Examples
Linear regression
julia> using DataFrames, GLM, StatsBase
julia> data = DataFrame(X=[1,2,3], Y=[2,4,7])
3×2 DataFrame
Row │ X Y
│ Int64 Int64
─────┼──────────────
1 │ 1 2
2 │ 2 4
3 │ 3 7
julia> ols = lm(@formula(Y ~ X), data)
LinearModel
Y ~ 1 + X
Coefficients:
─────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────
(Intercept) -0.666667 0.62361 -1.07 0.4788 -8.59038 7.25704
X 2.5 0.288675 8.66 0.0732 -1.16797 6.16797
─────────────────────────────────────────────────────────────────────────
julia> round.(stderror(ols), digits=5)
2-element Vector{Float64}:
0.62361
0.28868
julia> round.(predict(ols), digits=5)
3-element Vector{Float64}:
1.83333
4.33333
6.83333
julia> round.(confint(ols); digits=5)
2×2 Matrix{Float64}:
-8.59038 7.25704
-1.16797 6.16797
julia> round(r2(ols); digits=5)
0.98684
julia> round(adjr2(ols); digits=5)
0.97368
julia> round(deviance(ols); digits=5)
0.16667
julia> dof(ols)
3
julia> dof_residual(ols)
1.0
julia> round(aic(ols); digits=5)
5.84252
julia> round(aicc(ols); digits=5)
-18.15748
julia> round(bic(ols); digits=5)
3.13835
julia> round(dispersion(ols.model); digits=5)
0.40825
julia> round(loglikelihood(ols); digits=5)
0.07874
julia> round(nullloglikelihood(ols); digits=5)
-6.41736
julia> round.(vcov(ols); digits=5)
2×2 Matrix{Float64}:
0.38889 -0.16667
-0.16667 0.08333By default, the lm method uses the Cholesky factorization which is known as fast but numerically unstable, especially for ill-conditioned design matrices. Also, the Cholesky method aggressively detects multicollinearity. You can use the method keyword argument to apply a more stable QR factorization method.
julia> data = DataFrame(X=[1,2,3], Y=[2,4,7]);
julia> ols = lm(@formula(Y ~ X), data; method=:qr)
LinearModel
Y ~ 1 + X
Coefficients:
─────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────
(Intercept) -0.666667 0.62361 -1.07 0.4788 -8.59038 7.25704
X 2.5 0.288675 8.66 0.0732 -1.16797 6.16797
─────────────────────────────────────────────────────────────────────────The following example shows that QR decomposition works better for an ill-conditioned design matrix. The linear model with the QR method is a better model than the linear model with Cholesky decomposition method since the estimated loglikelihood of previous model is higher. Note that, the condition number of the design matrix is quite high (≈ 3.52e7).
julia> X = [-0.4011512997627107 0.6368622664511552;
-0.0808472925693535 0.12835204623364604;
-0.16931095045225217 0.2687956795496601;
-0.4110745650568839 0.6526163576003452;
-0.4035951747670475 0.6407421349445884;
-0.4649907741370211 0.7382129928076485;
-0.15772708898883683 0.25040532268222715;
-0.38144358562952446 0.6055745630707645;
-0.1012787681395544 0.16078875117643368;
-0.2741403589052255 0.4352214984054432];
julia> y = [4.362866166172215,
0.8792840060172619,
1.8414020451091684,
4.470790758717395,
4.3894454833815395,
5.0571760643993455,
1.7154177874916376,
4.148527704012107,
1.1014936742570425,
2.9815131910316097];
julia> modelqr = lm(X, y; method=:qr)
LinearModel
Coefficients:
────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────
x1 5.00389 0.0560164 89.33 <1e-12 4.87472 5.13307
x2 10.0025 0.035284 283.48 <1e-16 9.92109 10.0838
────────────────────────────────────────────────────────────────
julia> modelchol = lm(X, y; method=:cholesky)
LinearModel
Coefficients:
────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────
x1 5.17647 0.0849184 60.96 <1e-11 4.98065 5.37229
x2 10.1112 0.053489 189.03 <1e-15 9.98781 10.2345
────────────────────────────────────────────────────────────────
julia> loglikelihood(modelqr) > loglikelihood(modelchol)
trueSince the Cholesky method with dropcollinear = true aggressively detects multicollinearity, if you ever encounter multicollinearity in any GLM model with Cholesky, it is worth trying the same model with QR decomposition. The following example is taken from Introductory Econometrics: A Modern Approach, 7e" by Jeffrey M. Wooldridge. The dataset is used to study the relationship between firm size—often measured by annual sales—and spending on research and development (R&D). The following shows that for the given model, the Cholesky method detects multicollinearity in the design matrix with dropcollinear=true and hence does not estimate all parameters as opposed to QR.
julia> y = [9.42190647125244, 2.084805727005, 3.9376676082611, 2.61976027488708, 4.04761934280395, 2.15384602546691,
2.66240668296813, 4.39475727081298, 5.74520826339721, 3.59616208076477, 1.54265284538269, 2.59368276596069,
1.80476510524749, 1.69270837306976, 3.04201245307922, 2.18389105796813, 2.73844122886657, 2.88134002685546,
2.46666669845581, 3.80616021156311, 5.12149810791015, 6.80378007888793, 3.73669862747192, 1.21332454681396,
2.54629635810852, 5.1612901687622, 1.86798071861267, 1.21465551853179, 6.31019830703735, 1.02669405937194,
2.50623273849487, 1.5936255455017];
julia> x = [4570.2001953125, 2830, 596.799987792968, 133.600006103515, 42, 390, 93.9000015258789, 907.900024414062,
19773, 39709, 2936.5, 2513.80004882812, 1124.80004882812, 921.599975585937, 2432.60009765625, 6754,
1066.30004882812, 3199.89990234375, 150, 509.700012207031, 1452.69995117187, 8995, 1212.30004882812,
906.599975585937, 2592, 201.5, 2617.80004882812, 502.200012207031, 2824, 292.200012207031, 7621, 1631.5];
julia> rdchem = DataFrame(rdintens=y, sales=x);
julia> mdl = lm(@formula(rdintens ~ sales + sales^2), rdchem; method=:cholesky)
LinearModel
rdintens ~ 1 + sales + :(sales ^ 2)
Coefficients:
───────────────────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.0 NaN NaN NaN NaN NaN
sales 0.000852509 0.000156784 5.44 <1e-05 0.000532313 0.00117271
sales ^ 2 -1.97385e-8 4.56287e-9 -4.33 0.0002 -2.90571e-8 -1.04199e-8
───────────────────────────────────────────────────────────────────────────────────────
julia> mdl = lm(@formula(rdintens ~ sales + sales^2), rdchem; method=:qr)
LinearModel
rdintens ~ 1 + sales + :(sales ^ 2)
Coefficients:
─────────────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────────────
(Intercept) 2.61251 0.429442 6.08 <1e-05 1.73421 3.49082
sales 0.000300571 0.000139295 2.16 0.0394 1.56805e-5 0.000585462
sales ^ 2 -6.94594e-9 3.72614e-9 -1.86 0.0725 -1.45667e-8 6.7487e-10
─────────────────────────────────────────────────────────────────────────────────Probit regression
julia> data = DataFrame(X=[1,2,2], Y=[1,0,1])
3×2 DataFrame
Row │ X Y
│ Int64 Int64
─────┼──────────────
1 │ 1 1
2 │ 2 0
3 │ 2 1
julia> probit = glm(@formula(Y ~ X), data, Binomial(), ProbitLink())
GeneralizedLinearModel
Y ~ 1 + X
Coefficients:
────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept) 9.63839 293.909 0.03 0.9738 -566.414 585.69
X -4.81919 146.957 -0.03 0.9738 -292.849 283.211
────────────────────────────────────────────────────────────────────────Negative binomial regression
julia> using GLM, RDatasets
julia> quine = dataset("MASS", "quine")
146×5 DataFrame
Row │ Eth Sex Age Lrn Days
│ Cat… Cat… Cat… Cat… Int32
─────┼───────────────────────────────
1 │ A M F0 SL 2
2 │ A M F0 SL 11
3 │ A M F0 SL 14
4 │ A M F0 AL 5
5 │ A M F0 AL 5
6 │ A M F0 AL 13
7 │ A M F0 AL 20
8 │ A M F0 AL 22
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
140 │ N F F3 AL 3
141 │ N F F3 AL 3
142 │ N F F3 AL 5
143 │ N F F3 AL 15
144 │ N F F3 AL 18
145 │ N F F3 AL 22
146 │ N F F3 AL 37
131 rows omitted
julia> nbrmodel = glm(@formula(Days ~ Eth+Sex+Age+Lrn), quine, NegativeBinomial(2.0), LogLink())
GeneralizedLinearModel
Days ~ 1 + Eth + Sex + Age + Lrn
Coefficients:
────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────────
(Intercept) 2.88645 0.227144 12.71 <1e-36 2.44125 3.33164
Eth: N -0.567515 0.152449 -3.72 0.0002 -0.86631 -0.26872
Sex: M 0.0870771 0.159025 0.55 0.5840 -0.224606 0.398761
Age: F1 -0.445076 0.239087 -1.86 0.0627 -0.913678 0.0235251
Age: F2 0.0927999 0.234502 0.40 0.6923 -0.366816 0.552416
Age: F3 0.359485 0.246586 1.46 0.1449 -0.123814 0.842784
Lrn: SL 0.296768 0.185934 1.60 0.1105 -0.0676559 0.661191
────────────────────────────────────────────────────────────────────────────
julia> nbrmodel = negbin(@formula(Days ~ Eth+Sex+Age+Lrn), quine, LogLink())
GeneralizedLinearModel
Days ~ 1 + Eth + Sex + Age + Lrn
Coefficients:
────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────────
(Intercept) 2.89453 0.227415 12.73 <1e-36 2.4488 3.34025
Eth: N -0.569341 0.152656 -3.73 0.0002 -0.868541 -0.270141
Sex: M 0.0823881 0.159209 0.52 0.6048 -0.229655 0.394431
Age: F1 -0.448464 0.238687 -1.88 0.0603 -0.916281 0.0193536
Age: F2 0.0880506 0.235149 0.37 0.7081 -0.372834 0.548935
Age: F3 0.356955 0.247228 1.44 0.1488 -0.127602 0.841513
Lrn: SL 0.292138 0.18565 1.57 0.1156 -0.0717297 0.656006
────────────────────────────────────────────────────────────────────────────
julia> println("Estimated theta = ", round(nbrmodel.rr.d.r, digits=5))
Estimated theta = 1.27489
Julia and R comparisons
An example of a simple linear model in R is
> coef(summary(lm(optden ~ carb, Formaldehyde)))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.005085714 0.007833679 0.6492115 5.515953e-01
carb 0.876285714 0.013534536 64.7444207 3.409192e-07The corresponding model with the GLM package is
julia> using GLM, RDatasets
julia> form = dataset("datasets", "Formaldehyde")
6×2 DataFrame
Row │ Carb OptDen
│ Float64 Float64
─────┼──────────────────
1 │ 0.1 0.086
2 │ 0.3 0.269
3 │ 0.5 0.446
4 │ 0.6 0.538
5 │ 0.7 0.626
6 │ 0.9 0.782
julia> lm1 = fit(LinearModel, @formula(OptDen ~ Carb), form)
LinearModel
OptDen ~ 1 + Carb
Coefficients:
───────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────────
(Intercept) 0.00508571 0.00783368 0.65 0.5516 -0.0166641 0.0268355
Carb 0.876286 0.0135345 64.74 <1e-06 0.838708 0.913864
───────────────────────────────────────────────────────────────────────────A more complex example in R is
> coef(summary(lm(sr ~ pop15 + pop75 + dpi + ddpi, LifeCycleSavings)))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.5660865407 7.3545161062 3.8841558 0.0003338249
pop15 -0.4611931471 0.1446422248 -3.1885098 0.0026030189
pop75 -1.6914976767 1.0835989307 -1.5609998 0.1255297940
dpi -0.0003369019 0.0009311072 -0.3618293 0.7191731554
ddpi 0.4096949279 0.1961971276 2.0881801 0.0424711387with the corresponding Julia code
julia> LifeCycleSavings = dataset("datasets", "LifeCycleSavings")
50×6 DataFrame
Row │ Country SR Pop15 Pop75 DPI DDPI
│ String15 Float64 Float64 Float64 Float64 Float64
─────┼─────────────────────────────────────────────────────────────
1 │ Australia 11.43 29.35 2.87 2329.68 2.87
2 │ Austria 12.07 23.32 4.41 1507.99 3.93
3 │ Belgium 13.17 23.8 4.43 2108.47 3.82
4 │ Bolivia 5.75 41.89 1.67 189.13 0.22
5 │ Brazil 12.88 42.19 0.83 728.47 4.56
6 │ Canada 8.79 31.72 2.85 2982.88 2.43
7 │ Chile 0.6 39.74 1.34 662.86 2.67
8 │ China 11.9 44.75 0.67 289.52 6.51
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
44 │ United States 7.56 29.81 3.43 4001.89 2.45
45 │ Venezuela 9.22 46.4 0.9 813.39 0.53
46 │ Zambia 18.56 45.25 0.56 138.33 5.14
47 │ Jamaica 7.72 41.12 1.73 380.47 10.23
48 │ Uruguay 9.24 28.13 2.72 766.54 1.88
49 │ Libya 8.89 43.69 2.07 123.58 16.71
50 │ Malaysia 4.71 47.2 0.66 242.69 5.08
35 rows omitted
julia> fm2 = fit(LinearModel, @formula(SR ~ Pop15 + Pop75 + DPI + DDPI), LifeCycleSavings)
LinearModel
SR ~ 1 + Pop15 + Pop75 + DPI + DDPI
Coefficients:
─────────────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────────────
(Intercept) 28.5661 7.35452 3.88 0.0003 13.7533 43.3788
Pop15 -0.461193 0.144642 -3.19 0.0026 -0.752518 -0.169869
Pop75 -1.6915 1.0836 -1.56 0.1255 -3.87398 0.490983
DPI -0.000336902 0.000931107 -0.36 0.7192 -0.00221225 0.00153844
DDPI 0.409695 0.196197 2.09 0.0425 0.0145336 0.804856
─────────────────────────────────────────────────────────────────────────────────The glm function (or equivalently, fit(GeneralizedLinearModel, ...)) works similarly to the R glm function except that the family argument is replaced by a Distribution type and, optionally, a Link type. The first example from ?glm in R is
glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : (slightly modified)
glm> counts <- c(18,17,15,20,10,21,25,13,13)
glm> outcome <- gl(3,1,9)
glm> treatment <- gl(3,3)
glm> print(d.AD <- data.frame(treatment, outcome, counts))
treatment outcome counts
1 1 1 18
2 1 2 17
3 1 3 15
4 2 1 20
5 2 2 10
6 2 3 21
7 3 1 25
8 3 2 13
9 3 3 13
glm> glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
glm> anova(glm.D93)
Analysis of Deviance Table
Model: poisson, link: log
Response: counts
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 8 10.3928
outcome 2 5.2622 6 5.1307
treatment 2 0.0132 4 5.1175
glm> ## No test:
glm> summary(glm.D93)
Call:
glm(formula = counts ~ outcome + treatment, family = poisson())
Deviance Residuals:
1 2 3 4 5 6 7 8 9
-0.6122 1.0131 -0.2819 -0.2498 -0.9784 1.0777 0.8162 -0.1155 -0.8811
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.0313 0.1712 17.711 <2e-16 ***
outcome2 -0.4543 0.2022 -2.247 0.0246 *
outcome3 -0.2513 0.1905 -1.319 0.1870
treatment2 0.0198 0.1990 0.100 0.9207
treatment3 0.0198 0.1990 0.100 0.9207
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 10.3928 on 8 degrees of freedom
Residual deviance: 5.1175 on 4 degrees of freedom
AIC: 56.877
Number of Fisher Scoring iterations: 4In Julia this becomes
julia> using DataFrames, CategoricalArrays, GLM
julia> dobson = DataFrame(Counts = [18.,17,15,20,10,21,25,13,13],
Outcome = categorical([1,2,3,1,2,3,1,2,3]),
Treatment = categorical([1,1,1,2,2,2,3,3,3]))
9×3 DataFrame
Row │ Counts Outcome Treatment
│ Float64 Cat… Cat…
─────┼─────────────────────────────
1 │ 18.0 1 1
2 │ 17.0 2 1
3 │ 15.0 3 1
4 │ 20.0 1 2
5 │ 10.0 2 2
6 │ 21.0 3 2
7 │ 25.0 1 3
8 │ 13.0 2 3
9 │ 13.0 3 3
julia> gm1 = fit(GeneralizedLinearModel, @formula(Counts ~ Outcome + Treatment), dobson, Poisson())
GeneralizedLinearModel
Counts ~ 1 + Outcome + Treatment
Coefficients:
────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────────
(Intercept) 3.03128 0.171155 17.71 <1e-69 2.69582 3.36674
Outcome: 2 -0.454255 0.202171 -2.25 0.0246 -0.850503 -0.0580079
Outcome: 3 -0.251314 0.190476 -1.32 0.1870 -0.624641 0.122012
Treatment: 2 0.0198026 0.199017 0.10 0.9207 -0.370264 0.409869
Treatment: 3 0.0198026 0.199017 0.10 0.9207 -0.370264 0.409869
────────────────────────────────────────────────────────────────────────────
julia> round(deviance(gm1), digits=5)
5.11746Linear regression with PowerLink
In this example, we choose the best model from a set of λs, based on minimum BIC.
julia> using GLM, RDatasets, StatsBase, DataFrames, Optim
julia> trees = DataFrame(dataset("datasets", "trees"))
31×3 DataFrame
Row │ Girth Height Volume
│ Float64 Int64 Float64
─────┼──────────────────────────
1 │ 8.3 70 10.3
2 │ 8.6 65 10.3
3 │ 8.8 63 10.2
4 │ 10.5 72 16.4
5 │ 10.7 81 18.8
6 │ 10.8 83 19.7
7 │ 11.0 66 15.6
8 │ 11.0 75 18.2
⋮ │ ⋮ ⋮ ⋮
25 │ 16.3 77 42.6
26 │ 17.3 81 55.4
27 │ 17.5 82 55.7
28 │ 17.9 80 58.3
29 │ 18.0 80 51.5
30 │ 18.0 80 51.0
31 │ 20.6 87 77.0
16 rows omitted
julia> bic_glm(λ) = bic(glm(@formula(Volume ~ Height + Girth), trees, Normal(), PowerLink(λ)));
julia> optimal_bic = optimize(bic_glm, -1.0, 1.0);
julia> round(optimal_bic.minimizer, digits = 5) # Optimal λ
0.40935
julia> glm(@formula(Volume ~ Height + Girth), trees, Normal(), PowerLink(optimal_bic.minimizer)) # Best model
GeneralizedLinearModel
Volume ~ 1 + Height + Girth
Coefficients:
────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────────
(Intercept) -1.07586 0.352543 -3.05 0.0023 -1.76684 -0.384892
Height 0.0232172 0.00523331 4.44 <1e-05 0.0129601 0.0334743
Girth 0.242837 0.00922555 26.32 <1e-99 0.224756 0.260919
────────────────────────────────────────────────────────────────────────────
julia> round(optimal_bic.minimum, digits=5)
156.37638