jma: JMA: Jackknife Model Averaging
In MAMI: Model Averaging and Model Selection after Multiple Imputation

Description Usage Arguments Details Value Author(s) References Examples

Performs model averaging on a set of (linear) candidate models with the weight vector chosen such that the leave-one-out cross validation error is minimized.

1 2	jma(y,x,ma.method=c("JMA","MMA"),model.subset=c("nested","all"),factor.variables=NULL,pd=T, calc.var=c("none","boot"), bsa=100)

`y`	The response vector.
`x`	A matrix or dataframe containing the covariates.
`ma.method`	A character vector specifiying whether jackknife model averaging (JMA) or Mallow's model averaging (see `mma`) should be performed.
`model.subset`	A character vector specifiying whether all 2^p candidate models should be considered or the nested subset of p models.
`factor.variables`	A (vector of) string(s) specifying which variables should be treated as factors, i.e. recoded into dummy variables. Factor variables will automatically be recoded if not specified here.
`pd`	A logical value specifying whether messages should be printed or not.
`calc.var`	A character vector specifying whether no variance should be estimated or based on bootstrapping (`"boot"`).
`bsa`	A positive integer specifying the number of bootstrap samples used if `variance` `=` `"boot"`.

This function utilizes Jackknife model averaging as described in Hansen and Racine (2012), see reference below.

If subset = "all", then 2^p candidate models are being evaluated. This means p can't be too large (say<20) and occasionally the residual matrix used in the quadtratic programming problem may not be positive definite. In the latter case, this matrix is altered by jma such that it is positive definite, but results should be interpreted with care.

Returns an object of class ‘jma’:

`betahat`	estimates coefficients
`se`	standarde error
`lci`	lower confidence limit
`uci`	upper confidence limit
`weight`	JMA weight vector
`yhat`	fitted values
`ehat`	fitted residuals
`y`	outcome variable
`x`	matrix of covariates

Michael Schomaker (based on the file of Bruce Hansen at https://www.ssc.wisc.edu/~bhansen/progs/joe_12.html)

Hansen, B. and Racine, J. (2012), Jackknife Model Averaging, Journal of Econometrics, 167:38-46

data(Prostate)
jma(y=Prostate$lpsa,x=Prostate[,-9])
jma(y=Prostate$lpsa,x=Prostate[,-9], calc.var="boot", bsa=100)

# can also perform Mallow's Model Averaging as in mma()
jma(y=Prostate$lpsa,x=Prostate[,-9],ma.method="MMA")
mma(Prostate, formula=lpsa~.,ycol="lpsa")