum)

#socialconnect_itemComplete

# Calculate the percentage of missing
socialconnect_totalItem <- 7 * 14 #4 items * 14 timepoints 
socialconnect_itemComplete$percentComplete <- round(socialconnect_itemComplete$itemComplete/socialconnect_totalItem,3) #round to 3 decimal points

#socialconnect_itemComplete #NEEDS UPDATED - MORE THAN 100% COMPLETE - WHY?

# Extract the PID with less than 15% completion rate across both row and column 
#Brendan suggests higher threshold here bc it is related to the reliability of the items. You can systematically look at the reliability and plot it as a functions of items. 
socialconnect_exclude2 <- socialconnect_itemComplete$PID[socialconnect_itemComplete$percentComplete < 0.15]

#socialconnect_exclude2 

# Calculate the percentage of participant who are excluded based on this scale
length(socialconnect_exclude2)/113
## [1] 0
#Remove participants from the scale dataset who have been excluded based on the second criteria
socialconnect_scale_include2 <- socialconnect_scale_include1 %>%
  filter(! PID %in% socialconnect_exclude2)

#socialconnect_scale_include2

Step 3: If a scale has 70% of participant responses after previous steps, scale included, Count the percentage of participants remaining after step 1 and 2 who are included in the scale.

# Count the percentage of participants remaining after step 1 and 2 who are included in the scale
totalIncluded <- length(unique(socialconnect_scale_include2$PID))
percentageIncluded <- round((totalIncluded/113),3)
percentageIncluded
## [1] 0.982

Step 4: Impute series means for any remaining missing data

socialconnect_scale_include2$soc_numdifpeople_imp <- ifelse(is.na(socialconnect_scale_include2$soc_numdifpeople), mean(socialconnect_scale_include2$soc_numdifpeople, na.rm=TRUE), socialconnect_scale_include2$soc_numdifpeople)

socialconnect_scale_include2$soc_numpeople_imp <- ifelse(is.na(socialconnect_scale_include2$soc_numpeople), mean(socialconnect_scale_include2$soc_numpeople, na.rm=TRUE), socialconnect_scale_include2$soc_numpeople)

socialconnect_scale_include2$soc_positive_imp <- ifelse(is.na(socialconnect_scale_include2$soc_positive), mean(socialconnect_scale_include2$soc_positive, na.rm=TRUE), socialconnect_scale_include2$soc_positive)

socialconnect_scale_include2$soc_neg_imp <- ifelse(is.na(socialconnect_scale_include2$soc_neg), mean(socialconnect_scale_include2$soc_neg, na.rm=TRUE), socialconnect_scale_include2$soc_neg)

socialconnect_scale_include2$soc_howpos_imp <- ifelse(is.na(socialconnect_scale_include2$soc_howpos), 
mean(socialconnect_scale_include2$soc_howpos, na.rm=TRUE), 
socialconnect_scale_include2$soc_howpos) #WHY ISN'T THIS ONE IMPUTING?

socialconnect_scale_include2$soc_howneg_imp <- ifelse(is.na(socialconnect_scale_include2$soc_howneg), mean(socialconnect_scale_include2$soc_howneg, na.rm=TRUE), socialconnect_scale_include2$soc_howneg)

socialconnect_scale_include2$soc_closeness_imp <- ifelse(is.na(socialconnect_scale_include2$soc_closeness), mean(socialconnect_scale_include2$soc_closeness, na.rm=TRUE), socialconnect_scale_include2$soc_closeness)

Step 5: Creating composite scores for Social Connection

Re-coding score “soc_closeness” - score will now reflect that higher scores on soc_closeness_imp will equate to greater social connectedness.

socialconnect_scale_include2$soc_closeness_imp <- 5-socialconnect_scale_include2$soc_closeness_imp

Correlation Matrix - All items

socialconnect_scale_include_cor <- dplyr::select(socialconnect_scale_include2, soc_numdifpeople_imp:soc_closeness_imp)

# All Items
sapply(socialconnect_scale_include_cor, is.factor)
## soc_numdifpeople_imp    soc_numpeople_imp     soc_positive_imp 
##                FALSE                FALSE                FALSE 
##          soc_neg_imp       soc_howpos_imp       soc_howneg_imp 
##                FALSE                FALSE                FALSE 
##    soc_closeness_imp 
##                FALSE
cor <- round(cor(socialconnect_scale_include_cor[sapply(socialconnect_scale_include_cor, function(x) !is.factor(x))], use = "complete.obs"),2) #Correlation matrix of indices 

#cor

library(reshape2)
melted_cormat <- melt(cor)
#head(melted_cormat)

# Get lower triangle of the correlation matrix
  get_lower_tri<-function(cormat){
    cormat[upper.tri(cormat)] <- NA
    return(cormat)
  }
  # Get upper triangle of the correlation matrix
  get_upper_tri <- function(cormat){
    cormat[lower.tri(cormat)]<- NA
    return(cormat)
  }
  
  upper_tri <- get_upper_tri(cor)
#upper_tri
  
  # Melt the correlation matrix
library(reshape2)
melted_cormat <- melt(upper_tri, na.rm = TRUE)


reorder_cormat <- function(cor){
# Use correlation between variables as distance
dd <- as.dist((1-cor)/2)
hc <- hclust(dd)
cor <-cor[hc$order, hc$order]
}

# Reorder the correlation matrix
cor <- reorder_cormat(cor)
upper_tri <- get_upper_tri(cor)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
library(ggplot2)
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
 geom_tile(color = "white")+
 scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
   midpoint = 0, limit = c(-1,1), space = "Lab", 
    name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1, 
    size = 12, hjust = 1))+
 coord_fixed()


cor_heatmap <- ggheatmap + 
geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

cor_heatmap

Looking at the correlation matrix for the social connection items, we see that soc_neg_imp and soc_howneg_imp do not correlate well with the other scale items. We will create a composite of the remaining items and then a composite of the negative social connection items. (see below).

Items / Composites that will be analyzed

Positive, close, and connected social interactions

1a - Total number of social interactions (soc_numpeople_imp) (N/N/N)

1b - Number different people interacted with (soc_numdifpeople_imp) (N/Y/Y)

2 - Social closeness/connection (soc_closeness_imp) (N/Y/Y)(N/Y/Y)

3a - Number of positive social interactions (soc_positive_imp) (N/N/N)

3b - Degree of positive social interactions (soc_howpos_imp) (N/N/N)

Negative Interactions

3c - Number of negative social interactions (soc_neg_imp) (N/N/Y)

3d - Degree of negative social interactions (soc_howneg_imp) (N/N/N)

4 - Composite of positive, close, and connected social interactions items (EMA_social_comp) (N/Y/Y)

5 - Composite of negative interactions (EMA_social_comp_neg) (N/Y/Y)

Checking for internal validity of Social connection composite. and creating composite score.

#Checking for internal validity of positive, close, and connected social connection composite 

df_social_connect_comp <- dplyr::select(socialconnect_scale_include2, soc_numdifpeople_imp, soc_numpeople_imp, soc_positive_imp, soc_howpos_imp, soc_closeness_imp) 
                            
psych::alpha(df_social_connect_comp) #calculating chronbach's alpha for pos social connect items
## 
## Reliability analysis   
## Call: psych::alpha(x = df_social_connect_comp)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.74      0.79     0.8      0.43 3.7 0.01  2.5 0.91     0.38
## 
##  lower alpha upper     95% confidence boundaries
## 0.72 0.74 0.76 
## 
##  Reliability if an item is dropped:
##                      raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## soc_numdifpeople_imp      0.67      0.73    0.74      0.40 2.7   0.0126 0.036
## soc_numpeople_imp         0.65      0.72    0.70      0.39 2.5   0.0128 0.023
## soc_positive_imp          0.62      0.69    0.70      0.36 2.2   0.0149 0.042
## soc_howpos_imp            0.75      0.82    0.81      0.52 4.4   0.0115 0.041
## soc_closeness_imp         0.80      0.78    0.78      0.46 3.5   0.0067 0.070
##                      med.r
## soc_numdifpeople_imp  0.38
## soc_numpeople_imp     0.38
## soc_positive_imp      0.30
## soc_howpos_imp        0.54
## soc_closeness_imp     0.47
## 
##  Item statistics 
##                         n raw.r std.r r.cor r.drop mean   sd
## soc_numdifpeople_imp 1345  0.75  0.78  0.73   0.62  3.0 1.03
## soc_numpeople_imp    1345  0.78  0.80  0.79   0.64  3.2 1.19
## soc_positive_imp     1345  0.84  0.85  0.83   0.73  2.6 1.24
## soc_howpos_imp       1345  0.49  0.58  0.41   0.39  2.2 0.53
## soc_closeness_imp    1345  0.76  0.68  0.54   0.44  1.7 2.01
alpha_connect <- psych::alpha(df_social_connect_comp)$total$std.alpha #extracting just the alpha value for items  

alpha_connect <- round(alpha_connect, 2)  

print(alpha_connect) #Good internal consistency
## [1] 0.79
#Creating column with social connection composite score ("EMA_social_comp")

socialconnect_scale_include2$EMA_social_comp <-socialconnect_scale_include2$soc_numdifpeople_imp + socialconnect_scale_include2$soc_numpeople_imp + socialconnect_scale_include2$soc_positive_imp + socialconnect_scale_include2$soc_howpos_imp + socialconnect_scale_include2$soc_closeness_imp

Checking for internal validity of positive, close, and connected social connection composite reveals a chrnobach’s alpha (a= .79).

Checking for internal validity of negative social interaction composite.

#Checking for internal validity of Negative social connection composite 

df_neg_social_connect_comp <- dplyr::select(socialconnect_scale_include2, soc_neg_imp, soc_howneg_imp) 
                            
psych::alpha(df_neg_social_connect_comp) #calculating chronbach's alpha for neg social connect items
## 
## Reliability analysis   
## Call: psych::alpha(x = df_neg_social_connect_comp)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.55       0.6    0.43      0.43 1.5 0.021 0.93 0.58     0.43
## 
##  lower alpha upper     95% confidence boundaries
## 0.51 0.55 0.59 
## 
##  Reliability if an item is dropped:
##                raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## soc_neg_imp         0.43      0.43    0.19      0.43  NA       NA  0.43  0.43
## soc_howneg_imp      0.19      0.43      NA        NA  NA       NA  0.19  0.43
## 
##  Item statistics 
##                   n raw.r std.r r.cor r.drop mean   sd
## soc_neg_imp    1345  0.92  0.85  0.56   0.43  0.6 0.84
## soc_howneg_imp 1345  0.75  0.85  0.56   0.43  1.3 0.51
## 
## Non missing response frequency for each item
##                   0 0.602400600150037    1 1.25987841945289    2    3    4 5
## soc_neg_imp    0.57              0.01 0.30             0.00 0.09 0.03 0.01 0
## soc_howneg_imp 0.00              0.00 0.76             0.02 0.18 0.04 0.00 0
##                miss
## soc_neg_imp       0
## soc_howneg_imp    0
alpha_neg <- psych::alpha(df_neg_social_connect_comp)$total$std.alpha #extracting just the alpha value for items  

alpha_neg <- round(alpha_neg, 2)  

print(alpha_neg) #Acceptable internal consistency
## [1] 0.6
#Creating column with social connection composite score ("EMA_social_comp")

socialconnect_scale_include2$EMA_neg_social_comp <-socialconnect_scale_include2$soc_neg_imp + socialconnect_scale_include2$soc_howneg_imp

Checking for internal validity of negative social connection composite reveals a chronbach’s alpha (a= .60)

Re-labeling condition labels. The conditions were originally labeled as 1 and 2 in the dataset, this function changes this such that condition 1 is now condition 0 and condition 2 is now condition 1.

library(tidyr)
socialconnect_scale_include2 <- socialconnect_scale_include2 %>% 
  separate (EMA_timepoint, into = c("pre_post", "timepoint")) %>% 
  mutate(Condition = Condition -1) #the conditions were originally labeled as 1 and 2 in the dataset, this function changes this such that condition 1 is now condition 0 and condition 2 is now condition 1. 
#Note: 
  # Original condition 1 in the dataset is now "Condition 0"
  # Original condition 2 in the dataset is now "Condition 1"

#socialconnect_scale_include2

Creating separate dataframes for 1-week and 14-week follow-up

socialconnect_scale_1wkpost <- socialconnect_scale_include2 %>% 
  filter(pre_post == 'Base')

socialconnect_scale_14wkpost <- socialconnect_scale_include2 %>% 
  filter(pre_post == 'End')

Mixed models for EMA Social Connection Scales

library(lme4)
library(lmerTest)

socialconnect_scale_include2 <- socialconnect_scale_include2 %>% 
  mutate(pre_post = as.factor(pre_post), 
         timepoint = as.factor(timepoint))

POSITIVE, CLOSE, and CONNECTED SOCIAL CONNECTION COMPOSITE

Describing the data

# Descriptives - EMA Social Connection Composite (EMA_social_comp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$EMA_social_comp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 323 13.57223 3.910311     14 13.80629 4.4478   0  22    22
## X12    2      1    1 347 12.90287 4.359070     13 13.17271 4.4478   1  22    21
##           skew    kurtosis        se
## X11 -0.5183477  0.07009815 0.2175755
## X12 -0.5203587 -0.16305883 0.2340071
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$EMA_social_comp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 344 12.65017 4.463040     13 12.74781 4.4478  -2  22    24
## X12    2      1    1 331 11.58084 5.138869     12 11.79064 5.9304  -2  22    24
##           skew   kurtosis        se
## X11 -0.2983468  0.3683010 0.2406310
## X12 -0.3430122 -0.3647332 0.2824578
# Post
psych::describeBy(socialconnect_scale_include2$EMA_social_comp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed     mad min max
## X11    1      0    1 667 13.09669 4.226561     13 13.25728 4.44780  -2  22
## X12    2      1    1 678 12.25745 4.798022     13 12.51417 4.76008  -2  22
##     range       skew   kurtosis        se
## X11    24 -0.4221188  0.3060899 0.1636531
## X12    24 -0.4717985 -0.1965581 0.1842669
boxplot(EMA_social_comp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Social Connection Comp Score")

Mixed model for Social Connection Composite (POST)

# NEED TO UPDATE THESE TO REMOVE "PRE_POST" FROM (1 + pre_post | PID) once I confirm with David. 

# Model 1 Social Comp

EQ_MM_Social_model <- lmer (EMA_social_comp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
summary(EQ_MM_Social_model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EMA_social_comp ~ pre_post * Condition + (1 + pre_post | PID)
##    Data: socialconnect_scale_include2
## 
## REML criterion at convergence: 7280.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3066 -0.5551  0.0798  0.6238  3.3182 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  PID      (Intercept)  7.245   2.692        
##           pre_postEnd  2.284   1.511    0.37
##  Residual             10.426   3.229        
## Number of obs: 1345, groups:  PID, 111
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            13.6231     0.4060 110.2270  33.551  < 2e-16 ***
## pre_postEnd            -0.9319     0.3251 103.6763  -2.867  0.00502 ** 
## Condition              -0.6194     0.5704 109.1143  -1.086  0.27990    
## pre_postEnd:Condition  -0.3074     0.4598 103.2911  -0.669  0.50522    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) pr_psE Condtn
## pre_postEnd -0.045              
## Condition   -0.712  0.032       
## pr_pstEnd:C  0.032 -0.707 -0.038
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.62 12.83 – 14.42 <0.001
pre_post [End] -0.93 -1.57 – -0.29 0.004
Condition -0.62 -1.74 – 0.50 0.278
pre_post [End] *
Condition 		-0.31 -1.21 – 0.59 0.504
Random Effects
σ2 10.43
τ00 PID 7.24
τ11 PID.pre_postEnd 2.28
ρ01 PID 0.37
ICC 0.49
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.021 / 0.498 
#This model does not yet account for person-level predictors 

# Plotting model

library(sjPlot)
plot_model(model = EQ_MM_Social_model, type = "int")

WRITE UP - Collapsing 1-week + 14-week in Mixed Model Post Social Composite

Running a regression model to look at the effect of condition at fourteen-week post for composite score for positive, close, and connected social interactions reveals a non-significant effect (t(?)=-1.09, p = 0.28).

Regression for Social Connection Composite (1-WEEK POST)

# Regression for Social Connection Composite (1-WEEK POST)
EQ_MM_Social_model_1wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_1wkpost) 

View(socialconnect_scale_1wkpost)
summary(EQ_MM_Social_model_1wk)
## 
## Call:
## lm(formula = EMA_social_comp ~ Condition, data = socialconnect_scale_1wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.5722  -2.5722   0.3678   3.3078   9.0971 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  13.5722     0.2308  58.793   <2e-16 ***
## Condition    -0.6694     0.3208  -2.087   0.0373 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.149 on 668 degrees of freedom
## Multiple R-squared:  0.006477,   Adjusted R-squared:  0.004989 
## F-statistic: 4.355 on 1 and 668 DF,  p-value: 0.03729
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_1wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.57 13.12 – 14.03 <0.001
Condition -0.67 -1.30 – -0.04 0.037
Observations 670
R2 / R2 adjusted 0.006 / 0.005

WRITE UP - 1-week Post Composite

Running a regression model to look at the effect of condition at one-week post for composite score for positive, close, and connected social interactions reveals a significant effect (t(668)=-2.09, p = 0.04).

Regression for Social Connection Composite (14-WEEK POST)

# Regression for Social Connection Composite (14-WEEK POST)

EQ_MM_Social_model_14wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_14wkpost) 

summary(EQ_MM_Social_model_14wk)
## 
## Call:
## lm(formula = EMA_social_comp ~ Condition, data = socialconnect_scale_14wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.6502  -3.5808   0.3498   3.4192  10.4192 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.6502     0.2591   48.82  < 2e-16 ***
## Condition    -1.0693     0.3701   -2.89  0.00398 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.806 on 673 degrees of freedom
## Multiple R-squared:  0.01225,    Adjusted R-squared:  0.01079 
## F-statistic:  8.35 on 1 and 673 DF,  p-value: 0.003981
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_14wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 12.65 12.14 – 13.16 <0.001
Condition -1.07 -1.80 – -0.34 0.004
Observations 675
R2 / R2 adjusted 0.012 / 0.011

WRITE UP - 14-week Post Composite

Running a regression model to look at the effect of condition at fourteen-week post for composite score for positive, close, and connected social interactions reveals a significant effect (t(673)=-2.89, p = 0.00).

Plotting EMA_comp scores

library(ggplot2)

#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)
    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }
    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )
    # Rename the "mean" column    
    datac <- rename(datac, c("mean" = measurevar))

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}

soc_connect_summary <- summarySE(socialconnect_scale_include2, measurevar="EMA_social_comp", groupvars=c("pre_post", "Condition"))
soc_connect_summary
##   pre_post Condition   N EMA_social_comp       sd        se        ci
## 1     Base         0 323        13.57223 3.910311 0.2175755 0.4280490
## 2     Base         1 347        12.90287 4.359070 0.2340071 0.4602555
## 3      End         0 344        12.65017 4.463040 0.2406310 0.4732981
## 4      End         1 331        11.58084 5.138869 0.2824578 0.5556450
soc_connect_summary$Condition <- as.factor(soc_connect_summary$Condition)

library(plyr)
soc_connect_summary$pre_post<- revalue(soc_connect_summary$pre_post, c("Base"="1-Week Post", "End"="14-Weeks Post"))

#Basic line plot 
p<- ggplot(data=soc_connect_summary, aes(x=as.factor(pre_post), y=EMA_social_comp, group=Condition, color=Condition)) +
  geom_line(aes(linetype=Condition)) + geom_point(aes(shape=Condition))+
  scale_color_brewer(palette="Paired")+
  labs(title="Social Connection at 1-week and 14-weeks Post",x="Timepoint", y = "Social Connection")+
  theme_classic() 
p + theme_classic() + scale_color_manual(values=c('orange1','olivedrab3'))

#Basic line plot with confidence intervals
p<- ggplot(data=soc_connect_summary, aes(x=as.factor(pre_post), y=EMA_social_comp, group=Condition, color=Condition)) +
  geom_line(aes(linetype=Condition)) + geom_point(aes(shape=Condition))+
  scale_color_brewer(palette="Paired")+
  labs(title="Social Connection at 1-week and 14-weeks Post",x="Timepoint", y = "Social Connection")+
  theme_classic() +
  geom_errorbar(aes(ymin=EMA_social_comp-ci, ymax=EMA_social_comp+ci), width=.2,
                 position=position_dodge(0.05))
p + theme_classic() + scale_color_manual(values=c('orange1','olivedrab3'))

# Does it make sense to have an N? Do I use N for Pps or for observations

Removing Outliers

#Obtaining values of outliers 

library(dplyr)
library(ggplot2)

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

socialconnect_scale_include2 %>%
  group_by(Condition) %>%
  mutate(outlier = ifelse(is_outlier(EMA_social_comp), EMA_social_comp, as.numeric(NA))) %>%
  ggplot(., aes(x = factor(Condition), y = EMA_social_comp)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)

# NEED TO FIGURE OUT HOW TO Remove outliers at both levels

Removing Outliers

socialconnect_scale_include2_out <- socialconnect_scale_include2 %>% 
  filter(Condition == 0, EMA_social_comp > 3) 

socialconnect_scale_include2_1 <- socialconnect_scale_include2 %>% 
  filter(Condition == 1, EMA_social_comp > -2) 

socialconnect_scale_out <- rbind(socialconnect_scale_include2_out, socialconnect_scale_include2_1)
#run the models with and without these outliers

Mixed model for EMA Social Connection (outliers removed)

library(lme4)
library(lmerTest)

socialconnect_scale_out <- socialconnect_scale_out %>% 
  mutate(pre_post = as.factor(pre_post), 
         timepoint = as.factor(timepoint))

EQ_MM_SocialConnect_model_out <- lmer (EMA_social_comp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_out) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_SocialConnect_model_out)
sjPlot::tab_model(EQ_MM_SocialConnect_model_out)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.68 12.90 – 14.47 <0.001
pre_post [End] -0.73 -1.33 – -0.12 0.019
Condition -0.67 -1.77 – 0.43 0.230
pre_post [End] *
Condition 		-0.45 -1.31 – 0.41 0.305
Random Effects
σ2 9.74
τ00 PID 7.06
τ11 PID.pre_postEnd 1.92
ρ01 PID 0.29
ICC 0.48
N PID 111
Observations 1328
Marginal R2 / Conditional R2 0.022 / 0.494 
#This model does not yet account for person-level predictors 
library(sjPlot)
plot_model(model = EQ_MM_SocialConnect_model_out, type = "int")

Creating separate dataframes for 1-week and 14-week follow-up (outliers removed)

socialconnect_scale_out_1wkpost <- socialconnect_scale_out %>% 
  filter(pre_post == 'Base')

socialconnect_scale_out_14wkpost <- socialconnect_scale_out %>% 
  filter(pre_post == 'End')

Regression for Social Connection Composite (1-WEEK POST / outliers removed)

EQ_MM_Social_model_1wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_out_1wkpost) 

summary(EQ_MM_Social_model_1wk)
## 
## Call:
## lm(formula = EMA_social_comp ~ Condition, data = socialconnect_scale_out_1wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.9029  -2.6537   0.3463   3.3078   9.0971 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  13.6537     0.2284  59.776   <2e-16 ***
## Condition    -0.7508     0.3169  -2.369   0.0181 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.092 on 666 degrees of freedom
## Multiple R-squared:  0.008357,   Adjusted R-squared:  0.006868 
## F-statistic: 5.613 on 1 and 666 DF,  p-value: 0.01811
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_1wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.65 13.21 – 14.10 <0.001
Condition -0.75 -1.37 – -0.13 0.018
Observations 668
R2 / R2 adjusted 0.008 / 0.007 
#ggplot(socialconnect_scale_out_1wkpost) +
    #geom_bar( aes(x=Condition, y=EMA_social_comp), stat="identity", fill="skyblue", #alpha=0.7)

Running a regression model to look at the effect of condition at one-week post for composite score for positive, close, and connected social interactions reveals a significant effect (t(666)=-2.37, p = 0.02).

Regression for Social Connection Composite (14-WEEK POST / outliers removed)

EQ_MM_Social_model_14wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_out_14wkpost) 

summary(EQ_MM_Social_model_14wk)
## 
## Call:
## lm(formula = EMA_social_comp ~ Condition, data = socialconnect_scale_out_14wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.7051  -3.0592  -0.0592   3.2949  10.2949 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  13.0592     0.2473  52.817  < 2e-16 ***
## Condition    -1.3542     0.3507  -3.861 0.000124 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.505 on 658 degrees of freedom
## Multiple R-squared:  0.02215,    Adjusted R-squared:  0.02067 
## F-statistic: 14.91 on 1 and 658 DF,  p-value: 0.0001241
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_14wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.06 12.57 – 13.54 <0.001
Condition -1.35 -2.04 – -0.67 <0.001
Observations 660
R2 / R2 adjusted 0.022 / 0.021

Running a regression model to look at the effect of condition at one-week post for composite score for positive, close, and connected social interactions reveals a significant effect (t(658)=-3.86, p = 0.00).

Plotting EMA_comp scores w/ outliers removed

library(ggplot2)

soc_connect_summary_out <- summarySE(socialconnect_scale_out, measurevar="EMA_social_comp", groupvars=c("pre_post", "Condition"))
soc_connect_summary_out
##   pre_post Condition   N EMA_social_comp       sd        se        ci
## 1     Base         0 321        13.65368 3.782837 0.2111374 0.4153927
## 2     Base         1 347        12.90287 4.359070 0.2340071 0.4602555
## 3      End         0 332        13.05921 3.963221 0.2175100 0.4278763
## 4      End         1 328        11.70506 4.994264 0.2757621 0.5424916
soc_connect_summary_out$Condition <- as.factor(soc_connect_summary_out$Condition)

library(plyr)
soc_connect_summary_out$pre_post<- revalue(soc_connect_summary_out$pre_post, c("Base"="1-Week Post", "End"="14-Weeks Post"))

#Basic line plot 
p<- ggplot(data=soc_connect_summary_out, aes(x=as.factor(pre_post), y=EMA_social_comp, group=Condition, color=Condition)) +
  geom_line(aes(linetype=Condition)) + geom_point(aes(shape=Condition))+
  scale_color_brewer(palette="Paired")+
  labs(title="Social Connection at 1-week and 14-weeks Post",x="Timepoint", y = "Social Connection")+
  theme_classic() 
p + theme_classic() + scale_color_manual(values=c('orange1','olivedrab3'))

#Basic line plot with confidence intervals
p<- ggplot(data=soc_connect_summary_out, aes(x=as.factor(pre_post), y=EMA_social_comp, group=Condition, color=Condition)) +
  geom_line(aes(linetype=Condition)) + geom_point(aes(shape=Condition))+
  scale_color_brewer(palette="Paired")+
  labs(title="Social Connection at 1-week and 14-weeks Post",x="Timepoint", y = "Social Connection")+
  theme_classic() +
  geom_errorbar(aes(ymin=EMA_social_comp-ci, ymax=EMA_social_comp+ci), width=.2,
                 position=position_dodge(0.05))
p + theme_classic() + scale_color_manual(values=c('orange1','olivedrab3'))

# Does it make sense to have an N? Do I use N for Pps or for observations

///NEGATIVE SOCIAL INTERACTIONS COMPOSITE///

Describing the data

# Descriptives - EMA Social Connection Composite (EMA_social_comp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$EMA_neg_social_comp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd   median  trimmed       mad min
## X11    1      0    1 323 1.956048 1.221739 1.259878 1.740402 0.3852957   1
## X12    2      1    1 347 1.926877 1.099753 2.000000 1.757586 1.4826000   1
##          max    range      skew    kurtosis         se
## X11 6.000000 5.000000 1.2762762  0.94670774 0.06797938
## X12 5.259878 4.259878 0.9822507 -0.01134774 0.05903781
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$EMA_neg_social_comp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed mad min max range
## X11    1      0    1 344 1.871247 1.180213      1 1.645613   0   1   6     5
## X12    2      1    1 331 1.693735 1.132470      1 1.463573   0   1   8     7
##         skew  kurtosis         se
## X11 1.316492 0.9790247 0.06363285
## X12 1.975102 4.5200139 0.06224619
# Post
psych::describeBy(socialconnect_scale_include2$EMA_neg_social_comp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed mad min max range
## X11    1      0    1 667 1.912313 1.200348      1 1.691502   0   1   6     5
## X12    2      1    1 678 1.813057 1.121102      1 1.605171   0   1   8     7
##         skew  kurtosis         se
## X11 1.300701 0.9831728 0.04647765
## X12 1.459353 2.0726548 0.04305567
boxplot(EMA_neg_social_comp~Condition,socialconnect_scale_include2, main="Brief Negative Social Connection Scores by Condition",
   xlab="Condition", ylab="Negative Social Interactions Comp Score")

Mixed model for Negative Social Interactions Composite (POST)

# NEED TO UPDATE THESE TO REMOVE "PRE_POST" FROM (1 + pre_post | PID) once I confirm with David. 

# Model 1 Comp

EQ_MM_Social_model_negcomp <- lmer (EMA_neg_social_comp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_Social_model)

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_negcomp)
  EMA neg social comp
Predictors Estimates CI p
(Intercept) 1.95 1.74 – 2.16 <0.001
pre_post [End] -0.07 -0.28 – 0.13 0.484
Condition 0.00 -0.29 – 0.30 0.993
pre_post [End] *
Condition 		-0.15 -0.44 – 0.14 0.310
Random Effects
σ2 0.89
τ00 PID 0.47
τ11 PID.pre_postEnd 0.30
ρ01 PID -0.42
ICC 0.34
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.006 / 0.348 
#This model does not yet account for person-level predictors 

# Plotting model

library(sjPlot)
plot_model(model = EQ_MM_Social_model_negcomp, type = "int")

# Regression for Negative Social Interactions Composite (1-WEEK POST)

EQ_MM_Social_model_neg_1wk <- lm (EMA_neg_social_comp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_neg_1wk)
  EMA neg social comp
Predictors Estimates CI p
(Intercept) 1.96 1.83 – 2.08 <0.001
Condition -0.03 -0.21 – 0.15 0.745
Observations 670
R2 / R2 adjusted 0.000 / -0.001

Regression for Negative Social Interactions Composite (14-WEEK POST)

EQ_MM_Social_model_neg_14wk <- lm (EMA_neg_social_comp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_neg_14wk)
  EMA neg social comp
Predictors Estimates CI p
(Intercept) 1.87 1.75 – 1.99 <0.001
Condition -0.18 -0.35 – -0.00 0.047
Observations 675
R2 / R2 adjusted 0.006 / 0.004

Plotting EMA_neg_social_comp scores

library(ggplot2)

neg_soc_connect_summary <- summarySE(socialconnect_scale_include2, measurevar="EMA_neg_social_comp", groupvars=c("pre_post", "Condition"))
neg_soc_connect_summary
##   pre_post Condition   N EMA_neg_social_comp       sd         se        ci
## 1     Base         0 323            1.956048 1.221739 0.06797938 0.1337398
## 2     Base         1 347            1.926877 1.099753 0.05903781 0.1161182
## 3      End         0 344            1.871247 1.180213 0.06363285 0.1251597
## 4      End         1 331            1.693735 1.132470 0.06224619 0.1224494
neg_soc_connect_summary$Condition <- as.factor(neg_soc_connect_summary$Condition)

library(plyr)
neg_soc_connect_summary$pre_post<- revalue(neg_soc_connect_summary$pre_post, c("Base"="1-Week Post", "End"="14-Weeks Post"))

#Basic line plot with confidence intervals
p<- ggplot(data=neg_soc_connect_summary, aes(x=as.factor(pre_post), y=EMA_neg_social_comp, group=Condition, color=Condition)) +
  geom_line(aes(linetype=Condition)) + geom_point(aes(shape=Condition))+
  scale_color_brewer(palette="Paired")+
  labs(title="Negative Social Interactions at 1-week and 14-weeks Post",x="Timepoint", y = "Negative Social Interactions")+
  theme_classic() +
  geom_errorbar(aes(ymin=EMA_neg_social_comp-ci, ymax=EMA_neg_social_comp+ci), width=.2,
                 position=position_dodge(0.05))
p + theme_classic() + scale_color_manual(values=c('orange1','olivedrab3'))

# Does it make sense to have an N? Do I use N for Pps or for observations

MODELS FOR INDIVIDUAL ITEMS

NUMBER OF SOCIAL INTERACTIONS

Describing the data

# Total number of social interactions (soc_numpeople_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_numpeople_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 323 3.431447 1.140636      3 3.472423 1.4826   1   5     4
## X12    2      1    1 347 3.303624 1.068567      3 3.302357 1.4826   1   5     4
##            skew   kurtosis         se
## X11 -0.21942506 -0.8245707 0.06346670
## X12 -0.05801778 -0.7291598 0.05736367
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_numpeople_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 344 3.080047 1.241756      3 3.110639 1.4826   0   5     5
## X12    2      1    1 331 2.903863 1.241911      3 2.902561 1.4826   0   5     5
##            skew   kurtosis         se
## X11 -0.03365631 -0.8264586 0.06695100
## X12 -0.01720361 -0.6383279 0.06826158
# Post
psych::describeBy(socialconnect_scale_include2$soc_numpeople_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 667 3.250216 1.205842      3 3.285783 1.4826   0   5     5
## X12    2      1    1 678 3.108461 1.172758      3 3.127824 1.4826   0   5     5
##           skew   kurtosis         se
## X11 -0.1448039 -0.8151606 0.04669038
## X12 -0.1068199 -0.5861024 0.04503951
boxplot(soc_numpeople_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Total number of social interactions")

Mixed model for Number of social interactions (POST)

# NEED TO UPDATE THESE TO REMOVE "PRE_POST" FROM (1 + pre_post | PID) once I confirm with David. 

EQ_MM_Social_model7 <- lmer (soc_numpeople_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 

sjPlot::tab_model(EQ_MM_Social_model7)
  soc numpeople imp
Predictors Estimates CI p
(Intercept) 3.43 3.21 – 3.66 <0.001
pre_post [End] -0.33 -0.52 – -0.14 0.001
Condition -0.09 -0.40 – 0.22 0.570
pre_post [End] *
Condition 		-0.05 -0.31 – 0.22 0.722
Random Effects
σ2 0.65
τ00 PID 0.59
τ11 PID.pre_postEnd 0.27
ρ01 PID 0.02
ICC 0.53
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.024 / 0.541 
#This model does not yet account for person-level predictors 

# Plotting model

library(sjPlot)
plot_model(model = EQ_MM_Social_model7, type = "int")

Regression for Number of social interactions (1-WEEK POST)

EQ_MM_Social_model7_1wk <- lm (soc_numpeople_imp ~ Condition, data = socialconnect_scale_1wkpost) 

summary(EQ_MM_Social_model7_1wk)
## 
## Call:
## lm(formula = soc_numpeople_imp ~ Condition, data = socialconnect_scale_1wkpost)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4314 -0.4315 -0.3036  0.6964  1.6964 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.43145    0.06142  55.866   <2e-16 ***
## Condition   -0.12782    0.08535  -1.498    0.135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.104 on 668 degrees of freedom
## Multiple R-squared:  0.003347,   Adjusted R-squared:  0.001855 
## F-statistic: 2.243 on 1 and 668 DF,  p-value: 0.1347
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model7_1wk)
  soc numpeople imp
Predictors Estimates CI p
(Intercept) 3.43 3.31 – 3.55 <0.001
Condition -0.13 -0.30 – 0.04 0.135
Observations 670
R2 / R2 adjusted 0.003 / 0.002

Running a regression model to look at the effect of condition at one-week post for number of social interactions reveals a non-significant effect (t(668)=-1.50, p = 0.14).

Regression for Number of social interactions (14-WEEK POST)

EQ_MM_Social_model7_14wk <- lm (soc_numpeople_imp ~ Condition, data = socialconnect_scale_14wkpost) 

summary(EQ_MM_Social_model7_14wk)
## 
## Call:
## lm(formula = soc_numpeople_imp ~ Condition, data = socialconnect_scale_14wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.08005 -0.90386 -0.08005  0.91995  2.09614 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.08005    0.06696  46.002   <2e-16 ***
## Condition   -0.17618    0.09561  -1.843   0.0658 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.242 on 673 degrees of freedom
## Multiple R-squared:  0.00502,    Adjusted R-squared:  0.003541 
## F-statistic: 3.395 on 1 and 673 DF,  p-value: 0.06582
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model7_14wk)
  soc numpeople imp
Predictors Estimates CI p
(Intercept) 3.08 2.95 – 3.21 <0.001
Condition -0.18 -0.36 – 0.01 0.066
Observations 675
R2 / R2 adjusted 0.005 / 0.004

Running a regression model to look at the effect of condition at fourteen-week post for number of social interactions reveals a non-significant effect (t(673)=-1.84, p = 0.07).

NUMBER OF UNIQUE SOCIAL INTERACTIONS

Describing the data

# Number of unique social interactions (soc_numdifpeople_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_numdifpeople_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd median  trimmed    mad min max
## X11    1      0    1 323 3.213456 0.9656918      3 3.188981 1.4826   0   5
## X12    2      1    1 347 3.066127 0.9758412      3 3.039234 1.4826   1   5
##     range        skew    kurtosis         se
## X11     5 -0.02211768 -0.01772336 0.05373253
## X12     4  0.09159777 -0.39869228 0.05238590
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_numdifpeople_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 344 2.961974 1.091318      3 2.927244 1.4826   0   5     5
## X12    2      1    1 331 2.652487 1.022515      3 2.637634 1.4826   0   5     5
##          skew   kurtosis         se
## X11 0.1288718 -0.2819911 0.05883995
## X12 0.2051521  0.2586399 0.05620252
# Post 
psych::describeBy(socialconnect_scale_include2$soc_numdifpeople_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 667 3.083756 1.039263      3 3.053954 1.4826   0   5     5
## X12    2      1    1 678 2.864188 1.019381      3 2.847278 1.4826   0   5     5
##           skew    kurtosis         se
## X11 0.02519652 -0.17335532 0.04024041
## X12 0.11463383 -0.08426436 0.03914910
boxplot(soc_numdifpeople_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Total number of unique social interactions")

#THIS LOOKS OFF....

Mixed model for Number of unique social interactions (POST)

# NEED TO UPDATE THESE TO REMOVE "PRE_POST" FROM (1 + pre_post | PID) once I confirm with David. 

EQ_MM_Social_model6 <- lmer (soc_numdifpeople_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 

sjPlot::tab_model(EQ_MM_Social_model6)
  soc numdifpeople imp
Predictors Estimates CI p
(Intercept) 3.22 3.04 – 3.40 <0.001
pre_post [End] -0.25 -0.42 – -0.07 0.005
Condition -0.14 -0.40 – 0.11 0.269
pre_post [End] *
Condition 		-0.13 -0.38 – 0.12 0.301
Random Effects
σ2 0.60
τ00 PID 0.37
τ11 PID.pre_postEnd 0.23
ρ01 PID -0.11
ICC 0.43
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.033 / 0.449 
#This model does not yet account for person-level predictors 

# Plotting model

library(sjPlot)
plot_model(model = EQ_MM_Social_model6, type = "int")

# Running models with outliers removed

Detecting Outliers

#Obtaining values of outliers 

library(dplyr)
library(ggplot2)

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

socialconnect_scale_include2 %>%
  group_by(Condition) %>%
  mutate(outlier = ifelse(is_outlier(soc_numdifpeople_imp), soc_numdifpeople_imp, as.numeric(NA))) %>%
  ggplot(., aes(x = factor(Condition), y = soc_numdifpeople_imp)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)

# Removing Outliers

socialconnect_out1 <- socialconnect_scale_include2 %>% 
  filter(Condition == 1, soc_numdifpeople_imp > 0) 

socialconnect_out2 <- socialconnect_scale_include2 %>% 
  filter(Condition == 2, 5 > soc_numdifpeople_imp, soc_numdifpeople_imp > 0) 

socialconnect_out <- rbind(socialconnect_out1, socialconnect_out2)

Re-running Mixed model for Number of unique social interactions (POST, outliers removed)

# NEED TO UPDATE THESE TO REMOVE "PRE_POST" FROM (1 + pre_post | PID) once I confirm with David. 

EQ_MM_Social_model_out <- lmer (soc_numdifpeople_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_out) 
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
sjPlot::tab_model(EQ_MM_Social_model_out) #Why isn't condition showing up???
  soc numdifpeople imp
Predictors Estimates CI p
(Intercept) 3.08 2.89 – 3.26 <0.001
pre_post [End] -0.35 -0.53 – -0.17 <0.001
Random Effects
σ2 0.56
τ00 PID 0.40
τ11 PID.pre_postEnd 0.26
ρ01 PID -0.29
ICC 0.44
N PID 56
Observations 673
Marginal R2 / Conditional R2 0.030 / 0.457 
#This model does not yet account for person-level predictors 

# Plotting model

library(sjPlot)
plot_model(model = EQ_MM_Social_model_out, type = "int")
## Error: Confidence intervals could not be computed.
## * Reason: "non-conformable arguments"
## * Source: mm %*% vcm

Regression for Number of unique social interactions (1-WEEK POST, outliers included)

EQ_MM_Social_model6_1wk <- lm (soc_numdifpeople_imp ~ Condition, data = socialconnect_scale_1wkpost) 

summary(EQ_MM_Social_model6_1wk)
## 
## Call:
## lm(formula = soc_numdifpeople_imp ~ Condition, data = socialconnect_scale_1wkpost)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2135 -0.2135 -0.0661  0.7865  1.9339 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.21346    0.05403  59.480   <2e-16 ***
## Condition   -0.14733    0.07507  -1.963   0.0501 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.971 on 668 degrees of freedom
## Multiple R-squared:  0.005733,   Adjusted R-squared:  0.004244 
## F-statistic: 3.851 on 1 and 668 DF,  p-value: 0.05012
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model6_1wk)
  soc numdifpeople imp
Predictors Estimates CI p
(Intercept) 3.21 3.11 – 3.32 <0.001
Condition -0.15 -0.29 – 0.00 0.050
Observations 670
R2 / R2 adjusted 0.006 / 0.004

Running a regression model to look at the effect of condition at one-week post for number of unique social interactions reveals a significant effect (t(668)=-1.96, p = 0.05).

Regression for Number of unique social interactions (14-WEEK POST, outliers included)

EQ_MM_Social_model6_14wk <- lm (soc_numdifpeople_imp ~ Condition, data = socialconnect_scale_14wkpost) 

summary(EQ_MM_Social_model6_14wk)
## 
## Call:
## lm(formula = soc_numdifpeople_imp ~ Condition, data = socialconnect_scale_14wkpost)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.96197 -0.65249  0.03803  0.34751  2.34751 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.96197    0.05705  51.918  < 2e-16 ***
## Condition   -0.30949    0.08147  -3.799 0.000159 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.058 on 673 degrees of freedom
## Multiple R-squared:  0.02099,    Adjusted R-squared:  0.01954 
## F-statistic: 14.43 on 1 and 673 DF,  p-value: 0.0001586
library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model6_14wk)
  soc numdifpeople imp
Predictors Estimates CI p
(Intercept) 2.96 2.85 – 3.07 <0.001
Condition -0.31 -0.47 – -0.15 <0.001
Observations 675
R2 / R2 adjusted 0.021 / 0.020

Running a regression model to look at the effect of condition at fourteen-week post for number of unique social interactions reveals a significant effect (t(668)=-1.96, p = 0.05).

/// SOCIAL CLOSENESS / CONNECTION ///

# Descriptives - Social closeness/connection (soc_closeness_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_closeness_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 323 2.029290 1.781433      3 2.264327 1.4826  -3   4     7
## X12    2      1    1 347 1.625134 2.027648      3 1.874270 1.4826  -3   4     7
##          skew   kurtosis         se
## X11 -1.075461  0.5779050 0.09912159
## X12 -0.906075 -0.1162136 0.10884983
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_closeness_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 344 1.823230 1.970237      3 2.076779 1.4826  -3   4     7
## X12    2      1    1 331 1.452358 2.210033      1 1.682001 2.9652  -3   4     7
##           skew   kurtosis        se
## X11 -0.9601319  0.1325495 0.1062281
## X12 -0.6562069 -0.7061560 0.1214744
# Post
psych::describeBy(socialconnect_scale_include2$soc_closeness_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd   median  trimmed      mad min max
## X11    1      0    1 667 1.923016 1.882594 3.000000 2.167573 1.482600  -3   4
## X12    2      1    1 678 1.540784 2.118843 1.730337 1.780610 1.882402  -3   4
##     range       skew   kurtosis         se
## X11     7 -1.0252306  0.3657528 0.07289433
## X12     7 -0.7804077 -0.4308346 0.08137366
boxplot(soc_closeness_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Perceived social closeness / connection")

Mixed model for social closeness (POST)

#Model 4 social closeness
EQ_MM_Social_model_close <- lmer (soc_closeness_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
summary(EQ_MM_Social_model_close)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: soc_closeness_imp ~ pre_post * Condition + (1 + pre_post | PID)
##    Data: socialconnect_scale_include2
## 
## REML criterion at convergence: 5452.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1960 -0.4915  0.1954  0.6618  3.2262 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  PID      (Intercept) 0.8587   0.9267       
##           pre_postEnd 0.5958   0.7719   0.07
##  Residual             2.8257   1.6810       
## Number of obs: 1345, groups:  PID, 111
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)             2.05936    0.15678 113.45533  13.136   <2e-16 ***
## pre_postEnd            -0.23918    0.16792 104.66484  -1.424   0.1573    
## Condition              -0.40727    0.21975 111.25272  -1.853   0.0665 .  
## pre_postEnd:Condition   0.03363    0.23742 104.35512   0.142   0.8876    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) pr_psE Condtn
## pre_postEnd -0.305              
## Condition   -0.713  0.217       
## pr_pstEnd:C  0.216 -0.707 -0.296
sjPlot::tab_model(EQ_MM_Social_model_close)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 2.06 1.75 – 2.37 <0.001
pre_post [End] -0.24 -0.57 – 0.09 0.154
Condition -0.41 -0.84 – 0.02 0.064
pre_post [End] *
Condition 		0.03 -0.43 – 0.50 0.887
Random Effects
σ2 2.83
τ00 PID 0.86
τ11 PID.pre_postEnd 0.60
ρ01 PID 0.07
ICC 0.30
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.012 / 0.308 
#This model does not yet account for person-level predictors

#Model 4 
library(sjPlot)
plot_model(model = EQ_MM_Social_model_close, type = "int")

Regression for Social closeness/connection (1-WEEK POST)

EQ_MM_Social_model2_1wk <- lm (soc_closeness_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model2_1wk)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 2.03 1.82 – 2.24 <0.001
Condition -0.40 -0.69 – -0.11 0.006
Observations 670
R2 / R2 adjusted 0.011 / 0.010

Regression for Social closeness/connection (14-WEEK POST)

EQ_MM_Social_model2_14wk <- lm (soc_closeness_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model2_14wk)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 1.82 1.60 – 2.04 <0.001
Condition -0.37 -0.69 – -0.05 0.022
Observations 675
R2 / R2 adjusted 0.008 / 0.006

Removing outliers

#Obtaining values of outliers 

library(dplyr)
library(ggplot2)

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

socialconnect_scale_include2 %>%
  group_by(Condition) %>%
  mutate(outlier = ifelse(is_outlier(soc_closeness_imp), soc_closeness_imp, as.numeric(NA))) %>%
  ggplot(., aes(x = factor(Condition), y = soc_closeness_imp)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)

# NEED TO FIGURE OUT HOW TO Remove outliers at both levels

Removing Outliers

socialconnect_scale_include2_out4 <- socialconnect_scale_include2 %>% 
  filter(Condition == 0, soc_closeness_imp > -3) 

socialconnect_scale_include2_1_4 <- socialconnect_scale_include2 %>% 
  filter(Condition == 1, soc_closeness_imp > -3) 

socialconnect_scale_out4 <- rbind(socialconnect_scale_include2_out4, socialconnect_scale_include2_1_4)
#run the models with and without these outliers

Mixed model for EMA Social Connection - Closeness (outliers removed)

library(lme4)
library(lmerTest)

socialconnect_scale_out4 <- socialconnect_scale_out4 %>% 
  mutate(pre_post = as.factor(pre_post), 
         timepoint = as.factor(timepoint))

EQ_MM_SocialConnect_model_out4 <- lmer (soc_closeness_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_out4) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_SocialConnect_model_out4)
sjPlot::tab_model(EQ_MM_SocialConnect_model_out4)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 2.22 1.99 – 2.45 <0.001
pre_post [End] -0.12 -0.41 – 0.18 0.440
Condition -0.22 -0.55 – 0.11 0.182
pre_post [End] *
Condition 		-0.05 -0.46 – 0.37 0.833
Random Effects
σ2 1.90
τ00 PID 0.44
τ11 PID.pre_postEnd 0.55
ρ01 PID 0.04
ICC 0.28
N PID 111
Observations 1253
Marginal R2 / Conditional R2 0.007 / 0.283 
#This model does not yet account for person-level predictors 
library(sjPlot)
plot_model(model = EQ_MM_SocialConnect_model_out4, type = "int")

Creating separate dataframes for 1-week and 14-week follow-up (outliers removed)

socialconnect_scale_out_1wkpost <- socialconnect_scale_out4 %>% 
  filter(pre_post == 'Base')

socialconnect_scale_out_14wkpost <- socialconnect_scale_out4 %>% 
  filter(pre_post == 'End')

###Check to see if outliers were adequately removed.

Regression for Social closeness/connection (1-WEEK POST / outliers removed)

EQ_MM_Social_model2_out_1wk <- lm (soc_closeness_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model2_out_1wk)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 2.03 1.82 – 2.24 <0.001
Condition -0.40 -0.69 – -0.11 0.006
Observations 670
R2 / R2 adjusted 0.011 / 0.010

Regression for Social closeness/connection (14-WEEK POST / outliers removed)

EQ_MM_Social_model2_out_14wk <- lm (soc_closeness_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model2_out_14wk)
  soc closeness imp
Predictors Estimates CI p
(Intercept) 1.82 1.60 – 2.04 <0.001
Condition -0.37 -0.69 – -0.05 0.022
Observations 675
R2 / R2 adjusted 0.008 / 0.006

/// POSITIVE SOCIAL INTERACTIONS ///

# Number of positive social interactions (soc_positive_imp) 

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_positive_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 323 2.697095 1.116944      3 2.718771 1.4826   0   5     5
## X12    2      1    1 347 2.666172 1.183559      3 2.681583 1.4826   0   5     5
##            skew   kurtosis         se
## X11 -0.06471052 -0.5336181 0.06214843
## X12 -0.06403106 -0.6010315 0.06353680
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_positive_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 344 2.553266 1.277810      2 2.475809 1.4826   0   5     5
## X12    2      1    1 331 2.406589 1.338948      2 2.398418 1.4826   0   5     5
##           skew   kurtosis         se
## X11 0.34129538 -0.5951527 0.06889492
## X12 0.07087303 -0.7941901 0.07359526
# Post
psych::describeBy(socialconnect_scale_include2$soc_positive_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean       sd median  trimmed    mad min max range
## X11    1      0    1 667 2.622916 1.203855      3 2.591561 1.4826   0   5     5
## X12    2      1    1 678 2.539443 1.267544      3 2.543644 1.4826   0   5     5
##            skew   kurtosis         se
## X11  0.16173932 -0.5744157 0.04661346
## X12 -0.02326539 -0.6910354 0.04867975
boxplot(soc_positive_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Positive Interactions")

Mixed model for positive social interactions (POST)

#Model positive social interactions
EQ_MM_Social_model_pos <- lmer (soc_positive_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00279627 (tol = 0.002, component 1)
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_Social_model_close)
sjPlot::tab_model(EQ_MM_Social_model_pos)
  soc positive imp
Predictors Estimates CI p
(Intercept) 2.70 2.46 – 2.94 <0.001
pre_post [End] -0.14 -0.31 – 0.03 0.112
Condition -0.02 -0.36 – 0.32 0.901
pre_post [End] *
Condition 		-0.06 -0.31 – 0.18 0.600
Random Effects
σ2 0.64
τ00 PID 0.71
τ11 PID.pre_postEnd 0.20
ρ01 PID 0.18
ICC 0.58
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.005 / 0.581 
#This model does not yet account for person-level predictors

#Model 4 
library(sjPlot)
plot_model(model = EQ_MM_Social_model_pos, type = "int")

# Regression for positive social interactions (1-WEEK POST)

EQ_MM_Social_model4_1wk <- lm (soc_positive_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model4_1wk)
  soc positive imp
Predictors Estimates CI p
(Intercept) 2.70 2.57 – 2.82 <0.001
Condition -0.03 -0.21 – 0.14 0.729
Observations 670
R2 / R2 adjusted 0.000 / -0.001

Regression for positive social interactions (14-WEEK POST)

EQ_MM_Social_model4_14wk <- lm (soc_positive_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model4_14wk)
  soc positive imp
Predictors Estimates CI p
(Intercept) 2.55 2.41 – 2.69 <0.001
Condition -0.15 -0.34 – 0.05 0.146
Observations 675
R2 / R2 adjusted 0.003 / 0.002

Removing Outliers

/// DEGREE OF POSITIVE SOCIAL INTERACTIONS ///

# Degree of positive social interactions (soc_howpos_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_howpos_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd  median  trimmed       mad min max
## X11    1      0    1 323 2.200946 0.4747391 2.21063 2.200407 0.3122799   1   3
## X12    2      1    1 347 2.241808 0.5133123 2.21063 2.264901 0.3122799   1   3
##     range        skew  kurtosis         se
## X11     2 -0.06938184 0.9631913 0.02641519
## X12     2 -0.25338194 0.5105925 0.02755605
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_howpos_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd median  trimmed       mad min max
## X11    1      0    1 344 2.231654 0.5278829      2 2.241627 0.0000000   1   3
## X12    2      1    1 331 2.165545 0.5784341      2 2.206775 0.3122799   1   3
##     range        skew    kurtosis         se
## X11     2 -0.00536284  0.01646169 0.02846154
## X12     2 -0.20002055 -0.10722114 0.03179361
# Post
psych::describeBy(socialconnect_scale_include2$soc_howpos_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd  median  trimmed       mad min max
## X11    1      0    1 667 2.216783 0.5027090 2.00000 2.221672 0.3122799   1   3
## X12    2      1    1 678 2.204576 0.5470021 2.21063 2.243939 0.3122799   1   3
##     range        skew  kurtosis         se
## X11     2 -0.02187152 0.4266876 0.01946497
## X12     2 -0.24739523 0.1921725 0.02100749
boxplot(soc_howpos_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Degree of Positive Interactions")

Mixed model for Degree of positive social interactions (POST)

#Model degree of positive social interactions
EQ_MM_Social_model9 <- lmer (soc_howpos_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
 
sjPlot::tab_model(EQ_MM_Social_model9)
  soc howpos imp
Predictors Estimates CI p
(Intercept) 2.21 2.14 – 2.28 <0.001
pre_post [End] 0.03 -0.06 – 0.11 0.535
Condition 0.04 -0.06 – 0.14 0.438
pre_post [End] *
Condition 		-0.10 -0.22 – 0.02 0.097
Random Effects
σ2 0.22
τ00 PID 0.03
τ11 PID.pre_postEnd 0.03
ρ01 PID 0.33
ICC 0.21
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.003 / 0.213 
#This model does not yet account for person-level predictors

#Model 4 
library(sjPlot)
plot_model(model = EQ_MM_Social_model9, type = "int")

# Regression for Degree of positive social interactions (1-WEEK POST)

EQ_MM_Social_model9_1wk <- lm (soc_howpos_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model9_1wk)
  soc howpos imp
Predictors Estimates CI p
(Intercept) 2.20 2.15 – 2.26 <0.001
Condition 0.04 -0.03 – 0.12 0.286
Observations 670
R2 / R2 adjusted 0.002 / 0.000

Regression for Degree of positive social interactions (14-WEEK POST)

EQ_MM_Social_model9_14wk <- lm (soc_howpos_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model9_14wk)
  soc howpos imp
Predictors Estimates CI p
(Intercept) 2.23 2.17 – 2.29 <0.001
Condition -0.07 -0.15 – 0.02 0.121
Observations 675
R2 / R2 adjusted 0.004 / 0.002

Removing Outliers

/// NEGATIVE SOCIAL INTERACTIONS ///

# Number of negative social interactions (soc_neg_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_neg_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n      mean        sd median   trimmed mad min max range
## X11    1      0    1 323 0.7065164 0.9232930      0 0.5374703   0   0   5     5
## X12    2      1    1 347 0.6219228 0.7769684      0 0.4939326   0   0   4     4
##         skew kurtosis         se
## X11 1.556559 2.616528 0.05137340
## X12 1.213313 1.258263 0.04170985
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_neg_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n      mean        sd median   trimmed mad min max range
## X11    1      0    1 344 0.6174698 0.8644165      0 0.4616290   0   0   5     5
## X12    2      1    1 331 0.4646743 0.7969989      0 0.3011593   0   0   5     5
##         skew kurtosis         se
## X11 1.580285 2.805182 0.04660621
## X12 2.477937 8.423070 0.04380702
# Post
psych::describeBy(socialconnect_scale_include2$soc_neg_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n      mean        sd median   trimmed mad min max range
## X11    1      0    1 667 0.6605913 0.8938489      0 0.4983447   0   0   5     5
## X12    2      1    1 678 0.5451540 0.7901541      0 0.3889971   0   0   5     5
##         skew kurtosis         se
## X11 1.577675 2.758382 0.03460997
## X12 1.824306 4.592551 0.03034569
boxplot(soc_neg_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Negative Interactions")

#WHY ARE THESE SO SIMILAR?

Mixed model for negative social interactions (POST)

#Model negative social interactions
EQ_MM_Social_model_neg <- lmer (soc_neg_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 
 
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_Social_model_close)
sjPlot::tab_model(EQ_MM_Social_model_neg)
  soc neg imp
Predictors Estimates CI p
(Intercept) 0.70 0.55 – 0.85 <0.001
pre_post [End] -0.08 -0.24 – 0.08 0.320
Condition -0.06 -0.27 – 0.15 0.585
pre_post [End] *
Condition 		-0.07 -0.29 – 0.15 0.556
Random Effects
σ2 0.47
τ00 PID 0.25
τ11 PID.pre_postEnd 0.19
ρ01 PID -0.43
ICC 0.35
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.008 / 0.350 
#This model does not yet account for person-level predictors

#Model 4 
library(sjPlot)
plot_model(model = EQ_MM_Social_model_neg, type = "int")

# Regression for negative social interactions (1-WEEK POST)

EQ_MM_Social_model5_1wk <- lm (soc_neg_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model5_1wk)
  soc neg imp
Predictors Estimates CI p
(Intercept) 0.71 0.61 – 0.80 <0.001
Condition -0.08 -0.21 – 0.04 0.199
Observations 670
R2 / R2 adjusted 0.002 / 0.001

Regression for negative social interactions (14-WEEK POST)

EQ_MM_Social_model5_14wk <- lm (soc_neg_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model5_14wk)
  soc neg imp
Predictors Estimates CI p
(Intercept) 0.62 0.53 – 0.71 <0.001
Condition -0.15 -0.28 – -0.03 0.017
Observations 675
R2 / R2 adjusted 0.008 / 0.007

Removing outliers

/// DEGREE OF NEGATIVE SOCIAL INTERACTIONS ///

# Degree of negative social interactions (soc_howneg_imp)

# 1-week post
psych::describeBy(socialconnect_scale_1wkpost$soc_howneg_imp, socialconnect_scale_1wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd median  trimmed mad min max range
## X11    1      0    1 323 1.249532 0.5063675      1 1.141308   0   1   3     2
## X12    2      1    1 347 1.304954 0.5327504      1 1.210821   0   1   3     2
##         skew kurtosis         se
## X11 1.987501 3.157264 0.02817504
## X12 1.571219 1.567486 0.02859954
# 14-weeks post
psych::describeBy(socialconnect_scale_14wkpost$soc_howneg_imp, socialconnect_scale_14wkpost$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd median  trimmed mad min max range
## X11    1      0    1 344 1.253777 0.5127616      1 1.146012   0   1   3     2
## X12    2      1    1 331 1.229061 0.4957564      1 1.116299   0   1   3     2
##         skew kurtosis         se
## X11 1.936428 2.911768 0.02764625
## X12 2.142560 3.822752 0.02724924
# Post
psych::describeBy(socialconnect_scale_include2$soc_howneg_imp, socialconnect_scale_include2$Condition, mat = TRUE)
##     item group1 vars   n     mean        sd median  trimmed mad min max range
## X11    1      0    1 667 1.251721 0.5092972      1 1.143735   0   1   3     2
## X12    2      1    1 678 1.267903 0.5160411      1 1.164776   0   1   3     2
##         skew kurtosis         se
## X11 1.965323 3.046739 0.01972007
## X12 1.827956 2.499665 0.01981844
boxplot(soc_howneg_imp~Condition,socialconnect_scale_include2, main="Brief Social Connection Scores by Condition",
   xlab="Condition", ylab="Degree of Negative Interactions")

#WHY ARE THESE LIKE THIS?

Mixed model for degree of negative social interactions (POST)

EQ_MM_Social_model_negd <- lmer (soc_howneg_imp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_include2) 

sjPlot::tab_model(EQ_MM_Social_model_negd)
  soc howneg imp
Predictors Estimates CI p
(Intercept) 1.25 1.17 – 1.32 <0.001
pre_post [End] 0.01 -0.07 – 0.08 0.865
Condition 0.06 -0.04 – 0.16 0.253
pre_post [End] *
Condition 		-0.08 -0.19 – 0.02 0.126
Random Effects
σ2 0.22
τ00 PID 0.04
τ11 PID.pre_postEnd 0.01
ρ01 PID -0.17
ICC 0.16
N PID 111
Observations 1345
Marginal R2 / Conditional R2 0.003 / 0.160 
#This model does not yet account for person-level predictors

#Model 4 
library(sjPlot)
plot_model(model = EQ_MM_Social_model_negd, type = "int")

# Regression for degree of negative social interactions (1-WEEK POST)

EQ_MM_Social_model5d_1wk <- lm (soc_howneg_imp ~ Condition, data = socialconnect_scale_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model5d_1wk)
  soc howneg imp
Predictors Estimates CI p
(Intercept) 1.25 1.19 – 1.31 <0.001
Condition 0.06 -0.02 – 0.13 0.169
Observations 670
R2 / R2 adjusted 0.003 / 0.001

Regression for degree of negative social interactions (14-WEEK POST)

EQ_MM_Social_model5d_14wk <- lm (soc_howneg_imp ~ Condition, data = socialconnect_scale_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model5d_14wk)
  soc howneg imp
Predictors Estimates CI p
(Intercept) 1.25 1.20 – 1.31 <0.001
Condition -0.02 -0.10 – 0.05 0.525
Observations 675
R2 / R2 adjusted 0.001 / -0.001

Removing Outliers

******BELOW NEEDS TO BE UPDATED AND ADDED TO NEG COMP SECTION - CODE IS FROM COMP 1****** # Removing Outliers

#Obtaining values of outliers 

library(dplyr)
library(ggplot2)

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

socialconnect_scale_include2 %>%
  group_by(Condition) %>%
  mutate(outlier = ifelse(is_outlier(EMA_social_comp), EMA_social_comp, as.numeric(NA))) %>%
  ggplot(., aes(x = factor(Condition), y = EMA_social_comp)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)

# NEED TO FIGURE OUT HOW TO Remove outliers at both levels

Removing Outliers

socialconnect_scale_include2_out <- socialconnect_scale_include2 %>% 
  filter(Condition == 0, EMA_social_comp > 3) 

socialconnect_scale_include2_1 <- socialconnect_scale_include2 %>% 
  filter(Condition == 1, EMA_social_comp > -2) 

socialconnect_scale_out <- rbind(socialconnect_scale_include2_out, socialconnect_scale_include2_1)
#run the models with and without these outliers

Mixed model for EMA Social Connection (outliers removed)

library(lme4)
library(lmerTest)

socialconnect_scale_out <- socialconnect_scale_out %>% 
  mutate(pre_post = as.factor(pre_post), 
         timepoint = as.factor(timepoint))

EQ_MM_SocialConnect_model_out <- lmer (EMA_social_comp ~ pre_post * Condition + #fixed effects
      (1 + pre_post | PID), data = socialconnect_scale_out) 
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00379648 (tol = 0.002, component 1)
#this is a group by time interaction
#3 predictors: 1) Condition(Group), 2) Time, 3) time by condition (group x time)
# "1 + pre_post | PID" estimate a random intercept and a random slope for each individual 
#summary(EQ_MM_SocialConnect_model_out)
sjPlot::tab_model(EQ_MM_SocialConnect_model_out)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.68 12.90 – 14.47 <0.001
pre_post [End] -0.73 -1.33 – -0.12 0.019
Condition -0.67 -1.77 – 0.43 0.230
pre_post [End] *
Condition 		-0.45 -1.31 – 0.41 0.305
Random Effects
σ2 9.74
τ00 PID 7.06
τ11 PID.pre_postEnd 1.92
ρ01 PID 0.29
ICC 0.48
N PID 111
Observations 1328
Marginal R2 / Conditional R2 0.022 / 0.494 
#This model does not yet account for person-level predictors 
library(sjPlot)
plot_model(model = EQ_MM_SocialConnect_model_out, type = "int")

Creating separate dataframes for 1-week and 14-week follow-up (outliers removed)

socialconnect_scale_out_1wkpost <- socialconnect_scale_out %>% 
  filter(pre_post == 'Base')

socialconnect_scale_out_14wkpost <- socialconnect_scale_out %>% 
  filter(pre_post == 'End')

Regression for Social Connection Composite (1-WEEK POST / outliers removed) (Note: unclear whether this is a meaningful composite score)

EQ_MM_Social_model_1wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_out_1wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_1wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.65 13.21 – 14.10 <0.001
Condition -0.75 -1.37 – -0.13 0.018
Observations 668
R2 / R2 adjusted 0.008 / 0.007

Regression for Social Connection Composite (14-WEEK POST / outliers removed) (Note: unclear whether this is a meaningful composite score)

EQ_MM_Social_model_14wk <- lm (EMA_social_comp ~ Condition, data = socialconnect_scale_out_14wkpost) 

library(sjPlot)
sjPlot::tab_model(EQ_MM_Social_model_14wk)
  EMA social comp
Predictors Estimates CI p
(Intercept) 13.06 12.57 – 13.54 <0.001
Condition -1.35 -2.04 – -0.67 <0.001
Observations 660
R2 / R2 adjusted 0.022 / 0.021