Linear regression, prediction, and survey weighting

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 (Särndal, Swensson, and Wretman 1992), 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
##         mean     SE
## api00 656.58 9.2497
svymean(~ api00, cal)  # GREG estimate
##         mean     SE
## api00 663.86 4.1942

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

mean(apipop$api00)
## [1] 664.7126

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 R-hat convergence diagnostic.

library(mcmcsae)
set.seed(1)
sampler <- create_sampler(model, data=apisrs)
sim <- MCMCsim(sampler, verbose=FALSE)
(summ <- summary(sim))
## llh_ :
##       Mean   SD t-value   MCSE q0.05  q0.5 q0.95 n_eff R_hat
## llh_ -1104 2.12    -520 0.0418 -1108 -1104 -1101  2581     1
## 
## sigma_ :
##        Mean   SD t-value   MCSE q0.05 q0.5 q0.95 n_eff R_hat
## sigma_ 60.5 3.11    19.4 0.0631  55.5 60.4    66  2433     1
## 
## reg1 :
##                Mean     SD t-value    MCSE    q0.05    q0.5    q0.95 n_eff R_hat
## (Intercept)  778.32 24.600   31.64 0.44912  737.311  778.33 818.5935  3000 1.000
## ell           -1.72  0.298   -5.79 0.00544   -2.210   -1.72  -1.2382  3000 1.000
## meals         -1.75  0.275   -6.36 0.00502   -2.209   -1.75  -1.3115  3000 1.000
## stypeH      -108.81 14.023   -7.76 0.25603 -131.403 -108.60 -86.1115  3000 1.002
## stypeM       -59.05 12.117   -4.87 0.22122  -78.968  -59.08 -39.2607  3000 0.999
## hsg           -0.70  0.415   -1.69 0.00758   -1.408   -0.70  -0.0101  3000 1.000
## col.grad       1.22  0.487    2.51 0.00889    0.421    1.22   2.0163  3000 1.000
## grad.sch       2.20  0.501    4.39 0.00937    1.402    2.19   3.0274  2859 1.000
compute_DIC(sim)
##         DIC       p_DIC 
## 2217.368326    8.910283

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 ⊂ U the set of sample (observed) units, and let yi denote the value of the variable of interest taken by the ith 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 Ȳ 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)
## List of 3
##  $ : num [1:250, 1:5994] 699 623 586 599 608 ...
##  $ : num [1:250, 1:5994] 704 672 619 746 692 ...
##  $ : num [1:250, 1:5994] 587 701 743 673 582 ...
##  - attr(*, "class")= chr "dc"
samplesum <- sum(apisrs$api00)
summary(transform_dc(predictions, fun = function(x) (samplesum + sum(x))/N))
##      Mean   SD t-value  MCSE q0.05 q0.5 q0.95 n_eff R_hat
## [1,]  664 4.12     161 0.151   657  664   671   750     1

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)
)
##      Mean   SD t-value  MCSE q0.05 q0.5 q0.95 n_eff R_hat
## [1,]  664 4.21     158 0.083   657  664   671  2565 0.999

For any linear model one can obtain the same result more efficiently by precomputing covariate population totals. Posterior draws for Ȳ 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)
##      Mean  SD t-value   MCSE q0.05 q0.5 q0.95 n_eff R_hat
## [1,]  664 4.2     158 0.0797   657  664   671  2782 0.999

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)
## [1] 663.8594
print(summary(sim, "linpred_"), digits=6)
## linpred_ :
##             Mean      SD t-value      MCSE   q0.05    q0.5   q0.95 n_eff    R_hat
## linpred_ 663.792 4.35517 152.415 0.0795141 656.689 663.682 671.264  3000 0.999854

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, burnin=1000, n.iter=5000, thin=2, verbose=FALSE)
(summ <- summary(sim))
## llh_ :
##       Mean   SD t-value  MCSE q0.05  q0.5 q0.95 n_eff R_hat
## llh_ -1080 8.14    -133 0.623 -1094 -1081 -1067   171  1.01
## 
## sigma_ :
##        Mean   SD t-value  MCSE q0.05 q0.5 q0.95 n_eff R_hat
## sigma_ 49.5 4.37    11.3 0.302  42.5 49.5  56.9   210  1.01
## 
## linpred_ :
##          Mean   SD t-value   MCSE q0.05 q0.5 q0.95 n_eff R_hat
## linpred_  643 3.89     165 0.0476   636  643   649  6689     1
## 
## reg1 :
##                 Mean     SD t-value    MCSE   q0.05     q0.5    q0.95 n_eff R_hat
## (Intercept)  793.897 26.247   30.25 0.42886  749.96  794.435 836.5747  3746     1
## ell           -1.487  0.363   -4.10 0.00768   -2.09   -1.494  -0.8790  2235     1
## meals         -2.079  0.360   -5.78 0.00930   -2.68   -2.072  -1.4895  1496     1
## stypeH      -105.353 12.887   -8.18 0.15612 -126.36 -105.405 -83.7746  6813     1
## stypeM       -56.827 10.959   -5.19 0.13320  -74.72  -56.762 -38.7692  6769     1
## hsg           -0.682  0.455   -1.50 0.00614   -1.42   -0.680   0.0643  5492     1
## col.grad       0.967  0.477    2.03 0.00654    0.20    0.965   1.7645  5314     1
## grad.sch       2.114  0.466    4.54 0.00612    1.34    2.111   2.8830  5786     1
## 
## vfac1_df :
##          Mean   SD t-value  MCSE q0.05 q0.5 q0.95 n_eff R_hat
## vfac1_df 7.74 4.43    1.75 0.491  3.63 6.36  16.6  81.5  1.03
plot(sim, "vfac1_df")

acceptance_rates(sim)
## $vfac1_df
## $vfac1_df[[1]]
## [1] 0.2642
## 
## $vfac1_df[[2]]
## [1] 0.2692
## 
## $vfac1_df[[3]]
## [1] 0.2648
compute_DIC(sim)
##        DIC      p_DIC 
## 2194.70217   33.70512
predictions <- predict(sim, newdata=apipop[-m, ], show.progress=FALSE,
                       fun=function(x, p) (samplesum + sum(x))/N)
summary(predictions)
##      Mean   SD t-value   MCSE q0.05 q0.5 q0.95 n_eff R_hat
## [1,]  664 3.96     168 0.0482   657  664   670  6739     1
plot(weights(cal)/N, weights(sim)); abline(0, 1)

summary(get_means(sim, "Q_")[["Q_"]])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2114  0.9291  1.0761  1.0004  1.1287  1.1686

References

Särndal, C.-E., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. Springer.