R: Data on Mental Health and Socioeconomic Status

mentalHealth {gnm} | R Documentation

## Data on Mental Health and Socioeconomic Status

### Description

A 2-way contingency table from a sample of residents of Manhattan. Classifying variables are child's mental impairment (`MHS`) and parents' socioeconomic status (`SES`).

### Usage

    mentalHealth

### Format

A data frame with 24 observations on the following 3 variables.

`count`

  a numeric vector

`SES`

  an ordered factor with levels `A` < `B` < `C` < `D` < `E` < `F`

`MHS`

  an ordered factor with levels `well` < `mild` < `moderate` < `impaired`

### Source

From Agresti (2002, p381); originally in Srole et al. (1978, p289).

### References

Agresti, A. (2002). Categorical Data Analysis (2nd edn). New York: Wiley.

Srole, L, Langner, T. S., Michael, S. T., Opler, M. K. and Rennie, T. A. C. (1978), Mental Health in the Metropolis: The Midtown Manhattan Study. New York: NYU Press.

### Examples

    set.seed(1)

    ##  Goodman Row-Column association model fits well (deviance 3.57, df 8)
    mentalHealth$MHS <- C(mentalHealth$MHS, treatment)
    mentalHealth$SES <- C(mentalHealth$SES, treatment)
    RC1model <- gnm(count ~ SES + MHS + Mult(SES, MHS),
                    family = poisson, data = mentalHealth)
    ## Row scores and column scores are both unnormalized in this
    ## parameterization of the model

    ## The scores can be normalized as in Agresti's eqn (9.15):
    rowProbs <- with(mentalHealth, tapply(count, SES, sum) / sum(count))
    colProbs <- with(mentalHealth, tapply(count, MHS, sum) / sum(count))
    mu <- getContrasts(RC1model, pickCoef(RC1model, "[.]SES"),
                       ref = rowProbs, scaleRef = rowProbs,
                       scaleWeights = rowProbs)
    nu <- getContrasts(RC1model, pickCoef(RC1model, "[.]MHS"),
                       ref = colProbs, scaleRef = colProbs,
                       scaleWeights = colProbs)
    all.equal(sum(mu$qv[,1] * rowProbs), 0)
    all.equal(sum(nu$qv[,1] * colProbs), 0)
    all.equal(sum(mu$qv[,1]^2 * rowProbs), 1)
    all.equal(sum(nu$qv[,1]^2 * colProbs), 1)

[Package gnm version 1.0-8 Index]