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User:Faridani

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This user is a PhD student in the field of operations research at Berkeley. He usually contributes to the following general topics:

  • Stochastic Processes
  • Optimization Algorithms
  • Python Programming
  • Micro Electro Mechanical Systems

Kaveh Zamani's Funcrasing



Contributions

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At this moment, He is contributing to the following articles:

Social Network Analysis and Text Mining


Production and Logistics Sciences:

User Interfaces:

Entrepreneurship:

Other contributions:

People:

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To Do

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To Learn

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Stochastic Analysis

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Short Term TODO

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Poisson Regression in SPSS

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  1. Sorting in SPSS
  2. Poisson regression in SPSS
  3. Annotated SPSS Output

2D Spatial Statistical Analysis

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Generalized Linear Models: logistic regression, Poisson regression, etc.

Friends

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  • [User:Vantelimus]
  • [User:Michael_Hardy]
  • [User:Melcombe]
  • [User:Mpdelbuono]

Useful Stuff

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Use # for auto numbering, * for bullets use <code></code> for Code Texts. When you want the text and the link to be different use this [[Optimal_control_theory|control]]control

{{Reflist|3}} Multicol {{colbegin|3}} {{lorem}} {{lorem}} {{colend}}

{{colbegin|3}}
{{lorem}}
{{lorem}}
{{colend}}
<syntaxhighlight lang="python">
Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103
</syntaxhighlight>

For codes

Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103

This is the table I usually use:

α β γ δ ε ζ
η θ ι κ λ μ ν
ξ ο π ρ σ ς
τ υ φ χ ψ ω
Γ Δ Θ Λ Ξ Π
Σ Φ Ψ Ω
∫ ∑ ∏ √ − ± ∞
≈ ∝ = ≡ ≠ ≤ ≥
× · ÷ ∂ ′ ″
∇ ‰ ° ∴ Ø ø
∈ ∉ ∩ ∪ ⊂ ⊃ ⊆ ⊇
¬ ∧ ∨ ∃ ∀
⇒ ⇔ → ↔ ↑
ℵ - – —

Poisson regression in R

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The R function for fitting a generalized linear model is glm(). A Poisson regression is done when a counting process is being observed.

Syntax

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glm( numData˜roadType+weekDay, family=poisson(link=log), data=roadData) fits a model → Poisson(), where log() = . setting family=poisson.

The following code does the Poisson regression in R

X<-read.table("C:/poissonvisits.txt", header = TRUE)
names(X)
attach(X)
hourofday <- Time
foo <- split(Visits, hourofday)
foo <- sapply(foo, sum)
barplot(foo, space = 0, xlab = "hour of the day", ylab = "total count")

#Starting from here we do the GLM
w <- Time/24 * 2 * pi
out1 <- glm(Visits ~ I(sin(w)) + I(cos(w)), family = poisson)
summary(out1)

out2 <- update(out1, . ~ . + I(sin(2 * w)) + I(cos(2 * w)))
summary(out2)

out3 <- update(out2, . ~ . + I(sin(3 * w)) + I(cos(3 * w)))
summary(out3)

anova(out1, out2, out3, test = "Chisq")
plot(hourofday, Visits, xlab = "hour of the day")

curve(predict(out2, data.frame(w = x/24 * 2 * pi), type = "response"),add = TRUE)

where the file poissonvisits.txt is defined as

Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103

For more information about glm() in R type ?glm

See Also

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ANOVA

References

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  • Generalized linear models in R [1]
  • Generalized Linear Models in R, GillWard [2]
  • Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
  • Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
  • McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.


  • Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.