Description Usage Arguments Value Author(s) References See Also Examples
Compute conditional midcumulative probabilities
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formula 
an object of class " 
data 
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. By default the variables are taken from the environment from which the call is made. 
ecdf_est 
estimator of the (standard) conditional cumulative distribution. The options are: 
bws 
optional bandwidth specification. See 
theta 
values of the ArandaOrdaz transformation parameter for grid search when 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
weights 
an optional vector of weights to be used in the fitting process. Not currently implemented. 
na.action 
a function which indicates what should happen when the data contain 
contrasts 
an optional list. See the contrasts.arg of 
x 
design matrix of dimension n * p. 
y 
vector of observations of length n. 
intercept 
logical flag. Does 
An object of class class
cmidecdf
with midcumulative probabilities. This is a list that contains:
G 
Estimated conditional midprobabilities. This is a n * k matrix, where n is the sample size and k is the number of unique values of 
Fhat 
Estimated (standard) cumulative probabilities. 
Fse 
Standard error for Fhat. 
yo 
unique values of 
bw 

ecdf_est 
estimator used. 
Marco Geraci with contributions from Alessio Farcomeni
Geraci, M. and A. Farcomeni. Midquantile regression for discrete responses. arXiv:1907.01945 [stat.ME]. URL: http://arxiv.org/abs/1907.01945.
Li, Q. and J. S. Racine (2008). Nonparametric estimation of conditional cdf and quantile functions with mixed categorical and continuous data. Journal of Business and Economic Statistics 26(4), 423434.
Peracchi, F. (2002). On estimating conditional quantiles and distribution functions. Computational Statistics and Data Analysis 38(4), 433447.
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