dput because that data,copy indigenous https://pastebin.com/1f7VuBkx (too large to include here)

data.frame": 972 obs. Of 7 variables:$ data_mTBS : num 20.3 22.7 0 47.8 58.7 ...$ data_tooth: num 1 1 1 1 1 1 1 1 1 1 ...$ Adhesive : factor w/ 4 levels "C-SE2","C-UBq",..: 2 2 2 2 2 2 2 2 2 2 ...$ technique : element w/ 2 level "ER","SE": 1 1 1 1 1 1 1 1 1 1 ...$ Aging : aspect w/ 2 levels "1w","6m": 1 1 1 1 1 1 2 2 2 2 ...$ data_name : factor w/ 40 levels "C-SE2-1","C-SE2-10",..: 11 11 11 11 11 11 11 11 11 11 ...$ wait : element w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...head(Data) data_mTBS data_tooth Adhesive method Aging data_name wait1 20.27 1 C-UBq ER 1w C-UBq-1 no2 22.73 1 C-UBq ER 1w C-UBq-1 three 0.00 1 C-UBq ER 1w C-UBq-1 no4 47.79 1 C-UBq ER 1w C-UBq-1 no5 58.73 1 C-UBq ER 1w C-UBq-1 no6 57.02 1 C-UBq ER 1w C-UBq-1 nowhen I operation the complying with code without "wait", it functions perfectly, yet when I shot run it v "wait" included in the version it provides the singularity problem.

LME_011|data_name);Error in MEEM(object, conLin, control$niterEM) : singularity inbacksolve at level 0, block 1

contrast_Aginglist(Aging =c("1w"),Adhesive = levels(Data$Adhesive),Approach = levels(Data$Approach) ),b = list(Aging =c("6m"), Adhesive = levels(Data$Adhesive),Approach = levels(Data$Approach)))c1&

contrast_Approachlist(Approach = c("SE"), Aging =levels(Data$Aging) ,Adhesive = levels(Data$Adhesive)), b = list(Approach = c("ER"), Aging =levels(Data$Aging) ,Adhesive = levels(Data$Adhesive)))c2Thanks in advance.




You are watching: Error in meem(object, conlin, control$niterem) : singularity in backsolve at level 0, block 1

asked By: Mohammed Ahmed
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HongOoi is informing you, wait and also Adhesive are confounded in your model. Lme is a little stupider/more stubborn than countless of the various other modeling features in R, which will either warn you clearly that you have confounded fixed effects or automatically drop several of them because that you.

It"s a bit less complicated to view this if girlfriend plot the data:

## source("SO50505290_data.txt")library(ggplot2)ggplot(dd,aes(Adhesive,data_mTBS, fill=Aging, alpha=Approach))+ facet_grid(.~wait,scale="free_x",space="free", labeller=label_both)+ guides(alpha = guide_legend(override.aes = list(fill = "darkgray")))+ geom_boxplot()ggsave("SO50505290.png")

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This shows you that learning that wait=="no" is the very same as learning that Adhesive=="C-UBq".

It would more than likely make an ext sense to back up and also think around the concerns you"re asking, yet if you carry out this with lme4::lmer it will tell girlfriend

fixed-effect model matrix is location deficient therefore dropping 16 columns / coefficients

library(lme4)LME_021|data_name), na.action=na.exclude,data = dd)


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reply By: Mohammed Ahmed