R: Bayesian Ordered Logistic or Probit Regression

bayespolr {arm} | R Documentation

## Bayesian Ordered Logistic or Probit Regression

### Description

Bayesian functions for ordered logistic or probit modeling with independent normal, t, or Cauchy prior distribution for the coefficients.

### Usage

    bayespolr(formula, data, weights, start,  ...,
        subset, na.action, contrasts = NULL,
        Hess = TRUE, model = TRUE,
        method = c("logistic", "probit", "cloglog", "cauchit"),
        drop.unused.levels=TRUE,
        prior.mean = 0,
        prior.scale = 2.5,
        prior.df = 1,
        prior.counts.for.bins = NULL,
        min.prior.scale=1e-12,
        scaled = TRUE,
        maxit = 100,
        print.unnormalized.log.posterior = FALSE)

### Arguments

`formula` | a formula expression as for regression models, of the form `response ~ predictors`. The response should be a factor (preferably an ordered factor), which will be interpreted as an ordinal response, with levels ordered as in the factor. A proportional odds model will be fitted. The model must have an intercept: attempts to remove one will lead to a warning and be ignored. An offset may be used. See the documentation of `formula` for other details.
`data` | an optional data frame in which to interpret the variables occurring in `formula`.
`weights` | optional case weights in fitting. Default to 1.
`start` | initial values for the parameters. This is in the format `c(coefficients, zeta)`
`...` | additional arguments to be passed to `optim`, most often a `control` argument.
`subset` | expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
`na.action` | a function to filter missing data.
`contrasts` | a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
`Hess` | logical for whether the Hessian (the observed information matrix) should be returned.
`model` | logical for whether the model matrix should be returned.
`method` | logistic or probit or complementary log-log or cauchit (corresponding to a Cauchy latent variable and only available in R >= 2.1.0).
`drop.unused.levels` | default `TRUE`, if `FALSE`, it interpolates the intermediate values if the data have integer levels.
`prior.mean` | prior mean for the coefficients: default is 0. Can be a vector of length equal to the number of predictors (not counting the intercepts). If it is a scalar, it is expanded to the length of this vector.
`prior.scale` | prior scale for the coefficients: default is 2.5. Can be a vector of length equal to the number of predictors (not counting the intercepts). If it is a scalar, it is expanded to the length of this vector.
`prior.df` | for t distribution: default is 1 (Cauchy). Set to `Inf` to get normal prior distributions. Can be a vector of length equal to the number of predictors (not counting the intercepts). If it is a scalar, it is expanded to the length of this vector.
`prior.counts.for.bins` | default is `NULL`, which will augment the data by giving each cut point a `1/levels(y)`. To use a noninformative prior, assign prior.counts.for.bins = 0. If it is a scalar, it is expanded to the number of levels of y.
`min.prior.scale` | Minimum prior scale for the coefficients: default is 1e-12.
`scaled` | if `scaled = TRUE`, then the prior distribution is rescaled. Can be a vector of length equal to the number of cutpoints (intercepts). If it is a scalar, it is expanded to the length of this vector.
`maxit` | integer giving the maximal number of IWLS iterations, default is 100. This can also be controlled by `control`.
`print.unnormalized.log.posterior` | display the unnormalized log posterior likelihood for bayesglm fit, default=`FALSE`

### Details

The program is a simple alteration of `polr` in `VR` version 7.2-31 that augments the loglikelihood with the log of the t prior distributions for the coefficients.

We use Student-t prior distributions for the coefficients. The prior distributions for the intercepts (the cutpoints) are set so they apply to the value when all predictors are set to their mean values.

If scaled=TRUE, the scales for the prior distributions of the coefficients are determined as follows: For a predictor with only one value, we just use `prior.scale`. For a predictor with two values, we use prior.scale/range(x). For a predictor with more than two values, we use prior.scale/(2*sd(x)).

### Value

See `polr` for details.

`prior.mean` | prior means for the cofficients.
`prior.scale` | prior scales for the cofficients.
`prior.df` | prior dfs for the cofficients.
`prior.counts.for.bins` | prior counts for the cutpoints.

### Author(s)

Andrew Gelman gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn; Maria Grazia Pittau grazia@stat.columbia.edu

### See Also

`bayesglm`, `polr`

### Examples

        M1 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
        display (M1)

        M2 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing,
            prior.scale=Inf, prior.df=Inf) # Same as M1
        display (M2)

        M3 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
        display (M3)

        M4 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing,
            prior.scale=2.5, prior.df=1)  # Same as M3
        display (M4)

        M5 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing,
            prior.scale=2.5, prior.df=7)
        display (M5)

        M6 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing,
            prior.scale=2.5, prior.df=Inf)
        display (M6)

        # Assign priors
        M7 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing,
            prior.mean=rep(0,6), prior.scale=rep(2.5,6), prior.df=c(1,1,1,7,7,7))
        display (M7)

        #### Another example
        y <- factor (rep (1:10,1:10))
        x <- rnorm (length(y))
        x <- x - mean(x)

        M8 <- polr (y ~ x)
        display (M8)

        M9 <- bayespolr (y ~ x,  prior.scale=Inf, prior.df=Inf, prior.counts.for.bins=0)
        display (M9) # same as M1

        M10 <- bayespolr (y ~ x,  prior.scale=Inf, prior.df=Inf, prior.counts.for.bins=10000)
        display (M10)

        #### Another example

        y <- factor (rep (1:3,1:3))
        x <- rnorm (length(y))
        x <- x - mean(x)

        M11 <- polr (y ~ x)
        display (M11)

        M12 <- bayespolr (y ~ x,  prior.scale=Inf, prior.df=Inf, prior.counts.for.bins=0)
        display (M12) # same as M1

        M13 <- bayespolr (y ~ x,  prior.scale=Inf, prior.df=Inf, prior.counts.for.bins=1)
        display (M13)

        M14 <- bayespolr (y ~ x,  prior.scale=Inf, prior.df=Inf, prior.counts.for.bins=10)
        display (M14)

[Package arm version 1.9-3 Index]