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### Contour-enhanced funnel plots using metafor ###

# Load metafor package

library("metafor")

# Load dataset

dat <- get(data(dat.molloy2014)) 

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# Load package

library(pwr)

# Perform power analysis

pwr.t2n.test(sig.level = 0.05, n1= 32,n2= 23, power = 0.8,
             alternative="two.sided")

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# Load TOSTER package

library(TOSTER)

# Perform equivalence test

TOSTtwo(m1=37.33,m2=39.65 ,sd1=13.40,sd2=15.91,n1=23,n2=32,
        low_eqbound_d=-0.26,high_eqbound_d=0.26,
        plot = TRUE)

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# Write function

t.test2 <- function(m1,m2,s1,s2,n1,n2,m0=0,equal.variance=FALSE)
{
  if( equal.variance==FALSE ) 
  {
    se <- sqrt( (s1^2/n1) + (s2^2/n2) )
    # welch-satterthwaite df
    df <- ( (s1^2/n1 + s2^2/n2)^2 )/( (s1^2/n1)^2/(n1-1) + (s2^2/n2)^2/(n2-1) )
  } else

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install.packages("europepmc")
install.packages("cowplot")
install.packages("tidyverse")

library(europepmc)
library(cowplot)
library(tidyverse)

ot_trend <- europepmc::epmc_hits_trend(query = "oxytocin", 
                                       period = 2008:2018)

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library(synthpop) 
library(tidyverse) 
library(cowplot)

h_dat <- read_csv("help.csv")

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> summary(aov(happy_o ~drugcond*emocond,data=h_dat)) # Interaction effect on happiness

                 Df Sum Sq Mean Sq F value   Pr(>F)    
drugcond          1   1.70    1.70   1.436   0.2352    
emocond           1  34.21   34.21  28.950 1.16e-06 ***
drugcond:emocond  1   4.40    4.40   3.726   0.0581 .  
Residuals        63  74.44    1.18                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
49 observations deleted due to missingness

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> m1 = lm(happy_o ~ 1 + drugcond + emocond, 
        data = h_dat) # Model 1 with main effects only

> m2 = lm(happy_o ~ 1 + drugcond + emocond + 
        drugcond:emocond, data = h_dat) # Model with main effects and interaction

> summary(m2) # Summary of model 2 (with same p-value as original ANOVA for the interaction)

Call:
lm(formula = happy_o ~ 1 + drugcond + emocond + drugcond:emocond, 

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h_dat_s <- syn(h_dat, m = 1, seed = 1969)

compare(h_dat_s, h_dat,
        stat = "counts", # Selecting counts instead of percentage
        cols = c("#62B6CB", "#1B4965")) # Changing colours

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> help_glm_h <- lm(happy_o ~ 1 + drugcond + 
                 emocond + drugcond:emocond,
               data = h_dat) # Model from observed data
               
> help_syn_glm_h <- lm.synds(happy_o ~ 1 + drugcond + 
                           emocond + drugcond:emocond,
                         data = h_dat_s) # Model from synthesized data


> compare(help_syn_glm_h, h_dat) # A comparison of the models