Data on Mental Health and Socioeconomic Status
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
).
mentalHealth
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
From Agresti (2002, p381); originally in Srole et al. (1978, p289).
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.
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)
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