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foto51
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I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judge and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects.
# m1 = Your model including the fixed effects.
# Set REML = FALSE for a meaningful model comparison
# Basically, ANOVA uses ML for mer objects.

m0 <- lmer(rating ~ 1 + (1|judge) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE
# This is often recommended since it usually gives better estimates for random effects.

mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judge and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects.
# m1 = Your model including the fixed effects.
# Set REML = FALSE for a meaningful model comparison
# Basically, ANOVA uses ML for mer objects.

m0 <- lmer(rating ~ 1 + (1|judge) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE
# This is often recommended since it usually gives better estimates for random effects.

mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judge and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects.
# m1 = Your model including the fixed effects.
# Set REML = FALSE for a meaningful model comparison

m0 <- lmer(rating ~ 1 + (1|judge) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE
# This is often recommended since it usually gives better estimates for random effects.

mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

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foto51
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I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judgesjudge and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects.
# m1 = Your model including the fixed effects.
# Set REML = FALSE for a meaningful model comparison
# Basically, ANOVA uses ML for mer objects.

m0 <- lmer(rating ~ 1 + (1|judges1|judge) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judges1|judge) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE
# This is often recommended since it usually gives better estimates for random effects.

mF <- lmer(rating ~ condition + (1|judges1|judge) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judges and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects
# m1 = Your model including the fixed effects
# Set REML = FALSE for model comparison

m0 <- lmer(rating ~ 1 + (1|judges) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE

mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judge and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects.
# m1 = Your model including the fixed effects.
# Set REML = FALSE for a meaningful model comparison
# Basically, ANOVA uses ML for mer objects.

m0 <- lmer(rating ~ 1 + (1|judge) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF)

# If significant, fit the final model using REML = TRUE
# This is often recommended since it usually gives better estimates for random effects.

mF <- lmer(rating ~ condition + (1|judge) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

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foto51
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I would go for package lme4.

If I understand correctly, mFmF below should be your model. It has conditioncondition as a fixed effect, while judges and judgessubject as a random interceptintercepts.

library(lme4)

# m0 = Null model without the fixed effects
# m1 = Your model including the fixed effects
# Set REML = FALSE for model comparison

m0 <- lmer(rating ~ 1 + (1|judges) + (1|subject), data = data, REML = FALSE ) 
mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE ) 

summary(m0)
summary(mF)

anova(m0, mF) 

# If significant, fit the final model using REML = TRUE

mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect and judges as a random intercept.

library(lme4)

m0 <- lmer(rating ~ 1 + (1|judges), data = data, REML = FALSE )
mF <- lmer(rating ~ condition + (1|judges), data = data, REML = FALSE )

summary(m0)
summary(mF)

anova(m0,mF)

I would go for package lme4.

If I understand correctly, mF below should be your model. It has condition as a fixed effect, while judges and subject as a random intercepts.

library(lme4)

# m0 = Null model without the fixed effects
# m1 = Your model including the fixed effects
# Set REML = FALSE for model comparison

m0 <- lmer(rating ~ 1 + (1|judges) + (1|subject), data = data, REML = FALSE) 
mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE) 

summary(m0)
summary(mF)

anova(m0, mF) 

# If significant, fit the final model using REML = TRUE

mF <- lmer(rating ~ condition + (1|judges) + (1|subject), data = data, REML = FALSE)

Edit: Per discussion below.

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