# Dunnett analysis error \$ operator is invalid for atomic vectors

Here is my real data. Group A is my reference and I want to compare the other groups to group A (dunnett analysis). I want to simulate another 5 random numbers for Group A based on the mean and std for my current Group A. and use the combined the data to do the analysis.
Group Y
A 30.17
B 30.65
A 30.35
C 30.57
D 30.75
A 30.52
E 30.05
F 30.56
G 30.86
A 30.89
H 30.69
I 31.19
J 30.68
K 30.84
A 30.82
B 24.13
C 24.5
D 24.28

Here is my R code:
library(emmeans)

# import the real data

View(data)

A_sd<-sd(data[which(data\$Group=="A"),]\$Y)
A_mean<-mean(data[which(data\$Group=="A"),]\$Y)

sim=100

# create vectors to save the anona results.

res = vector("list",sim)

# Create data frames for the simulated the data

df_sim<- replicate(n=sim,data.frame(Group = rep('A',5), Y=rep(0,5)),simplify=FALSE)

# create data frames for the combined data( real data merged with simulated data)

df_merge<-replicate(n=sim,data.frame(Group=rep(NA,23),Y=rep(0,23)),simplify=FALSE)

for (i in 1:sim)
{
Y<-rnorm(n=5,A_mean,A_sd)
df_sim[[i]]\$Y[which(df_sim[[i]]\$Group=='A')]<-Y

df_merge[[i]]<-rbind(data,df_sim[[i]])

df_merge[[i]]\$Group<-as.factor( df_merge[[i]]\$Group)

res[[i]]<- lm(df_merge[[i]]\$Y ~ df_merge[[i]]\$Group, data = df_merge[[i]])
summary(res[[i]])

#Dunnect analysis comparing other groups to group A

# got error df_merge[[i]]Group : operator is invalid for atomic vectors

lssquare[[i]]=emmeans(res[[i]],specs=trt.vs.ctrlk~Group)

}

The code works well until I do the Dunnnet analysis using the emmeans function. I got the error df_merge[[i]]Group : operator is invalid for atomic vectors.

If I change my code to lssquare[[i]]=emmeans(res[[i]],specs=trt.vs.ctrlk~getElement(df_merge[[i]],"Group"),data=df_merge[[i]])

i got the error:Error in `[.data.frame`(tbl, , vars, drop = FALSE) :
undefined columns selected

Any ideas on how to fix this error? Appreciated for anly help

Try with:

``````lm(Y ~ Group, data = df_merge[[i]])
``````
1 Like

Thanks Alexis,it works! Appreciated!

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