Model constructors
The LinearMixedModel type represents a linear mixed-effects model. Typically it is constructed from a Formula and an appropriate Table type, usually a DataFrame.
Examples of linear mixed-effects model fits
For illustration, several data sets from the lme4 package for R are made available in .arrow format in this package. Often, for convenience, we will convert these to DataFrames. These data sets include the dyestuff and dyestuff2 data sets.
using DataFrames, MixedModels, StatsModels
dyestuff = MixedModels.dataset(:dyestuff)Arrow.Table with 30 rows, 2 columns, and schema:
:batch String
:yield Int16describe(DataFrame(dyestuff))| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | DataType | |
| 1 | batch | A | F | 0 | String | ||
| 2 | yield | 1527.5 | 1440 | 1530.0 | 1635 | 0 | Int16 |
The @formula language in Julia
MixedModels.jl builds on the the Julia formula language provided by StatsModels.jl, which is similar to the formula language in R and is also based on the notation from Wilkinson and Rogers (1973). There are two ways to construct a formula in Julia. The first way is to enclose the formula expression in the @formula macro:
StatsModels.@formula Macro
@formula(ex)Capture and parse a formula expression as a Formula struct.
A formula is an abstract specification of a dependence between left-hand and right-hand side variables as in, e.g., a regression model. Each side specifies at a high level how tabular data is to be converted to a numerical matrix suitable for modeling. This specification looks something like Julia code, is represented as a Julia Expr, but uses special syntax. The @formula macro takes an expression like y ~ 1 + a*b, transforms it according to the formula syntax rules into a lowered form (like y ~ 1 + a + b + a&b), and constructs a Formula struct which captures the original expression, the lowered expression, and the left- and right-hand-side.
Operators that have special interpretations in this syntax are
~is the formula separator, where it is a binary operator (the first argument is the left-hand side, and the second is the right-hand side.+concatenates variables as columns when generating a model matrix.&represents an interaction between two or more variables, which corresponds to a row-wise kronecker product of the individual terms (or element-wise product if all terms involved are continuous/scalar).*expands to all main effects and interactions:a*bis equivalent toa+b+a&b,a*b*ctoa+b+c+a&b+a&c+b&c+a&b&c, etc.1,0, and-1indicate the presence (for1) or absence (for0and-1) of an intercept column.
The rules that are applied are
The associative rule (un-nests nested calls to
+,&, and*).The distributive rule (interactions
&distribute over concatenation+).The
*rule expandsa*btoa+b+a&b(recursively).Subtraction is converted to addition and negation, so
x-1becomesx + -1(applies only to subtraction of literal 1).Single-argument
&calls are stripped, so&(x)becomes the main effectx.
The second way is to combine Terms with operators like +, &, ~, and others at "run time". This is especially useful if you wish to create a formula from a list a variable names. For instance, the following are equivalent:
@formula(y ~ 1 + a + b + a & b) == (term(:y) ~ term(1) + term(:a) + term(:b) + term(:a) & term(:b))trueMixedModels.jl provides additional formula syntax for representing random-effects terms. Most importantly, | separates random effects and their grouping factors (as in the formula extension used by the R package lme4. Much like with the base formula language, | can be used within the @formula macro and to construct a formula programmatically:
@formula(y ~ 1 + a + b + (1 + a + b | g))FormulaTerm
Response:
y(unknown)
Predictors:
1
a(unknown)
b(unknown)
(a,b,g)->(1 + a + b) | gterms = sum(term(t) for t in [1, :a, :b])
group = term(:g)
response = term(:y)
response ~ terms + (terms | group)FormulaTerm
Response:
y(unknown)
Predictors:
1
a(unknown)
b(unknown)
(1 + a + b | g)Models with simple, scalar random effects
A basic model with simple, scalar random effects for the levels of batch (the batch of an intermediate product, in this case) is declared and fit as
fm = @formula(yield ~ 1 + (1|batch))
fm1 = fit(MixedModel, fm, dyestuff)Linear mixed model fit by maximum likelihood
yield ~ 1 + (1 | batch)
logLik -2 logLik AIC AICc BIC
-163.6635 327.3271 333.3271 334.2501 337.5307
Variance components:
Column Variance Std.Dev.
batch (Intercept) 1388.3332 37.2603
Residual 2451.2501 49.5101
Number of obs: 30; levels of grouping factors: 6
Fixed-effects parameters:
────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
────────────────────────────────────────────────
(Intercept) 1527.5 17.6946 86.33 <1e-99
────────────────────────────────────────────────(If you are new to Julia you may find that this first fit takes an unexpectedly long time, due to Just-In-Time (JIT) compilation of the code. The subsequent calls to such functions are much faster.)
using BenchmarkTools
dyestuff2 = MixedModels.dataset(:dyestuff2)
@benchmark fit(MixedModel, $fm, $dyestuff2)BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
Range (min … max): 52.000 μs … 41.175 ms ┊ GC (min … max): 0.00% … 95.18%
Time (median): 57.708 μs ┊ GC (median): 0.00%
Time (mean ± σ): 62.120 μs ± 411.184 μs ┊ GC (mean ± σ): 6.31% ± 0.95%
▄▅▅█▅▆▇▅▄▆▃▁▁
▂▂▂▂▂▂▁▁▁▁▁▁▁▂▂▂▃▆▇█████████████▇▆▅▄▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▄
52 μs Histogram: frequency by time 65.4 μs <
Memory estimate: 50.61 KiB, allocs estimate: 1084.By default, the model is fit by maximum likelihood. To use the REML criterion instead, add the optional named argument REML=true to the call to fit
fm1reml = fit(MixedModel, fm, dyestuff, REML=true)Linear mixed model fit by REML
yield ~ 1 + (1 | batch)
REML criterion at convergence: 319.65427684225943
Variance components:
Column Variance Std.Dev.
batch (Intercept) 1764.0537 42.0006
Residual 2451.2492 49.5101
Number of obs: 30; levels of grouping factors: 6
Fixed-effects parameters:
────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
────────────────────────────────────────────────
(Intercept) 1527.5 19.3834 78.80 <1e-99
────────────────────────────────────────────────Floating-point type in the model
The type of fm1
typeof(fm1)LinearMixedModel{Float64}includes the floating point type used internally for the various matrices, vectors, and scalars that represent the model. At present, this will always be Float64 because the parameter estimates are optimized using the NLopt package which calls compiled C code that only allows for optimization with respect to a Float64 parameter vector.
So in theory other floating point types, such as BigFloat or Float32, can be used to define a model but in practice only Float64 works at present.
In theory, theory and practice are the same. In practice, they aren't. – Anon
Simple, scalar random effects
A simple, scalar random effects term in a mixed-effects model formula is of the form (1|G). All random effects terms end with |G where G is the grouping factor for the random effect. The name or, more generally the expression, G, should evaluate to a categorical array that has a distinct set of levels. The random effects are associated with the levels of the grouping factor.
A scalar random effect is, as the name implies, one scalar value for each level of the grouping factor. A simple, scalar random effects term is of the form, (1|G). It corresponds to a shift in the intercept for each level of the grouping factor.
Models with vector-valued random effects
The sleepstudy data are observations of reaction time, reaction, on several subjects, subj, after 0 to 9 days of sleep deprivation, days. A model with random intercepts and random slopes for each subject, allowing for within-subject correlation of the slope and intercept, is fit as
sleepstudy = MixedModels.dataset(:sleepstudy)
fm2 = fit(MixedModel, @formula(reaction ~ 1 + days + (1 + days|subj)), sleepstudy)Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 + days | subj)
logLik -2 logLik AIC AICc BIC
-875.9697 1751.9393 1763.9393 1764.4249 1783.0971
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 565.51066 23.78047
days 32.68212 5.71683 +0.08
Residual 654.94145 25.59182
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
──────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
──────────────────────────────────────────────────
(Intercept) 251.405 6.63226 37.91 <1e-99
days 10.4673 1.50224 6.97 <1e-11
──────────────────────────────────────────────────Models with multiple, scalar random-effects terms
A model for the Penicillin data incorporates random effects for the plate, and for the sample. As every sample is used on every plate these two factors are crossed.
penicillin = MixedModels.dataset(:penicillin)
fm3 = fit(MixedModel, @formula(diameter ~ 1 + (1|plate) + (1|sample)), penicillin)Linear mixed model fit by maximum likelihood
diameter ~ 1 + (1 | plate) + (1 | sample)
logLik -2 logLik AIC AICc BIC
-166.0942 332.1883 340.1883 340.4761 352.0676
Variance components:
Column Variance Std.Dev.
plate (Intercept) 0.714979 0.845565
sample (Intercept) 3.135193 1.770648
Residual 0.302426 0.549933
Number of obs: 144; levels of grouping factors: 24, 6
Fixed-effects parameters:
─────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
─────────────────────────────────────────────────
(Intercept) 22.9722 0.744596 30.85 <1e-99
─────────────────────────────────────────────────In contrast, the cask grouping factor is nested within the batch grouping factor in the Pastes data.
pastes = DataFrame(MixedModels.dataset(:pastes))
describe(pastes)| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | DataType | |
| 1 | batch | A | J | 0 | String | ||
| 2 | cask | a | c | 0 | String | ||
| 3 | strength | 60.0533 | 54.2 | 59.3 | 66.0 | 0 | Float64 |
This can be expressed using the solidus (the "/" character) to separate grouping factors, read "cask nested within batch":
fm4a = fit(MixedModel, @formula(strength ~ 1 + (1|batch/cask)), pastes)Linear mixed model fit by maximum likelihood
strength ~ 1 + (1 | batch) + (1 | batch & cask)
logLik -2 logLik AIC AICc BIC
-123.9972 247.9945 255.9945 256.7217 264.3718
Variance components:
Column Variance Std.Dev.
batch & cask (Intercept) 8.433617 2.904069
batch (Intercept) 1.199180 1.095071
Residual 0.678002 0.823409
Number of obs: 60; levels of grouping factors: 30, 10
Fixed-effects parameters:
─────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
─────────────────────────────────────────────────
(Intercept) 60.0533 0.642136 93.52 <1e-99
─────────────────────────────────────────────────If the levels of the inner grouping factor are unique across the levels of the outer grouping factor, then this nesting does not need to expressed explicitly in the model syntax. For example, defining sample to be the combination of batch and cask, yields a naming scheme where the nesting is apparent from the data even if not expressed in the formula. (That is, each level of sample occurs in conjunction with only one level of batch.) As such, this model is equivalent to the previous one.
pastes.sample = (string.(pastes.cask, "&", pastes.batch))
fm4b = fit(MixedModel, @formula(strength ~ 1 + (1|sample) + (1|batch)), pastes)Linear mixed model fit by maximum likelihood
strength ~ 1 + (1 | sample) + (1 | batch)
logLik -2 logLik AIC AICc BIC
-123.9972 247.9945 255.9945 256.7217 264.3718
Variance components:
Column Variance Std.Dev.
sample (Intercept) 8.433617 2.904069
batch (Intercept) 1.199178 1.095070
Residual 0.678002 0.823409
Number of obs: 60; levels of grouping factors: 30, 10
Fixed-effects parameters:
─────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
─────────────────────────────────────────────────
(Intercept) 60.0533 0.642136 93.52 <1e-99
─────────────────────────────────────────────────In observational studies it is common to encounter partially crossed grouping factors. For example, the InstEval data are course evaluations by students, s, of instructors, d. Additional covariates include the academic department, dept, in which the course was given and service, whether or not it was a service course.
insteval = MixedModels.dataset(:insteval)
fm5 = fit(MixedModel, @formula(y ~ 1 + service * dept + (1|s) + (1|d)), insteval)Linear mixed model fit by maximum likelihood
y ~ 1 + service + dept + service & dept + (1 | s) + (1 | d)
logLik -2 logLik AIC AICc BIC
-118792.7767 237585.5534 237647.5534 237647.5804 237932.8763
Variance components:
Column Variance Std.Dev.
s (Intercept) 0.105418 0.324681
d (Intercept) 0.258416 0.508347
Residual 1.384728 1.176745
Number of obs: 73421; levels of grouping factors: 2972, 1128
Fixed-effects parameters:
────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
────────────────────────────────────────────────────────────────
(Intercept) 3.27628 0.0793647 41.28 <1e-99
service: Y 0.0116044 0.0699321 0.17 0.8682
dept: D02 -0.0411091 0.120331 -0.34 0.7326
dept: D03 0.00967413 0.108411 0.09 0.9289
dept: D04 0.105017 0.0944964 1.11 0.2664
dept: D05 0.0828643 0.11148 0.74 0.4573
dept: D06 -0.01194 0.0978342 -0.12 0.9029
dept: D07 0.0992679 0.110598 0.90 0.3694
dept: D08 0.0575337 0.127935 0.45 0.6529
dept: D09 -0.00263181 0.107085 -0.02 0.9804
dept: D10 -0.223423 0.099838 -2.24 0.0252
dept: D11 0.0129816 0.110639 0.12 0.9066
dept: D12 0.00503825 0.0944243 0.05 0.9574
dept: D14 0.0050827 0.109041 0.05 0.9628
dept: D15 -0.0466719 0.101942 -0.46 0.6471
service: Y & dept: D02 -0.144352 0.0929527 -1.55 0.1204
service: Y & dept: D03 0.0174078 0.0927237 0.19 0.8511
service: Y & dept: D04 -0.0381262 0.0810901 -0.47 0.6382
service: Y & dept: D05 0.0596632 0.123952 0.48 0.6303
service: Y & dept: D06 -0.254044 0.080781 -3.14 0.0017
service: Y & dept: D07 -0.151634 0.11157 -1.36 0.1741
service: Y & dept: D08 0.0508942 0.112189 0.45 0.6501
service: Y & dept: D09 -0.259448 0.0899448 -2.88 0.0039
service: Y & dept: D10 0.25907 0.111369 2.33 0.0200
service: Y & dept: D11 -0.276577 0.0819621 -3.37 0.0007
service: Y & dept: D12 -0.0418489 0.0792928 -0.53 0.5977
service: Y & dept: D14 -0.256742 0.0931016 -2.76 0.0058
service: Y & dept: D15 0.24042 0.0982071 2.45 0.0144
────────────────────────────────────────────────────────────────Simplifying the random effect correlation structure
MixedModels.jl estimates not only the variance of the effects for each random effect level, but also the correlation between the random effects for different predictors. So, for the model of the sleepstudy data above, one of the parameters that is estimated is the correlation between each subject's random intercept (i.e., their baseline reaction time) and slope (i.e., their particular change in reaction time per day of sleep deprivation). In some cases, you may wish to simplify the random effects structure by removing these correlation parameters. This often arises when there are many random effects you want to estimate (as is common in psychological experiments with many conditions and covariates), since the number of random effects parameters increases as the square of the number of predictors, making these models difficult to estimate from limited data.
The special syntax zerocorr can be applied to individual random effects terms inside the @formula:
fm2zerocorr_fm = fit(MixedModel, @formula(reaction ~ 1 + days + zerocorr(1 + days|subj)), sleepstudy)Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + zerocorr(1 + days | subj)
logLik -2 logLik AIC AICc BIC
-876.0016 1752.0033 1762.0033 1762.3481 1777.9680
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 584.25897 24.17145
days 33.63281 5.79938 .
Residual 653.11578 25.55613
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
──────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
──────────────────────────────────────────────────
(Intercept) 251.405 6.70771 37.48 <1e-99
days 10.4673 1.51931 6.89 <1e-11
──────────────────────────────────────────────────Alternatively, correlations between parameters can be removed by including them as separate random effects terms:
fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj) + (days|subj)), sleepstudy)Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 | subj) + (days | subj)
logLik -2 logLik AIC AICc BIC
-876.0016 1752.0033 1762.0033 1762.3481 1777.9680
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 584.25897 24.17145
days 33.63281 5.79938 .
Residual 653.11578 25.55613
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
──────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
──────────────────────────────────────────────────
(Intercept) 251.405 6.70771 37.48 <1e-99
days 10.4673 1.51931 6.89 <1e-11
──────────────────────────────────────────────────Finally, for predictors that are categorical, MixedModels.jl will estimate correlations between each level. Notice the large number of correlation parameters if we treat days as a categorical variable by giving it contrasts:
fit(MixedModel, @formula(reaction ~ 1 + days + (1 + days|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 + days | subj)
logLik -2 logLik AIC AICc BIC
-805.3994 1610.7987 1742.7987 1821.0642 1953.5339
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 954.66067 30.89758
days: 1 498.34749 22.32370 -0.30
days: 2 916.26827 30.26992 -0.57 +0.75
days: 3 1268.96763 35.62257 -0.37 +0.72 +0.87
days: 4 1487.09850 38.56292 -0.31 +0.59 +0.67 +0.91
days: 5 2302.20224 47.98127 -0.25 +0.46 +0.45 +0.70 +0.85
days: 6 3851.78092 62.06272 -0.27 +0.30 +0.48 +0.70 +0.77 +0.75
days: 7 1808.39158 42.52519 -0.16 +0.22 +0.47 +0.50 +0.63 +0.64 +0.71
days: 8 3159.98677 56.21376 -0.20 +0.29 +0.36 +0.56 +0.73 +0.90 +0.73 +0.74
days: 9 3086.04128 55.55215 +0.06 +0.26 +0.16 +0.38 +0.59 +0.78 +0.38 +0.53 +0.85
Residual 18.79114 4.33488
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 7.35396 34.90 <1e-99
days: 1 7.84395 5.45654 1.44 0.1506
days: 2 8.71009 7.27954 1.20 0.2315
days: 3 26.3402 8.51975 3.09 0.0020
days: 4 31.9976 9.2035 3.48 0.0005
days: 5 51.8666 11.4012 4.55 <1e-05
days: 6 55.5265 14.6995 3.78 0.0002
days: 7 62.0988 10.1269 6.13 <1e-09
days: 8 79.9777 13.3283 6.00 <1e-08
days: 9 94.1994 13.1733 7.15 <1e-12
───────────────────────────────────────────────────Separating the 1 and days random effects into separate terms removes the correlations between the intercept and the levels of days, but not between the levels themselves:
fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj) + (days|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 | subj) + (days | subj)
logLik -2 logLik AIC AICc BIC
-827.7749 1655.5498 1769.5498 1823.7465 1951.5483
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 789.6792 28.1012
days: 1 0.0000 0.0000 .
days: 2 357.7766 18.9150 . +NaN
days: 3 684.7231 26.1672 . +NaN +0.81
days: 4 949.6394 30.8162 . +NaN +0.57 +0.91
days: 5 1751.2433 41.8479 . +NaN +0.26 +0.66 +0.87
days: 6 3357.4474 57.9435 . +NaN +0.45 +0.72 +0.80 +0.76
days: 7 1539.1760 39.2323 . +NaN +0.39 +0.42 +0.59 +0.62 +0.71
days: 8 2735.1879 52.2990 . +NaN +0.22 +0.52 +0.75 +0.93 +0.72 +0.75
days: 9 2767.1610 52.6038 . +NaN -0.05 +0.28 +0.57 +0.80 +0.34 +0.52 +0.87
Residual 135.0061 11.6192
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 7.16738 35.81 <1e-99
days: 1 7.84395 3.87307 2.03 0.0428
days: 2 8.71009 5.90569 1.47 0.1402
days: 3 26.3402 7.28291 3.62 0.0003
days: 4 31.9976 8.23155 3.89 0.0001
days: 5 51.8667 10.5968 4.89 <1e-06
days: 6 55.5265 14.196 3.91 <1e-04
days: 7 62.0988 10.0255 6.19 <1e-09
days: 8 79.9777 12.9211 6.19 <1e-09
days: 9 94.1994 12.9897 7.25 <1e-12
───────────────────────────────────────────────────(Notice that the variance component for days: 1 is estimated as zero, so the correlations for this component are undefined and expressed as NaN, not a number.)
An alternative is to force all the levels of days as indicators using fulldummy encoding.
MixedModels.fulldummy Function
fulldummy(term::CategoricalTerm)Assign "contrasts" that include all indicator columns (dummy variables) and an intercept column.
This will result in an under-determined set of contrasts, which is not a problem in the random effects because of the regularization, or "shrinkage", of the conditional modes.
The interaction of fulldummy with complex random effects is subtle and complex with numerous potential edge cases. As we discover these edge cases, we will document and determine their behavior. Until such time, please check the model summary to verify that the expansion is working as you expected. If it is not, please report a use case on GitHub.
fit(MixedModel, @formula(reaction ~ 1 + days + (1 + fulldummy(days)|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 + days | subj)
logLik -2 logLik AIC AICc BIC
-805.3991 1610.7982 1764.7982 1882.5630 2010.6559
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 727.90862 26.97978
days: 0 499.02116 22.33878 -0.22
days: 1 381.88986 19.54200 -0.07 +0.44
days: 2 463.96481 21.53984 -0.34 +0.05 +0.53
days: 3 379.47587 19.48014 +0.28 -0.45 +0.18 +0.63
days: 4 327.96770 18.10988 +0.65 -0.82 -0.38 -0.07 +0.66
days: 5 1051.33792 32.42434 +0.42 -0.52 -0.30 -0.27 +0.20 +0.62
days: 6 2321.59696 48.18295 +0.27 -0.48 -0.44 -0.08 +0.36 +0.58 +0.54
days: 7 1134.97511 33.68939 +0.27 -0.12 -0.28 +0.06 -0.03 +0.18 +0.32 +0.48
days: 8 1870.82916 43.25308 +0.35 -0.41 -0.41 -0.27 +0.04 +0.45 +0.82 +0.55 +0.55
days: 9 2277.31514 47.72122 +0.47 -0.14 -0.15 -0.34 -0.10 +0.32 +0.67 +0.07 +0.32 +0.78
Residual 19.21115 4.38305
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 7.36068 34.87 <1e-99
days: 1 7.84395 5.4558 1.44 0.1505
days: 2 8.71009 7.28336 1.20 0.2317
days: 3 26.3402 8.52163 3.09 0.0020
days: 4 31.9976 9.20409 3.48 0.0005
days: 5 51.8667 11.3962 4.55 <1e-05
days: 6 55.5265 14.7077 3.78 0.0002
days: 7 62.0988 10.1303 6.13 <1e-09
days: 8 79.9777 13.3249 6.00 <1e-08
days: 9 94.1994 13.1611 7.16 <1e-12
───────────────────────────────────────────────────This fit produces a better fit as measured by the objective (negative twice the log-likelihood is 1610.8) but at the expense of adding many more parameters to the model. As a result, model comparison criteria such, as AIC and BIC, are inflated.
But using zerocorr on the individual terms does remove the correlations between the levels:
fit(MixedModel, @formula(reaction ~ 1 + days + zerocorr(1 + days|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + zerocorr(1 + days | subj)
logLik -2 logLik AIC AICc BIC
-882.9138 1765.8276 1807.8276 1813.6757 1874.8797
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 958.4877 30.9595
days: 1 0.0000 0.0000 .
days: 2 0.0000 0.0000 . .
days: 3 0.0000 0.0000 . . .
days: 4 0.0000 0.0000 . . . .
days: 5 519.6167 22.7951 . . . . .
days: 6 1704.1051 41.2808 . . . . . .
days: 7 608.7869 24.6736 . . . . . . .
days: 8 1273.0228 35.6794 . . . . . . . .
days: 9 1753.8569 41.8791 . . . . . . . . .
Residual 434.8673 20.8535
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 8.79822 29.17 <1e-99
days: 1 7.84395 6.95116 1.13 0.2591
days: 2 8.71009 6.95116 1.25 0.2102
days: 3 26.3402 6.95116 3.79 0.0002
days: 4 31.9976 6.95116 4.60 <1e-05
days: 5 51.8667 8.78557 5.90 <1e-08
days: 6 55.5265 11.9579 4.64 <1e-05
days: 7 62.0988 9.06312 6.85 <1e-11
days: 8 79.9777 10.9106 7.33 <1e-12
days: 9 94.1994 12.0729 7.80 <1e-14
───────────────────────────────────────────────────fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj) + zerocorr(days|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + (1 | subj) + zerocorr(days | subj)
logLik -2 logLik AIC AICc BIC
-882.9138 1765.8276 1807.8276 1813.6757 1874.8797
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 958.4877 30.9595
days: 1 0.0000 0.0000 .
days: 2 0.0000 0.0000 . .
days: 3 0.0000 0.0000 . . .
days: 4 0.0000 0.0000 . . . .
days: 5 519.6167 22.7951 . . . . .
days: 6 1704.1051 41.2808 . . . . . .
days: 7 608.7869 24.6736 . . . . . . .
days: 8 1273.0228 35.6794 . . . . . . . .
days: 9 1753.8569 41.8791 . . . . . . . . .
Residual 434.8673 20.8535
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 8.79822 29.17 <1e-99
days: 1 7.84395 6.95116 1.13 0.2591
days: 2 8.71009 6.95116 1.25 0.2102
days: 3 26.3402 6.95116 3.79 0.0002
days: 4 31.9976 6.95116 4.60 <1e-05
days: 5 51.8667 8.78557 5.90 <1e-08
days: 6 55.5265 11.9579 4.64 <1e-05
days: 7 62.0988 9.06312 6.85 <1e-11
days: 8 79.9777 10.9106 7.33 <1e-12
days: 9 94.1994 12.0729 7.80 <1e-14
───────────────────────────────────────────────────fit(MixedModel, @formula(reaction ~ 1 + days + zerocorr(1 + fulldummy(days)|subj)), sleepstudy,
contrasts = Dict(:days => DummyCoding()))Linear mixed model fit by maximum likelihood
reaction ~ 1 + days + zerocorr(1 + days | subj)
logLik -2 logLik AIC AICc BIC
-878.9843 1757.9686 1801.9686 1808.4145 1872.2137
Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 1135.22369 33.69308
days: 0 775.53471 27.84842 .
days: 1 357.33070 18.90319 . .
days: 2 221.08627 14.86897 . . .
days: 3 0.00000 0.00000 . . . .
days: 4 44.25126 6.65216 . . . . .
days: 5 670.03272 25.88499 . . . . . .
days: 6 1739.38985 41.70599 . . . . . . .
days: 7 908.48034 30.14101 . . . . . . . .
days: 8 1457.44632 38.17652 . . . . . . . . .
days: 9 2027.51717 45.02796 . . . . . . . . . .
Residual 181.13013 13.45846
Number of obs: 180; levels of grouping factors: 18
Fixed-effects parameters:
───────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────────
(Intercept) 256.652 10.7804 23.81 <1e-99
days: 1 7.84395 9.11387 0.86 0.3894
days: 2 8.71009 8.68869 1.00 0.3161
days: 3 26.3402 7.95052 3.31 0.0009
days: 4 31.9976 8.10366 3.95 <1e-04
days: 5 51.8666 10.0217 5.18 <1e-06
days: 6 55.5264 12.6429 4.39 <1e-04
days: 7 62.0988 10.6622 5.82 <1e-08
days: 8 79.9777 12.0075 6.66 <1e-10
days: 9 94.1994 13.2609 7.10 <1e-11
───────────────────────────────────────────────────Fitting generalized linear mixed models
To create a GLMM representation, the distribution family for the response, and possibly the link function, must be specified.
verbagg = MixedModels.dataset(:verbagg)
verbaggform = @formula(r2 ~ 1 + anger + gender + btype + situ + mode + (1|subj) + (1|item));
gm1 = fit(MixedModel, verbaggform, verbagg, Bernoulli())Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 1)
r2 ~ 1 + anger + gender + btype + situ + mode + (1 | subj) + (1 | item)
Distribution: Bernoulli{Float64}
Link: LogitLink()
logLik deviance AIC AICc BIC
-4067.9164 8135.8329 8153.8329 8153.8566 8216.2370
Variance components:
Column Variance Std.Dev.
subj (Intercept) 1.793488 1.339212
item (Intercept) 0.117149 0.342271
Number of obs: 7584; levels of grouping factors: 316, 24
Fixed-effects parameters:
──────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
──────────────────────────────────────────────────────
(Intercept) -0.152977 0.385222 -0.40 0.6913
anger 0.0573961 0.0167528 3.43 0.0006
gender: M 0.320783 0.191207 1.68 0.0934
btype: scold -1.05988 0.184159 -5.76 <1e-08
btype: shout -2.10389 0.186517 -11.28 <1e-28
situ: self -1.05438 0.151194 -6.97 <1e-11
mode: want 0.706981 0.151007 4.68 <1e-05
──────────────────────────────────────────────────────The canonical link, which is LogitLink for the Bernoulli distribution, is used if no explicit link is specified.
Note that, in keeping with convention in the GLM package, the distribution family for a binary (i.e. 0/1) response is the Bernoulli distribution. The Binomial distribution is only used when the response is the fraction of trials returning a positive, in which case the number of trials must be specified as the case weights.
Optional arguments to fit
An alternative approach is to create the GeneralizedLinearMixedModel object then call fit! on it. The optional arguments fast and/or nAGQ can be passed to the optimization process via both fit and fit! (i.e these optimization settings are not used nor recognized when constructing the model).
As the name implies, fast=true, provides a faster but somewhat less accurate fit. These fits may suffice for model comparisons.
gm1a = fit(MixedModel, verbaggform, verbagg, Bernoulli(), fast = true)
deviance(gm1a) - deviance(gm1)0.33801335722546355@benchmark fit(MixedModel, $verbaggform, $verbagg, Bernoulli())BenchmarkTools.Trial: 4 samples with 1 evaluation per sample.
Range (min … max): 1.380 s … 1.402 s ┊ GC (min … max): 0.00% … 0.00%
Time (median): 1.386 s ┊ GC (median): 0.00%
Time (mean ± σ): 1.388 s ± 9.796 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
█ █ █ █
█▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█ ▁
1.38 s Histogram: frequency by time 1.4 s <
Memory estimate: 29.89 MiB, allocs estimate: 472507.@benchmark fit(MixedModel, $verbaggform, $verbagg, Bernoulli(), fast = true)BenchmarkTools.Trial: 54 samples with 1 evaluation per sample.
Range (min … max): 93.465 ms … 105.314 ms ┊ GC (min … max): 0.00% … 10.40%
Time (median): 93.883 ms ┊ GC (median): 0.00%
Time (mean ± σ): 94.346 ms ± 1.916 ms ┊ GC (mean ± σ): 0.22% ± 1.42%
▁▄▃█▂
█████▃▆▃▄▃▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃ ▁
93.5 ms Histogram: frequency by time 101 ms <
Memory estimate: 9.54 MiB, allocs estimate: 83085.The optional argument nAGQ=k causes evaluation of the deviance function to use a k point adaptive Gauss-Hermite quadrature rule. This method only applies to models with a single, simple, scalar random-effects term, such as
contraception = MixedModels.dataset(:contra)
contraform = @formula(use ~ 1 + age + abs2(age) + livch + urban + (1|dist));
bernoulli = Bernoulli()
deviances = Dict{Symbol,Float64}()
b = @benchmarkable deviances[:default] = deviance(fit(MixedModel, $contraform, $contraception, $bernoulli));
run(b)
b = @benchmarkable deviances[:fast] = deviance(fit(MixedModel, $contraform, $contraception, $bernoulli, fast = true));
run(b)
b = @benchmarkable deviances[:nAGQ] = deviance(fit(MixedModel, $contraform, $contraception, $bernoulli, nAGQ=9));
run(b)
b = @benchmarkable deviances[:nAGQ_fast] = deviance(fit(MixedModel, $contraform, $contraception, $bernoulli, nAGQ=9, fast=true));
run(b)
sort(deviances)OrderedCollections.OrderedDict{Symbol, Float64} with 4 entries:
:default => 2372.73
:fast => 2372.78
:nAGQ => 2372.46
:nAGQ_fast => 2372.51Extractor functions
LinearMixedModel and GeneralizedLinearMixedModel are subtypes of StatsAPI.RegressionModel which, in turn, is a subtype of StatsBase.StatisticalModel. Many of the generic extractors defined in the StatsBase package have methods for these models.
Model-fit statistics
The statistics describing the quality of the model fit include
StatsAPI.loglikelihood Function
loglikelihood(model::StatisticalModel)
loglikelihood(model::StatisticalModel, observation)Return the log-likelihood of the model.
With an observation argument, return the contribution of observation to the log-likelihood of model.
If observation is a Colon, return a vector of each observation's contribution to the log-likelihood of the model. In other words, this is the vector of the pointwise log-likelihood contributions.
In general, sum(loglikehood(model, :)) == loglikelihood(model).
StatsAPI.aic Function
aic(model::StatisticalModel)Akaike's Information Criterion, defined as k its number of consumed degrees of freedom (as returned by dof).
StatsAPI.bic Function
bic(model::StatisticalModel)Bayesian Information Criterion, defined as dof), and nobs).
StatsAPI.dof Function
dof(model::StatisticalModel)Return the number of degrees of freedom consumed in the model, including when applicable the intercept and the distribution's dispersion parameter.
sourceStatsAPI.nobs Function
nobs(model::StatisticalModel)Return the number of independent observations on which the model was fitted. Be careful when using this information, as the definition of an independent observation may vary depending on the model, on the format used to pass the data, on the sampling plan (if specified), etc.
sourceloglikelihood(fm1)-163.6635299405715aic(fm1)333.327059881143bic(fm1)337.5306520261295dof(fm1) # 1 fixed effect, 2 variances3nobs(fm1) # 30 observations30loglikelihood(gm1)-4067.9164291743905In general the deviance of a statistical model fit is negative twice the log-likelihood adjusting for the saturated model.
StatsAPI.deviance Method
deviance(m::GeneralizedLinearMixedModel{T}, nAGQ=1)::T where {T}Return the deviance of m evaluated by the Laplace approximation (nAGQ=1) or nAGQ-point adaptive Gauss-Hermite quadrature.
If the distribution D does not have a scale parameter the Laplace approximation is the squared length of the conditional modes,
Because it is not clear what the saturated model corresponding to a particular LinearMixedModel should be, negative twice the log-likelihood is called the objective.
MixedModels.objective Function
objective(m::LinearMixedModel)Return negative twice the log-likelihood of model m
This value is also accessible as the deviance but the user should bear in mind that this doesn't have all the properties of a deviance which is corrected for the saturated model. For example, it is not necessarily non-negative.
objective(fm1)327.327059881143deviance(fm1)327.327059881143The value optimized when fitting a GeneralizedLinearMixedModel is the Laplace approximation to the deviance or an adaptive Gauss-Hermite evaluation.
MixedModels.deviance! Function
deviance!(m::GeneralizedLinearMixedModel, nAGQ=1)Update m.η, m.μ, etc., install the working response and working weights in m.LMM, update m.LMM.A and m.LMM.R, then evaluate the deviance.
MixedModels.deviance!(gm1)8135.832858348763Fixed-effects parameter estimates
The coef and fixef extractors both return the maximum likelihood estimates of the fixed-effects coefficients. They differ in their behavior in the rank-deficient case. The associated coefnames and fixefnames return the corresponding coefficient names.
StatsAPI.coefnames Function
coefnames(model::StatisticalModel)Return the names of the coefficients.
sourceMixedModels.fixef Function
fixef(m::MixedModel)Return the fixed-effects parameter vector estimate of m.
In the rank-deficient case the truncated parameter vector, of length rank(m) is returned. This is unlike coef which always returns a vector whose length matches the number of columns in X.
MixedModels.fixefnames Function
fixefnames(m::MixedModel)Return a (permuted and truncated in the rank-deficient case) vector of coefficient names.
sourcecoef(fm1)
coefnames(fm1)1-element Vector{String}:
"(Intercept)"fixef(fm1)
fixefnames(fm1)1-element Vector{String}:
"(Intercept)"An alternative extractor for the fixed-effects coefficient is the β property. Properties whose names are Greek letters usually have an alternative spelling, which is the name of the Greek letter.
fm1.β1-element Vector{Float64}:
1527.4999999999989fm1.beta1-element Vector{Float64}:
1527.4999999999989gm1.β7-element Vector{Float64}:
-0.15297658506693748
0.05739606276708647
0.3207830298410298
-1.059879787138412
-2.103893961040908
-1.054376691878555
0.706980989641357A full list of property names is returned by propertynames
propertynames(fm1)(:formula, :reterms, :Xymat, :feterm, :sqrtwts, :parmap, :dims, :A, :L, :optsum, :θ, :theta, :β, :beta, :βs, :betas, :λ, :lambda, :stderror, :σ, :sigma, :σs, :sigmas, :σρs, :sigmarhos, :b, :u, :lowerbd, :X, :y, :corr, :vcov, :PCA, :rePCA, :objective, :pvalues)propertynames(gm1)(:A, :L, :theta, :beta, :coef, :λ, :σ, :sigma, :X, :y, :lowerbd, :objective, :σρs, :σs, :corr, :vcov, :PCA, :rePCA, :LMM, :β, :θ, :b, :u, :resp, :wt)The variance-covariance matrix of the fixed-effects coefficients is returned by
StatsAPI.vcov Function
vcov(model::StatisticalModel)Return the variance-covariance matrix for the coefficients of the model.
sourcevcov(fm2)2×2 Matrix{Float64}:
43.9868 -1.37039
-1.37039 2.25671vcov(gm1)7×7 Matrix{Float64}:
0.148396 -0.00561488 -0.00982342 … -0.0112072 -0.011347
-0.00561488 0.000280656 7.19122e-5 -1.47964e-5 1.0241e-5
-0.00982342 7.19122e-5 0.0365602 -8.04427e-5 5.25878e-5
-0.0167988 -1.43709e-5 -9.25629e-5 0.000265802 -0.000172097
-0.0166229 -2.90553e-5 -0.000162391 0.000658966 -0.000520523
-0.0112072 -1.47964e-5 -8.04427e-5 … 0.0228597 -0.000247782
-0.011347 1.0241e-5 5.25878e-5 -0.000247782 0.0228032The standard errors are the square roots of the diagonal elements of the estimated variance-covariance matrix of the fixed-effects coefficient estimators.
StatsAPI.stderror Function
stderror(model::StatisticalModel)Return the standard errors for the coefficients of the model.
sourcestderror(fm2)2-element Vector{Float64}:
6.632257724308144
1.5022355036397133stderror(gm1)7-element Vector{Float64}:
0.38522187715517414
0.016752777502287746
0.19120720019964096
0.1841585773287559
0.18651728135544754
0.15119425239025674
0.15100718227007012Finally, the coeftable generic produces a table of coefficient estimates, their standard errors, and their ratio. The p-values quoted here should be regarded as approximations.
StatsAPI.coeftable Function
coeftable(model::StatisticalModel; level::Real=0.95)Return a table with coefficients and related statistics of the model. level determines the level for confidence intervals (by default, 95%).
The returned CoefTable object implements the Tables.jl interface, and can be converted e.g. to a DataFrame via using DataFrames; DataFrame(coeftable(model)).
coeftable(fm2)──────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
──────────────────────────────────────────────────
(Intercept) 251.405 6.63226 37.91 <1e-99
days 10.4673 1.50224 6.97 <1e-11
──────────────────────────────────────────────────Covariance parameter estimates
The covariance parameters estimates, in the form shown in the model summary, are a VarCorr object
VarCorr(fm2)Variance components:
Column Variance Std.Dev. Corr.
subj (Intercept) 565.51066 23.78047
days 32.68212 5.71683 +0.08
Residual 654.94145 25.59182VarCorr(gm1)Variance components:
Column Variance Std.Dev.
subj (Intercept) 1.793488 1.339212
item (Intercept) 0.117149 0.342271Individual components are returned by other extractors
MixedModels.varest Function
varest(m::LinearMixedModel)Returns the estimate of σ², the variance of the conditional distribution of Y given B.
sourcevarest(m::GeneralizedLinearMixedModel)Returns the estimate of ϕ², the variance of the conditional distribution of Y given B.
For models with a dispersion parameter ϕ, this is simply ϕ². For models without a dispersion parameter, this value is missing. This differs from disperion, which returns 1 for models without a dispersion parameter.
For Gaussian models, this parameter is often called σ².
sourceMixedModels.sdest Function
sdest(m::LinearMixedModel)Return the estimate of σ, the standard deviation of the per-observation noise.
sourcesdest(m::GeneralizedLinearMixedModel)Return the estimate of the dispersion, i.e. the standard deviation of the per-observation noise.
For models with a dispersion parameter ϕ, this is simply ϕ. For models without a dispersion parameter, this value is missing. This differs from disperion, which returns 1 for models without a dispersion parameter.
For Gaussian models, this parameter is often called σ.
sourcevarest(fm2)654.9414500374786sdest(fm2)25.591823890404502fm2.σ25.591823890404502Conditional modes of the random effects
The ranef extractor
MixedModels.ranef Function
ranef(m::LinearMixedModel; uscale=false)Return, as a Vector{Matrix{T}}, the conditional modes of the random effects in model m.
If uscale is true the random effects are on the spherical (i.e. u) scale, otherwise on the original scale.
For a named variant, see raneftables.
ranef(fm1)1-element Vector{Matrix{Float64}}:
[-16.628221011733647 0.36951602248392174 … 53.57982326003439 -42.49434258554296]fm1.b1-element Vector{Matrix{Float64}}:
[-16.628221011733647 0.36951602248392174 … 53.57982326003439 -42.49434258554296]returns the conditional modes of the random effects given the observed data. That is, these are the values that maximize the conditional density of the random effects given the observed data. For a LinearMixedModel these are also the conditional means.
These are sometimes called the best linear unbiased predictors or BLUPs but that name is not particularly meaningful.
At a superficial level these can be considered as the "estimates" of the random effects, with a bit of hand waving, but pursuing this analogy too far usually results in confusion.
To obtain tables associating the values of the conditional modes with the levels of the grouping factor, use
MixedModels.raneftables Function
raneftables(m::MixedModel; uscale = false)Return the conditional means of the random effects as a NamedTuple of Tables.jl-compliant tables.
Note
The API guarantee is only that the NamedTuple contains Tables.jl tables and not on the particular concrete type of each table.
as in
DataFrame(only(raneftables(fm1)))| Row | batch | (Intercept) |
|---|---|---|
| String | Float64 | |
| 1 | A | -16.6282 |
| 2 | B | 0.369516 |
| 3 | C | 26.9747 |
| 4 | D | -21.8014 |
| 5 | E | 53.5798 |
| 6 | F | -42.4943 |
The corresponding conditional variances are returned by
MixedModels.condVar Function
condVar(m::LinearMixedModel)Return the conditional variances matrices of the random effects.
The random effects are returned by ranef as a vector of length k, where k is the number of random effects terms. The ith element is a matrix of size vᵢ × ℓᵢ where vᵢ is the size of the vector-valued random effects for each of the ℓᵢ levels of the grouping factor. Technically those values are the modes of the conditional distribution of the random effects given the observed data.
This function returns an array of k three dimensional arrays, where the ith array is of size vᵢ × vᵢ × ℓᵢ. These are the diagonal blocks from the conditional variance-covariance matrix,
s² Λ(Λ'Z'ZΛ + I)⁻¹Λ'condVar(fm1)1-element Vector{Array{Float64, 3}}:
[362.3104675622471;;; 362.3104675622471;;; 362.3104675622471;;; 362.3104675622471;;; 362.3104675622471;;; 362.3104675622471]Case-wise diagnostics and residual degrees of freedom
The leverage values
StatsAPI.leverage Function
leverage(model::RegressionModel)Return the diagonal of the projection matrix of the model.
sourceleverage(fm1)30-element Vector{Float64}:
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
⋮
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315
0.15650534082766315are used in diagnostics for linear regression models to determine cases that exert a strong influence on their own predicted response.
The documentation refers to a "projection". For a linear model without random effects the fitted values are obtained by orthogonal projection of the response onto the column span of the model matrix and the sum of the leverage values is the dimension of this column span. That is, the sum of the leverage values is the rank of the model matrix and n - sum(leverage(m)) is the degrees of freedom for residuals. The sum of the leverage values is also the trace of the so-called "hat" matrix, H. (The name "hat matrix" reflects the fact that H puts a hat on y.)
For a linear mixed model the sum of the leverage values will be between p, the rank of the fixed-effects model matrix, and p + q where q is the total number of random effects. This number does not represent a dimension (or "degrees of freedom") of a linear subspace of all possible fitted values because the projection is not an orthogonal projection. Nevertheless, it is a reasonable measure of the effective degrees of freedom of the model and n - sum(leverage(m)) can be considered the effective residual degrees of freedom.
For model fm1 the dimensions are
n, p, q, k = size(fm1)(30, 1, 6, 1)which implies that the sum of the leverage values should be in the range [1, 7]. The actual value is
sum(leverage(fm1))4.695160224829895For model fm2 the dimensions are
n, p, q, k = size(fm2)(180, 2, 36, 1)providing a range of [2, 38] for the effective degrees of freedom for the model. The observed value is
sum(leverage(fm2))28.611526266274847When a model converges to a singular covariance, such as
fm3 = fit(MixedModel, @formula(yield ~ 1+(1|batch)), MixedModels.dataset(:dyestuff2))Linear mixed model fit by maximum likelihood
yield ~ 1 + (1 | batch)
logLik -2 logLik AIC AICc BIC
-81.4365 162.8730 168.8730 169.7961 173.0766
Variance components:
Column Variance Std.Dev.
batch (Intercept) 0.00000 0.00000
Residual 13.34610 3.65323
Number of obs: 30; levels of grouping factors: 6
Fixed-effects parameters:
───────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
───────────────────────────────────────────────
(Intercept) 5.6656 0.666986 8.49 <1e-16
───────────────────────────────────────────────the effective degrees of freedom is the lower bound.
sum(leverage(fm3))0.9999999999999998Models for which the estimates of the variances of the random effects are large relative to the residual variance have effective degrees of freedom close to the upper bound.
fm4 = fit(MixedModel, @formula(diameter ~ 1+(1|plate)+(1|sample)),
MixedModels.dataset(:penicillin))Linear mixed model fit by maximum likelihood
diameter ~ 1 + (1 | plate) + (1 | sample)
logLik -2 logLik AIC AICc BIC
-166.0942 332.1883 340.1883 340.4761 352.0676
Variance components:
Column Variance Std.Dev.
plate (Intercept) 0.714979 0.845565
sample (Intercept) 3.135193 1.770648
Residual 0.302426 0.549933
Number of obs: 144; levels of grouping factors: 24, 6
Fixed-effects parameters:
─────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
─────────────────────────────────────────────────
(Intercept) 22.9722 0.744596 30.85 <1e-99
─────────────────────────────────────────────────sum(leverage(fm4))27.46531792078765Also, a model fit by the REML criterion generally has larger estimates of the variance components and hence a larger effective degrees of freedom.
fm4r = fit(MixedModel, @formula(diameter ~ 1+(1|plate)+(1|sample)),
MixedModels.dataset(:penicillin), REML=true)Linear mixed model fit by REML
diameter ~ 1 + (1 | plate) + (1 | sample)
REML criterion at convergence: 330.8605889912561
Variance components:
Column Variance Std.Dev.
plate (Intercept) 0.716908 0.846704
sample (Intercept) 3.730907 1.931555
Residual 0.302416 0.549923
Number of obs: 144; levels of grouping factors: 24, 6
Fixed-effects parameters:
─────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|)
─────────────────────────────────────────────────
(Intercept) 22.9722 0.808572 28.41 <1e-99
─────────────────────────────────────────────────sum(leverage(fm4r))27.472361698411703