modi: Multivariate Outlier Detection and Imputation for Incomplete Survey Data

Description

Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) doi:10.17713/ajs.v45i1.86.

Author(s)

Maintainer: Beat Hulliger beat.hulliger@fhnw.ch

Other contributors:

• Martin Sterchi martin.sterchi@fhnw.ch [contributor]

• Tobias Schoch tobias.schoch@fhnw.ch [contributor]

See Also

Useful links:

• https://github.com/martinSter/modi

• Report bugs at https://github.com/martinSter/modi/issues

BACON-EEM Algorithm for multivariate outlier detection in incomplete multivariate survey data

Description

BEM starts from a set of uncontaminated data with possible missing values, applies a version of the EM-algorithm to estimate the center and scatter of the good data, then adds (or deletes) observations to the good data which have a Mahalanobis distance below a threshold. This process iterates until the good data remain stable. Observations not among the good data are outliers.

Usage

BEM(
  data,
  weights,
  v = 2,
  c0 = 3,
  alpha = 0.01,
  md.type = "m",
  em.steps.start = 10,
  em.steps.loop = 5,
  better.estimation = FALSE,
  monitor = FALSE
)

Arguments

Details

The BACON algorithm with v = 1 is not robust but affine equivariant while v = 1 is robust but not affine equivariant. The threshold for the (squared) Mahalanobis distances, beyond which an observation is an outlier, is a standardised chisquare quantile at (1 - alpha). For large data sets it may be better to choose alpha / n instead. The internal function EM.normal is usually called from BEM. EM.normal is implementing the EM-algorithm in such a way that part of the calculations can be saved to be reused in the BEM algorithm. EM.normal does not contain the computation of the observed sufficient statistics, they will be computed in the main program of BEM and passed as parameters as well as the statistics on the missingness patterns.

Value

BEM returns a list whose first component output is a sublist with the following components:

sample.size

Number of observations

discarded.observations

Number of discarded observations

number.of.variables

Number of variables

significance.level

The probability used for the cutpoint, i.e. alpha

initial.basic.subset.size

Size of initial good subset

final.basic.subset.size

Size of final good subset

number.of.iterations

Number of iterations of the BACON step

computation.time

Elapsed computation time

center

Final estimate of the center

scatter

Final estimate of the covariance matrix

cutpoint

The threshold MD-value for the cut-off of outliers

The further components returned by BEM are:

outind

Indicator of outliers

dist

Final Mahalanobis distances

Note

BEM uses an adapted version of the EM-algorithm in function .EM-normal.

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2008) The BACON-EEM Algorithm for Multivariate Outlier Detection in Incomplete Survey Data, Survey Methodology, Vol. 34, No. 1, pp. 91-103.

Billor, N., Hadi, A.S. and Vellemann, P.F. (2000). BACON: Blocked Adaptative Computationally-efficient Outlier Nominators. Computational Statistics and Data Analysis, 34(3), 279-298.

Schafer J.L. (2000), Analysis of Incomplete Multivariate Data, Monographs on Statistics and Applied Probability 72, Chapman & Hall.

Examples

# Bushfire data set with 20% MCAR
data(bushfirem, bushfire.weights)
bem.res <- BEM(bushfirem, bushfire.weights,
               alpha = (1 - 0.01 / nrow(bushfirem)))
print(bem.res$output)

Utility function for EAdet and EAimp

Description

Calculation of distances for EPIDEMIC Algorithm for multivariate outlier detection and imputation

Usage

EA.dist(
  data,
  n,
  p,
  weights,
  reach,
  transmission.function,
  power,
  distance.type,
  maxl
)

Arguments

Author(s)

Beat Hulliger

Epidemic Algorithm for detection of multivariate outliers in incomplete survey data

Description

In EAdet an epidemic is started at a center of the data. The epidemic spreads out and infects neighbouring points (probabilistically or deterministically). The last points infected are outliers. After running EAdet an imputation with EAimp may be run.

Usage

EAdet(
  data,
  weights,
  reach = "max",
  transmission.function = "root",
  power = ncol(data),
  distance.type = "euclidean",
  maxl = 5,
  plotting = TRUE,
  monitor = FALSE,
  prob.quantile = 0.9,
  random.start = FALSE,
  fix.start,
  threshold = FALSE,
  deterministic = TRUE,
  rm.missobs = FALSE,
  verbose = FALSE
)

Arguments

Details

The form and parameters of the transmission function should be chosen such that the infection times have at least a range of 10. The default cutting point to decide on outliers is the median infection time plus three times the mad of infection times. A better cutpoint may be chosen by visual inspection of the cdf of infection times. EAdet calls the function EA.dist, which passes the counterprobabilities of infection (a n * (n - 1) / 2 size vector!) and three parameters (sample spatial median index, maximal distance to nearest neighbor and transmission distance = reach) as arguments to EAdet. The distances vector may be too large to be passed as arguments. Then either the memory size must be increased. Former versions of the code used a global variable to store the distances in order to save memory.

Value

EAdet returns a list whose first component output is a sub-list with the following components:

sample.size

Number of observations

discarded.observations

Indices of discarded observations

missing.observations

Indices of completely missing observations

number.of.variables

Number of variables

n.complete.records

Number of records without missing values

n.usable.records

Number of records with less than half of values missing (unusable observations are discarded)

medians

Component wise medians

mads

Component wise mads

prob.quantile

Use this quantile if mads fail, i.e. if one of the mads is 0

quantile.deviations

Quantile of absolute deviations

start

Starting observation

transmission.function

Input parameter

power

Input parameter

maxl

Maximum number of steps without infection

min.nn.dist

Maximal nearest neighbor distance

transmission.distance

d0

threshold

Input parameter

distance.type

Input parameter

deterministic

Input parameter

number.infected

Number of infected observations

cutpoint

Cutpoint of infection times for outlier definition

number.outliers

Number of outliers

outliers

Indices of outliers

duration

Duration of epidemic

computation.time

Elapsed computation time

initialisation.computation.time

Elapsed computation time for standardisation and calculation of distance matrix

The further components returned by EAdet are:

infected

Indicator of infection

infection.time

Time of infection

outind

Indicator of outliers

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations, JRSS-A, 167, Part 2, pp. 275-294.

See Also

EAimp for imputation with the Epidemic Algorithm.

Examples

data(bushfirem, bushfire.weights)
det.res <- EAdet(bushfirem, bushfire.weights)

Epidemic Algorithm for imputation of multivariate outliers in incomplete survey data.

Description

After running EAdet an imputation of the detected outliers with EAimp may be run.

Usage

EAimp(
  data,
  weights,
  outind,
  reach = "max",
  transmission.function = "root",
  power = ncol(data),
  distance.type = "euclidean",
  duration = 5,
  maxl = 5,
  kdon = 1,
  monitor = FALSE,
  threshold = FALSE,
  deterministic = TRUE,
  fixedprop = 0
)

Arguments

Details

EAimp uses the distances calculated in EAdet (actually the counterprobabilities, which are stored in a global data set) and starts an epidemic at each observation to be imputed until donors for the missing values are infected. Then a donor is selected randomly.

Value

EAimp returns a list with two components: parameters and imputed.data. parameters contains the following elements:

sample.size

Number of observations

number.of.variables

Number of variables

n.complete.records

Number of records without missing values

n.usable.records

Number of records with less than half of values missing (unusable observations are discarded)

duration

Duration of epidemic

reach

Transmission distance (d0)

threshold

Input parameter

deterministic

Input parameter

computation.time

Elapsed computation time

imputed.data contains the imputed data.

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations, JRSS-A, 167, Part 2, pp. 275-294.

See Also

EAdet for outlier detection with the Epidemic Algorithm.

Examples

data(bushfirem, bushfire.weights)
det.res <- EAdet(bushfirem, bushfire.weights)
imp.res <- EAimp(bushfirem, bushfire.weights, outind = det.res$outind, kdon = 3)
print(imp.res$output)

EM for multivariate normal data

Description

This version of EM does not contain the computation of the observed sufficient statistics, they will be computed in the main program of BEM and passed as parameters as well as the statistics on the missingness patterns.

Usage

EM.normal(
  data,
  weights = rep(1, nrow(data)),
  n = sum(weights),
  p = ncol(data),
  s.counts,
  s.id,
  S,
  T.obs,
  start.mean = rep(0, p),
  start.var = diag(1, p),
  numb.it = 10,
  Estep.output = FALSE
)

Arguments

Author(s)

Beat Hulliger

Robust EM-algorithm ER

Description

The ER function is an implementation of the ER-algorithm of Little and Smith (1987).

Usage

ER(
  data,
  weights,
  alpha = 0.01,
  psi.par = c(2, 1.25),
  em.steps = 100,
  steps.output = FALSE,
  Estep.output = FALSE,
  tolerance = 1e-06
)

Arguments

Details

The M-step of the EM-algorithm uses a one-step M-estimator.

Value

sample.size

Number of observations

number.of.variables

Number of variables

significance.level

alpha

computation.time

Elapsed computation time

good.data

Indices of the data in the final good subset

outliers

Indices of the outliers

center

Final estimate of the center

scatter

Final estimate of the covariance matrix

dist

Final Mahalanobis distances

rob.weights

Robustness weights in the final EM step

Author(s)

Beat Hulliger

References

Little, R. and P. Smith (1987). Editing and imputation for quantitative survey data. Journal of the American Statistical Association, 82, 58-68.

See Also

BEM

Examples

data(bushfirem, bushfire.weights)
det.res <- ER(bushfirem, weights = bushfire.weights, alpha = 0.05,
steps.output = TRUE, em.steps = 100, tol = 2e-6)
PlotMD(det.res$dist, ncol(bushfirem))

Utility for ER function

Description

The ER function is an implementation of the ER-algorithm of Little and Smith (1987).

Usage

ER.normal(
  data,
  weights = rep(1, nrow(data)),
  psi.par = c(2, 1.25),
  np = sum(weights),
  p = ncol(data),
  s.counts,
  s.id,
  S,
  missing.items,
  nb.missing.items,
  start.mean = rep(0, p),
  start.var = diag(1, p),
  numb.it = 10,
  Estep.output = FALSE,
  tolerance = 1e-06
)

Arguments

Author(s)

Beat Hulliger

Gaussian imputation followed by MCD

Description

Gaussian imputation uses the classical non-robust mean and covariance estimator and then imputes predictions under the multivariate normal model. Outliers may be created by this procedure. Then a high-breakdown robust estimate of the location and scatter with the Minimum Covariance Determinant algorithm is obtained and finally outliers are determined based on Mahalanobis distances based on the robust location and scatter.

Usage

GIMCD(data, alpha = 0.05, seedem = 23456789, seedmcd)

Arguments

Details

Normal imputation from package norm and MCD from package MASS. Note that currently MCD does not accept weights.

Value

Result is stored in a global list GIMCD.r:

center

robust center

scatter

robust covariance

alpha

quantile for cut-off value

computation.time

elapsed computation time

outind

logical vector of outlier indicators

dist

Mahalanobis distances

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2008), The BACON-EEM Algorithm for Multivariate Outlier Detection, in Incomplete Survey Data, Survey Methodology, Vol. 34, No. 1, pp. 91-103.

See Also

cov.rob

Examples

data(bushfirem)
det.res <- GIMCD(bushfirem, alpha = 0.1)
print(det.res$center)
PlotMD(det.res$dist, ncol(bushfirem))

Mahalanobis distance (MD) for data with missing values

Description

For each observation the missing dimensions are omitted before calculating the MD. The MD contains a correction factor p/q to account for the number of observed values, where p is the number of variables and q is the number of observed dimensions for the particular observation.

Usage

MDmiss(data, center, cov)

Arguments

Details

The function loops over the observations. This is not optimal if only a few missingness patterns occur. If no missing values occur the function returns the Mahalanobis distance.

Value

The function returns a vector of the (squared) Mahalanobis distances.

Author(s)

Beat Hulliger

References

Béguin, C., and Hulliger, B. (2004). Multivariate outlier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A167 (Part 2.), pp. 275-294.

See Also

mahalanobis

Examples

data(bushfirem, bushfire)
MDmiss(bushfirem, apply(bushfire, 2, mean), var(bushfire))

Nearest Neighbour Imputation with Mahalanobis distance

Description

POEM takes into account missing values, outlier indicators, error indicators and sampling weights.

Usage

POEM(
  data,
  weights,
  outind,
  errors,
  missing.matrix,
  alpha = 0.5,
  beta = 0.5,
  reweight.out = FALSE,
  c = 5,
  preliminary.mean.imputation = FALSE,
  monitor = FALSE
)

Arguments

Details

POEM assumes that an multivariate outlier detection has been carried out beforehand and assumes the result is summarized in the vector outind. In addition, further observations may have been flagged as failing edit-rules and this information is given in the vector errors. The mean and covariance estimate is calculated with the good observations (no outliers and downweighted errors). Preliminary mean imputation is sometimes needed to avoid a non-positive definite covariance estimate at this stage. Preliminary mean imputation assumes that the problematic values of an observation (with errors, outliers or missing) can be replaced by the mean of the rest of the non-problematic observations. Note that the algorithm imputes these problematic observations afterwards and therefore the final covariance matrix with imputed data is not the same as the working covariance matrix (which may be based on preliminary mean imputation).

Value

POEM returns a list whose first component output is a sub-list with the following components:

preliminary.mean.imputation

Logical. TRUE if preliminary mean imputation should be used

completely.missing

Number of observations with no observed values

good.values

Weighted number of of good values (not missing, not outlying, not erroneous)

nonoutliers.before

Number of nonoutliers before reweighting

weighted.nonoutliers.before

Weighted number of nonoutliers before reweighting

nonoutliers.after

Number of nonoutliers after reweighting

weighted.nonoutliers.after

Weighted number of nonoutliers after reweighting

old.center

Coordinate means after weighting, before imputation

old.variances

Coordinate variances after weighting, before imputation

new.center

Coordinate means after weighting, after imputation

new.variances

Coordinate variances after weighting, after imputation

covariance

Covariance (of standardised observations) before imputation

imputed.observations

Indices of observations with imputed values

donors

Indices of donors for imputed observations

new.outind

Indices of new outliers

The further component returned by POEM is:

imputed.data

Imputed data set

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger B., (2002), EUREDIT Workpackage x.2 D4-5.2.1-2.C Develop and evaluate new methods for statistical outlier detection and outlier robust multivariate imputation, Technical report, EUREDIT 2002.

Examples

data(bushfirem, bushfire.weights)
outliers <- rep(0, nrow(bushfirem))
outliers[31:38] <- 1
imp.res <- POEM(bushfirem, bushfire.weights, outliers,
preliminary.mean.imputation = TRUE)
print(imp.res$output)
var(imp.res$imputed.data)

QQ-Plot of Mahalanobis distances

Description

QQ-plot of (squared) Mahalanobis distances vs. scaled F-distribution (or a scaled chisquare distribution). In addition, two default cutpoints are proposed.

Usage

PlotMD(dist, p, alpha = 0.95, chisquare = FALSE)

Arguments

Details

Scaling of the F-distribution as median(dist)*qf((1:n)/(n+1), p, n-p)/qf(0.5, p, n-p). First default cutpoint is median(dist)*qf(alpha, p, n-p)/qf(0.5, p, n-p) and the second default cutpoint is the alpha quantile of the Mahalanobis distances.

Value

Author(s)

Beat Hulliger

References

Little, R. & Smith, P. (1987) Editing and imputation for quantitative survey data, Journal of the American Statistical Association, 82, 58-68

Examples

data(bushfirem, bushfire.weights)
det.res <- TRC(bushfirem, weights = bushfire.weights)
PlotMD(det.res$dist, ncol(bushfirem))

Transformed rank correlations for multivariate outlier detection

Description

TRC starts from bivariate Spearman correlations and obtains a positive definite covariance matrix by back-transforming robust univariate medians and mads of the eigenspace. TRC can cope with missing values by a regression imputation using the a robust regression on the best predictor and it takes sampling weights into account.

Usage

TRC(
  data,
  weights,
  overlap = 3,
  mincor = 0,
  robust.regression = "rank",
  gamma = 0.5,
  prob.quantile = 0.75,
  alpha = 0.05,
  md.type = "m",
  monitor = FALSE
)

Arguments

Details

TRC is similar to a one-step OGK estimator where the starting covariances are obtained from rank correlations and an ad hoc missing value imputation plus weighting is provided.

Value

TRC returns a list whose first component output is a sublist with the following components:

sample.size

Number of observations

number.of.variables

Number of variables

number.of.missing.items

Number of missing values

significance.level

1 - alpha

computation.time

Elapsed computation time

medians

Componentwise medians

mads

Componentwise mads

center

Location estimate

scatter

Covariance estimate

robust.regression

Input parameter

md.type

Input parameter

cutpoint

The default threshold MD-value for the cut-off of outliers

The further components returned by TRC are:

outind

Indicator of outliers

dist

Mahalanobis distances (with missing values)

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations, JRSS-A, 167, Part 2, pp. 275-294.

Examples

data(bushfirem, bushfire.weights)
det.res <- TRC(bushfirem, weights = bushfire.weights)
PlotMD(det.res$dist, ncol(bushfirem))
print(det.res)

Winsorization followed by imputation

Description

Winsorization of outliers according to the Mahalanobis distance followed by an imputation under the multivariate normal model. Only the outliers are winsorized. The Mahalanobis distance MDmiss allows for missing values.

Usage

Winsimp(data, center, scatter, outind, seed = 1000003)

Arguments

Details

It is assumed that center, scatter and outind stem from a multivariate outlier detection algorithm which produces robust estimates and which declares outliers observations with a large Mahalanobis distance. The cutpoint is calculated as the least (unsquared) Mahalanobis distance among the outliers. The winsorization reduces the weight of the outliers:

\hat{y}_i = \mu_R + (y_i - \mu_R) \cdot c/d_i

where \mu_R is the robust center and d_i is the (unsquared) Mahalanobis distance of observation i.

Value

Winsimp returns a list whose first component output is a sublist with the following components:

cutpoint

Cutpoint for outliers

proc.time

Processing time

n.missing.before

Number of missing values before imputation

n.missing.after

Number of missing values after imputation

The further component returned by winsimp is:

imputed.data

Imputed data set

Author(s)

Beat Hulliger

References

Hulliger, B. (2007), Multivariate Outlier Detection and Treatment in Business Surveys, Proceedings of the III International Conference on Establishment Surveys, Montréal.

See Also

MDmiss. Uses imp.norm.

Examples

data(bushfirem, bushfire.weights)
det.res <- TRC(bushfirem, weight = bushfire.weights)
imp.res <- Winsimp(bushfirem, det.res$center, det.res$scatter, det.res$outind)
print(imp.res$n.missing.after)

Bushfire scars.

Description

The bushfire data set was used by Campbell (1984, 1989) to locate bushfire scars. The dataset contains satellite measurements on five frequency bands, corresponding to each of 38 pixels.

Usage

bushfire

Format

A data frame with 38 rows and 5 variables.

Details

The data contains an outlying cluster of observations 33 to 38 a second outlier cluster of observations 7 to 11 and a few more isolated outliers, namely observations 12, 13, 31 and 32.

For testing purposes weights are provided: bushfire.weights <- rep(c(1,2,5), length = nrow(bushfire))

References

Campbell, N. (1989) Bushfire Mapping using NOAA AVHRR Data. Technical Report. Commonwealth Scientific and Industrial Research Organisation, North Ryde.

Examples

data(bushfire)

Weights for Bushfire scars.

Description

The bushfire data set was used by Campbell (1984, 1989) to locate bushfire scars. The dataset contains satellite measurements on five frequency bands, corresponding to each of 38 pixels.

Usage

bushfire.weights

Format

A vector of length 38.

Details

For testing purposes, bushfire.weights provides artificial weights created according to: bushfire.weights <- rep(c(1,2,5), length = nrow(bushfire))

References

Campbell, N. (1989) Bushfire Mapping using NOAA AVHRR Data. Technical Report. Commonwealth Scientific and Industrial Research Organisation, North Ryde.

Examples

data(bushfire.weights)

Bushfire scars with missing data.

Description

The bushfire data set was used by Campbell (1984, 1989) to locate bushfire scars. The dataset contains satellite measurements on five frequency bands, corresponding to each of 38 pixels. However, this dataset contains missing values.

Usage

bushfirem

Format

A data frame with 38 rows and 5 variables.

Details

The data contains an outlying cluster of observations 33 to 38 a second outlier cluster of observations 7 to 11 and a few more isolated outliers, namely observations 12, 13, 31 and 32.

bushfirem is created from bushfire by setting a proportion of 0.2 of the values to missing.

For testing purposes weights are provided: bushfire.weights <- rep(c(1,2,5), length = nrow(bushfire))

References

Campbell, N. (1989) Bushfire Mapping using NOAA AVHRR Data. Technical Report. Commonwealth Scientific and Industrial Research Organisation, North Ryde.

Examples

data(bushfirem)

Addressing function for Epidemic Algorithm

Description

Utility function for Epidemic Algorithm.

Usage

ind.dij(i, j, n)

Arguments

Author(s)

Cédric Béguin

Addressing function for Epidemic Algorithm

Description

Utility function for Epidemic Algorithm.

Usage

ind.dijs(i, js, n)

Arguments

Author(s)

Cédric Béguin

Living Standards Measurement Survey Albania 2012

Description

The dataset is an extended version of the public micro data file of the LSMS 2012 of Albania available at (https://www.instat.gov.al/en/figures/micro-data/, accessed 13 February 2023). Documentation of the LSMS 2012 of Albania is from the World Bank (https://microdata.worldbank.org/index.php/catalog/1970, accessed 5 November 2020). The data set is ported to R and updated with approximate survey design information derived from the data itself. The units are households and the variables are expenditures on main categories, poverty measures and structural information including weights and sample design.

Usage

lival

Format

A data frame with 6671 rows and 26 variables

psu

primary sampling unit (psu)

hhid

unique household identifier (100*psu+hh)

hh

household number per psu

prefectu

prefecture

urban

urbanicity (Urban=1, Rural=2)

strat

stratum

region

region

totcons

total consumption of hh

rcons

real mean per capita consumption

rfood

real food consumption per capita

rtotnfoo

real non food consumption per capita

reduexpp

real education consumption per capita

rdurcons

real durable consumption per capita

rtotutil

real utilities consumption per capita

egap0

extreme headcount poverty

egap1

extreme poverty gap

egap2

extreme poverty depth

agap0

absolute headcount poverty

agap1

absolute poverty gap

agap2

absolute poverty depth

weight

final cross-sectional weight

nph

number of psu in stratum population

mph

number of households in stratum population

mphi

number of households in sampled psu

pi1

psu inclusion probability

pi2

household inclusion probability

Details

Absolute poverty measures use a poverty line of Lek 4891 (2002 prices). Extreme poverty measures use a poverty line where the basic nutritional needs are difficult to meet. The headcount poverty variable is an indicator for the income of the household y_i being below the (absolute or extreme) poverty line z. The poverty gap variable measures the relative distance to the poverty line: (z-y_i)/z. The poverty depth variable is the square of the poverty gap variable, i.e. [(z-y_i)/z]^2, giving more weight to the poorer among the poor and thus describing the inequality among the poor.

The survey design is a stratified clustered two stage design. The primary sampling units are enumeration zones. The strata are the crossing of prefecture and urbanicity and the allocation of the psu sample to the strata is proportional to the number of households. Within strata the psu are sampled with probability proportional to number of households. Within psu a simple random sample of 8 households was selected. The weights are calibrated to population margins. All survey design information except the strata and the weights are approximated through the weights using assumptions on the design. Since the data set has undergone data protection measures and the survey design is approximate only, inference to the population does not yield exact results. However, the complexity of the data and of the survey design are realistic.

The size of the household is not on the original data set. However, the transformation capita <- round(0.07527689 * totcons/rcons, 0) yields the number of persons in the household.

Note

With the survey design function svydesign of the package survey a survey design object can be built: svydesign(~psu + hhid , strata= ~strat, fpc= ~pi1 +pi2, weight= ~weight, data=lival, pps="brewer").

References

https://www.instat.gov.al/en/figures/micro-data/

Examples

data(lival)
lival$capita <- with(lival, round(0.07527689 * totcons / rcons, 0))
## Not run: 
library(survey)
lival.des <- svydesign(~psu + hhid , strata= ~strat, fpc= ~pi1 +pi2,
                      weight= ~weight, data=lival, pps="brewer")
svymean(~totcons, lival.des, deff=TRUE)

## End(Not run)

modi: Multivariate outlier detection for incomplete survey data.

Description

The package modi is a collection of functions for multivariate outlier detection and imputation. The aim is to provide a set of functions which cope with missing values and take sampling weights into account. The original functions were developed in the EUREDIT project. This work was partially supported by the EU FP5 ICT programme, the Swiss Federal Office of Education and Science and the Swiss Federal Statistical Office. Subsequent development was in the AMELI project of the EU FP7 SSH Programme and also supported by the University of Applied Sciences and Arts Northwestern Switzerland (FHNW).

modi functions

BACON-EEM algorithm in BEM(), Epidemic algorithm in EAdet() and EAimp(), Transformed Rank Correlations in TRC(), Gaussian imputation with MCD in GIMCD().

References

Béguin, C., and Hulliger, B. (2004). Multivariate outlier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A167 (Part 2.), pp. 275-294.

Béguin, C., and Hulliger, B. (2008). The BACON-EEM Algorithm for Multivariate Outlier Detection in Incomplete Survey Data, Survey Methodology, Vol. 34, No. 1, pp. 91-103.

Non-zero non-missing minimum function

Description

Returns the non-zero non-missing minimum.

Usage

nz.min(x)

Arguments

Author(s)

Beat Hulliger

Plot of infection times of the EA algorithm

Description

The (weighted) cdf of infection times is plotted. The infection times jumps of the cdf are shown by the points with the same infection times stacked vertically and respecting the weights.

Usage

plotIT(infection.time, weights, cutpoint)

Arguments

Details

The infection times of EAdet are the main input. In addition the weights may be needed. The default cutpoint from EAdet may be used for the cutpoint. Points that are never infected have a missing infection time. These missing infection times are (temporarily) imputed by 1.2 times the maximum infection time to show them on the plot marked with an x.

Author(s)

Beat Hulliger

Examples

it <- c(rep(NA, 3), rep(1:7, times=c(1, 4, 10, 8, 5, 3, 2)))
wt <- rep(c(1,2,5), times=12)
plotIT(it, wt, 6)

psi-function

Description

Defined in Little and Smith for ER algorithm

Usage

psi.lismi(d, present, psi.par = c(2, 1.25))

Arguments

Author(s)

Beat Hulliger

Sample Environment Protection Expenditure Survey.

Description

The sepe data set is a sample of the pilot survey in 1993 of the Swiss Federal Statistical Office on environment protection expenditures of Swiss private economy in the previous accounting year. The units are enterprises, the monetary variables are in thousand Swiss Francs (CHF). From the original sample a random subsample was chosen of which certain enterprises were excluded for confidentiality reasons. In addition, noise has been added to certain variables, and certain categories have been collapsed. The data set has missing values. The data set has first been prepared for the EU FP5 project EUREDIT and later been data protected for educational purposes.

Usage

sepe

Format

A data frame with 675 rows and 23 variables:

idnr

identifier (anonymous)

exp

categorical variable where 1 = 'non-zero total expenditure' and 2 = 'zero total expenditure, and 3 = 'no answer'

totinvwp

total investment for water protection

totinvwm

total investment for waste management

totinvap

total investment for air protection

totinvnp

total investment for noise protection

totinvot

total investment for other environmental protection

totinvto

overall total investment in all environmental protection areas

totexpwp

total current expenditure in environmental protection area water protection

totexpwm

total current expenditure in environmental protection area waste management

totexpap

total current expenditure in environmental protection area air protection

totexpnp

total current expenditure in environmental protection area noise protection

totexpot

total current expenditure in other environmental protection

totexpto

overall total current expenditure in all environmental protection

subtot

total subsidies for environmental protection received

rectot

total receipts from environmental protection

employ

number of employees

sizeclass

size class (according to number of employees)

stratum

stratum number of sample design

activity

code of economic activity (aggregated)

popsize

number of enterprises in the population-stratum

popempl

number of employees in population activity group

weight

sampling weight (for extrapolation to the population)

Details

The sample design is stratified random sampling with different sampling rates. Use package survey or sampling to obtain correct point and variance estimates. In addition a ratio estimator may be built using the variable popemple which gives the total employment per activity.

There are two balance rules: the subtotals of the investment variables should sum to totinvto and the expenditure subtotals should sum to totexpto.

The missing values stem from the survey itself. In the actual survey the missing values were declared as 'guessed' rather than copied from records.

The sampling weight weight is adjusted for non-response in the stratum, i.e. weight=popsize/sampsize.

References

Swiss Federal Statistical Office (1996), Umweltausgaben und -investitionen in der Schweiz 1992/1993, Ergebnisse einer Pilotstudie.

Charlton, J. (ed.), Towards Effective Statistical Editing and Imputation Strategies - Findings of the Euredit project, unpublished manuscript available from Eurostat and https://www.cs.york.ac.uk/euredit/euredit-main.html.

Examples

data(sepe)

Sweep operator

Description

Definition of the sweep and reverse-sweep operator (Schafer pp 159-160)

Usage

sweep.operator(M, k, reverse = FALSE)

Arguments

Author(s)

Beat Hulliger

Quantiles of a weighted cdf

Description

A weighted cdf is calculated and quantiles are evaluated. Missing values are discarded.

Usage

weighted.quantile(x, w, prob = 0.5, plot = FALSE)

Arguments

Details

Weighted linear interpolation in case of non-unique inverse. Gives a warning when the contribution of the weight of the smallest observation to the total weight is larger than prob.

Value

The quantile according to prob (by default it returns the weighted median).

Note

No variance calculation.

Author(s)

Beat Hulliger

See Also

svyquantile

Examples

x <- rnorm(100)
x[sample(1:100, 20)] <- NA
w <- rchisq(100, 2)
weighted.quantile(x, w, 0.2, TRUE)

Weighted univariate variance coping with missing values

Description

This function is analogous to weighted.mean.

Usage

weighted.var(x, w, na.rm = FALSE)

Arguments

Details

The weights are standardised such that \sum_{observed} w_i equals the number of observed values in x. The function calculates

\sum_{observed} w_i(x_i - weighted.mean(x, w, na.rm = TRUE))^2/((\sum_{observed} w_i) - 1)

Value

The weighted variance of x with weights w (with missing values removed when na.rm = TRUE).

Author(s)

Beat Hulliger

See Also

weighted.mean

Examples

x <- rnorm(100)
x[sample(1:100, 20)] <- NA
w <- rchisq(100, 2)
weighted.var(x, w, na.rm = TRUE)

Utility function for TRC.R among others

Description

Sum of weights for observations < value (if lt = TRUE) or observations=value (if lt = FALSE).

Usage

weightsum(observations, weights, value, lt = TRUE)

Arguments

Author(s)

Beat Hulliger