summary.mipfp: Summarizing objects of class mipfp

In mipfp: Multidimensional Iterative Proportional Fitting and Alternative Models

Description

Summary method for class mipfp.

Usage

## S3 method for class 'mipfp'
summary(object, cov.method = "delta", prop = FALSE, 
        target.list = NULL, l.names = 0, ...)

## S3 method for class 'summary.mipfp'
print(x, ...)

Arguments

object	An object of class mipfp, usually a result of a call to Estimate
x	An object of class summary.mipfp, usually a result of a call to summary.mipfp.
cov.method	Indicates which method to use to compute the covariance. Possible values are Delta (delta, default) or Lang (lang).
prop	If set to FALSE (the default), the results return counts, probabilities otherwise.
target.list	The list of the dimensions of the targets used by for the estimation process (see Estimate for more details).
l.names	If set to a value greater than 0, then the names of the categories will be shorten to a length of l.names characters.
...	Further arguments passed to the underlying print and flat method, or from other methods.

Details

The function summary.mipfp compute and returns a list of summary statistics of the estimates (covariance, t-statistics, goodness-of-fit statistics, associated degrees of freedom).

Value

The function summary.mipfp returns an object of class summary.mipfp having the following components:

call	A call object in which all the specified arguments are given by their full names.
conv	A Boolean indicating if the specified method converged to a solution (TRUE) or not (FALSE).
method	The method used to generate estimates.
df	Degrees of freedom of the estimates.
estimates	Estimates generated by the selected method with standard deviations and associated t- and p-values.
error.margins	A list returning, for each margin, the absolute maximum deviation between the desired and generated margin.
vcov	A covariance matrix of the estimates (last index move fastest) computed using the method specified in cov.method.
tab.gof	A table containing the Log-likelihood (G2), Wald (W2) and Pearson chi-squared (X2) statistics with their associated p-values.
stats.df	Degrees of freedom for the G2, W2 and X2 statistics.
dim.names	Original dimension names of the estimated table.
l.names	The value of the parameter l.names.

Note

When using print for printing the resulting mipfp object, you can also have a look at the options of the method flat.

Author(s)

Johan Barthelemy.

Maintainer: Johan Barthelemy [e-mail].

See Also

The estimation function Estimate.

The function coef.mipfp to extract the estimates.

gof.estimates for the computation of the G2, W2 and X2 statistics.

vcov.mipfp for the details of the covariance computation.

Examples

# loading the data
data(spnamur, package = "mipfp")
# subsetting the data frame, keeping only the first 3 variables
spnamur.sub <- subset(spnamur, select = Household.type:Prof.status)
# true table
true.table <- table(spnamur.sub)
# extracting the margins
tgt.v1        <- apply(true.table, 1, sum)
tgt.v1.v2     <- apply(true.table, c(1,2), sum)
tgt.v2.v3     <- apply(true.table, c(2,3), sum)
tgt.list.dims <- list(1, c(1,2), c(2,3))
tgt.data      <- list(tgt.v1, tgt.v1.v2, tgt.v2.v3)
# creating the seed, a 10 pct sample of spnamur
seed.df <- spnamur.sub[sample(nrow(spnamur), round(0.10*nrow(spnamur))), ]
seed.table <- table(seed.df)
# applying the different fitting methods
r.ipfp <- Estimate(seed=seed.table, target.list=tgt.list.dims, 
                   target.data = tgt.data)
# printing the summary                   
print(summary(r.ipfp))

mipfp documentation built on May 2, 2019, 6:01 a.m.