Drop a single coefficient (one level from categorical variable) from formula/modelmatrix for Likelihood ratio test

[MaximilianNuber]

Dear all,

I am fitting MixedModels for large single cell datasets. One approach to test for covariates is to drop a single coefficient from the modelmatrix, which belongs to one level of a categorical variable. I have not been able to find this possibility in Julia.

As an example, let me use the iris dataset. It may not be a mixed model, but in principle it is the same.

We have the full model: full = lm(@formula(SepalLength ~ Species), iris) with results

SepalLength ~ 1 + Species

Coefficients:
─────────────────────────────────────────────────────────────────────────────
                     Coef.  Std. Error      t  Pr(>|t|)  Lower 95%  Upper 95%
─────────────────────────────────────────────────────────────────────────────
(Intercept)          5.936   0.0728022  81.54    <1e-99   5.79213    6.07987
Species: setosa     -0.93    0.102958   -9.03    <1e-15  -1.13347   -0.726531
Species: virginica   0.652   0.102958    6.33    <1e-08   0.448531   0.855469
─────────────────────────────────────────────────────────────────────────────

My goal would be, to drop Species: setosa in the formula or model matrix for another linear model. This nested model would be compared to the full model by likelihood ratio test.

Everything I found so far would be dropping the Species variable entirely, but it is not what I want.

I have played around with contrasts, where in the example the modelmatrix column for Species: setosa would be all zeros, but got the warning that the model is not full rank.

Is there any solution to this, or possibly a workaround?

Thank you for any help, Max

[MaximilianNuber]

P.S. I am posting this question in MixedModels.jl, as in GLM.jl I would be able to use X, y as input for lm or glm. I haven't found this possibility for MixedModels, but I do see that for specifying random effects, we need the formula language. I just don´t know to do it in this case.

[dmbates]

What you want to do is related to profiling the log-likelihood with respect to the fixed-effects coefficients, for which the code is in src/profile/fixefpr.jl. That code is more general than you need but it shows that you can create a reduced model by creating a fixed-effects term and then copying the response, the reterm (which needs a deepcopy or the special copy method in the file mentioned above) and the formula. Try this.

using MixedModels

function dropcol(m::LinearMixedModel, j::Integer)
    fe = m.feterm        # model matrix X, column names, rank, etc.
    x = fe.x             # model matrix
    fe.rank == size(x, 2) || throw(ArgumentError("m.X is not of full column rank"))
    notj = deleteat!(collect(axes(x, 2)), j) # indirectly checks that j is in range
    feterm = MixedModels.FeTerm(x[:, notj], fe.cnames[notj])
    θbak = m.θ           # save a copy of the parameter estimates
    setθ!(m, m.optsum.initial) # ensure m is at initial values
    mnew = LinearMixedModel(collect(m.y), feterm, deepcopy(m.reterms), m.formula)
    objective!(m, θbak)  # restore parameter values in m
    return mnew
end

m1 = let f = @formula y ~ 1 + dept + service + (1|s) + (1|d)
    fit(MixedModel, f, MixedModels.dataset(:insteval))
end

m1a = fit!(dropcol(m1, 2))

MixedModels.likelihoodratiotest(m1a, m1)

[MaximilianNuber]

Dear @dmbates,

Thank you so much, this works perfectly.

If I may, I would like to ask a follow-up question, about the correctness of my application to GeneralizedLinearMixedModel :

I applied your dropcol to GeneralizedLinearMixedModel by adjusting the function to include a target-vector y. I assume that the LMM within a GLMM carries a y-vector adjusted to the Distribution of the GLMM, therefore we need to include the original response.

function dropcol2(m::LinearMixedModel, j::Integer, y2)
    fe = m.feterm        # model matrix X, column names, rank, etc.
    x = fe.x             # model matrix
    fe.rank == size(x, 2) || throw(ArgumentError("m.X is not of full column rank"))
    notj = deleteat!(collect(axes(x, 2)), j) # indirectly checks that j is in range
    feterm = MixedModels.FeTerm(x[:, notj], fe.cnames[notj])
    θbak = m.θ           # save a copy of the parameter estimates
    setθ!(m, m.optsum.initial) # ensure m is at initial values
    mnew = LinearMixedModel(collect(y2), feterm, deepcopy(m.reterms), m.formula)
    MixedModels.objective!(m, θbak)  # restore parameter values in m
    return mnew
end

Then, we contruct a new GeneralizedLinearMixedModel as in MixedModels.jl/src/generalizedlinearmixedmodel.jl:

function dropcol_glmm(glmm::GeneralizedLinearMixedModel, j::Integer)
    MixedModels.unfit!(glmm)
    LMM = dropcol2(glmm.LMM, j, glmm.y)
    wts = []
    offset = []
    T = eltype(LMM.Xymat)
    y = copy(LMM.y)
    d = Bernoulli()
    l = canonicallink(d)
    dofit = size(LMM.X, 2) != 0 # GLM.jl kwarg
    gl = glm(LMM.X, y, d, l;
            wts=convert(Vector{T}, wts),
            dofit,
            offset=convert(Vector{T}, offset)
    )
    β = dofit ? coef(gl) : T[]
    u = [fill(zero(eltype(y)), MixedModels.vsize(t), MixedModels.nlevs(t)) for t in LMM.reterms]
    vv = length(u) == 1 ? vec(first(u)) : similar(y, 0)

    res = GeneralizedLinearMixedModel{T,typeof(d)}(
        LMM,
        β,
        copy(β),
        LMM.θ,
        copy.(u),
        u,
        zero.(u),
        gl.rr,
        similar(y),
        oftype(y, wts),
        similar(vv),
        similar(vv),
        similar(vv),
        similar(vv),
    )
    return res
end

As an adhoc example:

data = MixedModels.dataset(:insteval)
data = DataFrame(data)
data.service = map(x -> ifelse(x == "N", 0, 1), data.service)

fm = @formula(service ~ studage + lectage + (1|s))
m = GeneralizedLinearMixedModel(fm, data, Bernoulli())
fit!(m; fast = true)

Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 1)
  service ~ 1 + studage + lectage + (1 | s)
  Distribution: Bernoulli{Float64}
  Link: LogitLink()

   logLik     deviance      AIC         AICc        BIC     
 -47205.8133  94411.6267  94431.6267  94431.6297  94523.6663

Variance components:
     Column   Variance Std.Dev. 
s (Intercept)  0.437793 0.661659

 Number of obs: 73421; levels of grouping factors: 2972

Fixed-effects parameters:
────────────────────────────────────────────────────
                 Coef.  Std. Error       z  Pr(>|z|)
────────────────────────────────────────────────────
(Intercept)  -0.123563   0.0285926   -4.32    <1e-04
studage: 4   -0.387704   0.0419093   -9.25    <1e-19
studage: 6   -1.04746    0.0421811  -24.83    <1e-99
studage: 8   -1.27521    0.0452258  -28.20    <1e-99
lectage: 2    0.184468   0.0233174    7.91    <1e-14
lectage: 3    0.530911   0.0279009   19.03    <1e-80
lectage: 4    0.718081   0.0297538   24.13    <1e-99
lectage: 5    1.16544    0.0340265   34.25    <1e-99
lectage: 6    1.52207    0.0301753   50.44    <1e-99
────────────────────────────────────────────────────

m2 = dropcol_glmm(m, 2)
fit!(m2, fast = true)

Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 1)
  service ~ 1 + studage + lectage + (1 | s)
  Distribution: Bernoulli{Float64}
  Link: LogitLink()

   logLik     deviance      AIC         AICc        BIC     
 -47294.2634  94588.5267  94606.5267  94606.5292  94689.3624

Variance components:
     Column   Variance Std.Dev. 
s (Intercept)  0.406962 0.637936

 Number of obs: 73421; levels of grouping factors: 2972

Fixed-effects parameters:
────────────────────────────────────────────────────
                 Coef.  Std. Error       z  Pr(>|z|)
────────────────────────────────────────────────────
(Intercept)  -0.298664   0.0107902  -27.68    <1e-99
studage: 6   -0.926317   0.0167307  -55.37    <1e-99
studage: 8   -1.0441     0.0178362  -58.54    <1e-99
lectage: 2    0.193725   0.0108125   17.92    <1e-71
lectage: 3    0.475043   0.0127252   37.33    <1e-99
lectage: 4    0.674609   0.0137594   49.03    <1e-99
lectage: 5    1.11943    0.0158697   70.54    <1e-99
lectage: 6    1.46943    0.0137901  106.56    <1e-99
────────────────────────────────────────────────────

My point in all of this is to recreate the approach of the MAST package for single-cell analysis, where I need to fit both a LinearMixedModel and a Logistic regression.

Again, thank you for your help.

Best, Max

[dmbates]

I think that will work. We could try to make it easier to construct a Generalized Linear Mixed Model from the feterm and reterms but I am a bit reluctant to make that too easy. In general it is not a good idea to concentrate on individual coefficients from a categorical covariate but I can understand that in single-cell analysis it may be a convenient way to phrase that problem.

I did write a reduced set of types and methods to fit a logistic GLMM in https://embraceuncertaintybook.com/aGHQ.html If you were interested we could try to modify those for a special-purpose GLMM implementation. If this seems worthwhile to you, could you suggest where I should start reading about the MAST approach? Is the 2015 Genome Biology paper the best place to start or should I start with the vignettes in the BioConductor package?

Feel free to contact me at my gmail.com (same username as on github) address to take this discussion offline.

[dmbates]

@MaximilianNuber Is it correct that there is only one random-effects term in the LMMs and GLMMs that you wish to fit and that it is of the form (1|samp)? If so, there are considerable simplifications that can be made in fitting the model and refitting it with individual columns in X removed.

[MaximilianNuber]

@dmbates Yes, the only random effect in the model is in the form (1|sample), with the goal to retain sample structure. In larger or several datasets as my own I have been wondering if the pool/sequencing run/dataset should be added as well, but I haven´t found this attempt elsewhere so I would keep it at the sample level for now.

For more in-depth information on your question I am preparing a mail.