| npconmode {np} | R Documentation |
npconmode performs kernel modal regression on mixed data,
and finds the
conditional mode given a set of training data, consisting of
explanatory data and dependent data, and possibly evaluation data.
Automatically computes various in sample and out of sample measures of
accuracy.
npconmode(bws, ...)
## S3 method for class 'formula':
npconmode(bws, data = NULL, newdata = NULL, ...)
## S3 method for class 'call':
npconmode(bws, ...)
## Default S3 method:
npconmode(bws, txdat, tydat, ...)
## S3 method for class 'conbandwidth':
npconmode(bws,
txdat = stop("invoked without training data 'txdat'"),
tydat = stop("invoked without training data 'tydat'"),
exdat,
eydat,
...)
bws |
a bandwidth specification. This can be set as a conbandwidth
object returned from an invocation of npcdensbw
|
... |
additional arguments supplied to specify the bandwidth type,
kernel types, and so on, detailed below.
This is necessary if you specify bws as a p+q-vector and not
a conbandwidth object, and you do not desire the default behaviours.
|
data |
an optional data frame, list or environment (or object
coercible to a data frame by as.data.frame) containing the variables
in the model. If not found in data, the variables are taken from
environment(bws), typically the environment from which
npcdensbw was called.
|
newdata |
An optional data frame in which to look for evaluation data. If omitted, the training data are used. |
txdat |
a p-variate data frame of explanatory data (conditioning data) used to calculate the regression estimators. Defaults to the training data used to compute the bandwidth object. |
tydat |
a one (1) dimensional vector of unordered or ordered factors, containing the dependent data. Defaults to the training data used to compute the bandwidth object. |
exdat |
a p-variate data frame of points on which the regression will be
estimated (evaluation data). By default,
evaluation takes place on the data provided by txdat.
|
eydat |
a one (1) dimensional numeric or integer vector of the true values
(outcomes) of the dependent variable. By default,
evaluation takes place on the data provided by tydat.
|
npconmode returns a conmode object with the following
components:
conmode |
a vector of type factor (or ordered
factor) containing the conditional mode at each evaluation
point |
condens |
a vector of numeric type containing the density estimates at each evaluation point |
xeval |
a data frame of evaluation points |
yeval |
a vector of type factor (or ordered
factor) containing the actual outcomes, or NA if not
provided |
confusion.matrix |
the confusion matrix or NA if outcomes
are not available |
CCR.overall |
the overall correct
classification ratio, or NA if outcomes are not available |
CCR.byoutcome |
a numeric vector containing the correct
classification ratio by outcome, or NA if outcomes are not
available |
fit.mcfadden |
the McFadden-Puig-Kerschner performance measure
or NA if outcomes are not available |
The functions mode, and fitted may be used to
extract the conditional mode estimates, and the conditional density
estimates at the conditional mode, respectively,
from the resulting object. Also, summary supports
conmode objects.
If you are using data of mixed types, then it is advisable to use the
data.frame function to construct your input data and not
cbind, since cbind will typically not work as
intended on mixed data types and will coerce the data to the same
type.
Tristen Hayfield hayfield@phys.ethz.ch, Jeffrey S. Racine racinej@mcmaster.ca
Aitchison, J. and C.G.G. Aitken (1976), “Multivariate binary discrimination by the kernel method,” Biometrika, 63, 413-420.
Hall, P. and J.S. Racine and Q. Li (2004), “Cross-validation and the estimation of conditional probability densities,” Journal of the American Statistical Association, 99, 1015-1026.
Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press.
McFadden, D. and C. Puig and D. Kerschner (1977), “Determinants of the long-run demand for electricity,” Proceedings of the American Statistical Association (Business and Economics Section), 109-117.
Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.
Scott, D.W. (1992), Multivariate Density Estimation. Theory, Practice and Visualization, New York: Wiley.
Silverman, B.W. (1986), Density Estimation, London: Chapman and Hall.
Wang, M.C. and J. van Ryzin (1981), “A class of smooth estimators for discrete distributions,” Biometrika, 68, 301-309.
# EXAMPLE 1 (INTERFACE=FORMULA): For this example, we use the
# birthweight data taken from the MASS library, and compute a parametric
# logit model and a nonparametric conditional mode model. We then
# compare their confusion matrices and summary measures of
# classification ability.
library("MASS")
data("birthwt")
attach(birthwt)
# Fit a parametric logit model with low (0/1) as the dependent
# variable and age, lwt, and smoke (0/1) as the covariates
# From ?birthwt
# 'low' indicator of birth weight less than 2.5kg
# 'smoke' smoking status during pregnancy
# 'race' mother's race ('1' = white, '2' = black, '3' = other)
# 'ht' history of hypertension
# 'ui' presence of uterine irritability
# 'ftv' number of physician visits during the first trimester
# 'age' mother's age in years
# 'lwt' mother's weight in pounds at last menstrual period
model.logit <- glm(low~factor(smoke)+
factor(race)+
factor(ht)+
factor(ui)+
ordered(ftv)+
age+
lwt,
family=binomial(link=logit))
# Generate the confusion matrix and correct classification ratio
cm <- table(low, ifelse(fitted(model.logit)>0.5, 1, 0))
ccr <- sum(diag(cm))/sum(cm)
# Now do the same with a nonparametric model. Note - this may take a
# few minutes depending on the speed of your computer... we override the
# default search tolerances for speed considerations...
bw <- npcdensbw(formula=factor(low)~factor(smoke)+
factor(race)+
factor(ht)+
factor(ui)+
ordered(ftv)+
age+
lwt,
tol=.1, ftol=.1)
model.np <- npconmode(bws=bw)
# Compare confusion matrices from the logit and nonparametric model
# Logit
cm
ccr
# Nonparametric
summary(model.np)
detach(birthwt)
## Not run:
# EXAMPLE 1 (INTERFACE=DATA FRAME): For this example, we use the
# birthweight data taken from the MASS library, and compute a parametric
# logit model and a nonparametric conditional mode model. We then
# compare their confusion matrices and summary measures of
# classification ability.
library("MASS")
data("birthwt")
attach(birthwt)
# Fit a parametric logit model with low (0/1) as the dependent
# variable and age, lwt, and smoke (0/1) as the covariates
# From ?birthwt
# 'low' indicator of birth weight less than 2.5kg
# 'smoke' smoking status during pregnancy
# 'race' mother's race ('1' = white, '2' = black, '3' = other)
# 'ht' history of hypertension
# 'ui' presence of uterine irritability
# 'ftv' number of physician visits during the first trimester
# 'age' mother's age in years
# 'lwt' mother's weight in pounds at last menstrual period
model.logit <- glm(low~factor(smoke)+
factor(race)+
factor(ht)+
factor(ui)+
ordered(ftv)+
age+
lwt,
family=binomial(link=logit))
# Generate the confusion matrix and correct classification ratio
cm <- table(low, ifelse(fitted(model.logit)>0.5, 1, 0))
ccr <- sum(diag(cm))/sum(cm)
# Now do the same with a nonparametric model...
X <- data.frame(factor(smoke),
factor(race),
factor(ht),
factor(ui),
ordered(ftv),
age,
lwt)
y <- factor(low)
# Note - this may take a few minutes depending on the speed of your
# computer... we override the default search tolerances for speed
# considerations...
bw <- npcdensbw(xdat=X, ydat=y, tol=.1, ftol=.1)
model.np <- npconmode(bws=bw)
# Compare confusion matrices from the logit and nonparametric model
# Logit
cm
ccr
# Nonparametric
summary(model.np)
detach(birthwt)
## End(Not run)