> indvar_FODs$Condition_FODs<-factor(indvar_FODs$Condition_FODs, levels=c("A", "B", "C", "D"), labels=c("A", "B", "C", "D")) > indvar_FODs$developmentaldisorder_FODs<-factor(indvar_FODs$developmentaldisorder_FODs, levels=c("No", "Yes"), labels=c("No", "Yes")) > indvar_FODs$categorical_FOD_FODs<-factor(indvar_FODs$categorical_FOD_FODs, levels=c("ascending", "identity", "descending"), labels=c("ascending", "identity", "descending")) > model1 <- glmer(categorical_FOD_FODs ~ Condition_FODs + developmentaldisorder_FODs + Condition_FODs:developmentaldisorder_FODs + (1|subject_FODs), + data = indvar_FODs, family = "binomial", control = glmerControl(optimizer = "bobyqa")) boundary (singular) fit: see help('isSingular') please write me the follow-up analysis in this situation

# variables that are categorical
class(data$variable)

# variables that are continuous
class(data$variable)

# check for missing values
colSums(is.na(data))

# check for outliers
boxplot(data$variable)

# check for normality
qqnorm(data$variable)
qqline(data$variable)

# convert categorical variable to a factor variable
data$variable <- factor(data$variable)

# linear regression
model <- lm(outcome ~ predictor)

# ANOVA
model <- aov(outcome ~ predictor)

# T-test
model <- t.test(outcome ~ predictor)

# Chi-square test
model <- chisq.test(outcome ~ predictor)

# simple correlations
cor(data$variable1, data$variable2)

# repeated measures ANOVA
model <- aov(outcome ~ predictor + Error(subject/predictor), data = data)

# paired T-test
model <- t.test(variable1 ~ variable2)

#