Fits generalized linear models to survey data (S.D. Langton).
|What output to display (
||Error distribution (
||Link function (
||Value at which to fix the residual variance, if missing the variance is estimated; default 1 for binomial or Poisson, otherwise
||Whether to estimate or omit constant term in fixed model (
||Limit on number of factors/covariates in a model term; default 3|
||Variables for which predictions are to be formed; default
||Levels or values at which predictions are to be made corresponding to
||Formula specifying fixed terms for which predicted means are to be printed; default
||Stratification factor; default
||Number of primary sampling units in each stratum|
||Factor indicating the primary sampling units; default
||Bootstrapping method (
||Number of bootstrap samples to use; default 0 uses a Taylor series approximation for
||Seed for random number generator for bootstrap; default 0|
||The probability level for the confidence intervals; default 0.95|
||Method for forming confidence intervals (
||Number of binomial trials for each unit (must be set if
||Variates to save residuals|
||Variates to save fitted values|
||Estimates of parameters for each
||Standard errors of the estimates|
||Variance-covariance matrix for the estimates|
||Lower confidence limits for estimates|
||Upper confidence limits for estimates|
||Pointers to save Wald statistics for each term (pointer contains name of term, Wald statistic, F statistic, degrees of freedom, and P-value)|
||Pointers to tables of predictions|
||Pointers to tables of standard errors of predictions|
||Lower confidence limits for predictions|
||Upper confidence limits for predictions|
||Variance-covariance matrix for the predictions|
SVGLM fits generalized linear models to data from one- or two-stage surveys. Variance estimates reflecting the survey design are estimated by a bootstrap method or a Taylor series approximation (Korn & Graubard 1999). Survey weights, which are supplied using the
WEIGHTS option and which may be calculated by
SVWEIGHT, are used to ensure that unbiased estimates of the finite survey population parameters are produced. It should be noted that using a weighted analysis is not the only way to handle such data; in some circumstances it may be preferable to use an unweighted analysis, including factors reflecting the survey design (see, for example, Chapter 5 of Korn & Graubard 1999 for discussion of this subject). Mixed models, such as those fitted by the
REML directive, the
GLMM procedure or the
HGANALYSE procedure may be another way of accounting for the correlations induced in the data by the survey design.
FACTORIAL options are used to specify the model in exactly the same way as in the
MODEL directive. Similarly the
Y parameter supplies the response variable to be analysed and, for the binomial distribution,
NBINOMIAL supplies the number of trials for each unit. The terms to be fitted are supplied using option
TERMS as either a formula or, if no interactions are fitted, a list of variates and factors.
Information on the survey design is provided using the
SAMPLINGUNITS options. The option
NUNITS can be used to list the number of primary sampling units per stratum, using a table or variate with one value for each stratum; this is used to calculate the appropriate degrees of freedom for test statistics and in construction of bootstrap samples.
The bootstrapping method is selected using the
METHOD option. In a one-stage design the default of
simple forms each bootstrap sample by sampling with replacement from the original sample within each stratum. In a two-stage design (i.e. if
SAMPLINGUNITS is set), primary sampling units are first sampled with replacement, and then secondary units are sampled with replacement within the selected primary units. Variance estimates from the boostrapping process will be biased where there are very few sampling units in each stratum and so the method is not recommended in this situation. For a cluster sample the setting
csimple should be used; this samples primary sampling units with replacement as for the two-stage design, but does not resampling within those secondary units. The setting
METHOD=sarndal constructs a “pseudo-population” by replicating each sampled unit by the rounded value of its weight, so that, for example, an observation with weight 16.1 is represented sixteen times in the pseudo-population (see Sarndal et al. 1992, page 442). The bootstrap sample is formed by sampling with replacement from this pseudo-population. At present this method is only available for single- stage sampling.
The number of bootstrap samples used is set by means of the
NBOOT parameter. For exploratory analyses a relatively low value (perhaps 20) may suffice, but where test statistics or confidence limits are required a value of at least 500 is recommended. For simple linear regression (i.e.
NBOOT to zero calculates variances of regression parameters by a linearization approach similar to that used for means and totals by
SVTABULATE (Binder 1983). For other generalized linear models setting
NBOOT to zero uses a simple approximation in which the weights are scaled to sum to the number of observations in the sample; this setting is only recommended for initial model fitting as variance estimates will be seriously inaccurate, particularly in two-stage designs.
Parameter estimates and their standard errors can be saved using the
SE parameters, whilst
VCOVARIANCE saves the full variance-covariance matrix. The
UPPER parameters save confidence limits for the estimates; by default 95% confidence limits are shown, but this may be changed by means of the
CIPROBABILITY option. The
CIMETHOD option controls how confidence limits are formed after bootstrapping:
percentile uses simple percentiles of the bootstrapped distribution, whilst
tdistribution calculates a standard error from the bootstrapped estimates and then uses the t-distribution to form intervals; the default of
automatic uses the percentile method unless less than 400 bootstrap samples have been made.
Wald statistics (Korn & Graubard 1999) for terms in the model can be saved using parameter
WALD, in the form of a pointer with elements corresponding to the term (as a text), the Wald statistic, the approximate F statistic, the two sets of degrees of freedom, and the probability value.
Predicted values can be formed from the analysis. These estimate the average value of the response variable that would have been expected in the population had all the units been in the specified group, or had had the specified covariate value. The averages are taken over the distribution of the other fitted variables within the population (as deduced from the weighted sample). Factors and variates for which predictions are required are specified using the
PFACTORS option and particular levels or values may be specified using
PLEVELS, which operates in the same way as the
LEVELS parameter of
PTERMS can be used to specify particular terms so that, for example,
PTERMS=A.B would produce a two-way table classified by factors
B. The parameters
UPPREDICTIONS save the tables of predictions, their standard errors, and the lower and upper confidence limits respectively.
VCPREDICTIONS saves the full variance-covariance matrix of the bootstrapped predictions.
Printing is controlled by the
PTERMS is set, predictions. The
monitor setting provides progress of the bootstrap samples.
Restricting the response variate
Y fits a model to the subpopulation defined by the restriction.
Binder, D.A. (1983). On the Variances of Asymptotically Normal Estimators from Complex Surveys. International Statistical Review, 51, 279-292.
Sarndal, C., Swenssion, B. & Wretman, J. (1992). Model Assisted Survey Sampling. Springer-Verlag, New York.
CAPTION 'SVGLM example',\ 'Data from Sampford, Table 5.1, page 61, using farms of Table 6.1.';\ STYLE=meta,plain FACTOR [LEVELS=3] stratum TABLE [CLASS=stratum; VALUES=12,12,11] N READ farm,stratum,crops,oats 6 1 60 15 7 1 62 20 8 1 65 18 12 1 74 18 13 2 78 23 15 2 91 27 17 2 96 25 23 2 190 60 26 3 240 28 31 3 324 128 33 3 356 69 34 3 410 72 : SVWEIGHT [PRINT=summary; STRATUM=stratum; NUNITS=N] OUTWEIGHTS=wts CALCULATE logcrops,logoats=log10(crops,oats) SVGLM [PRINT=model,estimates,wald;STRATUM=stratum;\ WEIGHTS=wts;TERMS=logcrops;NBOOT=0] logoats VARIATE [VALUES=1.7,1.8...2.7] xpred SVGLM [PRINT=model,estimates,wald,prediction,monitor; STRATUM=stratum;\ PFACTORS=logcrops; PLEVELS=xpred; WEIGHTS=wts; TERMS=logcrops;\ NBOOT=50; SEED=630232] logoats; PREDICTIONS=ypred;\ SEPREDICTIONS=sep; LOWPREDICTIONS=lo; UPPREDICTIONS=hi PEN 2,3,4; METHOD=line; LINE=1,2,2; COLOUR='red',2('limegreen');\ SYMBOL=0 DGRAPH logoats,ypred,lo,hi; logcrops,(xpred)3; DESCRIPTION=\ 'observed','fitted line','lower 95% limit','upper 95% limit'