Package 'aplore3' reference manual

Title:	Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd Ed., 2013)
Description:	An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
Authors:	Luca Braglia [aut, cre]
Maintainer:	Luca Braglia <[email protected]>
License:	GPL-3
Version:	0.9
Built:	2024-11-11 02:58:23 UTC
Source:	https://github.com/lbraglia/aplore3

Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd ed., 2013)

Description

This package is an unofficial companion to the textbook "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013).

Details

It includes all the datasets used in the book, both for easy reproducibility and algorithms benchmarking purposes.

Some analysis proposed in the text are reproduced in the examples, in order to provide data testing and code demos at the same time.

The vignette includes all the examples (with graphics too); therefore is organized per-dataset.

Datasets and variables have lower-case name with respect to the original sources. Categorical data were packaged as factor.

Regarding data coding, help pages list the internal/factor representation of the data (eg 1: No, 2: Yes), not the original one (eg 0: No, 1: Yes). This is intended to allow easier/safer recoding based on as.integer, especially for multinomial variables.

Source

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

APS data

Description

aps dataset.

Usage

aps
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")

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")

BURN_EVAL_1 data

Description

burn_eval_1 dataset.

Usage

burn_eval_1
burn_eval_1

Format

A data.frame with 500 rows and 9 variables: the covariate are the same as those from burn1000.

Source

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

Examples

head(burn_eval_1, n = 10)
summary(burn_eval_1)
head(burn_eval_1, n = 10)
summary(burn_eval_1)

BURN_EVAL_2 data

Description

burn_eval_2 dataset.

Usage

burn_eval_2
burn_eval_2

Format

A data.frame with 500 rows and 9 variables: the covariate are the same as those from burn1000.

Source

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

Examples

head(burn_eval_2, n = 10)
summary(burn_eval_2)
head(burn_eval_2, n = 10)
summary(burn_eval_2)

BURN1000 data

Description

burn1000 dataset.

Usage

burn1000
burn1000

Format

A data.frame with 1000 rows and 9 variables:

id: Identification code (1 - 1000)
facility: Burn facility (1 - 40)
death: Hospital discharge status (1: Alive, 2: Dead)
age: Age at admission (Years)
gender: Gender (1: Female, 2: Male)
race: Race (1: Non-White, 2: White)
tbsa: Total burn surface area (0 - 100%)
inh_inj: Burn involved inhalation injury (1: No, 2: Yes)
flame: Flame involved in burn injury (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(burn1000, n = 10)
summary(burn1000)

## Table 3.15 p. 80
summary(mod3.15 <- glm(death ~ tbsa + inh_inj + age + gender + flame + race,
                       family = binomial, data = burn1000 ))
head(burn1000, n = 10)
summary(burn1000)

## Table 3.15 p. 80
summary(mod3.15 <- glm(death ~ tbsa + inh_inj + age + gender + flame + race,
                       family = binomial, data = burn1000 ))

BURN13M data

Description

burn13m dataset.

Usage

burn13m
burn13m

Format

A data.frame with 388 rows and 11 variables: the covariate are the same as those from burn1000 with the addition of

pair: Pair Identification Code (1-119)
pairid: Subject Identification Code within pair (1-4)

Source

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

Examples

head(burn13m, n = 10)
summary(burn13m)
head(burn13m, n = 10)
summary(burn13m)

CHDAGE data

Description

chdage dataset.

Usage

chdage
chdage

Format

A data.frame with 100 rows and 4 variables:

id: Identification code (1 - 100)
age: Age (Years)
agegrp: Age group (1: 20-39, 2: 30-34, 3: 35-39, 4: 40-44, 5: 45-49, 6: 50-54, 7: 55-59, 8: 60-69)
chd: Presence of CHD (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(chdage,  n = 10)
summary(chdage)

## Figure 1.1 p. 5
plot(as.integer(chd)-1 ~ age,
     pch = 20,
     main = "Figure 1.1 p. 5",
     ylab = "Coronary heart disease",
     xlab = "Age (years)",
     data = chdage)

## Table 1.2
with(chdage, addmargins(table(agegrp)))
with(chdage, addmargins(table(agegrp, chd)))
(Means <- with(chdage, tapply(as.integer(chd)-1, list(agegrp), mean)))

## Figure 1.2 p. 6
midPoints <- c(24.5, seq(32, 57, 5), 64.5)
plot(midPoints, Means, pch = 20,
     ylab = "Coronary heart disease (mean)",
     xlab = "Age (years)", ylim = 0:1,
     main = "Figure 1.2 p. 6")
lines(midPoints, Means)

## Table 1.3
summary( mod1.3 <- glm( chd ~ age, family = binomial, data = chdage ))

## Table 1.4
vcov(mod1.3)

## Computing OddsRatio and confidence intervals for age ...
exp(coef(mod1.3))[-1]
exp(confint(mod1.3))[-1, ]
head(chdage,  n = 10)
summary(chdage)

## Figure 1.1 p. 5
plot(as.integer(chd)-1 ~ age,
     pch = 20,
     main = "Figure 1.1 p. 5",
     ylab = "Coronary heart disease",
     xlab = "Age (years)",
     data = chdage)

## Table 1.2
with(chdage, addmargins(table(agegrp)))
with(chdage, addmargins(table(agegrp, chd)))
(Means <- with(chdage, tapply(as.integer(chd)-1, list(agegrp), mean)))

## Figure 1.2 p. 6
midPoints <- c(24.5, seq(32, 57, 5), 64.5)
plot(midPoints, Means, pch = 20,
     ylab = "Coronary heart disease (mean)",
     xlab = "Age (years)", ylim = 0:1,
     main = "Figure 1.2 p. 6")
lines(midPoints, Means)

## Table 1.3
summary( mod1.3 <- glm( chd ~ age, family = binomial, data = chdage ))

## Table 1.4
vcov(mod1.3)

## Computing OddsRatio and confidence intervals for age ...
exp(coef(mod1.3))[-1]
exp(confint(mod1.3))[-1, ]

GLOW_BONEMED data

Description

glow_bonemed dataset.

Usage

glow_bonemed
glow_bonemed

Format

A data.frame with 500 rows and 18 variables: the covariate are the same as those from glow500 with the addition of

bonemed: Bone medications at enrollment (1: No, 2: Yes)
bonemed_fu: Bone medications at follow-up (1: No, 2: Yes)
bonetreat: Bone medications both at enrollment and follow-up (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(glow_bonemed, n = 10)
summary(glow_bonemed)
head(glow_bonemed, n = 10)
summary(glow_bonemed)

GLOW_MIS_COMP data

Description

glow_mis_comp dataset.

Usage

glow_mis_comp
glow_mis_comp

Format

A data.frame with 500 rows and 10 variables: the covariate are the same as those from glow500, without bmi, premeno, armassist, smoke and fracscore.

Source

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

Examples

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

GLOW_MIS_WMISSING data

Description

glow_mis_wmissing dataset.

Usage

glow_mis_wmissing
glow_mis_wmissing

Format

A data.frame with 500 rows and 10 variables: the covariate are the same as those from glow500, without bmi, premeno, armassist, smoke and fracscore.

Source

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

Examples

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

GLOW_RAND data

Description

glow_rand dataset.

Usage

glow_rand
glow_rand

Format

A data.frame with 500 rows and 15 variables: the covariate are the same as those from glow500.

Source

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

Examples

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

GLOW11M data

Description

glow11m dataset.

Usage

glow11m
glow11m

Format

A data.frame with 238 rows and 16 variables: the covariate are the same as those from glow500 with the addition of

pair: Pair Identification Code (1-119)

Source

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

Examples

head(glow11m, n = 10)
summary(glow11m)

## Table 7.2 p. 252
library(survival)
mod7.2 <- clogit(as.numeric(fracture) ~ height + weight + bmi +
                 priorfrac + premeno + momfrac + armassist + raterisk +
                 strata(pair), data = glow11m)
summary(mod7.2)
head(glow11m, n = 10)
summary(glow11m)

## Table 7.2 p. 252
library(survival)
mod7.2 <- clogit(as.numeric(fracture) ~ height + weight + bmi +
                 priorfrac + premeno + momfrac + armassist + raterisk +
                 strata(pair), data = glow11m)
summary(mod7.2)

GLOW500 data

Description

glow500 dataset.

Usage

glow500
glow500

Format

A data.frame with 500 rows and 15 variables:

sub_id: Identification Code (1 - n)
site_id: Study Site (1 - 6)
phy_id: Physician ID code (128 unique codes)
priorfrac: History of Prior Fracture (1: No, 2: Yes)
age: Age at Enrollment (Years)
weight: Weight at enrollment (Kilograms)
height: Height at enrollment (Centimeters)
bmi: Body Mass Index (Kg/m^2)
premeno: Menopause before age 45 (1: No, 2: Yes)
momfrac: Mother had hip fracture (1: No, 2: Yes)
armassist: Arms are needed to stand from a chair (1: No, 2: Yes)
smoke: Former or current smoker (1: No, 2: Yes)
raterisk: Self-reported risk of fracture (1: Less than others of the same age, 2: Same as others of the same age, 3: Greater than others of the same age)
fracscore: Fracture Risk Score (Composite Risk Score)
fracture: Any fracture in first year (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(glow500, n = 10)
summary(glow500)

## Table 2.2 p. 39
summary(mod2.2 <- glm(fracture ~ age + weight + priorfrac +
                                 premeno + raterisk,
                      family = binomial,
                      data = glow500))

## Table 2.3 p. 40
summary(mod2.3 <- update(mod2.2, . ~ . - weight - premeno))

## Table 2.4 p. 44
vcov(mod2.3)

## Table 3.6 p. 58
contrasts(glow500$raterisk)

## Contrasts: Table 3.8 and 3.9 p. 60
contrasts(glow500$raterisk) <- matrix(c(-1,-1,1,0,0,1), byrow= TRUE, ncol = 2)
summary(mod3.9 <- glm(fracture ~ raterisk, family = binomial,
                      data = glow500))
# cleaning modified dataset ...
rm(glow500)

## Table 5.1 pg 160 - Hosmer-Lemeshow test (with vcdExtra package)
mod4.16 <- glm(fracture ~ age * priorfrac + height + momfrac * armassist +
                          I(as.integer(raterisk) == 3) ,
               family = binomial,
               data = glow500)
library(vcdExtra)
summary(HLtest(mod4.16))

## Table 5.3 p. 171 - Classification table
glow500$pred4.16 <- predict(mod4.16, type = "response")
with(glow500, addmargins(table( pred4.16 > 0.5, fracture)))

## Sensitivy, specificity, ROC (using pROC)
library(pROC)

## Figure 5.3 p. 177 - ROC curve (using pROC package)
print(roc4.16 <- roc(fracture ~ pred4.16, data = glow500))
plot(roc4.16, main = "Figure 5.3 p. 177")

## Table 5.8 p. 175
vars <- c("thresholds","sensitivities","specificities")
tab5.8 <- data.frame(roc4.16[vars])
## Now, for printing/comparison purposes, steps below in order to find
## threshold values most similar to those in the table
findIndex <- function(x, y) which.min( (x-y)^2 )
cutPoints <- seq(0.05, 0.75, by = 0.05)
tableIndex <- mapply(findIndex, y = cutPoints,
                     MoreArgs = list(x = roc4.16$thresholds))
## And finally, let's print a reasonable approximation of table 5.8
writeLines("\nTable 5.8 p. 175\n")
tab5.8[tableIndex, ]

## Figure 5.1 p. 175
plot(specificities ~ thresholds, xlim = c(0, 1), type = "l",
     xlab = "Probabilty cutoff", ylab = "Sensitivity/specificity",
     ylim = c(0, 1), data = tab5.8, main = "Figure 5.1 p. 175")
with(tab5.8, lines(thresholds, sensitivities, col = "red"))
legend(x = 0.75, y = 0.55, legend = c("Sensitivity", "Specificity"),
       lty = 1, col = c("red","black"))
abline(h = c(0, 1), col = "grey80", lty = "dotted")
head(glow500, n = 10)
summary(glow500)

## Table 2.2 p. 39
summary(mod2.2 <- glm(fracture ~ age + weight + priorfrac +
                                 premeno + raterisk,
                      family = binomial,
                      data = glow500))

## Table 2.3 p. 40
summary(mod2.3 <- update(mod2.2, . ~ . - weight - premeno))

## Table 2.4 p. 44
vcov(mod2.3)

## Table 3.6 p. 58
contrasts(glow500$raterisk)

## Contrasts: Table 3.8 and 3.9 p. 60
contrasts(glow500$raterisk) <- matrix(c(-1,-1,1,0,0,1), byrow= TRUE, ncol = 2)
summary(mod3.9 <- glm(fracture ~ raterisk, family = binomial,
                      data = glow500))
# cleaning modified dataset ...
rm(glow500)

## Table 5.1 pg 160 - Hosmer-Lemeshow test (with vcdExtra package)
mod4.16 <- glm(fracture ~ age * priorfrac + height + momfrac * armassist +
                          I(as.integer(raterisk) == 3) ,
               family = binomial,
               data = glow500)
library(vcdExtra)
summary(HLtest(mod4.16))

## Table 5.3 p. 171 - Classification table
glow500$pred4.16 <- predict(mod4.16, type = "response")
with(glow500, addmargins(table( pred4.16 > 0.5, fracture)))

## Sensitivy, specificity, ROC (using pROC)
library(pROC)

## Figure 5.3 p. 177 - ROC curve (using pROC package)
print(roc4.16 <- roc(fracture ~ pred4.16, data = glow500))
plot(roc4.16, main = "Figure 5.3 p. 177")

## Table 5.8 p. 175
vars <- c("thresholds","sensitivities","specificities")
tab5.8 <- data.frame(roc4.16[vars])
## Now, for printing/comparison purposes, steps below in order to find
## threshold values most similar to those in the table
findIndex <- function(x, y) which.min( (x-y)^2 )
cutPoints <- seq(0.05, 0.75, by = 0.05)
tableIndex <- mapply(findIndex, y = cutPoints,
                     MoreArgs = list(x = roc4.16$thresholds))
## And finally, let's print a reasonable approximation of table 5.8
writeLines("\nTable 5.8 p. 175\n")
tab5.8[tableIndex, ]

## Figure 5.1 p. 175
plot(specificities ~ thresholds, xlim = c(0, 1), type = "l",
     xlab = "Probabilty cutoff", ylab = "Sensitivity/specificity",
     ylim = c(0, 1), data = tab5.8, main = "Figure 5.1 p. 175")
with(tab5.8, lines(thresholds, sensitivities, col = "red"))
legend(x = 0.75, y = 0.55, legend = c("Sensitivity", "Specificity"),
       lty = 1, col = c("red","black"))
abline(h = c(0, 1), col = "grey80", lty = "dotted")

ICU data

Description

icu dataset.

Usage

icu
icu

Format

A data.frame with 200 rows and 21 variables:

id: Identification code (ID Number)
sta: Vital Status at hospital discharge (1: Lived, 2: Died)
age: Age (Years)
gender: Gender (1: Male, 2: Female)
race: Race (1: White, 2: Black, 3: Other)
ser: Service at ICU admission (1: Medical, 2: Surgical)
can: Cancer part of present problem (1: No, 2: Yes)
crn: History of chronic renal failure (1: No, 2: Yes)
inf: Infection probable at ICU admission (1: No, 2: Yes)
cpr: CPR prior to ICU admission (1: No, 2: Yes)
sys: Systolic blood pressure at ICU admission (mm Hg)
hra: Heart rate at ICU admission (Beats/min)
pre: Previous admission to an ICU within 6 months (1: No, 2: Yes)
type: Type of admission (1: Elective, 2: Emergency)
fra: Long bone, multiple, neck, single area, or hip fracture (1: No, 2: Yes)
po2: PO2 from initial blood gases (1: > 60, 2: <= 60)
ph: PH from initial blood gases (1: >= 7.25, 2: < 7.25)
pco: PCO2 from initial blood gases (1: <= 45, 2: > 45)
bic: Bicarbonate from initial blood gases (1: >= 18, 2: < 18)
cre: Creatinine from initial blood gases (1: <= 2.0, 2: > 2.0)
loc: Level of consciousness at ICU admission (1: No coma or deep stupor, 2: Deep stupor, 3: Coma)

Source

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

Examples

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

LOWBWT data

Description

lowbwt dataset.

Usage

lowbwt
lowbwt

Format

A data.frame with 189 rows and 11 variables:

id: Identification Code
low: Low birth weight (1: >= 2500, 2: < 2500 g)
age: Age of mother (Years)
lwt: Weight of mother at last menstrual period (Pounds)
race: Race (1: White, 2: Black, 3: Other)
smoke: Smoking status during pregnancy (1: No, 2: Yes)
ptl: History of premature labor (1: None, 2: One, 3: Two, etc)
ht: History of hypertension (1: No, 2: Yes)
ui: Presence of Uterine irritability (1: No, 2: Yes)
ftv: Number of physician visits during the first trimester (1: None, 2: One, 3: Two, etc)
bwt: Recorded birth weight (Grams)

Source

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

Examples

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

MYOPIA data

Description

myopia dataset.

Usage

myopia
myopia

Format

A data.frame with 618 rows and 18 variables:

id: Subject identifier (1-1503)
studyyear: Year subject entered the study (Year)
myopic: Myopia within the first five years of follow up (1: No, 2: Yes)
age: Age at first visit (Years)
gender: Gender (1: Male, 2: Female)
spheq: Spherical Equivalent Refraction (diopter)
al: Axial Length (mm)
acd: Anterior Chamber Depth (mm)
lt: Lens Thickness (mm)
vcd: Vitreous Chamber Depth (mm)
sporthr: How many hours per week outside of school the child spent engaging in sports/outdoor activities (Hours per week)
readhr: How many hours per week outside of school the child spent reading for pleasure (Hours per week)
comphr: How many hours per week outside of school the child spent playing video/computer games or working on the computer (Hours per week)
studyhr: How many hours per week outside of school the child spent reading or studying for school assignments (Hours per week)
tvhr: How many hours per week outside of school the child spent watching television (Hours per week)
diopterhr: Composite of near-work activities (Hours per week)
mommy: Was the subject's mother myopic? (1: No, 2: Yes)
dadmy: Was the subject's father myopic? (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(myopia, n = 10)
summary(myopia)
head(myopia, n = 10)
summary(myopia)

NHANES data

Description

nhanes dataset.

Usage

nhanes
nhanes

Format

A data.frame with 6482 rows and 21 variables:

id: Identification Code (1 - 6482)
gender: Gender (1: Male, 2: Female)
age: Age at Screening (Years)
marstat: Marital Status (1: Married, 2: Widowed, 3: Divorced, 4: Separated, 5: Never Married, 6: Living Together)
samplewt: Statistical Weight (4084.478 - 153810.3)
psu: Pseudo-PSU (1, 2)
strata: Pseudo-Stratum (1 - 15)
tchol: Total Cholesterol (mg/dL)
hdl: HDL-Cholesterol (mg/dL)
sysbp: Systolic Blood Pressure (mm Hg)
dbp: Diastolic Blood Pressure (mm Hg)
wt: Weight (kg)
ht: Standing Height (cm)
bmi: Body mass Index (Kg/m^2)
vigwrk: Vigorous Work Activity (1: Yes, 2: No)
modwrk: Moderate Work Activity (1: Yes, 2: No)
wlkbik: Walk or Bicycle (1: Yes, 2: No)
vigrecexr: Vigorous Recreational Activities (1: Yes, 2: No)
modrecexr: Moderate Recreational Activities (1: Yes, 2: No)
sedmin: Minutes of Sedentary Activity per Week (1: Yes, 2: No)
obese: BMI>35 (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(nhanes, n = 10)
summary(nhanes)
head(nhanes, n = 10)
summary(nhanes)

POLYPHARM data

Description

polypharm dataset.

Usage

polypharm
polypharm

Format

A data.frame with 3500 rows and 14 variables:

id: Subject ID (1 - 500)
polypharmacy: Outcome; taking drugs from more than three different classes (1: No, 2: Yes)
mhv4: Number of outpatient Mental Health Visits (1: none, 2: one to five, 3: six to fourteen, 4: greater than 14)
inptmhv3: Number of inpatient Mental Health Visits (1: none, 2: one, 3: more than one)
year: Year (2002 to 2008)
group: Group (1: Covered Families and Children - CFC, 2: Aged, Blind or Disabled - ABD, 3: Foster Care - FOS)
urban: Location (1: Urban, 2: Rural)
comorbid: Comorbidity (1: No, 2: Yes)
anyprim: Any primary diagnosis (bipolar, depression, etc.) (1: No, 2: Yes)
numprim: Number of primary diagnosis (1: none, 2: one, 3: more than one)
gender: Gender (1: Female, 2: Male)
race: Race (1: White, 2: Black, 3: Other)
ethnic: Ethnic category (1: Non-Hispanic, 2: Hispanic)
age: Age (Years and months, two decimal places)

Source

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

Examples

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

SCALE_EXAMPLE data

Description

scale_example dataset.

Usage

scale_example
scale_example

Format

A data.frame with 500 rows and 2 variables:

y: a dicotomic variable (say 1: No, 2: Yes)
x: a numeric variable

Source

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

Examples

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

Package 'aplore3'

Help Index

Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd ed., 2013)

Description

Details

Source

APS data

Description

Usage

Format

Source

Examples

BURN_EVAL_1 data

Description

Usage

Format

Source

Examples

BURN_EVAL_2 data

Description

Usage

Format

Source

Examples

BURN1000 data

Description

Usage

Format

Source

Examples

BURN13M data

Description

Usage

Format

Source

Examples

CHDAGE data

Description

Usage

Format

Source

Examples

GLOW_BONEMED data

Description

Usage

Format

Source

Examples

GLOW_MIS_COMP data

Description

Usage

Format

Source

Examples

GLOW_MIS_WMISSING data

Description

Usage

Format

Source

Examples

GLOW_RAND data

Description

Usage

Format

Source

Examples

GLOW11M data

Description

Usage

Format

Source

Examples

GLOW500 data

Description

Usage

Format

Source

Examples

ICU data

Description