Asking for help on lme4 package

[cyq1998]

I'm trying to run a lmer package using these codes:

summary (lmer(elabe_data$DMN_vs_DMN_z ~ elabe_data$HRLR + elabe_data$child_sex + elabe_data$mri_test_scan_day_un + elabe_data$mat_med_risk_sum + elabe_data$maternal_age_birth + elabe_data$bmi_pre_pregnancy + (1|modid), data = elabe_data))

However, I got an error message: Error: number of levels of each grouping factor must be < number of observations (problems: modid).

Does anyone know what the problem here is? I'm trying to have the identifier number (modid) as the random variable. Thanks!

[williaml]

@cyq1998

Error: number of levels of each grouping factor must be < number of observations (problems: modid)

That's the error.

By the way, you don't need to have elabe_data$ in there because you have used data = elabe_data.

It would be good to have a reproducible example of your dataset:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[cyq1998]

Hi Thank you so much for your help. I have put together a reproducible dataset. It would be very helpful if anyone can help how to dissolve the error. Thanks!

elabe_data <- data.frame(
DMN_vs_DMN_z = c(-1.5537227756,1.2685453317,0.5076225730,0.1588330048,0.4783086631),
HRLR = c(0, 0, 0, 0, 0),
child_sex = c(1,2,1,2,1),
mri_test_scan_day_un = c(3,1,6,6,3),
mat_med_risk_sum = c(3,1,4,3,1),
maternal_age_birth = c(25.24624, 35.86932, 26.72197, 25.94714, 27.18194),
bmi_pre_pregnancy = c(37.4,38.9,28,25.5,27.5),
modid = c('MOD1022', 'MOD1025', 'MOD1039',' MOD1054', 'MOD1058')
)

A <- lmer(elabe_data$DMN_vs_DMN_z ~ elabe_data$HRLR + elabe_data$child_sex + elabe_data$mri_test_scan_day_un + elabe_data$mat_med_risk_sum + elabe_data$maternal_age_birth + elabe_data$bmi_pre_pregnancy + (1|modid), data = elabe_data)

summary(A)

The error message is Error: number of levels of each grouping factor must be < number of observations (problems: modid).

[williaml]

@cyq1998

modid

Your modid column isn't a grouping variable. It is a unique ID.

That is what is causing your error:

Error: number of levels of each grouping factor must be < number of observations

You could probably drop that column, but also choose an actual grouping variable.

More info:

Mixed Model Anova Error

Hello! I am trying to run a 3 way mixed model anova using my data (Sex x PCB x Day_2) and the lme4 package. This was my initial code: Pref_df <- read_xlsx("~/Documents/D_DSacBotStat.xlsx", sheet = "Pref") MMA_D2 <- lmer(Pref_df$Day_2 ~ PCB Treat* Sex + 1|id, data = D_DSacBotStat ) Error in model.frame.default(data = D_DSacBotStat, drop.unused.levels = TRUE, : variable lengths differ (found for 'PCB Treat') So I adjusted the file and changed the name to PCB_treatment and had this code: MMA_D2 <- lmer(Pref_df$Day_2 ~ Pref_df$PCB_Treatment * Pref_df$Sex + 1|id, data = D_DSacBotStat ) But I was met with this error: Error in model.frame.default(data = D_DSacBotStat, drop.unused.levels …

lmer error: grouping factor must be < number of observations

[cyq1998]

Thank you. This is very helpful!