The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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:
## [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
## 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, \(s \subset U\) 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)
## 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 \(\bar{Y}\) 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)
## 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
## $vfac1_df
## $vfac1_df[[1]]
## [1] 0.2642
##
## $vfac1_df[[2]]
## [1] 0.2692
##
## $vfac1_df[[3]]
## [1] 0.2648
## 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.