Package 'csurvey'

Title:	Constrained Regression for Survey Data
Description:	Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.
Authors:	Xiyue Liao [aut, cre]
Maintainer:	Xiyue Liao <[email protected]>
License:	GPL (>= 2)
Version:	1.15
Built:	2026-06-03 08:53:06 UTC
Source:	https://github.com/xliaosdsu/csurvey

Help Index

Specify a Block Monotonic Shape-Restriction in a CSVY Formula
Estimation of Domain Means with Monotonicity or Convexity Constraints
Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis
Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis
Control settings for plot.csvy
Plot method for csvy objects Generates a diagnostic or summary plot from a fitted "csvy" object. Supports both single-factor and two-factor visualization. Aesthetic settings can be customized using plot_csvy_control().
Create a Control List for plotpersp.csvy

Specify a Block Monotonic Shape-Restriction in a CSVY Formula

Description

A symbolic routine to define that a vector of domain means follows a monotonic ordering in a predictor in a formula argument to csvy. This is the unsmoothed version.

Usage

  block.Ord(x, order = NULL, numknots = 0, knots = 0, space = "E")
block.Ord(x, order = NULL, numknots = 0, knots = 0, space = "E")

Arguments

x

A numeric predictor which has the same length as the response vector.

order

A $1 \times M$ vector defining the order of domains when the shape constraint is block ordering.

numknots

The number of knots used to smoothly constrain a predictor. The value should be $0$ for a shape-restricted predictor without smoothing. The default value is $0$ .

knots

The knots used to smoothly constrain a predictor. The value should be $0$ for a shape-restricted predictor without smoothing. The default value is $0$ .

space

A character specifying the method to create knots. It will not be used for a shape-restricted predictor without smoothing. The default value is "E".

Value

The vector x with five attributes, i.e., name: the name of x; shape: 9("block ordering"); numknots: the numknots argument in "block.Ord"; knots: the knots argument in "block.Ord"; space: the space argument in "block.Ord".

Author(s)

Xiyue Liao

Estimation of Domain Means with Monotonicity or Convexity Constraints

Description

The csvy function performs design-based domain mean estimation with monotonicity and block-monotone shape constraints.

For example, in a one dimensional situation, we assume that $\bar{y}_{U_t}$ are non-decreasing over $T$ domains. If this monotonicity is not used in estimation, the population domain means can be estimated by the Horvitz-Thompson estimator or the Hajek estimator. To use the monotonicity information, this csvy function starts from the Hajek estimates $\bar{y}_{S_t} = (\sum_{k\in S_t}y_k/\pi_k)/N_t$ and the isotonic estimator $(\hat{\theta}_1,\ldots,\hat{\theta}_T)^T$ minimizes the weighted sum of squared deviations from the sample domain means over the set of ordered vectors; that is, $\bold{\hat{\theta}}$ is the minimizer of $(\tilde{\bold{y}}_{S} - \bold{\theta})^T \bold{W}_s (\tilde{\bold{y}}_{S} - \bold{\theta})$ subject to $\bold{A\theta} \geq \bold{0}$ , where $\bold{W}_S$ is the diagonal matrix with elements $\hat{N}_1/\hat{N},\ldots,\hat{N}_D/\hat{N}$ , and $\hat{N} = \sum_{t=1}^T \hat{N}_t$ and $\bold{A}$ is a $m\times T$ constraint matrix imposing the monotonicity constraint.

Domains can also be formed from multiple covariates. In that case, a grid will be used to represent the domains. For example, if there are two predictors $x_1$ and $x_2$ , and $x_1$ has values on $D_1$ domains: $1,\ldots,D_1$ , $x_2$ has values on $D_2$ domains: $1,\ldots,D_2$ , then the domains formed by $x_1$ and $x_2$ will be a $D_1\times D_2$ by $2$ grid.

To get $100(1-\alpha)\%$ approximate confidence intervals or surfaces for the domain means, we apply the method in Meyer, M. C. (2018). $\hat{p}_J$ is the estimated probability that the projection of $y_s$ onto $\cal C$ lands on $\cal F_J$ , and the $\hat{p}_J$ values are obtained by simulating many normal random vectors with estimated domain means and covariance matrix $I$ , where $I$ is a $M \times M$ matrix, and recording the resulting sets $J$ .

The user needs to provide a survey design, which is specified by the svydesign function in the survey package, and also a data frame containing the response, predictor(s), domain variable, sampling weights, etc. So far, only stratified sampling design with simple random sampling without replacement (STSI) is considered in the examples in this package.

Note that when there is any empty domain, the user must specify the total number of domains in the $nD$ argument.

Usage

csvy(formula, design, family=stats::gaussian(), multicore=getOption("csurvey.multicore"), 
  level=0.95, n.mix=100L, test=FALSE, subset=NULL)
## S3 method for class 'csvy'
summary(object,...)
## S3 method for class 'csvy'
vcov(object,...)
## S3 method for class 'csvy'
coef(object,...)
## S3 method for class 'csvy'
confint(object, parm=NULL, level = 0.95, type = c("link", "response"),...)
## S3 method for class 'csvy'
predict(object, newdata = NULL, type = c("link", "response"), 
    se.fit = TRUE, level = 0.95, n.mix = 100,...)
csvy(formula, design, family=stats::gaussian(), multicore=getOption("csurvey.multicore"), 
  level=0.95, n.mix=100L, test=FALSE, subset=NULL)
## S3 method for class 'csvy'
summary(object,...)
## S3 method for class 'csvy'
vcov(object,...)
## S3 method for class 'csvy'
coef(object,...)
## S3 method for class 'csvy'
confint(object, parm=NULL, level = 0.95, type = c("link", "response"),...)
## S3 method for class 'csvy'
predict(object, newdata = NULL, type = c("link", "response"), 
    se.fit = TRUE, level = 0.95, n.mix = 100,...)

Arguments

formula

A formula object which gives a symbolic description of the model to be fitted. It has the form "response ~ predictor". The response is a vector of length $n$ . A predictor can be a non-parametrically modelled variable with a monotonicity or convexity restriction, or a combination of both. In terms of a non-parametrically modelled predictor, the user is supposed to indicate the relationship between the domain mean and a predictor $x$ in the following way:

Assume that $\mu$ is the vector of domain means and $x$ is a predictor:

incr(x):: $\mu$ is increasing in $x$ .
decr(x):: $\mu$ is decreasing in $x$ .
block.Ord(x):: $\mu$ is has a block ordering in $x$ .

design

A survey design, which must be specified by the svydesign routine in the survey package.

family

A parameter indicating the error distribution and link function to be used in the model. It can be a character string naming a family function or the result of a call to a family function. This is borrowed from the glm routine in the stats package. There are four families used in csvy: Gaussian, binomial, poisson, and Gamma.

multicore

A parameter retrieving the current global option for "csurvey.multicore" and assigns its value to the multicore variable, allowing the function to respect a user-defined setting for parallel processing behavior across the package.

level

Confidence level of the approximate confidence surfaces. The default is 0.95.

n.mix

The number of simulations used to get the approximate confidence intervals or surfaces. If n.mix = 0, no simulation will be done and the face of the final projection will be used to compute the covariance matrix of the constrained estimate. The default is n.mix = 100L.

test

A logical scalar. If test == TRUE, then the p-value for the test $H_0:\theta$ is in $V$ versus $H_1:\theta$ is in $C$ is returned. $C$ is the constraint cone of the form $\{\beta: A\beta \ge 0\}$ , and $V$ is the null space of $A$ . The default is test = FALSE.

subset

Expression to select a subpopulation.

...

Extra arguments.

The coef function returns estimated systematic component of a csvy object.

The confint function returns the confidence interval of a csvy object. If type = "response", then the interval is for the mean; if type = "link", then the interval is for the systematic component.

parm

An argument in the generic confint function in the stats package. For now, this argument is not in use.

The following arguments are used in the predict function.

object

A csvy object.

newdata

A data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

type

If the response is Gaussian, type = "response" and type = "link" give the predicted mean; if the response is binomial, poisson or Gamma, type = "response" gives the predicted mean, and type = "link" gives the predicted systematic component.

se.fit

Logical switch indicating if confidence intervals are required.

Details

For binomial and Poisson families use family=quasibinomial() and family=quasipoisson() to avoid a warning about non-integer numbers of successes. The ‘quasi’ versions of the family objects give the same point estimates and standard errors and do not give the warning.

predict gives fitted values and sampling variability for specific new values of covariates. When newdata are the population mean it gives the regression estimator of the mean, and when newdata are the population totals and total is specified it gives the regression estimator of the population total. Regression estimators of mean and total can also be obtained with calibrate.

Value

The output is a list of values used for estimation, inference and visualization. Main output include:

survey.design

The survey design used in the model.

etahat

Estimated shape-constrained domain systematic component.

etahatu

Estimated unconstrained domain systematic component.

muhat

Estimated shape-constrained domain means.

muhatu

Estimated unconstrained domain means.

lwr

Approximate lower confidence band or surface for the shape-constrained domain mean estimate.

upp

Approximate upper confidence band or surface for the shape-constrained domain mean estimate.

lwru

Approximate lower confidence band or surface for the unconstrained domain mean estimate.

uppu

Approximate upper confidence band or surface for the unconstrained domain mean estimate.

amat

The $k \times M$ constraint matrix imposing shape constraints in each dimension, where $M$ is the total number of domains.

grid

A $M \times p$ grid, where $p$ is the total number of predictors or dimensions.

nd

A vector of sample sizes in all domains.

Ds

A vector of the number of domains in each dimension.

acov

Constrained mixture covariance estimate of domain means.

cov.un

Unconstrained covariance estimate of domain means.

CIC

The cone information criterion proposed in Meyer(2013a). It uses the "null expected degrees of freedom" as a measure of the complexity of the model. See Meyer(2013a) for further details of cic.

CIC.un

The cone information criterion for the unconstrained estimator.

zeros_ps

Index of empty domain(s).

nd

Sample size of each domain.

pval

p-value of the one-sided test.

family

The family parameter defined in a csvy formula.

df.residual

The observed degree of freedom for the residuals of a csvy fit.

df.null

The degree of freedom for the null model of a csvy fit.

domain

Index of each domain in the data set contained in the survey.design object.

null.deviance

The deviance for the null model of a csvy fit.

deviance

The residual deviance of a csvy fit.

Author(s)

Xiyue Liao

References

Xu, X. and Meyer, M. C. (2021) One-sided testing of population domain means in surveys.

Oliva, C., Meyer, M. C., and Opsomer, J.D. (2020) Estimation and inference of domain means subject to qualitative constraints. Survey Methodology

Meyer, M. C. (2018) A Framework for Estimation and Inference in Generalized Additive Models with Shape and Order Restrictions. Statistical Science 33(4) 595–614.

Wu, J., Opsomer, J.D., and Meyer, M. C. (2016) Survey estimation of domain means that respect natural orderings. Canadian Journal of Statistics 44(4) 431–444.

Meyer, M. C. (2013a) Semi-parametric additive constrained regression. Journal of Nonparametric Statistics 25(3), 715.

Lumley, T. (2004) Analysis of complex survey samples. Journal of Statistical Software 9(1) 1–19.

Examples

# Example 1: monotonic in 1 predictor
data(nhdat2, package = 'csurvey')
#specify the design:
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)

#uncomment to use parallel computing:
#options(csurvey.multicore=TRUE)
#mixture-variance-covariance matrix is simulated
set.seed(1)
ans <- csvy(chol ~ incr(age), design = dstrat, n.mix=5)

#check the constrained fit vs the unconstrained fit
summary(ans)
plot(ans, type = 'both')

## Not run: 
# Example 2: monotonic in 2 predictors and unconstrained in a 3rd predictor
data(nhdat2, package = 'csurvey')
#specify the design:
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)

#use parallel computing:
#options(csurvey.multicore=TRUE)
#mixture-variance-covariance matrix is simulated
#Average cholestorel level increases in age, waist, and unconstrained in income
set.seed(1)
ans <- csvy(chol ~ incr(age)*incr(wcat)*icat, design = dstrat, test=FALSE, n.mix=5)

#visualize the constrained estimation with confidence bands
plot(ans, x1='icat', x2='wcat')

#create control object
ctl <- plot_csvy_control(ribbon_fill = "pink",x1lab = 'income',x2lab = 'waist')

plot(ans, x1='icat', x2='wcat', control=ctl)

## End(Not run)

# Example 3: example with a binomial response
library(NHANES)
library(survey)
data(NHANES)

nh <- subset(NHANES, !is.na(Education) & !is.na(BMI) & !is.na(Weight))
nh$DiabetesBin <- as.integer(nh$Diabetes == "Yes")
nh$BMIgroup <- cut(nh$BMI, breaks = c(0, 18.5, 25, 30, 35, 40, Inf), labels = FALSE)

# specify the design
dsgn <- svydesign(ids = ~1, strata = ~BMIgroup, weights = ~Weight, data = nh)

ans <- csvy(DiabetesBin ~ decr(Education) * incr(BMIgroup), design = dsgn,
  family = quasibinomial(link='logit'), n.mix=5)

summary(ans)

plot(ans, x1 = 'BMIgroup', x2 = 'Education')

ctl <- plot_csvy_control(
  x1size = 1.5, x2size = 2,
  angle = 45, hjust = .3)

plot(ans, x1 = 'Education', x2 = 'BMIgroup', control = ctl)


# Example 1: monotonic in 1 predictor
data(nhdat2, package = 'csurvey')
#specify the design:
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)

#uncomment to use parallel computing:
#options(csurvey.multicore=TRUE)
#mixture-variance-covariance matrix is simulated
set.seed(1)
ans <- csvy(chol ~ incr(age), design = dstrat, n.mix=5)

#check the constrained fit vs the unconstrained fit
summary(ans)
plot(ans, type = 'both')

## Not run: 
# Example 2: monotonic in 2 predictors and unconstrained in a 3rd predictor
data(nhdat2, package = 'csurvey')
#specify the design:
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)

#use parallel computing:
#options(csurvey.multicore=TRUE)
#mixture-variance-covariance matrix is simulated
#Average cholestorel level increases in age, waist, and unconstrained in income
set.seed(1)
ans <- csvy(chol ~ incr(age)*incr(wcat)*icat, design = dstrat, test=FALSE, n.mix=5)

#visualize the constrained estimation with confidence bands
plot(ans, x1='icat', x2='wcat')

#create control object
ctl <- plot_csvy_control(ribbon_fill = "pink",x1lab = 'income',x2lab = 'waist')

plot(ans, x1='icat', x2='wcat', control=ctl)

## End(Not run)

# Example 3: example with a binomial response
library(NHANES)
library(survey)
data(NHANES)

nh <- subset(NHANES, !is.na(Education) & !is.na(BMI) & !is.na(Weight))
nh$DiabetesBin <- as.integer(nh$Diabetes == "Yes")
nh$BMIgroup <- cut(nh$BMI, breaks = c(0, 18.5, 25, 30, 35, 40, Inf), labels = FALSE)

# specify the design
dsgn <- svydesign(ids = ~1, strata = ~BMIgroup, weights = ~Weight, data = nh)

ans <- csvy(DiabetesBin ~ decr(Education) * incr(BMIgroup), design = dsgn,
  family = quasibinomial(link='logit'), n.mix=5)

summary(ans)

plot(ans, x1 = 'BMIgroup', x2 = 'Education')

ctl <- plot_csvy_control(
  x1size = 1.5, x2size = 2,
  angle = 45, hjust = .3)

plot(ans, x1 = 'Education', x2 = 'BMIgroup', control = ctl)

Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis

Description

A structured subset of the 2009 to 2010 NHANES data designed for illustrating constrained survey estimation methods.

Usage

data(nhdat)
data(nhdat)

Format

A data frame with 1,680 observations on the following 7 variables:

id: Cluster identifier derived from NHANES sequence number (SEQN).
chol: Binary indicator of high total cholesterol: 1 if total cholesterol > 200 mg/dL, 0 otherwise. Derived from LBXTC in TCHOL_F.XPT.
wcat: Four-level ordinal variable for waist to height ratio category, based on BMXWAIST and BMXHT from BMX_F.XPT.
gender: Gender of the participant (1 = male, 2 = female), from RIAGENDR in DEMO_F.XPT.
age: Age in years (continuous), from RIDAGEYR in DEMO_F.XPT.
wt: Sampling weight within strata, based on WTINT2YR from DEMO_G.XPT.
str: Stratum identifier, based on SDMVPSU from DEMO_G.XPT.

Details

This subset includes participants aged 21 to 45 years, selected for illustrating the estimation of the probability of high cholesterol using order constrained survey methods.

Source

National Center for Health Statistics. NHANES 2009 to 2010 Public Use Data Files.\ https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009

Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis

Description

A structured subset of the 2009 to 2010 NHANES data designed for illustrating constrained survey estimation methods.

Usage

data(nhdat2)data(nhdat2)

Format

A data frame with 1,933 observations and the following 8 variables:

id: An identification vector specifying cluster ids from largest level to smallest level, derived from NHANES sequence number SEQN.
chol: Total cholesterol, measured in mg/dL. This variable is derived from LBXTC in the laboratory file TCHOL_F.XPT.
wcat: A 4 level ordinal categorical variable representing waist to height ratio categories, computed from BMXWAIST (waist circumference in cm) and BMXHT (height in cm) in the body measures file BMX_F.XPT.
icat: A 4 level ordinal categorical variable. It categorizes income based on the ratio of family income to the federal poverty level (INDFMPIR), with cutpoints at 0.75, 1.38, and 3.5 to reflect meaningful policy thresholds. It is derived from INDFMIN2 in the demographics file DEMO_F.XPT.
gender: Gender of the participant, with values 1 (male) and 2 (female), derived from RIAGENDR in DEMO_F.XPT.
age: Age in years (continuous), derived from RIDAGEYR in the demographics file DEMO_F.XPT.
wt: Sampling weight within each stratum, derived from (WTINT2YR) from DEMO_G.XPT.
str: Stratum identifier, derived from (SDMVPSU) from DEMO_G.XPT.

Details

This subset includes participants aged 21 through 45, selected to demonstrate estimation of domain means using order constrained methods.

Source

National Center for Health Statistics. NHANES 2009 to 2010 Public Use Data Files. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009

Control settings for plot.csvy

Description

Creates a list of graphical options to customize plots generated by plot.csvy. This includes labels, text sizes, colors, shapes, themes, and other display features.

Usage

plot_csvy_control(
  x1lab = NULL,
  x1_labels = TRUE,
  x2lab = NULL,
  x2_labels = TRUE,
  x3lab = NULL,
  x3_labels = TRUE,
  x4_vals = NULL,
  x4_labels = NULL,
  ynm = NULL,
  ci = TRUE,
  legend = TRUE,
  ylab = TRUE,
  x1size = 3.8,
  x2size = 3.8,
  constrained_color = "cornflowerblue",
  unconstrained_color = "#A3C99A",
  constrained_shape = 16,
  unconstrained_shape = 18,
  ribbon_fill = "lightblue",
  line_color = "black",
  base_theme = ggplot2::theme_minimal(),
  subtitle.size = 12,
  angle = 0,
  hjust = 0.1
)
plot_csvy_control(
  x1lab = NULL,
  x1_labels = TRUE,
  x2lab = NULL,
  x2_labels = TRUE,
  x3lab = NULL,
  x3_labels = TRUE,
  x4_vals = NULL,
  x4_labels = NULL,
  ynm = NULL,
  ci = TRUE,
  legend = TRUE,
  ylab = TRUE,
  x1size = 3.8,
  x2size = 3.8,
  constrained_color = "cornflowerblue",
  unconstrained_color = "#A3C99A",
  constrained_shape = 16,
  unconstrained_shape = 18,
  ribbon_fill = "lightblue",
  line_color = "black",
  base_theme = ggplot2::theme_minimal(),
  subtitle.size = 12,
  angle = 0,
  hjust = 0.1
)

Arguments

x1lab

Character. Label for the first covariate (x-axis). Default is NULL.

x1_labels

Logical or Character vector. Custom tick labels for the first covariate. Default is TRUE. If it is TRUE, tick labels will be created; if it is a character vector, then this vector will be used as the tick labels.

x2lab

Character. Label for the second covariate (x-axis). Default is NULL.

x2_labels

Logical or Character vector. Custom tick labels for the second covariate. Default is TRUE. Default is TRUE. If it is TRUE, tick labels will be created; if it is a character vector, then this vector will be used as the tick labels.

x3lab

Character. Label for the third covariate, if used (for subtitles or grouping). Default is NULL.

x3_labels

Logical or Character vector. Custom labels for the third covariate. Default is TRUE. If it is TRUE, labels will be created; if it is a character vector, then this vector will be used as the labels.

x4_vals

Character vector. For models with more than three predictors, specifies the category to use for each additional predictor. Defaults to NULL, using the mode of each.

x4_labels

Character vector. Custom labels for the fourth covariate. Default is NULL.

ynm

Character. Label for the response. Default is NULL.

ci

Logical. If TRUE, confidence bands are displayed. Defaults to TRUE.

legend

Logical. If TRUE, legend for constrained fit or unconstrained fit will be shown. Defaults to TRUE.

ylab

Logical. If TRUE, the response name will be shown on the y-axis. Defaults to TRUE.

x1size

Numeric. Font size for annotation labels on the x1 axis. Default is 3.8.

x2size

Numeric. Font size for annotation labels on the x2 axis. Default is 3.8.

constrained_color

Character. Color used to display fitted values and intervals from the constrained model. Default is "cornflowerblue".

unconstrained_color

Character. Color used to display fitted values and intervals from the unconstrained model. Default is "#A3C99A".

constrained_shape

Integer. Shape code (used by ggplot2) for points corresponding to constrained fits. Default is 16 (solid circle).

unconstrained_shape

Integer. Shape code for points from unconstrained fits. Default is 18 (solid diamond).

ribbon_fill

Character. Fill color for the confidence ribbon around the fitted lines. Default is "lightblue".

line_color

Character. Color of the lines connecting the fitted values. Default is "black".

base_theme

A ggplot2 theme object used as the base plot theme. Default is ggplot2::theme_minimal().

subtitle.size

Numeric. Font size for the subtitle text in the plot. Default is 12.

angle

Numeric. Angle (in degrees) to rotate x-axis labels (typically for x1). Default is 0.

hjust

Numeric. Horizontal justification for rotated x-axis labels. Default is .1 (right-aligned).

Value

A named list of graphical control parameters to be passed to the control argument in plot.csvy.

Examples

data(nhdat2, package = 'csurvey')
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)
ans <- csvy(chol ~ incr(age), design = dstrat, n.mix=5)
#check the constrained fit vs the unconstrained fit with default aesthetics
ctl <- plot_csvy_control()
plot(ans, type = 'both', control = ctl)
#check the fit with user-defined aethetics
ctl <- list(x1lab = "Age Group", x2lab = "Region", constrained_color = "cornflowerblue", 
unconstrained_color = "gray80", x1size = 4.5)
plot(ans, type = 'both', control = ctl)
data(nhdat2, package = 'csurvey')
dstrat <- svydesign(ids = ~id, strata = ~str, data = nhdat2, weight = ~wt)
ans <- csvy(chol ~ incr(age), design = dstrat, n.mix=5)
#check the constrained fit vs the unconstrained fit with default aesthetics
ctl <- plot_csvy_control()
plot(ans, type = 'both', control = ctl)
#check the fit with user-defined aethetics
ctl <- list(x1lab = "Age Group", x2lab = "Region", constrained_color = "cornflowerblue", 
unconstrained_color = "gray80", x1size = 4.5)
plot(ans, type = 'both', control = ctl)

Plot method for csvy objects Generates a diagnostic or summary plot from a fitted `"csvy"` object. Supports both single-factor and two-factor visualization. Aesthetic settings can be customized using `plot_csvy_control()`.

Description

Plot method for csvy objects Generates a diagnostic or summary plot from a fitted "csvy" object. Supports both single-factor and two-factor visualization. Aesthetic settings can be customized using plot_csvy_control().

Usage

## S3 method for class 'csvy'
plot(
  x,
  x1 = NULL,
  x2 = NULL,
  domains = NULL,
  type = c("constrained", "unconstrained", "both"),
  control = plot_csvy_control(),
  ...
)
## S3 method for class 'csvy'
plot(
  x,
  x1 = NULL,
  x2 = NULL,
  domains = NULL,
  type = c("constrained", "unconstrained", "both"),
  control = plot_csvy_control(),
  ...
)

Arguments

x

An object of class "csvy".

x1

Optional. Name of the first factor to display in two-factor plots. Defaults to the first added variable.

x2

Optional. Name of the second factor to display in two-factor plots. Defaults to the second added variable.

domains

Optional. A data frame containing some domain(s) to be emphasized on the plot. Defaults to be NULL.

type

Character string, either "constrained", "unconstrained", or "both". Defaults to "constrained".

control

A list of display options returned by plot_csvy_control. Defaults to plot_csvy_control().

...

Additional arguments passed to ggplot2::geom_line() or geom_point(), such as linewidth, size, etc.

Value

A ggplot2 object.

Examples

# plot.csvy(fit)
# plot.csvy(fit, x1 = "education", x2 = "region", control = plot_csvy_control(x1lab = "Education"))
# plot.csvy(fit)
# plot.csvy(fit, x1 = "education", x2 = "region", control = plot_csvy_control(x1lab = "Education"))

Create a Control List for `plotpersp.csvy`

Description

Constructs a list of control parameters for use with plotpersp.csvy. It extends the default settings from plotpersp_control with options specific to csvy plots.

Usage

plotpersp_csvy_control(surface = c("C", "U"), x1nm = NULL, x2nm = NULL, categ = NULL,
  col = NULL, random = FALSE, ngrid = 12, xlim = NULL, ylim = NULL, zlim = NULL,
  xlab = NULL, ylab = NULL, zlab = NULL, th = NULL, ltheta = NULL, main = NULL,
  sub = NULL, ticktype = "simple", ci = c("none", "lwr", "up", "both"), 
  cex = 1, categnm = NULL, type = c("response", "link"),
  cex.main = 0.8, box = TRUE, axes = TRUE, nticks = 5, palette = NULL, 
  NCOL = NULL, transpose = FALSE)
plotpersp_csvy_control(surface = c("C", "U"), x1nm = NULL, x2nm = NULL, categ = NULL,
  col = NULL, random = FALSE, ngrid = 12, xlim = NULL, ylim = NULL, zlim = NULL,
  xlab = NULL, ylab = NULL, zlab = NULL, th = NULL, ltheta = NULL, main = NULL,
  sub = NULL, ticktype = "simple", ci = c("none", "lwr", "up", "both"), 
  cex = 1, categnm = NULL, type = c("response", "link"),
  cex.main = 0.8, box = TRUE, axes = TRUE, nticks = 5, palette = NULL, 
  NCOL = NULL, transpose = FALSE)

Arguments

surface

Plot the constrained ("C") or unconstrained ("U") fit.

x1nm, x2nm

Character strings naming the predictor variables for x and y axes.

categ

Optional character string naming a categorical covariate to stratify plots.

col

Color(s) for surfaces. Can be a palette or custom colors.

random

If TRUE, colors are assigned randomly.

ngrid

Number of grid points along each axis.

xlim, ylim, zlim

Optional limits for x, y, z axes.

xlab, ylab, zlab

Axis labels.

th, ltheta

Viewing and lighting angles for the plot.

main, sub

Plot title and subtitle.

ticktype

Type of ticks: "simple" or "detailed".

ci

Confidence interval display mode: "none", "lwr", "up", "both".

cex

Scaling factor for labels.

categnm

Labels for each level of the categorical covariate.

type

Scale of the surface: "response" (default) or "link".

cex.main

Scaling factor for main title text.

box, axes

Logical flags to show box and axes.

nticks

Number of tick marks along axes.

palette

Vector of colors for multi-surface plots.

NCOL

Number of columns in multi-panel layout.

transpose

Logical; if TRUE, transpose layout of multi-panel plots.

Value

A named list of graphical settings for use in plotpersp.csvy.

Examples

ctrl <- plotpersp_csvy_control(col = "topo", ci = "both", transpose = TRUE)
ctrl <- plotpersp_csvy_control(col = "topo", ci = "both", transpose = TRUE)

Package 'csurvey'

Help Index

Specify a Block Monotonic Shape-Restriction in a CSVY Formula

Description

Usage

Arguments

Value

Author(s)

See Also

Estimation of Domain Means with Monotonicity or Convexity Constraints

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis

Description

Usage

Format

Details

Source

Subset of NHANES 2009 to 2010 Data for Constrained Survey Analysis

Description

Usage

Format

Details

Source

Control settings for plot.csvy

Description

Usage

Arguments

Value

Examples

Plot method for csvy objects Generates a diagnostic or summary plot from a fitted "csvy" object. Supports both single-factor and two-factor visualization. Aesthetic settings can be customized using plot_csvy_control().

Description

Usage

Arguments

Value

See Also

Examples

Create a Control List for plotpersp.csvy

Description

Usage

Arguments

Value

See Also

Examples

Plot method for csvy objects Generates a diagnostic or summary plot from a fitted `"csvy"` object. Supports both single-factor and two-factor visualization. Aesthetic settings can be customized using `plot_csvy_control()`.

Create a Control List for `plotpersp.csvy`