How to understand output from R's polr function (ordered logistic regression)? I am new to R, ordered logistic regression, and polr.
The "Examples" section at the bottom of the help page for polr (that fits a logistic or probit regression model to an ordered factor response) shows
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
pr <- profile(house.plr)
plot(pr)
pairs(pr)



*

*What information does pr contain?  The help page on profile is
generic, and gives no guidance for polr.

*What is plot(pr) showing?  I see six graphs. Each has an X axis that is
numeric, although the label is an indicator variable (looks like an input variable that is an indicator for an ordinal value).  Then the Y axis
is "tau" which is completely unexplained.

*What is pairs(pr) showing?  It looks like a plot for each pair of input
variables, but again I see no explanation of the X or Y axes.

*How can one understand if the model gave a good fit?
summary(house.plr) shows Residual Deviance 3479.149 and AIC (Akaike
Information Criterion?) of 3495.149.  Is that good?  In the case those
are only useful as relative measures (i.e. to compare to another model
fit), what is a good absolute measure?  Is the residual deviance approximately chi-squared distributed?  Can one use "% correctly predicted" on the original data or some cross-validation?  What is the easiest way to do that?

*How does one apply and interpret anova on this model?  The docs say "There are methods for the standard model-fitting functions, including predict, summary, vcov, anova."  However, running anova(house.plr) results in anova is not implemented for a single "polr" object

*How does one interpret the t values for each coefficient?  Unlike some
model fits, there are no P values here.
I realize this is a lot of questions, but it makes sense to me to ask as one bundle ("how do I use this thing?") rather than 7 different questions.  Any information appreciated.
 A: I have greatly enjoyed the conversation here, however I feel that the answers did not correctly address all the (very good) components to the question you put forth. The second half of the example page for polr is all about profiling. A good technical reference here is Venerables and Ripley who discuss profiling and what it does. This is a critical technique when you step beyond the comfort zone of fitting exponential family models with full likelihood (regular GLMs). 
The key departure here is the use of categorical thresholds. You will notice POLR doesn't estimate a usual intercept term. Rather, there are $k-1$ nuisance parameters: thresholds for which the fitted risk tends to fall in a certain cumulative of the $k$ possible categories. Since these thresholds are never jointly estimated, their covariance with model parameters is unknown. Unlike GLMs we cannot "perturb" a coefficient by an amount and be certain of how it might affect other estimates. We use profiling to do this accounting for the nuisance thresholds. Profiling is an immense subject, but basically the goal is robustly measuring the covariance of regression coefficients when the model is maximizing an irregular likelihood, like with lmer, nls, polr, and glm.nb.
The help page for ?profile.glm should be of some use as polr objects are essentially GLMs (plus the categorical thresholds). Lastly, you can actually peak at the source code, if it's of any use, using getS3method('profile', 'polr'). I use this getS3method function a lot because, while R seems to insist many methods should be hidden, one can learn surprisingly much about implementation and methods by reviewing code.

•What information does pr contain? The help page on profile is
  generic, and gives no guidance for polr.

pr is a profile.polr, profile object (inherited class profile). There's an entry for each covariate. The profiler loops over each covariate, recalculates the optimal model fit with that covariate fixed to some slightly different amount. The output shows the covariate's fixed value measured as a scaled "z-score" difference from its estimated value and the resulting fixed effects in other covariates. For instance, if you look at pr$InflMedium, you'll note that, when "z" is 0, the other fixed effects are the same as are found in the original fit. 

•What is plot(pr) showing? I see six graphs. Each has an X axis that
  is numeric, although the label is an indicator variable (looks like an
  input variable that is an indicator for an ordinal value). Then the Y
  axis is "tau" which is completely unexplained.

Again, ?plot.profile gives the description. The plot roughly shows how the regression coefficients covary. tau is the scaled difference, the z score before, so it's 0 value gives the optimal fit coefficients, depicted with a tick mark. You wouldn't tell for this fit is so well behaved, but those "lines" are actually splines. If the likelihood were very irregularly behaved at the optimal fit, you would observe strange and unpredictable behavior in the plot. This would behoove you to estimate the output using a more robust error estimation (bootstrap/jackknife), to calculate CIs using method='profile', to recode variables, or to perform other diagnostics.

•What is pairs(pr) showing? It looks like a plot for each pair of
  input variables, but again I see no explanation of the X or Y axes.

The help file says: "The pairs method shows, for each pair of parameters x and y, two curves intersecting at the maximum likelihood estimate, which give the loci of the points at which the tangents to the contours of the bivariate profile likelihood become vertical and horizontal, respectively. In the case of an exactly bivariate normal profile likelihood, these two curves would be straight lines giving the conditional means of y|x and x|y, and the contours would be exactly elliptical." Basically, they again help you to visualize the confidence ellipses. Non-orthogonal axes indicate highly covariable measures, such as InfMedium and InfHigh which are intuitively very related. Again, irregular likelihoods would lead to images that are quite baffling here.

•How can one understand if the model gave a good fit?
  summary(house.plr) shows Residual Deviance 3479.149 and AIC (Akaike
  Information Criterion?) of 3495.149. Is that good? In the case those
  are only useful as relative measures (i.e. to compare to another model
  fit), what is a good absolute measure? Is the residual deviance
  approximately chi-squared distributed? Can one use "% correctly
  predicted" on the original data or some cross-validation? What is the
  easiest way to do that?

One assumption that is good to assess is the proportional odds assumption. This is reflected somewhat in the global test (which assesses polr against a saturated loglinear model). A limitation here is that with large data, global tests always fail. As a result, using graphics and inspecting estimates (betas) and precision (SEs) for the loglinear model and polr fit is a good idea. If they massively disagree, something is perhaps wrong. 
With ordered outcomes, it is hard to define percent agreement. How will you choose a classifier based on the model, and if you do how will you suss poor performance from a poor classifier. mode is a bad choice. If I have 10 category logits and my prediction is always but one category off, perhaps that is not a bad thing. Further, my model may correctly predict a 40% chance of a 0 response, but also 20% chances of 8, 9, 10. So if I observe 9 is that good or bad? If you must measure agreement use a weighted kappa, or even MSE. The loglinear model will always produce the best agreement. That is not what the POLR does.

•How does one apply and interpret anova on this model? The docs say
  "There are methods for the standard model-fitting functions, including
  predict, summary, vcov, anova." However, running anova(house.plr)
  results in anova is not implemented for a single "polr" object

You can test nested models with waldtest and lrtest in the lmtest package in R. This is equivalent to ANOVA. The interpretation is exactly the same as with GLMs.

•How does one interpret the t values for each coefficient? Unlike some
  model fits, there are no P values here.

Again, unlike linear models, the POLR model is capable of having issues with irregular likelihood so inference based on the Hessian can be very unstable. It is analogous to fitting mixed models, see for instance the helpfile on confint.merMod for the lme4 package. Here, the assessments made with profiling show the covariance is well behaved. The programmers would have done this by default, except that profiling can be computationally very intensive, and thus they leave it to your hands. If you must see the Wald based inference, use coeftest(house.plr) from the lrtest package.
A: I would suggest you look at books on categorical data analysis (cf. Alan Agresti's Categorical Data Analysis, 2002) for better explanation and understanding of ordered logistic regression. All the questions that you ask are basically answered by a few chapters in such books. If you are only interested in R related examples, Extending Linear Models in R by Julian Faraway (CRC Press, 2008) is a great reference.
Before I answer your questions, ordered logistic regression is a case of multinomial logit models in which the categories are ordered. Suppose we have $J$ ordered categories and that for individual $i$, with ordinal response $Y_i$,
$p_{ij}=P(Yi=j)$ for $j=1,..., J$. With an ordered response, it is often easier to work with the cumulative probabilities, $\gamma_{ij}=P(Y_i \le j)$. The cumulative probabilities are increasing and invariant to combining adjacent categories. Furthermore, $\gamma_{iJ}=1$, so we need only model $J–1$ probabilities.
Now we want to link $\gamma_{ij}$s to covariates $x$. In your case, Sat has 3 ordered levels: low, medium, high. It makes more sense to treat them as ordered rather than unordered. The remaining variables are your covariates. The specific model that you are considering is the proportional odds model and is mathematically equivalent to:
$$\mbox{logit } \gamma_j(x_i) = \theta_j - \beta^T x_i, j = 1 \ldots J-1$$
$$\mbox{where }\gamma_j(x_i)=P(Y_i \le j | x_i)$$ 
It is so called because the relative odds for $Y \le j$ comparing $x_1$ and $x_2$ are:
$$\left(\frac {\gamma_j(x_1)}{1-\gamma_j(x_1)}\right) / \left(\frac {\gamma_j(x_2)}{1-\gamma_j(x_2)}\right)=\exp(-\beta^T (x_1-x_2))$$
Notice, the above expression does not depend on $j$. Of course, the assumption of proportional odds does need to be checked for a given dataset.
Now, I will answer some (1, 2, 4) questions.

How can one understand if the model
  gave a good fit? summary(house.plr)
  shows Residual Deviance 3479.149 and
  AIC (Akaike Information Criterion?) of
  3495.149. Is that good? In the case those are only useful as relative
  measures (i.e. to compare to another
  model fit), what is a good absolute
  measure? Is the residual deviance
  approximately chi-squared distributed?
  Can one use "% correctly predicted" on
  the original data or some
  cross-validation? What is the easiest
  way to do that?

A model fit by polr is a special glm, so all the assumptions that hold for a traditional glm hold here. If you take care of the parameters properly, you can figure out the distribution. Specifically, to test the if the model is good or not you may want to do a goodness of fit test, which test the following null (notice this is subtle, mostly you want to reject the null, but here you don't want to reject it to get a good fit):
$$H_o: \mbox{ current model is good enough }$$
You would use the chi-square test for this. The p-value is obtained as:
1-pchisq(deviance(house.plr),df.residual(house.plr))

Most of the time you'd hope to obtain a p-value greater than 0.05 so that you don't reject the null to conclude that the model is good fit (philosophical correctness is ignored here).
AIC should be high for a good fit at the same time you don't want to have a large number of parameters. stepAIC is a good way to check this. 
Yes, you can definitely use cross validation to see if the predictions hold. See predict function (option: type = "probs") in ?polr. All you need to take care of is the covariates.

What information does pr contain? The
  help page on profile is generic, and
  gives no guidance for polr

As pointed by @chl and others, pr contains all the information needed for obtaining CIs and other likelihood related information of the polr fit. All glms are fit using iteratively weighted least square estimation method for the log likelihood. In this optimization you obtain a lot of information (please see the references) which will be needed for calculating Variance Covariance Matrix, CI, t-value etc. It includes all of it.

How does one interpret the t values for each coefficient? Unlike some model >fits, there are no P values here.

Unlike normal linear model (special glm) other glms are don't have the nice t-distribution for the regression coefficients. Therefore all you can get is the parameter estimates and their asymptotic variance covariance matrix using the max-likelihood theory. Therefore:
$$\text{Variance}(\hat \beta) = (X^T W X)^{-1}\hat \phi$$
Estimate divided by its standard error is what BDR and WV call t-value (I am assuming MASS convention here). It is equivalent to t-value from normal linear regression but does not follow a t-distribution. Using CLT, it is asymptotically normally distributed. But they prefer not to use this approx (I guess), hence no p-values. (I hope I am not wrong, and if I am, I hope BDR is not on this forum. I further hope, someone will correct me if I am wrong.)
A: To 'test' (i.e., evaluate) the proportional odds assumption in R, you can use residuals.lrm() in Frank Harrell Jr.'s Design package. If you type ?residuals.lrm , there is a quick-to-replicate example of how Frank Harrell recommends evaluating the proportional odds assumption (i.e., visually, rather than by a push-button test). Design estimates ordered logistic regressions using lrm(), which you can substitute for polr() from MASS. 
For a more formal example of how to visually test the proportional odds assumption in R, see:
Paper: Ordinal Response Regression Models in Ecology 
Author(s): Antoine Guisan and Frank E. Harrell 
Source: Journal of Vegetation Science, Vol. 11, No. 5 (Oct., 2000), pp. 617-626
