CBK S2 SM R-squared

data1 <- data %>% filter(!is.na(attention))
data2 <- data1 %>% filter(!is.na(attention), attention > 2)

data2$cost.benefit <- data2$cost / data2$benefit
data2$cost.benefit.centered <- scale(data2$cost.benefit, center = TRUE, scale = FALSE)
data2$cost.centered <- scale(data2$cost, center = TRUE, scale = FALSE)
data2$benefit.centered <- scale(data2$benefit, center = TRUE, scale = FALSE)

model7 <- lmer(kind ~ 1 + cost.centered + benefit.centered + (1 + cost.centered + benefit.centered | id), data = data2)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0305836 (tol = 0.002, component 1)

r_squared7 <- r.squaredGLMM(model7)

## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.

r_squared7

##            R2m       R2c
## [1,] 0.4473933 0.6679694

modelS7 <- lmer(kind ~ 1 + cost.benefit.centered + (1 + cost.benefit.centered | id), data = data2)
r_squaredS7 <- r.squaredGLMM(modelS7)
r_squaredS7

##             R2m       R2c
## [1,] 0.04946456 0.4489428

modelS8<- lmer(kind ~ 1 + benefit.centered + cost.centered + cost.benefit.centered + (1 + benefit.centered + cost.centered + cost.benefit.centered | id), data = data2)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0549992 (tol = 0.002, component 1)

r_squaredS8 <- r.squaredGLMM(modelS8)
r_squaredS8

##            R2m       R2c
## [1,] 0.4313048 0.6844915

modelS9<- lmer(kind ~ 1 + benefit.centered + cost.centered + cost.centered*benefit.centered + (1 + benefit.centered + cost.centered + cost.centered*benefit.centered | id), data = data2)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.095956 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

r_squaredS9 <- r.squaredGLMM(modelS9)
r_squaredS9

##            R2m       R2c
## [1,] 0.4493555 0.6792071