# OxBoatRace: Oxford-Cambridge Boat Race In glarma: Generalized Linear Autoregressive Moving Average Models

## Description

Results of the boat race between Oxford and Cambridge from 1829–2011.

## Usage

 `1` ```data(OxBoatRace) ```

## Format

A data frame containing the following columns:

 [, 1] Year Year in which the race occurred. Some years are missing when the race was not run. [, 2] Intercept A vector of ones, providing the intercept in the model. [, 3] Camwin A binary response, zero for an Oxford win, one for a Cambridge win. [, 4] WinnerWeight Weight of winning team's crew. [, 5] LoserWeight Weight of losing team's crew. [, 6] Diff Difference between winning team's weight and losing team's weight.

## Source

Klingenberg, Bernhard (2008) Regression models for binary time series with gaps. Computational Statistics & Data Analysis, 52, 4076–4090.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```### Example with Oxford-Cambridge Boat Race data(OxBoatRace) y1 <- OxBoatRace\$Camwin n1 <- rep(1, length(OxBoatRace\$Year)) Y <- cbind(y1, n1 - y1) X <- cbind(OxBoatRace\$Intercept, OxBoatRace\$Diff) colnames(X) <- c("Intercept", "Weight Diff") oxcamglm <- glm(Y ~ Diff + I(Diff^2), data = OxBoatRace, family = binomial(link = "logit"), x = TRUE) summary(oxcamglm) X <- oxcamglm\$x glarmamod <- glarma(Y, X, thetaLags = c(1, 2), type = "Bin", method = "NR", residuals = "Pearson", maxit = 100, grad = 1e-6) summary(glarmamod) likTests(glarmamod) ## Plot Probability of Cambridge win versus Cambridge Weight advantage: beta <- coef(glarmamod, "beta") par(mfrow = c(1, 1)) plot(OxBoatRace\$Diff, 1 / (1 + exp(-(beta[1] + beta[2] * OxBoatRace\$Diff + beta[3] * OxBoatRace\$Diff^2))), ylab = "Prob", xlab = "Weight Diff") title("Probability of Cambridge win \n versus Cambridge weight advantage") ## Residuals and fit plots par(mfrow=c(3, 2)) plot.glarma(glarmamod) ```

glarma documentation built on Feb. 9, 2018, 6:08 a.m.