Examples
Linear regression
julia> using DataFrames, GLM
julia> data = DataFrame(X=[1,2,3], Y=[2,4,7])
3×2 DataFrames.DataFrame
│ Row │ X │ Y │
│ │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1 │ 1 │ 2 │
│ 2 │ 2 │ 4 │
│ 3 │ 3 │ 7 │
julia> ols = lm(@formula(Y ~ X), data)
StatsModels.DataFrameRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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 Array{Float64,1}:
0.62361
0.28868
julia> round.(predict(ols), digits=5)
3-element Array{Float64,1}:
1.83333
4.33333
6.83333Probit regression
julia> data = DataFrame(X=[1,2,2], Y=[1,0,1])
3×2 DataFrames.DataFrame
│ Row │ X │ Y │
│ │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1 │ 1 │ 1 │
│ 2 │ 2 │ 0 │
│ 3 │ 2 │ 1 │
julia> probit = glm(@formula(Y ~ X), data, Binomial(), ProbitLink())
StatsModels.DataFrameRegressionModel{GeneralizedLinearModel{GlmResp{Array{Float64,1},Binomial{Float64},ProbitLink},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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 DataFrames.DataFrame
│ Row │ Eth │ Sex │ Age │ Lrn │ Days │
│ │ Categorical… │ Categorical… │ Categorical… │ Categorical… │ 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 │
⋮
│ 139 │ N │ F │ F3 │ 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 │
julia> nbrmodel = glm(@formula(Days ~ Eth+Sex+Age+Lrn), quine, NegativeBinomial(2.0), LogLink())
StatsModels.DataFrameRegressionModel{GeneralizedLinearModel{GlmResp{Array{Float64,1},NegativeBinomial{Float64},LogLink},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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())
StatsModels.DataFrameRegressionModel{GeneralizedLinearModel{GlmResp{Array{Float64,1},NegativeBinomial{Float64},LogLink},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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.model.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)
StatsModels.DataFrameRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: OptDen ~ 1 + Carb
───────────────────────────────────────────────────────────────────────────
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-6 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 │
│ │ String⍰ │ 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 │
⋮
│ 43 │ United Kingdom │ 7.81 │ 23.27 │ 4.46 │ 1813.93 │ 2.01 │
│ 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 │
julia> fm2 = fit(LinearModel, @formula(SR ~ Pop15 + Pop75 + DPI + DDPI), LifeCycleSavings)
StatsModels.DataFrameRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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 │ Categorical… │ Categorical… │
├─────┼─────────┼──────────────┼──────────────┤
│ 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())
StatsModels.DataFrameRegressionModel{GeneralizedLinearModel{GlmResp{Array{Float64,1},Poisson{Float64},LogLink},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: 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)
5.11746