npse function

Nonparametric Power Series Estimation

Use the optimal order of power series of covariates to predict outcome. The optimal order of power series is determined by cross-validation.

npse(formula, order = 3, m = 10, seed = NULL)

Arguments

• formula: specification of the outcome model in the form like either z ~ x1 + x2 or z ~ X where X is the covariate matrix.
• order: the maximal order of power series to be used.
• m: the number of folds to be used in cross-validation.
• seed: random starting number used to replicate cross-validation.

Details

This function predicts the outcome based on the optimal order of covariates power series. The optimal order of the power series is determined by cross-validation. For example, it can be used to predict the probabilty of receiving treatment inducment based on covariates.

Returns

• fitted: Predicted outcomes based on the estimated model. They are probabilities when the outcome is binary.

• Lambda: The optimal order of power series determined by cross-validation.

• Data.opt: The data including z and the optimal covariates power series.

• CV.Res: The residual sum of squares of the cross-validations.

• seed: The random seed.

References

Abadie, Alberto. 2003. "Semiparametric Instrumental Variable Estimation of Treatment Response Models." Journal of Econometrics 113: 231-263.

Author(s)

Weihua An, Departments of Sociology and Statistics, Indiana University Bloomington, weihuaan@indiana.edu .

Xuefu Wang, Department of Statistics, Indiana University Bloomington, wangxuef@umail.iu.edu .

See Also

larf, larf.fit

Examples

data(c401k)
attach(c401k)

## Not run:

# binary outcome
Z <- c401k$e401k

# covariates
X <- as.matrix(c401k[,c("inc", "male", "fsize"  )])

# get nonparametric power series estimation of the regression of Z on X
zp <- npse(Z~X, order = 5, m = 10, seed = 681)

# sum of residual squares of the cross-validations
zp$CV.Res

# the opitimal order of the power series
zp$Lambda

# summary of the predictions based on the optimal power series
summary(zp$fitted)
## End(Not run)