# Linear regression, prediction, and survey weighting In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

options(width=100)  # width of output


We use the api dataset from package survey to illustrate estimation of a population mean from a sample using a linear regression model. First let's estimate the population mean of the academic performance indicator 2000 from a simple random sample, apisrs. Using package survey's GREG estimator [@Sarndal1992], we find

library(survey)
data(api)
# define the regression model
model <- api00 ~ ell + meals + stype + hsg + col.grad + grad.sch
# compute corresponding population totals
XpopT <- colSums(model.matrix(model, apipop))
N <- XpopT[["(Intercept)"]]  # population size
# create the survey design object
des <- svydesign(ids=~1, data=apisrs, weights=~pw, fpc=~fpc)
# compute the calibration or GREG estimator
cal <- calibrate(des, formula=model, population=XpopT)
svymean(~ api00, des)  # equally weighted estimate
svymean(~ api00, cal)  # GREG estimate


The true population mean in this case can be obtained from the apipop dataset:

mean(apipop$api00)  Note that the GREG estimate is more accurate than the simple equally weighted estimate, which is also reflected by the smaller estimated standard error of the former. We can run the same linear model using package mcmcsae. In the next code chunk, function create_sampler sets up a sampler function that is used as input to function MCMCsim, which runs a simulation to obtain draws from the posterior distribution of the model parameters. By default three chains with independently generated starting values are run over 1250 iterations with the first 250 discarded as burnin. As the starting values for the MCMC simulation are randomly generated, we set a random seed for reproducibility. The results of the simulation are subsequently summarized, and the DIC model criterion is computed. The simulation summary shows several statistics for the model parameters, including the posterior mean, standard error, quantiles, as well as the Rhat convergence diagnostic. library(mcmcsae) set.seed(21) sampler <- create_sampler(model, data=apisrs) sim <- MCMCsim(sampler, verbose=FALSE) (summ <- summary(sim)) compute_DIC(sim)  The output of function MCMCsim is an object of class mcdraws. The package provides methods for the generic functions summary, plot, predict, residuals and fitted for this class. ## Prediction To compute a model-based estimate of the population mean, we need to predict the values of the target variable for the unobserved units. Let$U$denote the set of population units,$s \subset U$the set of sample (observed) units, and let$y_i$denote the value of the variable of interest taken by the$i$th unit. The population mean of the variable of interest is $$\bar{Y} = \frac{1}{N}\sum_{i=1}^N y_i = \frac{1}{N}\left(\sum_{i\in s} y_i + \sum_{i\in U\setminus s} y_i \right)\,.$$ Posterior draws for$\bar{Y}$can be obtained by generating draws for the non-sampled population units, summing them and adding the sample sum. This is done in the next code chunk, where method predict is used to generate draws from the posterior predictive distribution for the new, unobserved, units. m <- match(apisrs$cds, apipop$cds) # population units in the sample # use only a sample of 250 draws from each chain predictions <- predict(sim, newdata=apipop[-m, ], iters=sample(1:1000, 250), show.progress=FALSE) str(predictions) samplesum <- sum(apisrs$api00)
summary(transform_dc(predictions, fun = function(x) (samplesum + sum(x))/N))


The result for the population mean can also be obtained directly (which is more efficient memory wise) by supplying the appropriate aggregation function to the prediction method:

summary(predict(sim, newdata=apipop[-m, ], fun=function(x, p) (samplesum + sum(x))/N,
show.progress=FALSE)
)


For any linear model one can obtain the same result more efficiently by precomputing covariate population totals. Posterior draws for $\bar{Y}$ are then computed as

$$\bar{Y}_r = \frac{1}{N} \left( n\bar{y} + \beta_r'(X - n\bar{x}) + e_r\right)\,,$$

where $e_r \sim {\cal N}(0, (N-n)\sigma_r^2)$, representing the sum of $N-n$ independent normal draws. The code to do this is

n <- nrow(apisrs)
XsamT <- colSums(model.matrix(model, apisrs))
XpopR <- matrix(XpopT - XsamT, nrow=1)
predictions <- predict(sim, X=list(reg1=XpopR), var=N-n, fun=function(x, p) (samplesum + x)/N,
show.progress=FALSE)
summary(predictions)


## Weights

To compute weights corresponding to the population total:

sampler <- create_sampler(model, data=apisrs,
linpred=list(reg1=matrix(XpopT/N, nrow=1)),
compute.weights=TRUE)
sim <- MCMCsim(sampler, verbose=FALSE)
plot(weights(cal)/N, weights(sim)); abline(0, 1)
sum(weights(sim) * apisrs\$api00)
summary(sim, "linpred_")


Note the small difference between the weighted sample sum of the target variable and the posterior mean of the linear predictor. This is due to Monte Carlo error; the weighted sum is exact for the simple linear regression case.

## Outliers

One possible way to deal with outliers is to use a Student-t sampling distribution, which has fatter tails than the normal distribution. In the next example, the formula.V argument is used to add local variance parameters with inverse chi-squared distributions. The marginal sampling distribution then becomes Student-t. Here the degrees of freedom parameter is modeled, i.e. assigned a prior distribution and inferred from the data.

sampler <- create_sampler(model, formula.V=~vfac(prior=pr_invchisq(df="modeled")),
linpred=list(reg1=matrix(XpopR/N, nrow=1)),
data=apisrs, compute.weights=TRUE)
sim <- MCMCsim(sampler, n.iter=5000, burnin=1000, verbose=FALSE)
(summ <- summary(sim))
plot(sim, "vfac1_df")
acceptance_rates(sim)
compute_DIC(sim)
predictions <- predict(sim, newdata=apipop[-m, ], show.progress=FALSE,
fun=function(x, p) (samplesum + sum(x))/N)
summary(predictions)
plot(weights(cal)/N, weights(sim)); abline(0, 1)
summary(get_means(sim, "Q_")[["Q_"]])


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mcmcsae documentation built on Oct. 10, 2021, 5:07 p.m.