RobberyConvict: Court Convictions for Armed Robbery in New South Wales

In glarma: Generalized Linear Autoregressive Moving Average Models

Court Convictions for Armed Robbery in New South Wales

Description

Monthly counts of charges laid and convictions made in Local Courts and Higher Court in armed robbery in New South Wales from 1995–2007.

Usage

data(RobberyConvict)

Format

A data frame containing the following columns:

[, 1]	Date	Date in month/year format.
[, 2]	Incpt	A vector of ones, providing the intercept in the model.
[, 3]	Trend	Scaled time trend.
[, 4]	Step.2001	Unit step change from 2001 onwards.
[, 5]	Trend.2001	Change in trend term from 2001 onwards.
[, 6]	HC.N	Monthly number of cases for robbery (Higher Court).
[, 7]	HC.Y	Monthly number of convictions for robbery (Higher court).
[, 8]	HC.P	Proportion of convictions to charges for robbery (Higher court).
[, 9]	LC.N	Monthly number of cases for robbery (Lower court).
[, 10]	LC.Y	Monthly number of convictions for robbery (Lower court).
[, 11]	LC.P	Proportion of convictions to charges for robbery (Lower court).

Source

Dunsmuir, William TM, Tran, Cuong, and Weatherburn, Don (2008) Assessing the Impact of Mandatory DNA Testing of Prison Inmates in NSW on Clearance, Charge and Conviction Rates for Selected Crime Categories.

Examples

### Example with Robbery Convictions
data(RobberyConvict)
datalen <- dim(RobberyConvict)[1]
monthmat <- matrix(0, nrow = datalen, ncol = 12)
dimnames(monthmat) <- list(NULL, c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                                   "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
months <- unique(months(strptime(RobberyConvict$Date, format = "%m/%d/%Y"),
                        abbreviate=TRUE))


for (j in 1:12) {
  monthmat[months(strptime(RobberyConvict$Date,  "%m/%d/%Y"),
                  abbreviate = TRUE) == months[j], j] <- 1
}

RobberyConvict <- cbind(rep(1, datalen), RobberyConvict, monthmat)
rm(monthmat)

## LOWER COURT ROBBERY
y1 <- RobberyConvict$LC.Y
n1 <- RobberyConvict$LC.N

Y <- cbind(y1, n1-y1)

glm.LCRobbery <- glm(Y ~ Step.2001 +
                        I(Feb + Mar + Apr + May + Jun + Jul) +
                        I(Aug + Sep + Oct + Nov + Dec),
                     data = RobberyConvict, family = binomial(link = logit),
                     na.action = na.omit, x = TRUE)

summary(glm.LCRobbery, corr = FALSE)

X <- glm.LCRobbery$x


## Newton Raphson
glarmamod <- glarma(Y, X, phiLags = c(1), type = "Bin", method = "NR",
                    residuals = "Pearson", maxit = 100, grad = 1e-6)
glarmamod
summary(glarmamod)

## LRT, Wald tests.
likTests(glarmamod)

## Residuals and Fit Plots
plot.glarma(glarmamod)


## HIGHER COURT ROBBERY
y1 <- RobberyConvict$HC.Y
n1 <- RobberyConvict$HC.N

Y <- cbind(y1, n1-y1)

glm.HCRobbery <- glm(Y ~ Trend + Trend.2001 +
                       I(Feb + Mar + Apr + May + Jun) + Dec,
                     data = RobberyConvict, family = binomial(link = logit),
                     na.action = na.omit, x = TRUE)

summary(glm.HCRobbery,corr = FALSE)

X <- glm.HCRobbery$x


## Newton Raphson
glarmamod <- glarma(Y, X, phiLags = c(1, 2, 3), type = "Bin", method = "NR",
                    residuals = "Pearson", maxit = 100, grad = 1e-6)
glarmamod
summary(glarmamod)


## LRT, Wald tests.
likTests(glarmamod)

## Residuals and Fit Plots
plot.glarma(glarmamod)

glarma documentation built on April 4, 2025, 12:32 a.m.