This user is a PhD student in the field of operations research at Berkeley. He usually contributes to the following general topics:
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Generalized Linear Models: logistic regression, Poisson regression, etc.
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use <code></code> for Code Texts. When you want the text and the link to be different use this [[Optimal_control_theory|control]]control
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<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 - – — |
The R function for fitting a generalized linear model is glm(). A Poisson regression is done when a counting process is being observed.
glm( numData˜roadType+weekDay, family=poisson(link=log), data=roadData) fits a model 
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()inR type ?glm
