pim: Fit a PIM
In pimold: Probabilistic Index Models

Description Usage Arguments Details Value See Also Examples

Fit a PIM

pim(formula, data, link = c("logit", "identity", "probit", "inverse",
  "1/mu^2", "log"), blocking.variables = character(), poset = noselfposet,
  leftsuffix = "_L", rightsuffix = "_R", interpretation = c("difference",
  "regular", "marginal", "symmetric"), na.action = na.fail,
  estimator = estimator.nleqslv(),
  varianceestimator = varianceestimator.sandwich(), lhs = c("PO", "<",
  "<="), keep.data = FALSE, verbosity = 0, nicenames = TRUE,
  interactions.difference = (interpretation != "marginal"),
  extra.nicenames = data.frame(org = character(), nice = character(),
  stringsAsFactors = FALSE), check.symmetric = (interpretation == "regular"),
  threshold = 1e-06, weights = NULL,
  pseudoweights = pseudoweights.default)

`formula`	Original formula
`data`	Context where the formula `formula` is to be interpreted
`link`	Name of the link function (defaults to `"logit"`)
`blocking.variables`	Character vector holding column names that hold blocking variables.
`poset`	Matrix of two columns indicating what the original observation number is for the left and right real observation in the pseudo-observation. Alternatively, can be a function like `fullposet` and similar.
`leftsuffix, rightsuffix`	Suffixes that will be added to the 'left' and 'right' observation's column name in the pseudo-observation. Note: no checking is done that these suffixes are safe, so the wrong suffixes may lead to unexpected behaviour.
`interpretation`	If `"marginal"` parts of the formula are converted to imply marginal pim modeling (see e.g. `Mainreplacetext`). If it is `"difference"` (the default), then the design matrix of the PIM is the difference of the design matrices of each part of the pseudo-observations. A popular option is `"regular"`, which will interpret unaltered columns as differences. A new option is `"symmetric"`, which works the same as `"regular"`, but will enforce the symmetry condition by making the sign switch when changing the order (typically, this is achieved by subtracting the inverse for each dummy).
`na.action`	Defaults to `na.fail`: handles missing data in `data`.
`estimator`	Function like the result of `estimator.nleqslv()` (the default). See there to find the required form of this function or some provided alternatives.
`varianceestimator`	Function like the result of `varianceestimator.sandwich()` (the default). See there to find the required form of this function or some provided alternatives.
`lhs`	`"PO"`, `"<"` or `"<="`: Unequality used for the lefthandside of the formula. The default (`"PO"`) is the normal probabilistic index.
`keep.data`	If `TRUE` (not the default), the data and design matrices are maintained in the return value
`verbosity`	The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output)
`nicenames`	Defaults to `TRUE`: try to make the column names more readable.
`interactions.difference`	If `TRUE` (note that the default is `interpretation!="marginal"`) interaction terms will be interpreted as the differences of the onesided interaction terms (if this is possible at all). This is unsupported if `unsupported if` is `"marginal"`. Some special interaction terms with calculated columns may lead to unexpected behaviour.
`extra.nicenames`	Should be a `data.frame` containing two character columns: `org` and `nice`. For each "constructed" column name, provide a nicer one, that will make the results more readable. You may also use parts of constructed column names. Note: make sure to use `stringsAsFactor=FALSE` when creating the `data.frame`.
`check.symmetric`	Defaults to `TRUE` for `interpretation=="regular"`, `FALSE` otherwise: if the model does not support the symmetry condition, a warning is displayed. Note: this option is on by default for novice users, but it may be time- and resourceconsuming, so turn it off if you know what you are doing!
`threshold`	When checking the symmetry condition, how much digression is allowed.
`weights`	Defaults to `NULL`: vector of weights for every row of `data`, or `NULL` if no weighting is required.
`pseudoweights`	Defaults to `pseudoweights.default`: function that can convert weights by observation to weights per pseudo-observation. Should have the same signature and outcome as `pseudoweights.default`.

Some of the items in the return value may be missing if parts of the estimation fail. See pim.fit for this.

If estimator was estimator.glmnet, The return value gets as an additional class "glmnetpim".

For more details on how to use this function and its parameters, we refer the reader to vignette("pim").

An object of class pim,which is a list with items:

`coefficients`	Parameter estimates. See `estimator.nleqslv`
`morefitinfo`	Parameter estimation info. See `estimator.nleqslv`
`vcov`	Covariance estimates. See `varianceestimator.sandwich`
`morevarfitinfo`	Covariance estimation info. See `varianceestimator.sandwich`
`varestimationerror`	Only present if covariance estimation failed. See `pim.fit`
`call`	Call used when running this function.
`formula`	Passed in formula.
`link`	Name of the link function (defaults to `"identity"`)
`fitted.values`	Predicted pseudo-observation values.
`pfd`	Object of class `pimfitdata`. If `keep.data` was `TRUE`, the `X` and `Y` items are set to `NULL`.
`data`	Passed in `data` (may be altered by `poset`). Only present if `keep.data` was `TRUE`

estimator.nleqslv varianceestimator.sandwich

set.seed(1)
iris$out<-factor(sample(2, nrow(iris), replace=TRUE))
iris$xord<-as.ordered(iris$Species)
pima<-pim(out~Sepal.Length, data=iris, link="logit", interpretation="regular")
pimb<-pim(out~I((R(Sepal.Length) - L(Sepal.Length))/sqrt(R(Sepal.Length) * L(Sepal.Length)) ), data=iris, 
	link="logit", interpretation="regular", extra.nicenames=data.frame(
		org="I((R(Sepal.Length) - L(Sepal.Length))/sqrt(R(Sepal.Length) * L(Sepal.Length)) )", 
		nice="Sepal.Length.WDiff", stringsAsFactors=FALSE))
pimc<-pim(out~O(xord), data=iris, link="logit", interpretation="regular")
##And an example with weighted fitting
iris50<-iris[sample.int(nrow(iris), 50),]
rwt50<-runif(50) #50 random weights
pimd<-pim(out~O(xord), data=iris50, link="logit", interpretation="regular",
		weights = rwt50)