aps: APS data

In aplore3: Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd Ed., 2013)

Description

aps dataset.

Usage

aps

Format

A data.frame with 508 rows and 11 variables:

id

Identification Code (1 - 508)

place

Placement (1: Outpatient, 2: Day Treatment, 3: Intermediate Residential, 4: Residential)

place3

Placement Combined (1: Outpatient or Day Treatment, 2: Intermediate Residential, 3: Residential )

age

Age at Admission (Years)

race

Race (1: White, 2: Non-white)

gender

Gender (1: Female, 2: Male)

neuro

Neuropsychiatric Disturbance (1: None, 2: Mild, 3: Moderate, 4: Severe)

emot

Emotional Disturbance (1: Not Severe, 2: Severe)

danger

Danger to Others (1: Unlikely, 2: Possible, 3: Probable, 4: Likely)

elope

Elopement Risk (1: No Risk, 2: At Risk)

los

Length of Hospitalization (Days)

behav

Behavioral Symptoms Score (0 - 9)

custd

State Custody (1: No, 2: Yes)

viol

History of Violence (1: No, 2: Yes)

Source

Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (2013) Applied Logistic Regression, 3rd ed., New York: Wiley

Examples

head(aps, n = 10)
summary(aps)


## Table 8.2 p. 274
library(nnet)
modt8.2 <- multinom(place3 ~ viol, data = aps)
summary(modt8.2)
exp(coef(modt8.2)[, "violYes"])
t(exp(confint(modt8.2)["violYes", ,]))
## To test differences between b_2 and b_1 we need the estimated variance
## covariance matrix for the fitted model (Table 8.3 p. 274). 
vcov(modt8.2) # 'raw'
## To have exactly the same output as the text we need to rearrange just a
## minimum
VarCovM <- vcov(modt8.2)[c(2, 1, 4, 3), c(2, 1, 4, 3)]
VarCovM[upper.tri(VarCovM)] <- NA
VarCovM
## Testing against null model. 
modt8.2Null <- multinom(place3 ~ 1, data = aps)
anova(modt8.2, modt8.2Null, test = "Chisq")

aplore3 documentation built on May 2, 2019, 8:24 a.m.