Predicting ordered logit in R I'm trying to do an ordered logit regression.  I'm running the model like so (just a dumb little model estimating number of firms in a market from income and population measures).  My question is about predictions.
nfirm.opr<-polr(y~pop0+inc0, Hess = TRUE)
pr_out<-predict(nfirm.opr)

When I run predict (which I'm trying to use to get the predicted y), the outputs are either 0, 3, or 27, which in no way reflects what should seem to be the prediction based upon my manual predictions from the coefficient estimates and intercepts.  Does anyone know how get "accurate" predictions for my ordered logit model?
EDIT
To clarify my concern, my response data has observations across all the levels
>head(table(y))
y
0  1  2  3  4  5 
29 21 19 27 15 16 

where as my predict variable seems to be bunching up
> head(table(pr_out))
pr_out
0     1   2   3   4   5 
117   0   0 114   0   0 

 A: To manually verify the predictions derived from using polr() from package MASS, assume a situation with a categorical dependent variable $Y$ with ordered categories $1, \ldots, g, \ldots, k$ and predictors $X_{1}, \ldots, X_{j}, \ldots, X_{p}$. polr() assumes the proportional odds model
$$
\text{logit}(p(Y \leqslant g)) = \ln \frac{p(Y \leqslant g)}{p(Y > g)} = \beta_{0_g} - (\beta_{1} X_{1} + \dots + \beta_{p} X_{p})
$$
For possible choices implemented in other functions, see this answer. The logistic function is the inverse of the logit-function, so the predicted probabilities $\hat{p}(Y \leqslant g)$ are
$$
\hat{p}(Y \leqslant g) = \frac{e^{\hat{\beta}_{0_{g}} - (\hat{\beta}_{1} X_{1} + \dots + \hat{\beta}_{p} X_{p})}}{1 + e^{\hat{\beta}_{0_{g}} - (\hat{\beta}_{1} X_{1} + \dots + \hat{\beta}_{p} X_{p})}}
$$
The predicted category probabilities are $\hat{P}(Y=g) = \hat{P}(Y \leq g) - \hat{P}(Y \leq g-1)$. Here is a reproducible example in R with two predictors $X_{1}, X_{2}$. For an ordinal $Y$ variable, I cut a simulated continuous variable into 4 categories.
set.seed(1.234)
N     <- 100                                    # number of observations
X1    <- rnorm(N, 5, 7)                         # predictor 1
X2    <- rnorm(N, 0, 8)                         # predictor 2
Ycont <- 0.5*X1 - 0.3*X2 + 10 + rnorm(N, 0, 6)  # continuous dependent variable
Yord  <- cut(Ycont, breaks=quantile(Ycont), include.lowest=TRUE,
             labels=c("--", "-", "+", "++"), ordered=TRUE)    # ordered factor

Now fit the proportional odds model using polr() and get the matrix of predicted category probabilities using predict(polr(), type="probs").
> library(MASS)                              # for polr()
> polrFit <- polr(Yord ~ X1 + X2)            # ordinal regression fit
> Phat    <- predict(polrFit, type="probs")  # predicted category probabilities
> head(Phat, n=3)
         --         -         +        ++
1 0.2088456 0.3134391 0.2976183 0.1800969
2 0.1967331 0.3068310 0.3050066 0.1914293
3 0.1938263 0.3051134 0.3067515 0.1943088

To manually verify these results, we need to extract the parameter estimates, from these calculate the predicted logits, from these logits calculate the predicted probabilities $\hat{p}(Y \leqslant g)$, and then bind the predicted category probabilities to a matrix.
ce <- polrFit$coefficients         # coefficients b1, b2
ic <- polrFit$zeta                 # intercepts b0.1, b0.2, b0.3
logit1 <- ic[1] - (ce[1]*X1 + ce[2]*X2)
logit2 <- ic[2] - (ce[1]*X1 + ce[2]*X2)
logit3 <- ic[3] - (ce[1]*X1 + ce[2]*X2)
pLeq1  <- 1 / (1 + exp(-logit1))   # p(Y <= 1)
pLeq2  <- 1 / (1 + exp(-logit2))   # p(Y <= 2)
pLeq3  <- 1 / (1 + exp(-logit3))   # p(Y <= 3)
pMat   <- cbind(p1=pLeq1, p2=pLeq2-pLeq1, p3=pLeq3-pLeq2, p4=1-pLeq3)  # matrix p(Y = g)

Compare to the result from polr().
> all.equal(pMat, Phat, check.attributes=FALSE)
[1] TRUE

For the predicted categories, predict(polr(), type="class") just picks - for each observation - the category with the highest probability.
> categHat <- levels(Yord)[max.col(Phat)]   # category with highest probability
> head(categHat)
[1] "-"  "-"  "+"  "++" "+"  "--"

Compare to result from polr().
> facHat <- predict(polrFit, type="class")  # predicted categories
> head(facHat)
[1] -  -  +  ++ +  --
Levels: -- - + ++

> all.equal(factor(categHat), facHat, check.attributes=FALSE)  # manual verification
[1] TRUE

