Package 'IPWboxplot' reference manual

Package 'IPWboxplot'

Title:	Adapted Boxplot to Missing Observations
Description:	Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.
Authors:	Ana Maria Bianco [aut], Graciela Boente [aut], Ana Perez-Gonzalez [aut]
Maintainer:	Ana Perez-Gonzalez <[email protected]>
License:	GPL (>= 2)
Version:	0.1.2
Built:	2024-11-11 04:14:57 UTC
Source:	https://github.com/cran/IPWboxplot

Title:

Adapted Boxplot to Missing Observations

Description:

Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

Authors:

Ana Maria Bianco [aut], Graciela Boente [aut], Ana Perez-Gonzalez [aut]

Maintainer:

Ana Perez-Gonzalez <[email protected]>

License:

GPL (>= 2)

Version:

0.1.2

Built:

2024-11-11 04:14:57 UTC

Source:

https://github.com/cran/IPWboxplot

Help Index

Boxplot adapted to skewness and missing values

Description

The function draws a modified boxplot adapted to missing data and skewness. The drop-out probabilities can be given by the practitioner or fitted through a logistic model using auxiliary covariates. The plots are adapted to asymmetric distributions by correcting the whiskers through a measure of the skewness.

Usage

IPW.ASYM.boxplot(y,px=NULL,x=NULL,graph=c("IPW","both"),names=c("IPW Asymmetric Boxplot",
                "NAIVE Asymmetric Boxplot"), size.letter=1.2,
                method=c("quartile","octile"), ctea=-4, cteb=3, lim.inf=NULL,lim.sup=NULL,
                main=" ",xlab = " ", ylab ="  ",color="black")
IPW.ASYM.boxplot(y,px=NULL,x=NULL,graph=c("IPW","both"),names=c("IPW Asymmetric Boxplot",
                "NAIVE Asymmetric Boxplot"), size.letter=1.2,
                method=c("quartile","octile"), ctea=-4, cteb=3, lim.inf=NULL,lim.sup=NULL,
                main=" ",xlab = " ", ylab ="  ",color="black")

Arguments

`y`	Numerical vector of length n with possible missing values codified by NA or NAN.
`px`	Optional. Numerical vector of drop-out probabilities. If not provided a logistic fit is performed using `x` as predictive variable. Missing values are not admitted.
`x`	Optional. The matrix of fully observed variables used to estimate the missing model with dimension nrows=n and ncol=p. Missing values are not admitted. One of the vectors px or x must be supplied.
`graph`	Optional. Character string indicating if the plot contains two boxplots ("both") or only the boxplot computed with the inverse probability weighted quantiles ("IPW"). The default is "IPW".
`names`	Optional. Character string to name the boxplots. The default is "IPW Asymmetric Boxplot", when `graph="IPW"` and c("IPW Asymmetric Boxplot", "NAIVE Asymmetric Boxplot") when `graph="both"`.
`size.letter`	Optional. The font size of names. Default value is 1.2
`method`	Optional. Character string indicating if the skewness measure is based on the quartiles ("quartile") or the octiles ("octile"). The default is "quartile".
`ctea`	Optional. Scaling factors to compute the outlier boundary. The default is -4.
`cteb`	Optional. Scaling factors to compute the outlier boundary. The default is 3. When ctea=cteb=0 the IPW boxplot for symmetric data is obtained.
`lim.inf`	Optional. The lower limit of the plot if supplied by the user.
`lim.sup`	Optional. The upper limit of the plot if supplied by the user.
`main`	Optional. Character string to title the plot. By default no main title is given.
`xlab`	Optional. Character string to indicate the label of the horizontal axis.
`ylab`	Optional. Character string to indicate the label of the vertical axis.
`color`	Optional. Color for the IPW Boxplot.

Details

The function draws boxplots designed to adjust both for skewness and missingness. The drop-out probabilities can be supplied by the user or estimated through a logistic model from given covariates.

The function plots as default a modified boxplot based on the inverse probability weighted (IPW) quantiles adapting for missing observations as in Zhang et al.(2012), but using a correction factor to adjust for skewness. For that purpose, the function incorporates a skewness measure to compute the whiskers and the outlier cut–off values in a similar way to that considered in Hubert and Vandervieren (2008). The argument method selects quartiles (method="quartile") or octiles (method="octile") to calculate the skewness measure SKEW, respectively, as

$SKEW=\frac{(Q_{0.75}-Q_{0.5})-(Q_{0.5}-Q_{25})}{(Q_{0.75}-Q_{0.25})},$

$SKEW=\frac{(Q_{0.875}-Q_{0.5})-(Q_{0.5}-Q_{0.125})}{(Q_{0.875}-Q_{0.125})},$

where $Q\alpha$ denotes the $\alpha-$ quantile.

The whiskers and the outlier cut–off values are computed by means of an exponential model in the fashion of Hubert and Vandervieren (2008) taking into account the interval:

$(Q_{0.25}-1.5*\exp{(c_i*SKEW)}*IQR,Q_{0.75}+1.5*\exp{(c_s*SKEW)}*IQR),$

where $IQR=Q_{0.75}-Q_{0.25}$ and $c_i$ =ctea and $c_s$ =cteb if SKEW is positive, otherwise, $c_i$ =-cteb and $c_s$ =-ctea.

The default values for ctea and cteb are $-4$ and $3$ , however, the user may choose other values for these constants.

By specifying graph = "both", the function displays two parallel modified boxplots. The boxplot on the left corresponds to the IPW boxplot adapted for missingness and skewness, while that on the right, to its naive counterpart which is simply based on the observations y at hand without any correction for missingness.

The user can supply a vector of drop-out probabilities px or a set of covariates x to estimate the propensity. When both px and x are supplied, the IPW.ASYM.boxplot is executed using px. When px is not given, it is estimated assuming a logistic model depending on the covariates x . For more details, see Bianco et al. (2018).

Value

The output of the function is a list with components:

`px`	Numerical vector of drop-out probabilities.
`IPW.Quartiles`	Numerical vector of inverse probability weighted quartiles.
`IPW.whisker`	Numerical vector of lower and upper whiskers calculated from IPW quantiles.
`out.IPW`	Numerical vector of data points detected as atypical by the IPW boxplot adapted to skewness.
`SKEW.IPW`	Skewness measure based on the IPW quartiles (method="quartile") or IPW octiles (method="octile").
`NAIVE.Quartiles`	Numerical vector of naive quartiles computed from the subset of non-missing values of `y`. Returned only when graph="both".
`NAIVE.whisker`	Numerical vector of lower and upper whiskers obtained from the naive quantiles. Returned only when graph="both".
`out.NAIVE`	Numerical vector of data points detected as atypical by the naive boxplot. Returned only when graph="both".
`SKEW.NAIVE`	Skewness measure based on the naive quartiles (method="quartile") or Naive octiles (method="octile"), computed from the subset of non-missing values of `y`. Returned only when graph="both".

Note

The missing values of y must be codified as NA or NAN.

The numerical vector px and the matrix of covariates x must be fully observed. px or x must be supplied by the user.

The lengths of y, px, and nrow(x) must be equal.

Author(s)

Ana Maria Bianco <[email protected]>, Graciela Boente <[email protected]> and Ana Perez-Gonzalez <[email protected]>.

References

Bianco, A. M., Boente, G., and Perez-Gonzalez, A. (2018). A boxplot adapted to missing values: an R function when predictive covariates are available. Submitted.

Hubert, M. and Vandervieren, E. (2008). An adjusted boxplot for skewed distributions. Computational Statistics & Data Analysis, 52, 5186-5201.

Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J. (2012). Causal inference on quantiles with an obstetric application. Biometrics, 68, 697-706.

Examples


## A real data example


library(mice)
data(boys)
attach(boys)

# The function plots the IPW boxplot adapted to skewness.
# Some statistical summaries computed using the inverse probability weighting approach
# are also returned.
res1=IPW.ASYM.boxplot(hc,x=age,main="IPW boxplot adjusted for skewness of the head circumference")

# We can compare the naive and IPW approaches. We also can consider the skewness measure computed
# using the  quartiles (as default).
res2=IPW.ASYM.boxplot(hc,x=age,method="quartile",graph="both",main=" ")

# The results obtained if the skewness measure is computed with the octiles (method="octile") are:

res3=IPW.ASYM.boxplot(hc,x=age,method="octile",graph="both",main=" ")

## A real data example


library(mice)
data(boys)
attach(boys)

# The function plots the IPW boxplot adapted to skewness.
# Some statistical summaries computed using the inverse probability weighting approach
# are also returned.
res1=IPW.ASYM.boxplot(hc,x=age,main="IPW boxplot adjusted for skewness of the head circumference")

# We can compare the naive and IPW approaches. We also can consider the skewness measure computed
# using the  quartiles (as default).
res2=IPW.ASYM.boxplot(hc,x=age,method="quartile",graph="both",main=" ")

# The results obtained if the skewness measure is computed with the octiles (method="octile") are:

res3=IPW.ASYM.boxplot(hc,x=age,method="octile",graph="both",main=" ")

Boxplot adapted to missing values

Description

The function draws a modified boxplot adapted to missing values. The drop-out probabilities can be given by the practitioner or fitted through a logistic model using auxiliary covariates. The function returns the usual boxplot of the available data as well as a modified plot which takes into account the missing data model and weights the observations using the estimated/given propensity.

Usage

IPW.boxplot(y,px=NULL,x=NULL,graph=c("IPW","both"),
names=c("IPW  Boxplot", "NAIVE  Boxplot"), size.letter=1.2,
lim.inf=NULL,lim.sup=NULL,main=" ",xlab = " ", ylab =" ",color="black")
IPW.boxplot(y,px=NULL,x=NULL,graph=c("IPW","both"),
names=c("IPW  Boxplot", "NAIVE  Boxplot"), size.letter=1.2,
lim.inf=NULL,lim.sup=NULL,main=" ",xlab = " ", ylab =" ",color="black")

Arguments

`y`	Numerical vector of length n with possible missing values codified by NA or NAN.
`px`	Optional. Numerical vector of drop-out probabilities. If not provided a logistic fit is performed using `x` as predictive variable. Missing values are not admitted.
`x`	Optional. The matrix of fully observed variables used to estimate the missing model with dimension nrows=n and ncol=p. Missing values are not admitted. One of the vectors px or x must be supplied.
`graph`	Optional. Character string indicating if the plot contains two boxplots ("both") or only the boxplot computed with the inverse probability weighted quantiles("IPW"). The default is "IPW".
`names`	Optional. Character string to name the boxplots. The default is "IPW Boxplot", when `graph="IPW"` and c("IPW Boxplot", "NAIVE Boxplot") when `graph="both"`.
`size.letter`	Optional. The font size of names. Default value is 1.2
`lim.inf`	Optional. The lower limit of the plot if supplied by the user.
`lim.sup`	Optional. The upper limit of the plot if supplied by the user.
`main`	Optional. Character string to title the plot. By default no main title is given.
`xlab`	Optional. Character string to indicate the label of the horizontal axis.
`ylab`	Optional. Character string to indicate the label of the vertical axis.
`color`	Optional. Color for the IPW Boxplot.

Details

The function draws boxplots designed to adjust for missing values. The propensity can be supplied by the user or estimated through a logistic model from given covariates.

The function plots as default a modified boxplot based on the inverse probability weighted (IPW) quantiles adapting for missing observations as in Zhang et al.(2012).

By specifying graph = "both", the function displays two parallel boxplots. The boxplot on the left corresponds to the IPW boxplot adapted for missingness, while on the right, the naive boxplot, i.e., the usual boxplot simply computed with the observations y at hand, is displayed.

The user can supply a vector of probabilities px or a set of covariates x to estimate it. When both px and x are supplied, the IPW.boxplot is executed using px. When px is not supplied, it is estimated assuming a logistic model depending on the covariates x . For more details, see Bianco et al. (2018).

Value

The output of the function is a list with components:

`px`	Numerical vector of probabilities.
`IPW.Quartiles`	Numerical vector of inverse probability weighted quartiles.
`IPW.whisker`	Numerical vector of lower and upper whiskers calculated from IPW quartiles.
`out.IPW`	Numerical vector of data points detected as atypical by the IPW boxplot.
`NAIVE.Quartiles`	Numerical vector of naive quartiles computed from the subset of non-missing values of `y`. Returned only when graph="both".
`NAIVE.whisker`	Numerical vector of lower and upper whiskers obtained from the naive quantiles. Returned only when graph="both".
`out.NAIVE`	Numerical vector of data points detected as atypical by the naive boxplot. Returned only when graph="both".

Note

The missing values of y must be codified as NA or NAN.

The numerical vector px and the matrix of covariates x must be fully observed. px or x must be supplied by the user.

The lengths of y, px, and nrow(x) must be equal.

Author(s)

Ana Maria Bianco <[email protected]>, Graciela Boente <[email protected]> and Ana Perez-Gonzalez <[email protected]>.

References

Bianco, A. M., Boente, G., and Perez-Gonzalez, A. (2018). A boxplot adapted to missing values: an R function when predictive covariates are available. Submitted.

Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J. (2012). Causal inference on quantiles with an obstetric application. Biometrics, 68, 697-706.

Examples



## A real data example

library(mice)
data(boys)
attach(boys)

res1=IPW.boxplot(tv,x=age,main="IPW boxplot of the testicular volume")


# We  can compare the naive and IPW boxplots
res2=IPW.boxplot(tv,x=age,graph="both",main=" ")

## A real data example

library(mice)
data(boys)
attach(boys)

res1=IPW.boxplot(tv,x=age,main="IPW boxplot of the testicular volume")


# We  can compare the naive and IPW boxplots
res2=IPW.boxplot(tv,x=age,graph="both",main=" ")

Computes the IPW quantiles

Description

The function calculates the inverse probability weighted quantiles of a numeric vector.

Usage

IPW.quantile(y, px=NULL,x=NULL,probs = seq(0, 1, 0.25))
IPW.quantile(y, px=NULL,x=NULL,probs = seq(0, 1, 0.25))

Arguments

`y`	Numerical vector of length n with possible missing values codified by NA or NAN.
`px`	Optional. Numerical vector of drop-out probabilities. If not provided a logistic fit is performed using `x` as predictive variable. Missing values are not admitted.
`x`	Optional. The matrix of fully observed variables used to estimate the missing model with dimension nrows=n and ncol=p. Missing values are not admitted. One of the vectors px or x must be supplied.
`probs`	Required. Numeric vector of probabilities with values in (0,1).

Details

The function computes inverse probability weighted (IPW) quantiles of a numeric vector y adapting for missing observations as in Zhang et al.(2012).

The user can supply a vector of drop-out probabilities px or a set of covariates x to estimate the propensity. When both px and x are supplied, the IPW.quantile is executed using px. When px is not supplied, the happenstance probabilities are estimated assuming a logistic model depending on the covariates x. For more details, see Bianco et al. (2018).

We adapted the function weighted.fractile from the isotone package to missing values in variable y. See isotone for more details.

Value

The output of the function is a list with components:

ipw.quantile: Numerical vector of length length(probs) containing the estimated quantiles.
px: Numerical vector of drop-out probabilities.

Note

The missing values of y must be codified as NA or NAN.

The numerical vector px and the matrix of covariates x must be fully observed. px or x must be supplied by the user.

The lengths of y, px, and nrow(x) must be equal.

Author(s)

Ana Maria Bianco <[email protected]>, Graciela Boente <[email protected]> and Ana Perez-Gonzalez <[email protected]>.

References

Bianco, A. M., Boente, G. and Perez-Gonzalez, A. (2018). A boxplot adapted to missing values: an R function when predictive covariates are available. Submitted.

Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J. (2012). Causal inference on quantiles with an obstetric application. Biometrics, 68, 697-706.

Examples




## A real data example
library(mice)
data(boys)
attach(boys)
# As an illustration, we consider variable testicular volume, tv.
# To compute the inverse probability weighted (IPW) quartiles
# the covariate age is considered as covariate with predictive capability
# to estimate the vector of drop-out probabilities.

res=IPW.quantile(tv,x=age,probs=c(0.25,0.5,0.75))
res$IPW.quantile

# Compute the inverse probability weighted (IPW) quantiles
# corresponding to the fractiles  0.3, 0.8 and 0.9
# using the covariate age  to estimate the propensity.


res1=IPW.quantile(tv,x=age,probs=c(0.3,0.8,0.9))
res1$IPW.quantile

## A real data example
library(mice)
data(boys)
attach(boys)
# As an illustration, we consider variable testicular volume, tv.
# To compute the inverse probability weighted (IPW) quartiles
# the covariate age is considered as covariate with predictive capability
# to estimate the vector of drop-out probabilities.

res=IPW.quantile(tv,x=age,probs=c(0.25,0.5,0.75))
res$IPW.quantile

# Compute the inverse probability weighted (IPW) quantiles
# corresponding to the fractiles  0.3, 0.8 and 0.9
# using the covariate age  to estimate the propensity.


res1=IPW.quantile(tv,x=age,probs=c(0.3,0.8,0.9))
res1$IPW.quantile

Package 'IPWboxplot'

Help Index

Boxplot adapted to skewness and missing values

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

Boxplot adapted to missing values

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

Computes the IPW quantiles

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples