using Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
p1 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Geom.point)
p2 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Stat.binmean, Geom.point)
hstack(p1,p2)
SepalLength
4
5
6
7
8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
4
6
8
7.6253.0875
7.1400000000000013.2
6.8571428571428563.0714285714285716
6.68000000000000153.0300000000000002
6.5,3.0
6.39999999999999952.9571428571428577
6.2999999999999992.8555555555555556
6.22.8249999999999997
6.052.7916666666666665
5.9000000000000013.0666666666666664
5.7999999999999992.8857142857142857
5.7000000000000013.1
5.60000000000000052.816666666666667
5.4428571428571433.207142857142857
5.2,3.425
5.10000000000000053.477777777777778
4.96253.05625
4.7714285714285713.185714285714286
4.4888888888888893.0777777777777775
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
2.75
3.00
3.25
3.50
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
2.750
2.755
2.760
2.765
2.770
2.775
2.780
2.785
2.790
2.795
2.800
2.805
2.810
2.815
2.820
2.825
2.830
2.835
2.840
2.845
2.850
2.855
2.860
2.865
2.870
2.875
2.880
2.885
2.890
2.895
2.900
2.905
2.910
2.915
2.920
2.925
2.930
2.935
2.940
2.945
2.950
2.955
2.960
2.965
2.970
2.975
2.980
2.985
2.990
2.995
3.000
3.005
3.010
3.015
3.020
3.025
3.030
3.035
3.040
3.045
3.050
3.055
3.060
3.065
3.070
3.075
3.080
3.085
3.090
3.095
3.100
3.105
3.110
3.115
3.120
3.125
3.130
3.135
3.140
3.145
3.150
3.155
3.160
3.165
3.170
3.175
3.180
3.185
3.190
3.195
3.200
3.205
3.210
3.215
3.220
3.225
3.230
3.235
3.240
3.245
3.250
3.255
3.260
3.265
3.270
3.275
3.280
3.285
3.290
3.295
3.300
3.305
3.310
3.315
3.320
3.325
3.330
3.335
3.340
3.345
3.350
3.355
3.360
3.365
3.370
3.375
3.380
3.385
3.390
3.395
3.400
3.405
3.410
3.415
3.420
3.425
3.430
3.435
3.440
3.445
3.450
3.455
3.460
3.465
3.470
3.475
3.480
3.485
3.490
3.495
3.500
2.5
3.0
3.5
SepalWidth
SepalLength
4
5
6
7
8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
4
6
8
5.9,3.0
6.2,3.4
6.5,3.0
6.3,2.5
6.7,3.0
6.7,3.3
6.8,3.2
5.8,2.7
6.9,3.1
6.7,3.1
6.9,3.1
6.0,3.0
6.4,3.1
6.3,3.4
7.7,3.0
6.1,2.6
6.3,2.8
6.4,2.8
7.9,3.8
7.4,2.8
7.2,3.0
6.4,2.8
6.1,3.0
6.2,2.8
7.2,3.2
6.7,3.3
6.3,2.7
7.7,2.8
5.6,2.8
6.9,3.2
6.0,2.2
7.7,2.6
7.7,3.8
6.5,3.0
6.4,3.2
5.8,2.8
5.7,2.5
6.8,3.0
6.4,2.7
6.5,3.2
7.2,3.6
6.7,2.5
7.3,2.9
4.9,2.5
7.6,3.0
6.5,3.0
6.3,2.9
7.1,3.0
5.8,2.7
6.3,3.3
5.7,2.8
5.1,2.5
6.2,2.9
5.7,2.9
5.7,3.0
5.6,2.7
5.0,2.3
5.8,2.6
6.1,3.0
5.5,2.6
5.5,2.5
5.6,3.0
6.3,2.3
6.7,3.1
6.0,3.4
5.4,3.0
6.0,2.7
5.8,2.7
5.5,2.4
5.5,2.4
5.7,2.6
6.0,2.9
6.7,3.0
6.8,2.8
6.6,3.0
6.4,2.9
6.1,2.8
6.3,2.5
6.1,2.8
5.9,3.2
5.6,2.5
6.2,2.2
5.8,2.7
5.6,3.0
6.7,3.1
5.6,2.9
6.1,2.9
6.0,2.2
5.9,3.0
5.0,2.0
5.2,2.7
6.6,2.9
4.9,2.4
6.3,3.3
5.7,2.8
6.5,2.8
5.5,2.3
6.9,3.1
6.4,3.2
7.0,3.2
5.0,3.3
5.3,3.7
4.6,3.2
5.1,3.8
4.8,3.0
5.1,3.8
5.0,3.5
4.4,3.2
4.5,2.3
5.0,3.5
5.1,3.4
4.4,3.0
4.9,3.6
5.5,3.5
5.0,3.2
4.9,3.1
5.5,4.2
5.2,4.1
5.4,3.4
4.8,3.1
4.7,3.2
5.2,3.4
5.2,3.5
5.0,3.4
5.0,3.0
4.8,3.4
5.1,3.3
4.6,3.6
5.1,3.7
5.4,3.4
5.1,3.8
5.7,3.8
5.1,3.5
5.4,3.9
5.7,4.4
5.8,4.0
4.3,3.0
4.8,3.0
4.8,3.4
5.4,3.7
4.9,3.1
4.4,2.9
5.0,3.4
4.6,3.4
5.4,3.9
5.0,3.6
4.6,3.1
4.7,3.2
4.9,3.0
5.1,3.5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
2.0
2.5
3.0
3.5
4.0
4.5
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
1.99
2.00
2.01
2.02
2.03
2.04
2.05
2.06
2.07
2.08
2.09
2.10
2.11
2.12
2.13
2.14
2.15
2.16
2.17
2.18
2.19
2.20
2.21
2.22
2.23
2.24
2.25
2.26
2.27
2.28
2.29
2.30
2.31
2.32
2.33
2.34
2.35
2.36
2.37
2.38
2.39
2.40
2.41
2.42
2.43
2.44
2.45
2.46
2.47
2.48
2.49
2.50
2.51
2.52
2.53
2.54
2.55
2.56
2.57
2.58
2.59
2.60
2.61
2.62
2.63
2.64
2.65
2.66
2.67
2.68
2.69
2.70
2.71
2.72
2.73
2.74
2.75
2.76
2.77
2.78
2.79
2.80
2.81
2.82
2.83
2.84
2.85
2.86
2.87
2.88
2.89
2.90
2.91
2.92
2.93
2.94
2.95
2.96
2.97
2.98
2.99
3.00
3.01
3.02
3.03
3.04
3.05
3.06
3.07
3.08
3.09
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
3.18
3.19
3.20
3.21
3.22
3.23
3.24
3.25
3.26
3.27
3.28
3.29
3.30
3.31
3.32
3.33
3.34
3.35
3.36
3.37
3.38
3.39
3.40
3.41
3.42
3.43
3.44
3.45
3.46
3.47
3.48
3.49
3.50
3.51
3.52
3.53
3.54
3.55
3.56
3.57
3.58
3.59
3.60
3.61
3.62
3.63
3.64
3.65
3.66
3.67
3.68
3.69
3.70
3.71
3.72
3.73
3.74
3.75
3.76
3.77
3.78
3.79
3.80
3.81
3.82
3.83
3.84
3.85
3.86
3.87
3.88
3.89
3.90
3.91
3.92
3.93
3.94
3.95
3.96
3.97
3.98
3.99
4.00
4.01
4.02
4.03
4.04
4.05
4.06
4.07
4.08
4.09
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
4.26
4.27
4.28
4.29
4.30
4.31
4.32
4.33
4.34
4.35
4.36
4.37
4.38
4.39
4.40
4.41
4.42
4.43
4.44
4.45
4.46
4.47
4.48
4.49
4.50
2
3
4
5
SepalWidth
using DataFrames, Gadfly, Distributions
set_default_plot_size(21cm, 8cm)
x = -4:0.1:4
Da = [DataFrame(x=x, ymax=pdf.(Normal(μ),x), u="μ=$μ") for μ in [-1,1]]
Db = [DataFrame(x=randn(200).+μ, u="μ=$μ") for μ in [-1,1]]
p1 = plot(vcat(Da...), x=:x, y=:ymax, ymin=[0.0], ymax=:ymax, color=:u,
Geom.line, Geom.ribbon, Guide.ylabel("Density"), Theme(alphas=[0.6]),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Parametric PDF")
)
p2 = plot(vcat(Db...), x=:x, color=:u, Theme(alphas=[0.6]),
Stat.density(bandwidth=0.5), Geom.polygon(fill=true, preserve_order=true),
Coord.cartesian(xmin=-4, xmax=4, ymin=0, ymax=0.4),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Kernel PDF")
)
hstack(p1,p2)
x
-4
-2
0
2
4
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-4.00
-3.95
-3.90
-3.85
-3.80
-3.75
-3.70
-3.65
-3.60
-3.55
-3.50
-3.45
-3.40
-3.35
-3.30
-3.25
-3.20
-3.15
-3.10
-3.05
-3.00
-2.95
-2.90
-2.85
-2.80
-2.75
-2.70
-2.65
-2.60
-2.55
-2.50
-2.45
-2.40
-2.35
-2.30
-2.25
-2.20
-2.15
-2.10
-2.05
-2.00
-1.95
-1.90
-1.85
-1.80
-1.75
-1.70
-1.65
-1.60
-1.55
-1.50
-1.45
-1.40
-1.35
-1.30
-1.25
-1.20
-1.15
-1.10
-1.05
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
-5
0
5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
0.0
0.1
0.2
0.3
0.4
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
0.022
0.024
0.026
0.028
0.030
0.032
0.034
0.036
0.038
0.040
0.042
0.044
0.046
0.048
0.050
0.052
0.054
0.056
0.058
0.060
0.062
0.064
0.066
0.068
0.070
0.072
0.074
0.076
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Kernel PDF
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Density
Parametric PDF
using CategoricalArrays
using Gadfly
set_default_plot_size(14cm, 8cm)
n = 400
group = repeat([-1, 1], inner=200)
x = randn(n) .+ group
plot(x=x, color=categorical(group), Guide.colorkey(title="", pos=[3.6,0.7]),
layer(Stat.density, Geom.line, Geom.polygon(fill=true, preserve_order=true), alpha=[0.4]),
layer(Stat.quantile_bars(quantiles=[0.05, 0.95]), Geom.segment),
Guide.title("Density with bars showing the central 90% CI"),
Guide.ylabel("Density"), Coord.cartesian(xmin=-4, xmax=4)
)
x
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Density
Density with bars showing the central 90% CI
using DataFrames, Gadfly, RDatasets, Statistics
set_default_plot_size(21cm, 8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
df = combine(groupby(salaries, [:Rank, :Discipline]), :Salary.=>mean)
df.label = string.(round.(Int, df.Salary_mean))
p1 = plot(df, x=:Discipline, y=:Salary_mean, color=:Rank,
Scale.x_discrete(levels=["Discipline A", "Discipline B"]),
label=:label, Geom.label(position=:centered), Stat.dodge(position=:stack),
Geom.bar(position=:stack)
)
p2 = plot(df, y=:Discipline, x=:Salary_mean, color=:Rank,
Coord.cartesian(yflip=true), Scale.y_discrete,
label=:label, Geom.label(position=:right), Stat.dodge(axis=:y),
Geom.bar(position=:dodge, orientation=:horizontal),
Scale.color_discrete(levels=["Prof", "AssocProf", "AsstProf"]),
Guide.yticks(orientation=:vertical), Guide.ylabel(nothing)
)
hstack(p1, p2)
Salary_mean
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Prof
AssocProf
AsstProf
Rank
133
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Discipline B
Discipline A
Discipline B
Discipline A
Discipline
Discipline A
Discipline B
Prof
AsstProf
AssocProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Salary_mean
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
sigmoid(x) = 1 ./ (1 .+ exp.(-x))
npoints = 30
gshift, x = rand([0,2], npoints), range(-9, 9, length=npoints)
y, ye = sigmoid(x+gshift), 0.2*rand(npoints)
df = DataFrame(x=x, y=y, ymin=y-ye, ymax=y+ye, g=gshift)
plot(y=[sigmoid, x->sigmoid(x+2)], xmin=[-10], xmax=[10],
Geom.line, Stat.func(100), color=[0,2], Guide.xlabel("x"),
layer(df, x=:x, y=:y, ymin=:ymin, ymax=:ymax, color=:g,
Geom.point, Geom.yerrorbar, Stat.x_jitter(range=1)),
Scale.color_discrete_manual("deepskyblue","yellow3", levels=[0,2]),
Guide.colorkey(title="Function", labels=["Sigmoid(x)", "Sigmoid(x+2)"]),
Theme(errorbar_cap_length=0mm, key_position=:inside)
)
x
-10
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-10
0
10
9.0141046600311480.9998766054240137
8.6927540025953610.9997704844756888
8.1439459673205050.9995731370872474
7.5339262418893150.9992062365897446
6.6189131357520840.9998000496254946
5.547565393392760.9996281141773367
5.0757871084144220.9949124687501931
4.3911606127179690.9987143089615129
4.1168057252619660.9976109861916291
3.1354122445179220.9955650454954019
2.73212982377129960.9917814049431126
2.3910512889365950.8977447634316474
1.2028286926363470.8251626114337308
0.76812760129307690.7172851124799586
0.151905903768971230.9097301769472593
-0.249320961278248680.8441788063113398
-0.8281913982033450.2827148875200414
-1.58342552357296950.610229226597668
-2.10681175390071160.45700301154629586
-3.18894341318910430.05769799230857057
-3.2464350186055960.03186716596599677
-4.1491536301362420.11562972700203106
-4.6106589343894060.009422642306984511
-4.9267611518636330.005087531249807014
-5.8616504657392570.019907474356029627
-6.7756507565596650.0014755595155596822
-7.01153531072300850.00583556787482027
-7.9328016384674130.00042686291275275794
-8.4381743082221340.00022951552431103904
-8.9822199994533580.0009110511944006454
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Sigmoid(x)
Sigmoid(x+2)
Function
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1.50
-0.5
0.0
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1.0
1.5
y
using Distributions, Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
iris, geyser = dataset.("datasets", ["iris", "faithful"])
df = combine(groupby(iris, :Species), :SepalLength=>(x->fit(Normal, x))=>:d)
ds2 = fit.([Normal, Uniform], [geyser.Eruptions])
yeqx(x=4:6) = layer(x=x, Geom.abline(color="gray80"))
xylabs = [Guide.xlabel("Theoretical q"), Guide.ylabel("Sample q")]
p1 = plot(df, x=:d, y=iris[:,1], color=:Species, Stat.qq, yeqx(4:8),
xylabs..., Guide.title("3 Samples, 1 Distribution"))
p2 = plot(geyser, x=ds2, y=:Eruptions, color=["Normal","Uniform"], Stat.qq,
yeqx(0:6), xylabs..., Guide.title("1 Sample, 2 Distributions"),
Theme(discrete_highlight_color=c->nothing, alphas=[0.5], point_size=2pt)
)
hstack(p1, p2)
Theoretical q
0
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8
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6.45
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6.85
6.90
6.95
7.00
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7.65
7.70
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7.80
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7.90
7.95
8.00
0
10
Normal
Uniform
Color
5.0935661764705885.0835606617647064
5.0806985294117645.050187500000002
5.0678308823529415.01680330882353
5.0549632352941174.967362132352942
5.0420955882352934.933
5.0292279411764714.933
5.0163602941176464.917288602941177
5.0034926470588234.9
4.9906254.8920312500000005
4.9777573529411764.867652573529412
4.9648897058823534.8421562499999995
4.9520220588235294.833
4.9391544117647064.8257352941176475
4.9262867647058824.817
4.91341911764705854.80940625
4.9005514705882354.8
4.8876838235294114.8
4.8748161764705884.8
4.8619485294117644.8
4.8490808823529414.8
4.8362132352941174.792781249999999
4.8233455882352934.776264705882354
4.8104779411764714.75990625
4.7976102941176464.74296875
4.7847426470588234.72603125
4.7718754.7095
4.7590073529411764.7
4.7461397058823534.7
4.7332720588235294.7
4.7204044117647064.7
4.7075367647058824.7
4.69466911764705854.687321691176471
4.6818014705882354.667
4.6689338235294114.66059375
4.6560661764705884.64365625
4.6431985294117644.633
4.6303308823529424.633
4.6174632352941174.627205882352941
4.6045955882352934.61090625
4.5917279411764714.6
4.5788602941176464.6
4.5659926470588234.6
4.5531254.59415625
4.5402573529411764.583
4.5273897058823534.583
4.5145220588235294.583
4.5016544117647064.577735294117647
4.4887867647058824.567
4.47591911764705854.567
4.4630514705882354.56159375
4.4501838235294114.54465625
4.4373161764705884.533
4.4244485294117644.533
4.4115808823529424.533
4.3987132352941174.533
4.3858455882352934.5282647058823535
4.3729779411764714.512031250000001
4.3601102941176464.5
4.3472426470588234.5
4.3343754.5
4.3215073529411764.5
4.3086397058823534.5
4.2957720588235294.5
4.2829044117647064.5
4.2700367647058824.49553125
4.25716911764705854.478852941176471
4.2443014705882354.467
4.2314338235294114.46271875
4.2185661764705884.45
4.2056985294117644.45
4.1928308823529424.44590625
4.1799632352941174.433
4.1670955882352934.429264705882353
4.1542279411764714.417
4.1413602941176464.417
4.1284926470588244.417
4.1156254.41328125
4.1027573529411764.396343750000001
4.0898897058823534.379617647058823
4.07702205882352954.367
4.0641544117647064.367
4.0512867647058824.366799632352941
4.03841911764705854.36285294117647
4.0255514705882354.35
4.0126838235294114.35
3.9998161764705884.35
3.98694852941176464.34690625
3.9740808823529414.333
3.96121323529411744.333
3.94834558823529364.333
3.935477941176474.333
3.9226102941176474.330382352941177
3.90974264705882354.3142812500000005
3.89687499999999964.3
3.88400735294117634.29740625
3.8711397058823534.283
3.85827205882352954.280676470588236
3.84540441176470574.267
3.8325367647058824.264656250000001
3.81966911764705854.25
3.8068014705882354.25
3.79393382352941134.25
3.7810661764705884.24790625
3.76819852941176464.233
3.7553308823529414.233
3.7424632352941184.229299632352941
3.72959558823529364.19815625
3.716727941176474.181323529411765
3.7038602941176474.167
3.69099264705882354.167
3.67812499999999964.167
3.66525735294117634.16546875
3.6523897058823534.15
3.63952205882352954.15
3.62665441176470574.15
3.6137867647058824.1487187500000005
3.60091911764705854.133
3.5880514705882354.131911764705882
3.57518382352941184.117
3.56231617647058844.1159687499999995
3.54944852941176464.1
3.5365808823529414.09909375
3.52371323529411744.083
3.5108455882352944.083
3.497977941176474.083
3.4851102941176474.083
3.4722426470588234.082441176470589
3.45937499999999964.067
3.44650735294117634.066531250000001
3.4336397058823534.04959375
3.42077205882352954.033
3.4079044117647064.032454044117647
3.3950367647058824.0
3.38216911764705854.0
3.3693014705882354.0
3.35643382352941184.0
3.34356617647058844.0
3.33069852941176464.0
3.31783088235294083.967303308823529
3.30496323529411743.9660128676470587
3.2920955882352943.9502647058823532
3.27922794117647073.95
3.2663602941176473.917788602941177
3.25349264705882353.917
3.24062499999999963.917
3.22775735294117633.8841875
3.2148897058823533.8512738970588236
3.20202205882352953.85
3.18915441176470573.8337812500000004
3.17628676470588233.833
3.16341911764705853.833
3.1505514705882353.833
3.13768382352941183.833
3.12481617647058843.818029411764706
3.11194852941176463.7704007352941176
3.09908088235294083.767
3.08621323529411743.75128125
3.0733455882352943.7343437500000003
3.06047794117647073.7183235294117645
3.0476102941176473.6859375
3.03474264705882353.607476102941177
3.02187499999999963.6
3.00900735294117633.6
2.9961397058823533.6
2.98327205882352953.5704577205882355
2.97040441176470573.567
2.95753676470588233.507512867647059
2.94466911764705853.5
2.9318014705882353.455974264705883
2.91893382352941183.421064338235294
2.90606617647058843.373341911764706
2.8931985294117653.3374375
2.88033088235294083.333
2.86746323529411743.3192058823529416
2.8545955882352943.1023860294117647
2.84172794117647072.9242518382352936
2.8288602941176472.88553125
2.81599264705882352.8126636029411767
2.80312499999999962.65909375
2.79025735294117632.6195588235294114
2.7773897058823532.5049227941176464
2.76452205882352952.4280404411764707
2.75165441176470572.417
2.73878676470588232.40296875
2.72591911764705852.4
2.7130514705882352.38609375
2.70018382352941182.369970588235294
2.68731617647058842.35321875
2.6744485294117652.33628125
2.66158088235294082.3201470588235296
2.64871323529411742.30340625
2.6358455882352942.28646875
2.62297794117647072.2703235294117645
2.6101102941176472.2535937500000003
2.59724264705882352.25
2.5843752.23671875
2.57150735294117632.233
2.5586397058823532.2206176470588237
2.54577205882352952.20390625
2.53290441176470572.2
2.52003676470588232.2
2.5071691176470592.18709375
2.4943014705882352.1709117647058824
2.48143382352941182.167
2.46856617647058842.15428125
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3.2926008739816763.7704007352941176
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3.22833215555653743.607476102941177
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3.1741453883223623.567
3.1632263386648713.507512867647059
3.15227739364742333.5
3.1412973721756593.455974264705883
3.13028507150361833.421064338235294
3.11923926616143143.373341911764706
3.1081587068386583.3374375
3.09704211922042473.333
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3.07469562947782033.1023860294117647
3.0634630425037272.9242518382352936
3.05218905482501322.88553125
3.04087224777002072.8126636029411767
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2.9951472576185732.4280404411764707
2.98359385545556942.417
2.9719883668628172.40296875
2.96032910465443562.4
2.9486143354943462.38609375
2.93684227763124462.369970588235294
2.92501109850901742.35321875
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2.28339763868930671.8950624999999999
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2.20728625115726641.8729117647058824
2.1873448808761741.867
2.16699691739678271.867
2.1462186657197281.867
2.1249843149087671.867
2.10326567341368961.867
2.08103186134597041.867
2.0582489509746111.867
2.0348795445621741.85678125
2.0108822758861011.85
1.9862112181735431.8399062499999999
1.96081517641967971.833
1.9346368357368671.833
1.90761172889015151.833
1.87966697463669471.833
1.8507197226121821.833
1.8206752183674471.833
1.78942437081922231.8239117647058825
1.75684065929212821.817
1.72277615129687131.817
1.6870563035580511.80753125
1.64947306822111871.8
1.60977559057729751.8
1.56765740578027681.8
1.52273841345155341.7907812499999998
1.47453882829363711.783
1.42244036820803781.7653474264705882
1.36562629931171961.75
1.30298470245474141.75
1.23294383281248091.75
1.15317237380319071.75
1.05998359781980871.75
0.94699976394484021.7412812500000001
0.80158086604138971.7161966911764706
0.59202273600605791.6833180147058824
0.178582551267737261.6333768382352942
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0
1
2
3
4
5
6
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
4.75
5.00
5.25
5.50
5.75
6.00
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
1.22
1.24
1.26
1.28
1.30
1.32
1.34
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.54
1.56
1.58
1.60
1.62
1.64
1.66
1.68
1.70
1.72
1.74
1.76
1.78
1.80
1.82
1.84
1.86
1.88
1.90
1.92
1.94
1.96
1.98
2.00
2.02
2.04
2.06
2.08
2.10
2.12
2.14
2.16
2.18
2.20
2.22
2.24
2.26
2.28
2.30
2.32
2.34
2.36
2.38
2.40
2.42
2.44
2.46
2.48
2.50
2.52
2.54
2.56
2.58
2.60
2.62
2.64
2.66
2.68
2.70
2.72
2.74
2.76
2.78
2.80
2.82
2.84
2.86
2.88
2.90
2.92
2.94
2.96
2.98
3.00
3.02
3.04
3.06
3.08
3.10
3.12
3.14
3.16
3.18
3.20
3.22
3.24
3.26
3.28
3.30
3.32
3.34
3.36
3.38
3.40
3.42
3.44
3.46
3.48
3.50
3.52
3.54
3.56
3.58
3.60
3.62
3.64
3.66
3.68
3.70
3.72
3.74
3.76
3.78
3.80
3.82
3.84
3.86
3.88
3.90
3.92
3.94
3.96
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
0
10
Sample q
1 Sample, 2 Distributions
Theoretical q
4
5
6
7
8
9
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
8.02
8.04
8.06
8.08
8.10
8.12
8.14
8.16
8.18
8.20
8.22
8.24
8.26
8.28
8.30
8.32
8.34
8.36
8.38
8.40
8.42
8.44
8.46
8.48
8.50
8.52
8.54
8.56
8.58
8.60
8.62
8.64
8.66
8.68
8.70
8.72
8.74
8.76
8.78
8.80
8.82
8.84
8.86
8.88
8.90
8.92
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3
6
9
setosa
versicolor
virginica
Species
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Sample q
3 Samples, 1 Distribution
using Compose, Gadfly, RDatasets
set_default_plot_size(21cm,8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
p = plot(salaries[salaries.Rank.=="Prof",:], x=:YrsService, y=:Salary,
color=:Sex, xgroup = :Discipline,
Geom.subplot_grid(Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.95, 0.99]), Geom.line, Geom.ribbon)),
Scale.xgroup(levels=["Discipline A", "Discipline B"]),
Guide.colorkey(title="", pos=[0.43w, -0.4h]),
Theme(point_size=2pt, alphas=[0.5])
)
YrsService by
Discipline B
Discipline A
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16.6
16.8
17.0
17.2
17.4
17.6
17.8
18.0
18.2
18.4
18.6
18.8
19.0
19.2
19.4
19.6
19.8
20.0
20.2
20.4
20.6
20.8
21.0
21.2
21.4
21.6
21.8
22.0
22.2
22.4
22.6
22.8
23.0
23.2
23.4
23.6
23.8
24.0
24.2
24.4
24.6
24.8
25.0
25.2
25.4
25.6
25.8
26.0
26.2
26.4
26.6
26.8
27.0
27.2
27.4
27.6
27.8
28.0
28.2
28.4
28.6
28.8
29.0
29.2
29.4
29.6
29.8
30.0
30.2
30.4
30.6
30.8
31.0
31.2
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
33.8
34.0
34.2
34.4
34.6
34.8
35.0
35.2
35.4
35.6
35.8
36.0
36.2
36.4
36.6
36.8
37.0
37.2
37.4
37.6
37.8
38.0
38.2
38.4
38.6
38.8
39.0
39.2
39.4
39.6
39.8
40.0
40.2
40.4
40.6
40.8
41.0
41.2
41.4
41.6
41.8
42.0
42.2
42.4
42.6
42.8
43.0
43.2
43.4
43.6
43.8
44.0
44.2
44.4
44.6
44.8
45.0
45.2
45.4
45.6
45.8
46.0
46.2
46.4
46.6
46.8
47.0
47.2
47.4
47.6
47.8
48.0
48.2
48.4
48.6
48.8
49.0
49.2
49.4
49.6
49.8
50.0
50.2
50.4
50.6
50.8
51.0
51.2
51.4
51.6
51.8
52.0
52.2
52.4
52.6
52.8
53.0
53.2
53.4
53.6
53.8
54.0
54.2
54.4
54.6
54.8
55.0
55.2
55.4
55.6
55.8
56.0
56.2
56.4
56.6
56.8
57.0
57.2
57.4
57.6
57.8
58.0
58.2
58.4
58.6
58.8
59.0
59.2
59.4
59.6
59.8
60.0
0
100
0
10
20
30
40
50
60
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
25.0
27.5
30.0
32.5
35.0
37.5
40.0
42.5
45.0
47.5
50.0
52.5
55.0
57.5
60.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
12.2
12.4
12.6
12.8
13.0
13.2
13.4
13.6
13.8
14.0
14.2
14.4
14.6
14.8
15.0
15.2
15.4
15.6
15.8
16.0
16.2
16.4
16.6
16.8
17.0
17.2
17.4
17.6
17.8
18.0
18.2
18.4
18.6
18.8
19.0
19.2
19.4
19.6
19.8
20.0
20.2
20.4
20.6
20.8
21.0
21.2
21.4
21.6
21.8
22.0
22.2
22.4
22.6
22.8
23.0
23.2
23.4
23.6
23.8
24.0
24.2
24.4
24.6
24.8
25.0
25.2
25.4
25.6
25.8
26.0
26.2
26.4
26.6
26.8
27.0
27.2
27.4
27.6
27.8
28.0
28.2
28.4
28.6
28.8
29.0
29.2
29.4
29.6
29.8
30.0
30.2
30.4
30.6
30.8
31.0
31.2
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
33.8
34.0
34.2
34.4
34.6
34.8
35.0
35.2
35.4
35.6
35.8
36.0
36.2
36.4
36.6
36.8
37.0
37.2
37.4
37.6
37.8
38.0
38.2
38.4
38.6
38.8
39.0
39.2
39.4
39.6
39.8
40.0
40.2
40.4
40.6
40.8
41.0
41.2
41.4
41.6
41.8
42.0
42.2
42.4
42.6
42.8
43.0
43.2
43.4
43.6
43.8
44.0
44.2
44.4
44.6
44.8
45.0
45.2
45.4
45.6
45.8
46.0
46.2
46.4
46.6
46.8
47.0
47.2
47.4
47.6
47.8
48.0
48.2
48.4
48.6
48.8
49.0
49.2
49.4
49.6
49.8
50.0
50.2
50.4
50.6
50.8
51.0
51.2
51.4
51.6
51.8
52.0
52.2
52.4
52.6
52.8
53.0
53.2
53.4
53.6
53.8
54.0
54.2
54.4
54.6
54.8
55.0
55.2
55.4
55.6
55.8
56.0
56.2
56.4
56.6
56.8
57.0
57.2
57.4
57.6
57.8
58.0
58.2
58.4
58.6
58.8
59.0
59.2
59.4
59.6
59.8
60.0
0
100
21,145.028
20,138.0
27,142.5
38,93.519
49,186.96
28,144.309
33,128.25
27,142.023
10,107.986
35,150.376
31,162.15
38,114.596
17,124.312
11,106.231
15,137.317
25,128.464
12,145.0
23,98.053
38,151.445
19,145.098
10,105.45
9,116.518
60,192.253
23,134.778
37,151.65
15,124.714
31,162.221
15,161.101
23,104.428
15,135.027
16,134.55
45,67.559
20,129.6
10,145.2
21,170.0
11,119.5
11,146.0
11,145.35
19,126.2
7,128.4
39,111.35
20,163.2
18,120.0
33,162.2
2,96.545
5,165.0
17,152.5
17,160.4
40,119.7
22,114.5
33,189.409
18,122.1
22,133.7
9,180.0
19,153.75
10,107.5
30,134.0
23,101.0
22,150.0
5,141.136
11,142.467
14,147.349
25,111.751
20,134.185
24,93.164
19,151.575
18,181.257
19,130.664
16,167.284
8,105.89
19,176.5
16,137.167
21,118.971
9,111.168
12,128.148
26,144.651
27,156.938
28,119.015
27,112.696
14,127.512
5,153.303
23,126.933
25,133.217
26,106.689
14,102.235
7,129.676
20,123.683
38,166.024
25,172.272
37,152.708
14,132.825
18,122.96
20,144.64
16,135.585
28,150.743
19,193.0
3,150.48
23,113.398
34,92.391
19,100.131
45,146.856
2,126.32
36,91.412
17,111.512
31,99.418
12,101.0
31,109.785
21,117.704
9,106.639
11,108.875
28,126.621
25,140.096
19,151.768
28,98.193
15,114.778
19,94.384
38,231.545
27,101.299
2,146.5
31,125.196
21,155.75
9,117.256
4,132.261
8,118.223
20,101.0
3,117.15
18,104.8
18,129.0
20,119.25
45,147.765
23,175.0
41,141.5
39,115.0
16,173.2
18,139.75
15,95.329
25,101.738
19,150.564
30,103.106
19,151.292
19,166.605
18,186.023
36,119.45
15,109.305
27,139.219
9,114.33
21,125.192
44,105.0
23,172.505
38,150.68
26,103.649
19,103.275
26,136.66
7,109.707
20,110.515
31,134.69
30,131.95
10,115.435
40,101.036
43,205.5
30,138.771
15,109.646
11,121.946
14,109.954
35,107.309
40,88.709
6,146.8
35,100.351
19,94.35
9,108.1
7,92.05
15,166.8
28,122.5
31,111.35
44,144.05
4,105.26
13,170.5
16,127.1
36,88.6
43,72.3
11,148.8
18,126.3
36,97.15
7,107.3
9,183.8
28,168.5
7,174.5
27,150.5
27,115.8
43,155.865
51,57.8
27,103.6
38,136.5
46,100.6
18,107.1
27,163.2
48,107.2
6,93.0
18,194.8
40,143.25
7,103.7
44,89.65
10,104.35
43,143.94
30,134.8
35,99.0
31,126.0
26,121.2
45,107.55
30,92.55
22,140.3
7,116.45
12,132.0
8,102.0
39,109.0
7,204.0
23,128.8
18,101.1
23,91.1
11,90.45
23,84.273
23,108.2
37,102.6
30,122.875
6,96.2
40,77.202
25,114.0
17,81.7
19,117.555
19,148.75
27,91.0
38,133.9
11,88.175
20,122.4
35,87.8
11,106.608
18,152.664
14,105.668
14,108.262
18,136.0
25,168.635
57,76.84
30,113.278
26,155.5
49,78.162
22,96.614
22,97.262
32,124.309
14,115.313
36,117.515
29,148.5
9,120.806
0,105.0
37,104.279
16,112.429
31,131.205
28,113.543
23,134.885
8,106.294
19,113.068
30,93.904
31,102.58
26,89.565
36,137.0
23,124.75
34,103.45
0
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Male
Female
Salary
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
x = range(0.1, stop=4.9, length=30)
D = DataFrame(x=x, y=x.+randn(length(x)))
p = plot(D, x=:x, y=:y, Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.90,0.99]), Geom.line, Geom.ribbon(fill=false)),
Theme(lowlight_color=c->"gray", line_style=[:solid, :dot])
)
x
0
1
2
3
4
5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
1.22
1.24
1.26
1.28
1.30
1.32
1.34
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.54
1.56
1.58
1.60
1.62
1.64
1.66
1.68
1.70
1.72
1.74
1.76
1.78
1.80
1.82
1.84
1.86
1.88
1.90
1.92
1.94
1.96
1.98
2.00
2.02
2.04
2.06
2.08
2.10
2.12
2.14
2.16
2.18
2.20
2.22
2.24
2.26
2.28
2.30
2.32
2.34
2.36
2.38
2.40
2.42
2.44
2.46
2.48
2.50
2.52
2.54
2.56
2.58
2.60
2.62
2.64
2.66
2.68
2.70
2.72
2.74
2.76
2.78
2.80
2.82
2.84
2.86
2.88
2.90
2.92
2.94
2.96
2.98
3.00
3.02
3.04
3.06
3.08
3.10
3.12
3.14
3.16
3.18
3.20
3.22
3.24
3.26
3.28
3.30
3.32
3.34
3.36
3.38
3.40
3.42
3.44
3.46
3.48
3.50
3.52
3.54
3.56
3.58
3.60
3.62
3.64
3.66
3.68
3.70
3.72
3.74
3.76
3.78
3.80
3.82
3.84
3.86
3.88
3.90
3.92
3.94
3.96
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
0
5
4.96.631090391647861
4.734482758620695.066086528811177
4.5689655172413795.609631415042641
4.4034482758620692.978507868064252
4.23793103448275855.455740571241266
4.0724137931034483.9852399569472987
3.9068965517241382.734988932595699
3.74137931034482742.838490010093868
3.5758620689655172.0906470084434403
3.4103448275862074.056501325888056
3.24482758620689672.797357588796709
3.0793103448275863.2090227386334593
2.9137931034482763.4626171324681927
2.74827586206896562.284481031092637
2.58275862068965533.9397380305346683
2.41724137931034471.4612513243678766
2.25172413793103443.4735120893328846
2.0862068965517240.5246289054909075
1.92068965517241370.997503212778759
1.75517241379310352.5842669378674685
1.5896551724137931.4504400808353632
1.42413793103448282.4157363486894994
1.25862068965517242.152708145435948
1.09310344827586212.4203181125855364
0.92758620689655170.7500129361442378
0.76206896551724132.064553997791261
0.5965517241379310.43545259681447346
0.43103448275862066-2.2441493197847717
0.2655172413793103-0.37138797002961077
0.1-0.00378042408790491
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-2.5
0.0
2.5
5.0
7.5
-2.5
-2.0
-1.5
-1.0
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0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
-2.50
-2.45
-2.40
-2.35
-2.30
-2.25
-2.20
-2.15
-2.10
-2.05
-2.00
-1.95
-1.90
-1.85
-1.80
-1.75
-1.70
-1.65
-1.60
-1.55
-1.50
-1.45
-1.40
-1.35
-1.30
-1.25
-1.20
-1.15
-1.10
-1.05
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
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y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(25), y=rand(25), Stat.step, Geom.line)
x
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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y
using DataFrames, Gadfly, Distributions
using Gadfly: w,h
set_default_plot_size(21cm, 8cm)
D = DataFrame(Dist=["Prior", "Posterior"],
Density=[Normal(-0.22, 0.02), Normal(-0.29, 0.015)])
xcoord = Coord.cartesian(xmin=-0.4, xmax=-0.1)
gck = Guide.colorkey(title="", pos=[0.5w, -0.4h])
p1 = plot(D, y=:Density, color=:Dist, Guide.title("color=:Dist"), gck,
layer(Stat.unidistribution, Geom.line, Geom.ribbon, alpha=[0.8]), xcoord)
p2 = plot(D, y=:Density, color=:Dist, layer(Stat.unidistribution, Geom.line),
layer(Stat.unidistribution([[0.0001, 0.05], [0.95, 0.9999]]), Geom.ribbon),
Guide.ylabel(nothing), Guide.title("color=:Dist"), gck)
p3 = plot(D, y=:Density, group=:Dist, xcoord, gck,
layer(Stat.unidistribution([[0.0001, 0.1],[0.1, 0.9], [0.9, 0.9999]]), Geom.ribbon, alpha=[0.8]),
Scale.color_discrete_manual("orange", "yellow", "coral"), Theme(lowlight_color=identity),
Guide.title("group=:Dist"), Guide.ylabel(nothing)
)
hstack(p1, p2, p3)
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