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Home Page: http://metidajl.org
License: MIT License
Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.
Home Page: http://metidajl.org
License: MIT License
Hi. Is there any detailed documetation about the parameters used in Metida
? Like, if I have a kinship matrix, which commonly used in genetic studies reflects the pairwised genetic relatedness, and i want to include it into LMM as a random effect, how could I possibly do it?
Add weights
Now it impossible to fit model with rank deficient fixed effect matrix. There are two ways to fix it:
First lead to lost compatibility with packages like Effects.jl
Second take tonnes of "hardcoding".
Add to model result check for positive definite G matrix
as deprecated
1.6 test error on experimental functions
Hi, new to Julia coming from R, I have a question about an error message I get trying to apply a mixed effects model using Metida on my data.
using Metida, CSV, DataFrames, CategoricalArrays
df = CSV.File("../data/mydata.csv") |> DataFrame;
transform!(df, :Peptide => categorical, renamecols=false);
transform!(df, :Protein => categorical, renamecols=false);
transform!(df, :Treatment => categorical, renamecols=false);
transform!(df, :Subject => categorical, renamecols=false);
df
24,104 rows × 5 columns
Peptide | Protein | Measurement | Treatment | Subject
Cat... | Cat... | Float64?.. | Cat... | Cat...
AACAQLNDFLQEYGTQGC | P0C0L4 | missing | G | 6
AACAQLNDFLQEYGTQGC | P0C0L5 | 22.3928 | G | 6
...
# This is the full model I want to specify
lmm = LMM(@formula(Measurement ~ Treatment * Peptide + Protein), df;
random = VarEffect(@covstr(1 | Subject / Protein), CSH));
MethodError: no method matching fulldummycodingdict(::FunctionTerm{typeof(/), var"#68#70", (:Subject, :Protein)})
...
# Removing the nested term lets me specify the formula
lmm = LMM(@formula(Measurement ~ Treatment * Peptide + Protein), df;
random = VarEffect(@covstr(1 | Subject), CSH));
# But the fit throws an error
fit!(lmm)
BoundsError: attempt to access 20683×1109 Matrix{Float64} at index [UInt32[0x00004dc9, 0x00004dca, 0x00004dcb, 0x00004dcc, 0x00004dcd, 0x00004dce, 0x00004dcf, 0x00004dd0, 0x00004dd1, 0x00004dd2 … 0x000051d7, 0x000051d8, 0x000051d9, 0x000051da, 0x000051db, 0x000051dc, 0x000051dd, 0x000051de, 0x000051df, 0x000051e0], 1:1109]
Stacktrace:
[1] throw_boundserror(A::Matrix{Float64}, I::Tuple{Vector{UInt32}, Base.Slice{Base.OneTo{Int64}}})
...
# This works in MixedModels.jl, perhaps the nested terms have different syntax i Metida?
fm = @formula(Measurement ~ 1 + Treatment*Peptide + Protein + (1 | Subject/Protein))
mm = fit(MixedModel, fm, df, REML=true)
However in MixedModels.jl I am unable to get degrees of freedom and confidence intervals for my model.
Ultimately I want to obtain pairwise contrasts from my model. In R using lme4 and emmeans I would do this:
lmm = lmer(Measurement ~ Treatment * Peptide + Protein + (1 | Subject / Protein), mydata)
emm <- emmeans(lmm, pairwise ~ Treatment | Peptide)
emm <- confint(emm)
emm <- as.data.frame(emm$contrasts)
Any help appreciated
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Bootstrap validation
There is no need to use multiple repeated effects in most cases, but in some rare cases, it can be useful.
Check rank before variance-covariance matrix of β calc
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