Kernel Modal Regression with Mixed Data Types

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.

Usage

npconmode(bws, 
          ...)

# S3 method for class 'formula'
npconmode(bws, 
          data = NULL,
          newdata = NULL, 
          ...,
          se = FALSE)

# Default S3 method
npconmode(bws, 
          txdat, 
          tydat, 
          nomad = FALSE,
          proper = NULL,
          proper.control = list(),
          probabilities = FALSE,
          gradients = FALSE,
          level = NULL,
          ...,
          se = FALSE)

# S3 method for class 'conbandwidth'
npconmode(bws,
          txdat = stop("invoked without training data 'txdat'"),
          tydat = stop("invoked without training data 'tydat'"),
          exdat,
          eydat,
          proper = NULL,
          proper.control = list(),
          probabilities = FALSE,
          gradients = FALSE,
          level = NULL,
          ...,
          se = FALSE)

# S3 method for class 'conmode'
predict(object,
          newdata = NULL,
          type = c("class", "prob"),
          se.fit = FALSE,
          ...)

# S3 method for class 'conmode'
plot(x, ...)

Details

Conditional fitting and replay use the literal categorical kernels for both explanatory and response variables. Near-upper categorical bandwidths are not replaced by approximate constant factors. The same target is used for levels, gradients, standard errors, conditional hats, and reconstructed plot uncertainty. This conditional-family policy does not change direct regression or unconditional kernel-sum evaluation and does not modify the caller's np.largelambda option.

New fitted objects record conditional.kernel.policy. Stored values in older objects remain available without refitting. A plot that combines such historical stored class probabilities or effects with newly reconstructed bootstrap or joint asymptotic uncertainty requires an explicit refit under the current policy. Stored pointwise probability standard errors remain readable. Explicit predictions at new evaluation data use the current conditional target and retain the object's probability-normalization controls.

With a formula-derived bandwidth object, supplying only tydat replaces the evaluated response in retained-training-sample order: one row per observation retained after the original subset and missing-value selection. Original excluded rows are not restored; row names or equal lengths do not imply original-data order. The effective na.action is then applied jointly to the retained predictors and replacement response; any new omission indices refer to this retained sample. The selected bandwidths remain fixed, and the returned object retains the replacement sample for subsequent refits. Use data for full formula-data replacement, not together with a partial response replacement. Explicit native training arguments retain their separate positional contract.

For native (non-formula) objects, named newdata columns are matched to the retained variable names and reordered as needed. Extra columns are ignored; missing, duplicate or ambiguous names are errors. An unnamed matrix or vector remains positional for a single evaluation role. Explicit native evaluation arguments retain their positional contract and take precedence over newdata.

For both formula and native bandwidth objects, newdata supplies evaluation predictors and, when present, named evaluation outcomes. Explicit native evaluation arguments take precedence. This evaluates at the requested rows without repeating bandwidth selection.

With evaluation predictors and outcomes, the confusion matrix and accuracy describe the evaluation sample. With evaluation predictors only, predictions are returned normally and these diagnostics are NA. Without evaluation inputs, diagnostics describe training data. Missing evaluation outcomes do not remove predictor-complete prediction rows: scores use observed outcome/prediction pairs, and are NA when none are available. Supplying outcomes never changes fitted class support, probabilities or gradients.

Formula variables of class ts, including lagged variables, are aligned on their common time support before fitting. Other variables must already have the length of that intersection. Supply explicitly aligned rows when using native data arguments instead of a formula.

Local-polynomial calculations inherit numerical ridging from npcdens. See that estimator's help for the solve policy and its distinct conditional uncertainty formula.

Documentation guide: see np.kernels for kernels, np.options for global options, and plot for plotting options.

If bws is not an explicit conbandwidth object, npconmode computes conditional density bandwidths by forwarding the call to npcdensbw. The resulting conbandwidth object is stored unchanged in the returned object's bws component. Regression type, local-polynomial degree, and NOMAD/search metadata are also mirrored on the returned conmode object for convenient inspection and summary reporting; the bandwidth object remains the canonical source.

For non-local-constant conditional-mode fits, npconmode constructs the full set of fitted probabilities over the discrete response support and projects them onto the probability simplex before selecting and reporting the modal outcome. Thus the probabilities used for modal selection are non-negative and sum to one over the discrete support. For binary outcomes this is the complement contract \(\Pr(Y=\ell_2\mid X=x)=1-\Pr(Y=\ell_1\mid X=x)\). Local-constant fits are already proper by construction, so omitted proper resolves to FALSE for regtype="lc" and to TRUE otherwise.

The predict method follows the usual S3 newdata convention. With no evaluation arguments, predict(fit) extracts the stored modal class and predict(fit, type="prob") extracts stored class probabilities when the object was fitted with probabilities=TRUE. With newdata, predict evaluates the conditional mode at the supplied rows using the stored bandwidth object. Use type="prob" to return the full matrix of fitted class probabilities at the evaluation rows. Use se.fit=TRUE with type="prob" to return a list containing the class-probability matrix and the matching asymptotic standard-error matrix. Native evaluator arguments exdat and eydat remain available for advanced workflows and take precedence if supplied.

Arguments

Data, Bandwidth Inputs And Formula Interface

These arguments identify the bandwidth specification, formula/data interface, and training data.

bws

a bandwidth specification. This can be set as a conbandwidth object returned from an invocation of npcdensbw

data

This argument belongs to the formula interface (including supported formula-bearing bandwidth-object refits). Native calls must supply their vectors, matrices or data frames directly, or use with(data, ...). Supplying data, including explicit data = NULL, to a native route that does not consume it is an error; see np-data-interfaces.

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.

txdat

a \(p\)-variate data frame of explanatory data (conditioning data) used to calculate the regression estimators. Defaults to the corresponding training data recoverable from the bandwidth object in the current session.

tydat

a one (1) dimensional vector of unordered or ordered factors, containing the dependent data. Defaults to the corresponding training data recoverable from the bandwidth object in the current session.

object

an object of class "conmode" returned by npconmode.

x

an object of class "conmode" returned by npconmode.

Evaluation Data

These arguments control where the conditional mode is evaluated.

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

Optional observed categorical outcomes at exdat, supplied as a factor or a one-column data frame. Used only for evaluation diagnostics. When exdat is supplied without outcomes, classification diagnostics are NA; training outcomes are not substituted.

newdata

An optional data frame in which to look for evaluation data. If omitted, the training data are used.

type

prediction type for predict.conmode. Use "class" to return modal class predictions and "prob" to return the full class-probability matrix. Probability prediction requires either a stored probability matrix or an evaluation path with probabilities=TRUE.

se.fit

a logical value for predict.conmode. If TRUE and type="prob", return a list with components fit and se.fit, containing the class-probability matrix and matching asymptotic standard-error matrix. Standard errors are for the smooth class-probability target, not for the discrete modal class label.

se

a scalar logical controlling raw class-probability asymptotic standard errors. Defaults to FALSE; class selection, fitted probabilities and requested probability gradients are unchanged. With TRUE, selected-probability errors are stored, together with the full error matrix when probabilities are retained. This does not add standard errors for the modal class label or probability gradients, or change the existing unavailable-error treatment after projection. To add uncertainty without repeating bandwidth search, use npconmode(bws=fit$bws, probabilities=TRUE, se=TRUE), supplying the original training/evaluation data if needed. Prediction with new evaluation rows and se.fit=TRUE requests this work explicitly; prediction without evaluation rows only extracts stored results.

Probability And Gradient Controls

These arguments control stored class probabilities, proper probability normalization, and level-specific class-probability effects.

probabilities

a logical value. If TRUE, store the full matrix of fitted probabilities over the discrete response support in the returned object. This support is retained from the fitted training data. Response values supplied for evaluation diagnostics, including unused or unseen factor levels, do not enlarge or reorder the predicted class support. The default FALSE keeps returned objects compact. If gradients=TRUE, probabilities are stored automatically because they are the object-fed target for plot.conmode.

gradients

a logical value. If TRUE, compute and store class-probability gradients/effects for one response level with respect to the conditioning variables. These are derivatives for continuous conditioning variables and the corresponding smooth finite-change effects for discrete conditioning variables as produced by the underlying npcdens gradient route. The target is the smooth class probability \(\Pr(Y=\ell\mid X=x)\), not the derivative of the discrete modal class label. With a heterogeneous local-polynomial conditional-density degree vector, a degree-zero continuous coordinate has no available first-derivative estimate and is stored as NA; the established all-degree-zero compatibility route is unchanged. Its plot panel remains present but empty when another derivative or categorical first-difference effect is available; a request with no available component is rejected.

level

response level for class-probability gradients/effects and plot.conmode class-probability displays. If omitted, the default is the base/reference response level, levels(y)[1], for both factor and ordered responses. Users should supply level explicitly when a particular binary or multinomial outcome probability is of substantive interest.

proper

a logical value or NULL. If TRUE, fitted probabilities over the discrete response support are projected onto the probability simplex before modal selection. If FALSE, legacy raw per-level values are used. If NULL, the default is estimator-aware: FALSE for regtype="lc" and TRUE otherwise.

proper.control

a list of controls for discrete probability properization. Currently tol may be supplied to set the numerical tolerance for non-negativity and unit-mass checks.

Local-Polynomial Degree And Bandwidth Search

This argument controls the recommended automatic local-polynomial NOMAD route, which jointly selects continuous polynomial degree and bandwidths when conditional-density bandwidths are computed inside npconmode.

nomad

logical shortcut passed through to npcdensbw when bandwidths are computed inside npconmode. When TRUE, the conditional-density bandwidth route fills any missing values among regtype, search.engine, degree.select, bernstein.basis, degree.min, degree.max, degree.verify, and bwtype with the recommended automatic local-polynomial degree-and-bandwidth NOMAD preset documented in npcdensbw. Additional NOMAD tuning arguments such as nomad.nmulti may also be supplied through ...; nmulti remains the outer restart count while nomad.nmulti controls inner native crs NOMAD API multistarts within each outer restart. Automatic NOMAD degree search requires at least one continuous explanatory variable. After fitting, inspect fit$bws$nomad.shortcut on the returned object fit to see the normalized shortcut metadata.

Additional Arguments

Further arguments are passed to the bandwidth-selection counterpart, prediction/evaluation route, or plot route as appropriate.

...

additional arguments supplied to npcdensbw when bandwidths are computed internally, or arguments needed to interpret a numeric bws vector. This is where bandwidth-selection controls such as bwmethod, bwtype, and bwscaling, kernel/support controls such as cxkertype, cykertype, cxkerorder, cykerorder, cxkerbound, and cykerbound, categorical kernel controls such as uxkertype, uykertype, oxkertype, and oykertype, search controls such as nmulti and scale.factor.search.lower, and local-polynomial/NOMAD controls such as regtype, degree, bernstein.basis, degree.select, and nomad.nmulti are supplied. In predict.conmode, additional arguments are passed to npconmode for evaluation with the stored bandwidth object; native exdat and eydat take precedence over newdata. In plot.conmode, additional arguments control the object-fed probability/effect display; common controls include gradients, level, output, data.rug, layout, legend, view, neval, grid.control, perspective, plot.vars, renderer, and graphics arguments. The default view="sample" draws the stored fitted probabilities/effects at the fitted evaluation points. The view="fixed" route constructs one-dimensional object-fed slices over each conditioning variable, holding the remaining variables at their uocquantile median/mode values; np_grid_control(xtrim=..., xq=...) may be used to adjust the slice range and holdout quantiles. With perspective=TRUE, the same fixed-grid route draws a base-graphics probability surface for one selected response level over two continuous conditioning variables; use plot.vars to name the displayed pair when more than two continuous conditioning variables are present; renderer="rgl" requests the corresponding interactive surface when rgl is available. Both renderers use the package-wide viridis surface palette and neutral-gray three-face reference grid. The public output="both" spelling is accepted as an alias for the historical output="plot-data" behavior. For class-probability plots, errors="asymptotic" adds probability-level asymptotic standard errors and intervals for the selected response level. These intervals target the smooth probability \(\Pr(Y=\ell\mid X=x)\), not the discrete modal class label. Rows whose probabilities were materially repaired by simplex projection report NA standard errors rather than treating raw conditional-density standard errors as projected-probability standard errors. Bootstrap intervals are available for one-dimensional view="sample" and view="fixed" probability plots and use the stored bandwidth object without recomputing bandwidths. Class-probability bootstrap plots default to B=399; an explicit B overrides this count. Asymptotic joint bands are available for regular local-constant and local-polynomial probability and first-gradient/effect routes. A joint Gaussian maximum is calibrated for one named response level on the declared grid within each panel. The complete response support enters the proper-probability Jacobian, including smooth positive normalization, clipping with a stable sign pattern, and projection onto a stable simplex face. Branch seams or unidentified zero-variance rows remain unavailable. A zero Jacobian caused by clipping or projection onto an inactive class or a singleton face does not certify a constant sampling target. Such a panel warns and returns unavailable simultaneous limits, retaining the plotted probabilities and any available pointwise and Bonferroni intervals. Proper probability bands use standard errors from that transformed influence; existing pointwise and Bonferroni probability bands retain their own convention in band="all".

Continuous gradient plots retain the raw underlying conditional-density derivative documented above, including when proper=TRUE; the derivative is not silently changed to a normalized-probability derivative. Categorical effects retain the existing view/engine policy: fixed-view and positive-degree sample effects difference endpoint probabilities, while local-constant and degree-zero sample effects retain their native raw contrast. New asymptotic gradient intervals use the covariance and marginal scale of that particular target. Bootstrap gradient intervals remain unsupported.

These are selected-smoothing variability approximations for the named level, not confidence bands for the winning class, all classes jointly, smoothing bias or values between grid points. Independent observations, regular local neighborhoods and valid covariance/marginal approximations are required. Local-polynomial and fixed-view response factors use local response moments as a working covariance approximation conditional on the accepted design and response map. At fixed nonshrinking bandwidths they need not equal a full empirical-caseweight derivative, which also perturbs the design map. Their leading interpretation requires smooth shrinking neighborhoods and nondegenerate local response variation. A growing or data-dependent sample grid additionally needs uniform linearization, non-dominating influences and a Gaussian-maximum approximation; arbitrary sample grids do not carry a uniform-coverage guarantee. gaussian.draws (default 9999) and random.seed control reproducible joint calibration; pointwise and Bonferroni-only gradient requests need no Gaussian simulation. Surface asymptotic intervals are available as output="data" payload columns and are drawn with graphical surface output. The two conditioning variables selected by plot.vars must be distinct. Surface bootstrap intervals remain unsupported. See npcdensbw and plot.np for the complete bandwidth-selection and plotting argument surfaces.

Value

npconmode returns a conmode object with the following components:

omit, rows.omit, nobs.omit

omitted-row information for the active result: training rows for training evaluation, otherwise evaluation rows. Existing NA padding of row outputs is unchanged.

train.rows.omit, train.nobs.omit

training omissions, retained separately even when the result evaluates external data.

eval.omit, eval.rows.omit, eval.nobs.omit

omission information for external evaluation data, when supplied.

bws

the conbandwidth object used to compute the conditional mode

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 modal density estimates at each evaluation point

conderr

a vector of numeric type containing asymptotic standard errors for the modal density estimates at each evaluation point. This component is NULL when se=FALSE. If a row is materially properized by probability projection, this value is set to NA for that row rather than reporting a standard error for the unrepaired raw probability.

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

diagnostics.sample

"training", "evaluation", or "unavailable", identifying the sample used for classification diagnostics.

diagnostics.nobs

Number of observed outcome/prediction pairs scored.

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

probabilities

if requested, a matrix of fitted probabilities over the discrete response support

probability.levels

if probabilities are stored, the response support corresponding to the probability matrix columns

probability.errors

if probabilities are stored, a matrix of asymptotic standard errors for the raw selected-level conditional probability estimates, with rows set to NA where simplex projection materially repaired the fitted probability vector; omitted when se=FALSE

se

whether probability uncertainty was requested

probability.repaired.rows

if probabilities are stored, a logical vector identifying rows whose fitted probability vector was materially repaired by proper probability projection

probability.gradients

if gradients=TRUE, a matrix whose entries are class-probability gradients/effects for one selected response level, indexed by evaluation row and conditioning variable

probability.gradient.level, probability.gradient.names, probability.gradient.info

metadata describing the response support, conditioning variables, and interpretation of probability.gradients

proper.requested, proper.applied, proper.info

metadata describing whether discrete probability properization was requested, whether any row was materially projected, and diagnostics for non-negativity and unit-mass checks

regtype, degree, nomad, search.engine

metadata mirrored from bws describing the conditional density regression type, local-polynomial degree, whether NOMAD automatic degree search was used, and the search engine when available

degree.search, nomad.shortcut, nomad.time, powell.time

detailed search metadata mirrored from bws when automatic degree search or NOMAD/Powell refinement was used

The function predict may be used to extract conditional mode class predictions, while fitted extracts the conditional density estimates at the conditional mode from the resulting object. The function gradients extracts class-probability gradients/effects when gradients=TRUE. Also, summary and plot support conmode objects. For plot.conmode, first fit with probabilities=TRUE so the plot can remain object-fed. The default plot displays the fitted probability for the base/reference response level levels(y)[1]; use plot(fit, level=...) to select another outcome and plot(fit, gradients=TRUE) to display stored class-probability effects. The default view="sample" draws stored object-fed probability/effect payloads at the fitted evaluation points. Use view="fixed" and neval to draw object-fed one-dimensional slices over each conditioning variable, with other variables held at their median/mode values. Use perspective=TRUE to draw a base-graphics probability surface for one selected response level over two continuous conditioning variables, and renderer="rgl" for the corresponding interactive surface. Use errors="asymptotic" for probability-level standard errors and intervals in one-dimensional plots and fixed surfaces. Surface band="all" draws the pointwise, simultaneous and Bonferroni endpoints already present in output="data"; coverage remains for the selected response level on the declared evaluation grid, not jointly across response levels. Automatic surface limits include finite interval endpoints without truncating them to \([0,1]\). Explicit zlim controls drawing only and does not alter returned endpoints. Unavailable interval cells remain missing. Surface bootstrap intervals and gradient surfaces are not implemented.

Book And Method Pointers

The conditional-mode target is \(\arg\max_y \Pr(Y=y\mid X=x)\) for a discrete response. In practice npconmode estimates the conditional probability of each response support point using npcdensbw / npcdens, optionally projects non-local-constant fitted probabilities onto the probability simplex, and then reports the modal support point.

Setting gradients=TRUE stores class-probability effects for one response level. If level is omitted, the base/reference response level levels(y)[1] is used. These effects are useful for asking how the fitted probability of a selected class changes with each covariate; they are not gradients of the arg max classification rule itself.

For book-length background, see Racine (2019), Chapter 4 Conditional Probability Density and Cumulative Distribution Functions, especially the binary and multinomial choice material, and Li and Racine (2007), Chapter 5 Conditional Density Estimation together with Chapter 4 Kernel Estimation with Mixed Data.

References

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.

Author

Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca

Usage Issues

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.

For formula fits, character-valued missing-data policies are resolved through the stats model-frame owner; pass a caller-local policy as a function object rather than its name. With replacement training data, an explicit na.action, including NULL, takes precedence over the saved policy. Native fitting still requires complete rows, so a permissive policy does not guarantee the same retained sample as stats::model.frame.

When reusing a bandwidth object without explicit training or evaluation data, newly constructed bandwidth objects use retained training values. A fit with explicit replacement training data retains that sample in its returned bandwidth object; evaluation grids do not replace training data. Older objects without retained samples use their stored call environment when available and may require explicit training inputs after saveRDS() and readRDS(). Custom formula transformations used on new evaluation data must still be available.

See also

np.kernels, np.options, plot, npcdensbw.

Examples

if (FALSE) { # \dontrun{
# 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")

birthwt$low <- factor(birthwt$low)
birthwt$smoke <- factor(birthwt$smoke)
birthwt$race <- factor(birthwt$race)
birthwt$ht <- factor(birthwt$ht)
birthwt$ui <- factor(birthwt$ui)
birthwt$ftv <- ordered(birthwt$ftv)

with(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~smoke+
                   race+
                   ht+
                   ui+
                   ftv+
                   age+
                   lwt,
                   family=binomial(link=logit))

# Generate the confusion matrix and correct classification ratio

cm <- table(factor(low, levels = 0:1),
            factor(ifelse(fitted(model.logit)>0.5, 1, 0), levels = 0:1))
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...

bw <- npcdensbw(formula=low~smoke+
                race+
                ht+
                ui+
                ftv+
                age+
                lwt,
                data=birthwt)

model.np <- npconmode(bws=bw)

# Compare confusion matrices from the logit and nonparametric model

# Logit

cm
ccr

# Nonparametric
summary(model.np)

# Predict modal classes and fitted class probabilities at selected rows
new.birthwt <- birthwt[1:5, c("smoke", "race", "ht", "ui", "ftv", "age", "lwt")]
predict(model.np, newdata=new.birthwt)
predict(npconmode(bws=bw, probabilities=TRUE),
        newdata=new.birthwt, type="prob")

})

# 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")
with(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(factor(low, levels = 0:1),
            factor(ifelse(fitted(model.logit)>0.5, 1, 0), levels = 0:1))
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...

bw <- npcdensbw(xdat=X, ydat=y)

model.np <- npconmode(bws=bw)

# Compare confusion matrices from the logit and nonparametric model

# Logit

cm
ccr

# Nonparametric
summary(model.np)

})

# EXAMPLE 3 (CLASS PROBABILITY EFFECTS): compute and plot
# class-probability gradients/effects for one selected response level.
# This example uses a small artificial sample so that it runs quickly.

set.seed(42)
n <- 100
x <- seq(-1, 1, length.out=n)
y <- factor(rbinom(n, 1, plogis(1.5*x)), levels=0:1)

model.effects <- npconmode(y~x,
                           regtype="ll",
                           bwmethod="cv.ls",
                           nmulti=1,
                           probabilities=TRUE,
                           gradients=TRUE)

gradients(model.effects)
plot(model.effects)
plot(model.effects, view="fixed", neval=25)
plot(model.effects, gradients=TRUE)
plot(model.effects, gradients=TRUE, view="fixed", neval=25)

## A two-continuous-predictor fit can be displayed as a probability surface.
## The default level is the base/reference response level.

z <- runif(n, -1, 1)
y2 <- factor(rbinom(n, 1, plogis(1.5*x - z)), levels=0:1)
model.surface <- npconmode(y2~x+z,
                           regtype="ll",
                           bwmethod="cv.ls",
                           nmulti=1,
                           probabilities=TRUE)
plot(model.surface, perspective=TRUE, neval=15)
} # }