RobberyConvict: Court Convictions for Armed Robbery in New South Wales
In glarma: Generalized Linear Autoregressive Moving Average Models
Description
Usage
Format
Source
Examples
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
1
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.
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### 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)
Example output
Call:
glm(formula = Y ~ Step.2001 + I(Feb + Mar + Apr + May + Jun +
Jul) + I(Aug + Sep + Oct + Nov + Dec), family = binomial(link = logit),
data = RobberyConvict, na.action = na.omit, x = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5435 -0.8978 0.1682 0.8011 2.6497
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.25685 0.15605 -1.646 0.09978 .
Step.2001 0.82315 0.08135 10.119 < 2e-16 ***
I(Feb + Mar + Apr + May + Jun + Jul) -0.37228 0.16188 -2.300 0.02146 *
I(Aug + Sep + Oct + Nov + Dec) -0.50068 0.16549 -3.025 0.00248 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 327.48 on 149 degrees of freedom
Residual deviance: 212.12 on 146 degrees of freedom
AIC: 684.79
Number of Fisher Scoring iterations: 4
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1), maxit = 100, grad = 1e-06)
GLARMA Coefficients:
phi_1
0.08175172
Linear Model Coefficients:
(Intercept) Step.2001
-0.2746835 0.8220330
I(Feb + Mar + Apr + May + Jun + Jul) I(Aug + Sep + Oct + Nov + Dec)
-0.3567715 -0.5003871
Degrees of Freedom: 149 Total (i.e. Null); 145 Residual
Null Deviance: 327.4831
Residual Deviance: 198.9089
AIC: 680.676
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1), maxit = 100, grad = 1e-06)
Pearson Residuals:
Min 1Q Median 3Q Max
-2.4456 -0.8159 0.1337 0.7301 2.4798
GLARMA Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
phi_1 0.08175 0.03298 2.479 0.0132 *
Linear Model Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
(Intercept) -0.27468 0.15711 -1.748 0.08041 .
Step.2001 0.82203 0.09571 8.589 < 2e-16 ***
I(Feb + Mar + Apr + May + Jun + Jul) -0.35677 0.15981 -2.232 0.02559 *
I(Aug + Sep + Oct + Nov + Dec) -0.50039 0.16333 -3.064 0.00219 **
Null deviance: 327.48 on 149 degrees of freedom
Residual deviance: 198.91 on 145 degrees of freedom
AIC: 680.676
Number of Newton Raphson iterations: 4
LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
Statistic p-value
LR Test 6.110 0.0134 *
Wald Test 6.144 0.0132 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Statistic p-value
LR Test 6.1104 0.01344 *
Wald Test 6.1443 0.01318 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call:
glm(formula = Y ~ Trend + Trend.2001 + I(Feb + Mar + Apr + May +
Jun) + Dec, family = binomial(link = logit), data = RobberyConvict,
na.action = na.omit, x = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.5072 -0.9098 0.1133 0.7891 3.6679
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04210 0.05394 -0.781 0.435016
Trend 0.17272 0.01214 14.224 < 2e-16 ***
Trend.2001 -0.17600 0.02145 -8.206 2.29e-16 ***
I(Feb + Mar + Apr + May + Jun) 0.14669 0.03950 3.713 0.000204 ***
Dec 0.38137 0.07292 5.230 1.69e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 584.85 on 149 degrees of freedom
Residual deviance: 247.87 on 145 degrees of freedom
AIC: 956.57
Number of Fisher Scoring iterations: 4
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1, 2, 3), maxit = 100, grad = 1e-06)
GLARMA Coefficients:
phi_1 phi_2 phi_3
0.01229693 0.04177964 0.02805464
Linear Model Coefficients:
(Intercept) Trend
-0.0351250 0.1707481
Trend.2001 I(Feb + Mar + Apr + May + Jun)
-0.1721641 0.1549353
Dec
0.3914685
Degrees of Freedom: 149 Total (i.e. Null); 142 Residual
Null Deviance: 584.8472
Residual Deviance: 231.9971
AIC: 948.9794
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1, 2, 3), maxit = 100, grad = 1e-06)
Pearson Residuals:
Min 1Q Median 3Q Max
-3.0349 -0.9824 0.1392 0.7787 3.4460
GLARMA Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
phi_1 0.01230 0.01523 0.807 0.41956
phi_2 0.04178 0.01494 2.796 0.00517 **
phi_3 0.02805 0.01403 2.000 0.04553 *
Linear Model Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
(Intercept) -0.03512 0.07270 -0.483 0.629008
Trend 0.17075 0.01668 10.236 < 2e-16 ***
Trend.2001 -0.17216 0.02911 -5.915 3.32e-09 ***
I(Feb + Mar + Apr + May + Jun) 0.15494 0.04462 3.472 0.000516 ***
Dec 0.39147 0.07074 5.534 3.12e-08 ***
Null deviance: 584.85 on 149 degrees of freedom
Residual deviance: 232.00 on 142 degrees of freedom
AIC: 948.9794
Number of Newton Raphson iterations: 6
LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
Statistic p-value
LR Test 13.59 0.00352 **
Wald Test 11.97 0.00749 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Statistic p-value
LR Test 13.591 0.003518 **
Wald Test 11.968 0.007495 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
glarma documentation built on May 2, 2019, 6:33 a.m.
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Description Usage Format Source Examples
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
1 |
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
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 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ### 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)
|
Example output
Call:
glm(formula = Y ~ Step.2001 + I(Feb + Mar + Apr + May + Jun +
Jul) + I(Aug + Sep + Oct + Nov + Dec), family = binomial(link = logit),
data = RobberyConvict, na.action = na.omit, x = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5435 -0.8978 0.1682 0.8011 2.6497
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.25685 0.15605 -1.646 0.09978 .
Step.2001 0.82315 0.08135 10.119 < 2e-16 ***
I(Feb + Mar + Apr + May + Jun + Jul) -0.37228 0.16188 -2.300 0.02146 *
I(Aug + Sep + Oct + Nov + Dec) -0.50068 0.16549 -3.025 0.00248 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 327.48 on 149 degrees of freedom
Residual deviance: 212.12 on 146 degrees of freedom
AIC: 684.79
Number of Fisher Scoring iterations: 4
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1), maxit = 100, grad = 1e-06)
GLARMA Coefficients:
phi_1
0.08175172
Linear Model Coefficients:
(Intercept) Step.2001
-0.2746835 0.8220330
I(Feb + Mar + Apr + May + Jun + Jul) I(Aug + Sep + Oct + Nov + Dec)
-0.3567715 -0.5003871
Degrees of Freedom: 149 Total (i.e. Null); 145 Residual
Null Deviance: 327.4831
Residual Deviance: 198.9089
AIC: 680.676
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1), maxit = 100, grad = 1e-06)
Pearson Residuals:
Min 1Q Median 3Q Max
-2.4456 -0.8159 0.1337 0.7301 2.4798
GLARMA Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
phi_1 0.08175 0.03298 2.479 0.0132 *
Linear Model Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
(Intercept) -0.27468 0.15711 -1.748 0.08041 .
Step.2001 0.82203 0.09571 8.589 < 2e-16 ***
I(Feb + Mar + Apr + May + Jun + Jul) -0.35677 0.15981 -2.232 0.02559 *
I(Aug + Sep + Oct + Nov + Dec) -0.50039 0.16333 -3.064 0.00219 **
Null deviance: 327.48 on 149 degrees of freedom
Residual deviance: 198.91 on 145 degrees of freedom
AIC: 680.676
Number of Newton Raphson iterations: 4
LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
Statistic p-value
LR Test 6.110 0.0134 *
Wald Test 6.144 0.0132 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Statistic p-value
LR Test 6.1104 0.01344 *
Wald Test 6.1443 0.01318 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Call:
glm(formula = Y ~ Trend + Trend.2001 + I(Feb + Mar + Apr + May +
Jun) + Dec, family = binomial(link = logit), data = RobberyConvict,
na.action = na.omit, x = TRUE)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.5072 -0.9098 0.1133 0.7891 3.6679
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04210 0.05394 -0.781 0.435016
Trend 0.17272 0.01214 14.224 < 2e-16 ***
Trend.2001 -0.17600 0.02145 -8.206 2.29e-16 ***
I(Feb + Mar + Apr + May + Jun) 0.14669 0.03950 3.713 0.000204 ***
Dec 0.38137 0.07292 5.230 1.69e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 584.85 on 149 degrees of freedom
Residual deviance: 247.87 on 145 degrees of freedom
AIC: 956.57
Number of Fisher Scoring iterations: 4
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1, 2, 3), maxit = 100, grad = 1e-06)
GLARMA Coefficients:
phi_1 phi_2 phi_3
0.01229693 0.04177964 0.02805464
Linear Model Coefficients:
(Intercept) Trend
-0.0351250 0.1707481
Trend.2001 I(Feb + Mar + Apr + May + Jun)
-0.1721641 0.1549353
Dec
0.3914685
Degrees of Freedom: 149 Total (i.e. Null); 142 Residual
Null Deviance: 584.8472
Residual Deviance: 231.9971
AIC: 948.9794
Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson",
phiLags = c(1, 2, 3), maxit = 100, grad = 1e-06)
Pearson Residuals:
Min 1Q Median 3Q Max
-3.0349 -0.9824 0.1392 0.7787 3.4460
GLARMA Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
phi_1 0.01230 0.01523 0.807 0.41956
phi_2 0.04178 0.01494 2.796 0.00517 **
phi_3 0.02805 0.01403 2.000 0.04553 *
Linear Model Coefficients:
Estimate Std.Error z-ratio Pr(>|z|)
(Intercept) -0.03512 0.07270 -0.483 0.629008
Trend 0.17075 0.01668 10.236 < 2e-16 ***
Trend.2001 -0.17216 0.02911 -5.915 3.32e-09 ***
I(Feb + Mar + Apr + May + Jun) 0.15494 0.04462 3.472 0.000516 ***
Dec 0.39147 0.07074 5.534 3.12e-08 ***
Null deviance: 584.85 on 149 degrees of freedom
Residual deviance: 232.00 on 142 degrees of freedom
AIC: 948.9794
Number of Newton Raphson iterations: 6
LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
Statistic p-value
LR Test 13.59 0.00352 **
Wald Test 11.97 0.00749 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Statistic p-value
LR Test 13.591 0.003518 **
Wald Test 11.968 0.007495 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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