Type: Package
Title: Automatic Linear and Logistic Regression and Survival Analysis
Version: 0.3.3
URL: https://github.com/cardiomoon/autoReg, https://cardiomoon.github.io/autoReg/
BugReports: https://github.com/cardiomoon/autoReg/issues
Description: Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.
License: GPL-3
LazyData: true
Encoding: UTF-8
Imports: moonBook(≥ 0.3.0), nortest, dplyr, crayon, stringr, tidyr, purrr, survival, mice, officer, flextable, rlang, patchwork, ggplot2, boot, broom, tidycmprsk, scales, maxstat, pammtools
Suggests: knitr, finalfit, lme4, TH.data, rmarkdown, survminer, asaur, cmprsk, PairedData
RoxygenNote: 7.2.3
VignetteBuilder: knitr
Depends: R (≥ 2.10)
NeedsCompilation: no
Packaged: 2023-11-14 01:31:21 UTC; cardiomoon
Author: Keon-Woong Moon [aut, cre]
Maintainer: Keon-Woong Moon <cardiomoon@gmail.com>
Repository: CRAN
Date/Publication: 2023-11-14 05:53:27 UTC

Draw an Observed vs Expected plot

Description

Draw an Observed vs Expected plot

Usage

OEplot(fit, xnames = NULL, no = 3, maxy.lev = 5, median = TRUE)

Arguments

fit

An object of class "coxph"

xnames

Character Names of explanatory variable to plot

no

integer Number of groups to be made

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

median

logical

Value

No return value, called for side effects

Examples

library(survival)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~rx+age+sex,data=colon)
OEplot(fit)
OEplot(fit,xnames="sex")
## Not run: 
fit=coxph(Surv(time,status)~age,data=colon)
OEplot(fit)
fit=coxph(Surv(time,status)~logWBC,data=anderson)
OEplot(fit)

## End(Not run)

Add model summary to an object of class gaze

Description

Add model summary to an object of class gaze

Usage

addFitSummary(df, fit, statsname = "")

Arguments

df

An object of class "gaze" or "autoReg"

fit

An object of class "glm" or "lm" or "crr"

statsname

character Name of statistics

Value

addFitSummary returns an object of gaze or autoReg - the same class as df

Examples

require(survival)
require(dplyr)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~rx+age+sex+nodes+obstruct+perfor,data=colon)
df=autoReg(fit,uni=FALSE)
final=fit2final(fit)
df %>% addFitSummary(final,statsname="HR (final)") %>% myft()

Add labels to data

Description

Add labels to data

Usage

addLabelData(data)

Arguments

data

A data.frame

Value

A data.frame

Examples

addLabelData(data.frame(ph.ecog=0:3,sex=c(1,2,2,2),age=c(20,30,40,70)))

Draw an expected plot

Description

Draw an expected plot

Usage

adjustedPlot(
  fit,
  xnames = NULL,
  pred.values = list(),
  newdata = NULL,
  maxy.lev = 5,
  median = TRUE,
  facet = NULL,
  se = FALSE,
  mark.time = FALSE,
  show.median = FALSE,
  type = "ggplot",
  ...
)

Arguments

fit

An object of class "coxph" or "survreg"

xnames

Character Names of explanatory variable to plot

pred.values

A list A list of predictor values

newdata

A data.frame or NULL

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

median

Logical

facet

Character Name of facet variable

se

logical Whether or not show se

mark.time

logical Whether or not mark time

show.median

logical

type

Character plot type

...

further arguments to be passed to plot.survfit

Value

A ggplot or no return value(called for side effects)

Examples

library(survival)
fit=coxph(Surv(time,status)~rx+logWBC,data=anderson)
adjustedPlot(fit)
adjustedPlot(fit,xnames="rx",se=TRUE,type="plot")
adjustedPlot(fit,xnames="rx",se=TRUE)
## Not run: 
anderson$WBCgroup=ifelse(anderson$logWBC<=2.73,0,1)
anderson$WBCgroup=factor(anderson$WBCgroup,labels=c("low","high"))
anderson$rx=factor(anderson$rx,labels=c("treatment","control"))
fit=coxph(Surv(time,status)~rx,data=anderson)
adjustedPlot(fit,xnames=c("rx"),show.median=TRUE)
fit=coxph(Surv(time,status)~rx*WBCgroup,data=anderson)
adjustedPlot(fit,xnames=c("rx","WBCgroup"),show.median=TRUE)
adjustedPlot(fit,xnames=c("rx","WBCgroup"),facet="WBCgroup",show.median=TRUE)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~rx+strata(sex)+age+differ,data =colon)
adjustedPlot(fit,xnames=c("sex"))
adjustedPlot(fit,xnames=c("sex"),pred.values=list(age=58,differ=3))
adjustedPlot(fit,xnames=c("sex","rx"),facet="sex")
adjustedPlot(fit,xnames=c("rx","sex","differ"),facet=c("sex","rx"),se=TRUE)
fit <- coxph(Surv(start, stop, event) ~ rx + number + size+ cluster(id), data = bladder2)
adjustedPlot(fit,xnames=c("rx","number","size"),facet=c("rx","size"),maxy.lev=8)

## End(Not run)

Draw predicted survival curve with an object survreg

Description

Draw predicted survival curve with an object survreg

Usage

adjustedPlot.survreg(
  x,
  xnames = NULL,
  pred.values = list(),
  maxy.lev = 5,
  median = TRUE,
  newdata = NULL,
  addCox = FALSE,
  legend.position = "topright",
  xlim = NULL,
  ylim = NULL
)

Arguments

x

An object of class survreg

xnames

Character Names of explanatory variable to plot

pred.values

A list A list of predictor values

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

median

Logical

newdata

A data.frame or NULL

addCox

logical Whether or not add KM

legend.position

Character Default value is "topright"

xlim

numeric

ylim

numeric

Value

No return value, called for side effects

Examples

library(survival)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
## Not run: 
x=survreg(Surv(time, status) ~ sex, data=lung,dist="weibull")
adjustedPlot(x,addCox=TRUE)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x)
x=survreg(Surv(time, status) ~ ph.ecog + age + sex, data=lung, dist="weibull")
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
adjustedPlot(x,pred.values=list(age=c(20,40,60,80),sex=2,ph.ecog=3),addCox=TRUE)
newdata=data.frame(ph.ecog=0:3,sex=c(1,2,2,2),age=c(20,40,60,80))
adjustedPlot(x,newdata=newdata,addCox=TRUE)

## End(Not run)

Draw a survfitted plot

Description

Draw a survfitted plot

Usage

adjustedPlot2(fit, se = FALSE, mark.time = FALSE)

Arguments

fit

An object of class coxph or survfit

se

logical Whether or not show se

mark.time

logical Whether or not mark time

Value

a ggplot

Examples

library(survival)
fit=coxph(Surv(time,status)~rx+logWBC,data=anderson)
plot(survfit(fit),conf.int=TRUE)
adjustedPlot2(fit,se=TRUE)

Draw predicted survival curve as a ggplot with an object survreg

Description

Draw predicted survival curve as a ggplot with an object survreg

Usage

adjustedPlot2.survreg(
  x,
  xnames = NULL,
  pred.values = list(),
  maxy.lev = 5,
  newdata = NULL,
  addCox = FALSE,
  autovar = TRUE,
  legend.position = NULL,
  facet = NULL
)

Arguments

x

An object of class survreg

xnames

Character Names of explanatory variable to plot

pred.values

A list A list of predictor values

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

newdata

A data.frame or NULL

addCox

logical Whether or not add KM

autovar

logical

legend.position

Character Default value is "topright"

facet

Character name(s) of facet variable(s)

Value

A ggplot

Examples

library(survival)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x,type="plot")
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
## Not run: 
x=survreg(Surv(time, status) ~ sex, data=lung,dist="weibull")
adjustedPlot(x,addCox=TRUE)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
adjustedPlot(x,addCox=TRUE)
x=survreg(Surv(time, status) ~ ph.ecog + age + sex, data=lung, dist="weibull")
pred.values=list(ph.ecog=0:3,sex=1:2,age=c(20,40,60,80))
adjustedPlot(x)
adjustedPlot(x,addCox=TRUE)
adjustedPlot(x,addCox=TRUE,xnames=c("ph.ecog","sex"),facet="sex")
adjustedPlot(x,pred.values=pred.values,addCox=TRUE,legend.position="top")+xlim(c(1,1000))
adjustedPlot(x,pred.values=pred.values,xnames=c("ph.ecog","sex","age"),facet=c("ph.ecog","sex"))
adjustedPlot(x,pred.values=pred.values,xnames=c("ph.ecog","sex","age"),facet=c("age","sex"))
adjustedPlot(x,pred.values=pred.values,addCox=TRUE)
adjustedPlot(x,addCox=TRUE)
adjustedPlot(x,pred.values=list(age=c(20,40,60,80),sex=1,ph.ecog=3),addCox=TRUE)

## End(Not run)

Remission survival times of 42 leukemia patients

Description

A dataset containing survival time of 42 leukemia patients

Usage

anderson

Format

A data.frame with 42 rows and 5 variables

time

survival time in weeks

status

censoring status 1=failure 0=censored

sex

sex 0=Female 1=Male

logWBC

log white blood cell count

rx

treatment status 1=control 0 =treatment

Source

David G. Kleinbaum and Mitchel Klein. Survival Analysis. A Self-Learning Text(3rd ed)(Springer,2012) ISBN: 978-1441966452

Remission survival times of 42 leukemia patients

Description

A dataset containing survival time of 42 leukemia patients This data is the same data with anderson, but sex and rx variable are factors not numeric

Usage

anderson1

Format

A data.frame with 42 rows and 5 variables

time

survival time in weeks

status

censoring status 1=failure 0=censored

sex

sex "Female" or "Male

logWBC

log white blood cell count

rx

treatment status "treatment" or "control"

Source

David G. Kleinbaum and Mitchel Klein. Survival Analysis. A Self-Learning Text(3rd ed)(Springer,2012) ISBN: 978-1441966452

Remission survival times of 31 leukemia patients

Description

This data is subdata of anderson with medium(2.3 < logWBC <= 2.96) and high WBC count(logWBC > 2.96)

Usage

anderson2

Format

A data.frame with 31 rows and 6 variables

time

survival time in weeks

status

censoring status 1=failure 0=censored

sex

sex 0=Female 1=Male

logWBC

log white blood cell count

rx

treatment status 1=control 0 =treatment

WBCCAT

WBC count group 1=medium 2=high

Details

A dataset containing survival time of 31 leukemia patients

Source

David G. Kleinbaum and Mitchel Klein. Survival Analysis. A Self-Learning Text(3rd ed)(Springer,2012) ISBN: 978-1441966452

Convert data.frame to printable form

Description

Calculate character length and apply all data

Usage

as_printable(
  data,
  align.first = "left",
  align.chr = "right",
  align.num = "right"
)

Arguments

data

A data.frame

align.first

character Alignment of first variable

align.chr

character Alignment of character variable

align.num

character Alignment of numeric variable

Value

A data.frame

Examples

as_printable(mtcars)
as_printable(iris)

Perform univariable and multivariable regression and stepwise backward regression automatically

Description

Perform univariable and multivariable regression and stepwise backward regression automatically

Usage

autoReg(x, ...)

## S3 method for class 'lm'
autoReg(x, ...)

## S3 method for class 'glm'
autoReg(x, ...)

## S3 method for class 'coxph'
autoReg(x, ...)

## S3 method for class 'survreg'
autoReg(x, ...)

Arguments

x

An object of class lm, glm or coxph

...

Further arguments

Value

autoReg returns an object of class "autoReg" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

attr(*,"model")

name of model. One of "lm","glm" or "coxph"

Methods (by class)

Examples

data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
autoReg(fit)
autoReg(fit,uni=FALSE,final=TRUE)
autoReg(fit,uni=FALSE,imputed=TRUE)
fit=lm(mpg~wt*hp+am+I(wt^2),data=mtcars)
autoReg(fit,final=TRUE)
autoReg(fit,imputed=TRUE)

perform automatic regression for a class of coxph

Description

perform automatic regression for a class of coxph

Usage

autoRegCox(
  x,
  threshold = 0.2,
  uni = FALSE,
  multi = TRUE,
  final = FALSE,
  imputed = FALSE,
  keepstats = FALSE,
  ...
)

Arguments

x

An object of class coxph

threshold

numeric

uni

logical whether or not perform univariable regression

multi

logical whether or not perform multivariable regression

final

logical whether or not perform stepwise backward elimination

imputed

logical whether or not perform multiple imputation

keepstats

logical whether or not keep statistic

...

Further arguments to be passed to gaze()

Value

autoRegCox returns an object of class "autoReg" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

attr(*,"model")

name of model. One of "lm","glm" or "coxph"

Examples

require(survival)
require(dplyr)
data(cancer)
fit=coxph(Surv(time,status==2)~log(bili)+age+cluster(edema),data=pbc)
autoReg(fit)
fit=coxph(Surv(time,status)~rx+age+sex+nodes+obstruct+perfor,data=colon)
autoReg(fit)
autoReg(fit,uni=TRUE,threshold=1)
autoReg(fit,uni=TRUE,final=TRUE) %>% myft()
data(colon_s,package="finalfit")
fit=coxph(Surv(time,status)~age.factor+sex.factor+obstruct.factor+perfor.factor,data=colon_s)
autoReg(fit,uni=TRUE,threshold=1)
autoReg(fit,uni=TRUE,imputed=TRUE)

Perform univariable and multivariable regression and stepwise backward regression automatically

Description

Perform univariable and multivariable regression and stepwise backward regression automatically

Usage

autoReg_sub(
  fit,
  threshold = 0.2,
  uni = FALSE,
  multi = TRUE,
  final = FALSE,
  imputed = FALSE,
  keepstats = FALSE,
  showstats = TRUE,
  ...
)

Arguments

fit

An object of class lm or glm

threshold

numeric

uni

logical whether or not perform univariate regression

multi

logical whether or not perform multivariate regression

final

logical whether or not perform stepwise backward elimination

imputed

logical whether or not include imputed model

keepstats

logical whether or not keep statistics

showstats

logical whether or not show descriptive statistics

...

Further arguments to be passed to imputedReg()

Value

An object of class "autoReg" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

attr(*,"model")

name of model. One of "lm","glm" or "coxph"

perform automatic regression for a class of survreg

Description

perform automatic regression for a class of survreg

Usage

autoRegsurvreg(
  x,
  threshold = 0.2,
  uni = FALSE,
  multi = TRUE,
  final = FALSE,
  imputed = FALSE,
  keepstats = FALSE,
  mode = 1,
  ...
)

Arguments

x

An object of class survreg

threshold

numeric

uni

logical whether or not perform univariable regression

multi

logical whether or not perform multivariable regression

final

logical whether or not perform stepwise backward elimination

imputed

logical whether or not perform multiple imputation

keepstats

logical whether or not keep statistic

mode

integer

...

Further arguments to be passed to gaze()

Value

autoRegsurvreg returns an object of class "autoReg" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

attr(*,"model")

name of model. One of "lm","glm","coxph" or "survreg"

Examples

require(survival)
require(dplyr)
data(cancer)
fit=survreg(Surv(time,status)~rx+age+sex+nodes+obstruct+perfor,data=colon)
autoReg(fit)
autoReg(fit,uni=TRUE,threshold=1)
autoReg(fit,uni=TRUE,final=TRUE)
autoReg(fit,uni=TRUE,final=TRUE) %>% myft()
## Not run: 
autoReg(fit,mode=2)
autoReg(fit,uni=TRUE,threshold=1,,mode=2)
autoReg(fit,uni=TRUE,final=TRUE,mode=2)
autoReg(fit,uni=TRUE,final=TRUE,mode=2) %>% myft()
autoReg(fit,final=TRUE,imputed=TRUE) %>% myft()
autoReg(fit,final=TRUE,imputed=TRUE,mode=2) %>% myft()

## End(Not run)

Whether a string vector can be converted to numeric

Description

Whether a string vector can be converted to numeric

Usage

beNumeric(x)

Arguments

x

A string vector

Value

A logical vector

Bootstrap simulation for model prediction

Description

Generate model predictions against a specified set of explanatory levels with bootstrapped confidence intervals.

Usage

bootPredict(fit, newdata, R = 100, type = "response", ...)

Arguments

fit

An object of class lm or glm

newdata

A data.frame

R

Number of simulations. Note default R=100 is very low.

type

he type of prediction required, see predict.glm. The default for glm models is on the scale of the response variable. Thus for a binomial model the default predictions are predicted probabilities.

...

Further arguments to be passed to boot::boot

Value

An object of class "data.frame"

Examples

data(GBSG2,package="TH.data")
fit=glm(cens~horTh+pnodes,data=GBSG2,family="binomial")
newdata=expand.grid(horTh=factor(c(1,2),labels=c("no","yes")),pnodes=1:51)
bootPredict(fit,newdata)
library(survival)
fit=coxph(Surv(time,cens)~age+horTh+progrec+pnodes,data=GBSG2)

Count groups

Description

Count groups

Usage

countGroups(data, yvars)

Arguments

data

A data.frame

yvars

variable names

Value

An object of class "tibble"

Examples

library(moonBook)
countGroups(acs,"sex")
countGroups(acs,c("sex","Dx"))

Graphical Test of Proportional Hazards

Description

Tis is a ggplot version of plot.cox.zph. Displays a graph of the scaled Schoenfeld residuals, along with a smooth curve.

Usage

coxzphplot(x, resid = TRUE, se = TRUE, var = NULL, hr = FALSE, add.lm = FALSE)

Arguments

x

result of the cox.zph function.

resid

a logical value, if TRUE the residuals are included on the plot, as well as the smooth fit.

se

a logical value, if TRUE, confidence bands at two standard errors will be added.

var

The set of variables for which plots are desired. It can be integer or variable names

hr

logical If true, plot for hazard ratio, If false, plot for coefficients

add.lm

logical If true, add linear regression line

Value

A facetted ggplot

Examples

library(survival)
vfit <- coxph(Surv(time,status) ~ trt + factor(celltype) + karno + age, data=veteran, x=TRUE)
x <- cox.zph(vfit)
coxzphplot(x)
coxzphplot(x,var="karno",add.lm=TRUE)

Extract statistics from an object of class crr

Description

Extract statistics from an object of class crr

Usage

crr2stats(x, digits = 2)

Arguments

x

an object of class crr

digits

integer indication the position of decimal place

Value

An object of class "data.frame"

Examples

data(melanoma,package="boot")
melanoma$status_crr=ifelse(melanoma$status==1,1,ifelse(melanoma$status==2,0,2))
x=crrFormula(time+status_crr~age+sex+thickness+ulcer,data=melanoma)
crr2stats(x)

Competing Risk Regression with Formula

Description

Competing Risk Regression with Formula

Usage

crrFormula(x, data, ...)

Arguments

x

formula time+status~explanatory variables

data

data a data.frame

...

Further arguments to be passed to crr

Value

An object of class "tidycrr" which is described in crr

Examples

data(melanoma,package="boot")
melanoma$status_crr=ifelse(melanoma$status==1,1,ifelse(melanoma$status==2,0,2))
crrFormula(time+status_crr~age+sex+thickness+ulcer,data=melanoma)

Make description for numeric summary

Description

Make description for numeric summary

Usage

descNum(method = 1, p = NULL)

Arguments

method

numeric

p

A numeric or NULL

Value

A character vector of length 1

Convert data.frame to flextable

Description

Convert data.frame to flextable

Usage

df2flextable(
  df,
  vanilla = FALSE,
  fontname = NULL,
  fontsize = 12,
  add.rownames = FALSE,
  even_header = "transparent",
  odd_header = "#5B7778",
  even_body = "#EFEFEF",
  odd_body = "transparent",
  vlines = TRUE,
  colorheader = FALSE,
  digits = 2,
  digitp = 3,
  align_header = "center",
  align_body = "right",
  align_rownames = "left",
  NA2space = TRUE,
  pcol = NULL,
  ...
)

Arguments

df

A data.frame

vanilla

A Logical

fontname

Font name

fontsize

font size

add.rownames

logical. Whether or not include rownames

even_header

background color of even_header

odd_header

background color of even_header

even_body

background color of even_body

odd_body

background color of even_body

vlines

Logical. Whether or not draw vertical lines

colorheader

Logical. Whether or not use color in header

digits

integer indicating the number of decimal places

digitp

integer indicating the number of decimal places of p values

align_header

alignment of header. Expected value is one of 'left', 'right', 'center', 'justify'.

align_body

alignment of body. Expected value is one of 'left', 'right', 'center', 'justify'.

align_rownames

alignment of rownames. Expected value is one of 'left', 'right', 'center', 'justify'.

NA2space

A logical. If true, convert NA value to space

pcol

An integer indicating p value. If specified, convert value less than 0.01 to "< 0.001" in given column.

...

further arguments to be passed to flextable

Value

An object of class "flextable" which is described in flextable

draw line character

Description

draw line character

Usage

drawline(n)

Arguments

n

Numeric

Value

No return value, called for side effects

Examples

drawline(10)

Draw an adjusted Plot for a numeric predictor

Description

Select cutpoint for a numeric predictor with maxstat.test() and draw survival plot with this cutpoint

Usage

expectedPlot(
  fit,
  xname = NULL,
  no = 2,
  maxy.lev = 5,
  median = TRUE,
  mark.time = FALSE,
  se = FALSE,
  type = "ggplot",
  ...
)

Arguments

fit

An object of class "coxph"

xname

Character Name of explanatory variable to plot

no

integer Number of groups to be made

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

median

Logical

mark.time

logical Whether or not mark time

se

logical Whether or not show se

type

Character "plot" or "ggplot"

...

further arguments to be passed to plot.survfit

Value

No return value, called for side effects

Examples

library(survival)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~age+sex,data =colon)
expectedPlot(fit,xname="age")
fit=coxph(Surv(time,status)~rx+logWBC,data=anderson)
expectedPlot(fit,xname="logWBC",no=3)

filldown vector with lead value

Description

filldown vector with lead value

Usage

filldown(x, what = c("", NA))

Arguments

x

a vector

what

Values to be filled

Value

A vector with the same class as x

Examples

x=rep(1:5,each=3)
x=removeDup(x,NA)
filldown(x)

Find first duplicated position

Description

Find first duplicated position

Usage

find1stDup(x)

Arguments

x

a vector

Value

A logical vector

Examples

x=rep(1:5,each=3)
which(find1stDup(x))

Find duplicated term

Description

Find duplicated term

Usage

findDup(x)

Arguments

x

A vector

Value

A logical vector

Examples

x=rep(1:5,each=3)
findDup(x)
x=c(6,x)
findDup(x)
which(!findDup(x))

Make final model using stepwise backward elimination

Description

Make final model using stepwise backward elimination

Usage

fit2final(fit, threshold = 0.2)

Arguments

fit

An object of class "coxph"

threshold

Numeric

Value

An object of class "coxph" which is described in coxph

Examples

require(survival)
data(cancer)
fit=coxph(Surv(time,status)~age+sex+obstruct+perfor,data=colon)
final=fit2final(fit)
fit2summary(final)

extract likelihood information with a coxph object

Description

extract likelihood information with a coxph object

Usage

fit2lik(x)

Arguments

x

An object of class "coxph" or "survreg"

Value

A string

Examples

library(survival)
fit=coxph(Surv(time,status) ~rx,data=anderson)
fit2lik(fit)

Make a list of univariable model with multivariable regression model

Description

Make a list of univariable model with multivariable regression model

Usage

fit2list(fit)

Arguments

fit

An object of class "lm" or "glm"

Value

An object of class "fitlist" which is a list of objects of class "lm" or "glm"

Examples

library(survival)
data(cancer)
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
fit2list(fit)
fit=lm(mpg~wt*hp+am,data=mtcars)
fit2list(fit)

Restore fit model data containing AsIs expressions

Description

Restore fit model data containing AsIs expressions

Usage

fit2model(fit)

Arguments

fit

An object of class "lm", "glm" or "coxph"

Value

An object of class "data.frame"

Examples

require(survival)
pbc$status2=ifelse(pbc$status==2,1,0)
fit=coxph(Surv(time,status2)~age+log(bili),data=pbc)
fit2model(fit)

Make multivariable regression model by selecting univariable models with p.value below threshold

Description

Make multivariable regression model by selecting univariable models with p.value below threshold

Usage

fit2multi(fit, threshold = 0.2)

Arguments

fit

An object of class "coxph"

threshold

Numeric

Value

An object of class "coxph"

Examples

require(survival)
data(cancer)
fit=coxph(Surv(time,status)~age+sex+obstruct+perfor,data=colon)
fit2multi(fit)

Make a new data of mean value or most frequent value

Description

Make a new data of mean value or most frequent value

Usage

fit2newdata(
  fit,
  xnames = NULL,
  pred.values = list(),
  maxy.lev = 5,
  median = TRUE,
  digits = 1
)

Arguments

fit

An object of class "coxph"

xnames

character Names of explanatory variable to plot

pred.values

A list A list of predictor values

maxy.lev

Integer Maximum unique length of a numeric variable to be treated as categorical variables

median

logical If TRUE, select median value for numerical variable. Otherwise select most frequent value

digits

integer indicating the number of decimal places

Value

A data.frame

Examples

require(survival)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~rx+sex+age,data=colon)
fit=coxph(Surv(time,status)~rx+age+strata(sex),data=colon)
fit=survreg(Surv(time, status) ~ ph.ecog + age + sex, data=lung, dist="weibull")
fit2newdata(fit)
fit2newdata(fit,pred.values=list(sex=0,age=58))
fit2newdata(fit,pred.values=list(age=c(20,40,60,80),sex=2,ph.ecog=3))

Summarize statistics with a model

Description

Summarize statistics with a model

Usage

fit2stats(fit, method = "default", digits = 2, mode = 1)

Arguments

fit

An object of class lm or glm or coxph or survreg

method

character choices are one of the c("likelihood","wald")

digits

integer indicating the number of decimal places

mode

integer

Value

An object of class "data.frame"

Examples

library(survival)
data(cancer)
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
fit2stats(fit)
fit=lm(mpg~wt*hp+am,data=mtcars)
fit2stats(fit)
fit=survreg(Surv(time,status)~rx+sex+age+obstruct+nodes,data=colon)
fit2stats(fit)

Summarize statistics with a model or model list

Description

Summarize statistics with a model or model list

Usage

fit2summary(fit, mode = 1, ...)

Arguments

fit

An object of class "lm" or "glm" or "fitlist" which is a result of fit2list

mode

integer

...

Further argument to be passed to fit2stats

Value

An object of class "data.frame"

Examples

library(survival)
data(cancer)
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
fit2summary(fit)
fitlist=fit2list(fit)
fit2summary(fitlist)
fit=survreg(Surv(time,status)~rx+sex+age+obstruct+nodes,data=colon)
fit2summary(fit)

Produce table for descriptive statistics

Description

Produce table for descriptive statistics by groups for several variables easily. Depending on the nature of these variables, different descriptive statistical methods were used(t-test, ANOVA, Kruskal-Wallis, chi-squared, Fisher's,...)

Usage

gaze(x, ...)

## S3 method for class 'formula'
gaze(x, ...)

## S3 method for class 'data.frame'
gaze(x, ...)

## S3 method for class 'coxph'
gaze(x, ...)

## S3 method for class 'survreg'
gaze(x, ...)

## S3 method for class 'glm'
gaze(x, ...)

## S3 method for class 'lm'
gaze(x, ...)

## S3 method for class 'tidycrr'
gaze(x, ...)

Arguments

x

An R object, formula or data.frame

...

arguments to be passed to gaze.data.frame or gaze.formula

Value

An object of class "gaze" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

Methods (by class)

Examples

library(moonBook)
library(dplyr)
gaze(acs)
gaze(~age+sex,data=acs)
gaze(sex~.,data=acs,digits=1,method=1,show.p=TRUE) %>% myft()

gaze(sex~age+Dx,data=acs)
gaze(EF~.,data=acs) %>% myft()
gaze(sex+Dx~.,data=acs,show.p=TRUE) %>% myft()
gaze(sex+Dx~.,data=acs)
gaze(Dx+sex~cardiogenicShock,data=acs,show.p=TRUE) %>% myft()
gaze(Dx+sex+HBP~cardiogenicShock,data=acs,show.p=TRUE)
gaze(~mpg+cyl,data=mtcars)
gaze(~.,data=mtcars)
gaze(cyl~.,data=mtcars,show.p=TRUE)
gaze(hp~.,data=mtcars)
gaze(cyl+am~.,data=mtcars)

library(survival)
x=coxph(Surv(time,status) ~rx,data=anderson1)
gaze(x)
x=coxph(Surv(time,status) ~rx*logWBC,data=anderson1)
gaze(x)
library(survival)
x=survreg(Surv(time, status) ~ rx, data=anderson,dist="exponential")
gaze(x)
x=survreg(Surv(time, status) ~ ph.ecog + age + sex, lung)
gaze(x)
data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
gaze(fit)
fit=lm(mpg~wt*hp+am+I(wt^2),data=mtcars)
gaze(fit)
data(melanoma,package="boot")
melanoma$status_crr=ifelse(melanoma$status==1,1,ifelse(melanoma$status==2,0,2))
fit=crrFormula(time+status_crr~age+sex+thickness+ulcer,data=melanoma)
gaze(fit)

Produce table for descriptive statistics

Description

Produce table for descriptive statistics by groups for several variables easily. Depending on the nature of these variables, different descriptive statistical methods were used(t-test, ANOVA, Kruskal-Wallis, chi-squared, Fisher's,...)

Usage

## S3 method for class 'formula_sub'
gaze(x, data, missing = FALSE, ...)

Arguments

x

An object of class "formula". Left side of ~ must contain the name of one grouping variable or two grouping variables in an additive way(e.g. sex+group~), and the right side of ~ must have variables in an additive way.

data

A data.frame

missing

logical If true, missing value analysis performed

...

Further arguments to be passed to gaze()

Value

An object of class "gaze" which inherits from the class "data.frame" with at least the following attributes:

attr(*,"yvars)

character. name of dependent variable

Summary function for categorical variable

Description

Summary function for categorical variable

Usage

gazeCat(
  data,
  x,
  y = NULL,
  max.ylev = 5,
  digits = 1,
  show.total = FALSE,
  show.n = FALSE,
  show.missing = FALSE,
  show.stats = TRUE,
  origData = NULL,
  show.p = TRUE,
  method = 1,
  catMethod = 2,
  maxCatLevel = 20,
  ...
)

Arguments

data

A data frame

x

Name of a categorical variable

y

Name of a variable, either continuous or categorical

max.ylev

max.ylev An integer indicating the maximum number of levels of grouping variable ('y'). If a column have unique values less than max.ylev it is treated as a categorical variable. Default value is 5.

digits

Numeric

show.total

logical. Whether or not show total column

show.n

logical. Whether or not show N column

show.missing

logical. Whether or not show missing column

show.stats

logical. Whether or not show stats column

origData

A data.frame containing original data

show.p

logical. Whether or not show p column

method

method An integer indicating methods for continuous variables. Possible values in methods are 1 forces analysis as normal-distributed 2 forces analysis as continuous non-normal 3 performs a Shapiro-Wilk test or nortest::ad.test to decide between normal or non-normal Default value is 1.

catMethod

An integer indicating methods for categorical variables. Possible values in methods are

0

Perform chisq.test first. If warning present, perform fisher test

1

Perform chisq.test without continuity correction

2

Perform chisq.test with continuity correction

3

perform fisher.test

4

perform prop.trend test

Default value is 2.

maxCatLevel

An integer indicating the maximum number of unique levels of categorical variable. If a column have unique values more than maxCatLevel, categorical summarization will not be performed.

...

Further arguments

Value

An object of class "data.frame" or "tibble"

Examples

require(moonBook)
gazeCat(acs,"Dx")
gazeCat(acs,"Dx","smoking")
gazeCat(acs,"sex","Dx",show.p=TRUE)
gazeCat(acs,"Dx","sex",show.p=TRUE)
gazeCat(acs,"Dx","EF")
gazeCat(acs,"sex","EF",method=2)
gazeCat(mtcars,"cyl","hp")

Summary function for continuous variable

Description

Summary function for continuous variable

Usage

gazeCont(
  data,
  x,
  y = NULL,
  max.ylev = 5,
  digits = 1,
  show.total = FALSE,
  show.n = FALSE,
  show.missing = FALSE,
  show.stats = TRUE,
  show.p = TRUE,
  method = 1,
  origData,
  ...
)

Arguments

data

A data.frame

x

A name of variable

y

A name of variable, either continuous or categorical

max.ylev

max.ylev An integer indicating the maximum number of levels of grouping variable ('y'). If a column have unique values less than max.ylev it is treated as a categorical variable. Default value is 5.

digits

integer indicating the number of decimal places

show.total

logical. Whether or not show total column

show.n

logical. Whether or not show N column

show.missing

logical. Whether or not show missing column

show.stats

logical. Whether or not show stats column

show.p

logical. Whether or not show p column

method

method An integer indicating methods for continuous variables. Possible values in methods are 1 forces analysis as normal-distributed 2 forces analysis as continuous non-normal 3 performs a Shapiro-Wilk test or nortest::ad.test to decide between normal or non-normal Default value is 1.

origData

A data.frame containing original data

...

Further arguments

Value

An object of class "data.frame" or "tibble"

Examples

gazeCont(mtcars,"hp")
gazeCont(mtcars,"hp","mpg")
require(moonBook)
gazeCont(acs,"log(age)")
gazeCont(acs,"age",method=2)
gazeCont(acs,"age","EF",method=2)
gazeCont(acs,"age","Dx",method=1)
gazeCont(acs,"age","Dx",show.p=TRUE,method=3)

Summary function for categorical/continuous variable

Description

Summary function for categorical/continuous variable

Usage

gaze_sub(data, xname, y = NULL, max.ylev = 5, autoCat = FALSE, ...)

Arguments

data

A data.frame

xname

A name of categorical/continuous vector

y

A name of vector, either continuous or categorical

max.ylev

max.ylev An integer indicating the maximum number of levels of grouping variable ('y'). If a column have unique values less than max.ylev it is treated as a categorical variable. Default value is 5.

autoCat

logical Whether or not use is.mynumeric() to determine whether a variable is numeric or not

...

Further arguments to be passed to gazeCont() or gazeCat()

Value

An object of class "data.frame" or "tibble"

Examples

require(moonBook)
gaze_sub(acs,"age")
gaze_sub(acs,"log(age)")
gaze_sub(acs,"I(age^2)")
gaze_sub(acs,"sex")
gaze_sub(acs,"age","EF")
gaze_sub(acs,"sex","EF")
gaze_sub(acs,"age","Dx")
gaze_sub(acs,"sex","Dx")
gaze_sub(iris,"Species","Sepal.Length")
gaze_sub(mtcars,"am")
gaze_sub(mtcars,"am",autoCat=TRUE)

Get interaction data from data

Description

Get interaction data from data

Usage

getInteraction(name, data)

Arguments

name

a string with interaction term

data

a data.frame

Value

An object of class "data.frame"

Examples

data(acs,package="moonBook")
getInteraction("TC:Dx:sex",data=acs)

Get number of data specified by 'name' and 'desc'

Description

Get number of data specified by 'name' and 'desc'

Usage

getN(name, desc, data)

Arguments

name

a string with interaction term

desc

character

data

a data.frame

Value

A numeric vector

Examples

data(acs,package="moonBook")
df=getInteraction("TC:Dx:sex",data=acs)
getN(name=df$name,desc=df$desc,data=acs)

Get explanatory variables of a model with significance level below the threshold

Description

Get explanatory variables of a model with significance level below the threshold

Usage

getSigVars(fit, threshold = 0.2, final = TRUE)

Arguments

fit

An object of class lm or glm

threshold

Numeric

final

logical if true, perform stepwise regression using step()

Value

A list containing the following components:

sigVars

names of explanatory variables which have significant levels below the threshold in univariable model

finalVars

names of explanatory variables included in final model as a result of step

Examples

library(survival)
data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
getSigVars(fit)
fit=lm(mpg~hp*wt+am,data=mtcars)
getSigVars(fit)

Draw Cumulative Incidence Curves for Competing Risks

Description

Draw Cumulative Incidence Curves for Competing Risks

Usage

ggcmprsk(x, data, id = NULL, se = FALSE, strata = NULL, facet = NULL, ...)

Arguments

x

A formula as time+status~1

data

A data.frame

id

character vector label for status

se

logical whether or not show confidence interval

strata

character vector label for strata

facet

numeric if facet is not NULL, draw plot with selected facets

...

Further arguments to be passed to tidycmprsk::cuminc

Value

An object of class "ggplot"

Examples

data(melanoma,package="boot")
melanoma$status1 = ifelse(melanoma$status==1,1,ifelse(melanoma$status==2,0,2))
melanoma$years=melanoma$time/365

ggcmprsk(years+status1~1,data=melanoma)
ggcmprsk(years+status1~1,data=melanoma,id=c("alive","melanoma","other"),se=TRUE)
ggcmprsk(years+status1~sex,data=melanoma)
ggcmprsk(years+status1~sex,data=melanoma,facet=1)
ggcmprsk(years+status1~sex,data=melanoma,
id=c("alive","melanoma","other"),strata=c("female","male"))
ggcmprsk(years+status1~sex,data=melanoma,
id=c("alive","melanoma","other"),strata=c("female","male"),facet=1)

Compare cumulative incidence to th Kaplan-Meier estimate

Description

Compare cumulative incidence to th Kaplan-Meier estimate

Usage

ggcmprsk2(
  x,
  data,
  id = c("disease", "other"),
  se = FALSE,
  xpos = c(2, 2),
  ypos = c(0.25, 0.7),
  ylabs = NULL,
  xlab = NULL,
  label = NULL,
  plot = TRUE
)

Arguments

x

A formula as time+status~1

data

A data.frame

id

Character vector of length2

se

logical whether or not show confidence interval

xpos

numeric x-axis position of label

ypos

numeric y-axis position of label

ylabs

string vector of length 2. y axis labels

xlab

A character. The x-axis label

label

string vector of length 2. Label names

plot

logical Whether or not print plot

Value

A list containing the following components:

df

A long-form data.frame consist of time, est, upper,lower, id, method

df3

A data.frame for label consist of x, y, label, id

p

A ggplot object

Examples

require(dplyr)
data(prostateSurvival,package="asaur")
prostateHighRisk <- prostateSurvival %>%
  filter(grade=="poor" & stage=="T2",ageGroup=="80+")
prostateHighRisk$years=prostateHighRisk$survTime/12
ggcmprsk2(years+status~1,data=prostateHighRisk,
  id=c("prostate cancer","other causes"))

Highlight a data.frame

Description

Highlight a data.frame

Usage

highlight2(x, i = NULL, j = NULL, style = NULL, include.colname = FALSE)

Arguments

x

A data.frame

i

numeric rows to highlight

j

numeric columns to hightlight

style

A style function or NULL

include.colname

logical Whether or not include colname

Value

a data.frame

Examples

head(mtcars) %>% highlight2(i=3) %>% printdf()
library(crayon)
head(mtcars) %>% highlight2(i=2) %>% highlight2(j=3,style=blue$bold) %>% printdf()
fit=lm(mpg~wt*hp,data=mtcars)
gaze(fit)
gaze(fit) %>% highlight2(j=4,include.colname=TRUE)
gaze(fit) %>% highlight2(i=2,j=4) %>% highlight2(i=2,j=2:3,style=blue$bold)
gaze(fit) %>% highlight2(i=2) %>% highlight2(j=3,style=blue$bold)

Make a multiple imputed model

Description

Make a multiple imputed model

Usage

imputedReg(fit, data = NULL, m = 20, seed = 1234, digits = 2, mode = 1, ...)

Arguments

fit

An object of class lm, glm, coxph or survreg

data

a data.frame

m

Number of multiple imputations. The default is m=20.

seed

An integer that is used as argument by the set.seed() for offsetting the random number generator.

digits

Integer indicating the number of decimal place

mode

integer indicating summary mode of class survreg

...

Further argument to be passed to mice

Value

An object of class "imputedReg" which inherits from the class "data.frame"

Examples

data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
imputedReg(fit)

library(survival)
fit=coxph(Surv(time,status)~rx+age+sex+nodes+obstruct+perfor,data=colon)
imputedReg(fit)
fit=survreg(Surv(time,status)~rx+age+sex+nodes+obstruct+perfor,data=colon)
imputedReg(fit)
imputedReg(fit,mode=2)

Decide whether a vector can be treated as a numeric variable

Description

Decide whether a vector can be treated as a numeric variable

Usage

is.mynumeric(x, maxy.lev = 5)

Arguments

x

A vector

maxy.lev

An integer indicating the maximum number of unique values of a numeric variable be treated as a categorical variable

Value

A logical value

Examples

x=1:5
is.mynumeric(x)
x=1:13
is.mynumeric(x)

takes the breaks as input and returns labels as output

Description

takes the breaks as input and returns labels as output

Usage

label_parse(breaks)

Arguments

breaks

character

Value

a character vector

Draw log-log plot

Description

Draw log-log plot

Usage

loglogplot(
  fit,
  xnames = NULL,
  main = NULL,
  labels = NULL,
  no = 3,
  add.loess = FALSE,
  add.lm = TRUE,
  type = "l",
  se = TRUE,
  what = "surv",
  legend.position = NULL,
  ...
)

Arguments

fit

An object of class "coxph" or "survfit"

xnames

character Names of explanatory variable to plot

main

String Title of plot

labels

String vector Used as legend in legend

no

Numeric The number of groups to be converted

add.loess

logical If true, add loess regression line

add.lm

logical If true, add linear regression line

type

character "l" or "p"

se

logical If true, add se

what

character One of c("surv","survOdds","failureOdds")

legend.position

legend position. One of c("left","top","bottom","right") or numeric vector of length 2.

...

Furhter arguments to be passed to plot()

Value

A ggplot or no return value, called for side effects

Examples

require(survival)
data(cancer,package="survival")
fit=coxph(Surv(time,status)~x,data=leukemia)
loglogplot(fit)
fit=survfit(Surv(time,status)~1,data=anderson)
loglogplot(fit)
fit=survfit(Surv(time,status)~sex,data=anderson)
loglogplot(fit)
fit=survfit(Surv(time,status)~logWBC,data=anderson)
loglogplot(fit)
loglogplot(fit,no=2)
fit=survfit(Surv(time,status)~logWBC+rx,data=anderson)
loglogplot(fit,no=2)
fit=survfit(Surv(time,status)~rx,data=anderson)
loglogplot(fit,type="p")
fit=survfit(Surv(time,status)~WBCCAT,data=anderson2)
loglogplot(fit,type="p",what="survOdds")
loglogplot(fit,type="p",what="failureOdds")

Return maximum character number except NA

Description

Return maximum character number except NA

Usage

maxnchar(x)

Arguments

x

a vector

Value

A numeric vector of length 1

Examples

x=c(1,2,"sadf",NA)
maxnchar(x)
data(acs,package="moonBook")
lapply(acs,maxnchar)

Draw coefficients/odds ratio/hazard ratio plot

Description

Draw coefficients/odds ratio/hazard ratio plot

Usage

modelPlot(
  fit,
  widths = NULL,
  change.pointsize = TRUE,
  show.OR = TRUE,
  show.ref = TRUE,
  bw = TRUE,
  legend.position = "top",
  ...
)

Arguments

fit

An object of class glm

widths

Numeric vector

change.pointsize

logical Whether or not change point size

show.OR

logical Whether or not show odds ratio

show.ref

logical Whether or not show reference

bw

logical If true, use grey scale

legend.position

legend position default value is 'top'

...

Further arguments to be passed to autoReg()

Value

modelPlot returns an object of class "modelPlot" An object of class modelPlot is a list containing at least of the following components:

tab1

The first table containing names

tab2

The 2nd table containing levels

tab3

The 3rd table containing coefficients or odds ratio or hazards ratio

p

A ggplot

widths

the widths of the tables and the ggplot

Examples

fit=lm(mpg~wt*hp+am,data=mtcars)
modelPlot(fit,widths=c(1,0,2,3))
modelPlot(fit,uni=TRUE,threshold=1,widths=c(1,0,2,3))
fit=lm(Sepal.Width~Sepal.Length*Species,data=iris)
modelPlot(fit)
modelPlot(fit,uni=TRUE,change.pointsize=FALSE)

data(cancer,package="survival")
fit=glm(status~rx+age+sex+nodes+obstruct+perfor,data=colon,family="binomial")
modelPlot(fit)
modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1)
modelPlot(fit,multi=TRUE,imputed=TRUE,change.pointsize=FALSE)
data(colon_s,package="finalfit")
fit=glm(mort_5yr~age.factor+sex.factor+obstruct.factor+perfor.factor,data=colon_s,family="binomial")
modelPlot(fit)
modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1)
modelPlot(fit,uni=TRUE,multi=TRUE)
modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1,show.ref=FALSE)
library(survival)
fit=coxph(Surv(time,status)~rx+age+sex+obstruct+perfor,data=colon)
modelPlot(fit)
modelPlot(fit,uni=TRUE,threshold=1)
modelPlot(fit,multi=FALSE,final=TRUE,threshold=1)
fit=coxph(Surv(time,status)~age.factor+sex.factor+obstruct.factor+perfor.factor,data=colon_s)
modelPlot(fit)
modelPlot(fit,uni=TRUE,threshold=1)
modelPlot(fit,uni=TRUE,threshold=1,show.ref=FALSE)
modelPlot(fit,imputed=TRUE)

Makes table summarizing list of models

Description

Makes table summarizing list of models

Usage

modelsSummary(fitlist, show.lik = FALSE)

Arguments

fitlist

A list of objects of class "coxph"

show.lik

logical Whether or not show likelihood test results

Value

No return value, called for side effects

Examples

library(survival)
fit1=coxph(Surv(time,status) ~rx,data=anderson)
fit2=coxph(Surv(time,status) ~rx+logWBC,data=anderson)
fit3=coxph(Surv(time,status) ~rx*logWBC,data=anderson)
fitlist=list(fit1,fit2,fit3)
modelsSummary(fitlist)

Makes flextable summarizing list of models

Description

Makes flextable summarizing list of models

Usage

modelsSummaryTable(fitlist, labels = NULL, show.lik = FALSE)

Arguments

fitlist

A list of objects of class "coxph"

labels

character labels of models

show.lik

logical Whether or not show likelihood test results

Value

A flextable

Examples

library(survival)
fit1=coxph(Surv(time,status) ~rx,data=anderson)
fit2=coxph(Surv(time,status) ~rx+logWBC,data=anderson)
fit3=coxph(Surv(time,status) ~rx*logWBC,data=anderson)
fitlist=list(fit1,fit2,fit3)
modelsSummaryTable(fitlist)

Statistical test for categorical variables Statistical test for categorical variables

Description

Statistical test for categorical variables Statistical test for categorical variables

Usage

my.chisq.test2(x, y, catMethod = 2, all = FALSE)

Arguments

x

a vector

y

a vector

catMethod

An integer indicating methods for categorical variables. Possible values in methods are

0

Perform chisq.test first. If warning present, perform fisher test

1

Perform chisq.test without continuity correction

2

Perform chisq.test with continuity correction

3

perform fisher.test

4

perform prop.trend test

Default value is 2.

all

A logical

Value

A numeric vector of length 1

Examples

library(moonBook)
x=acs$sex
y=acs$Dx
my.chisq.test2(x,y)

Statistical test for continuous variables

Description

Statistical test for continuous variables

Usage

my.t.test2(y, x, method = 1, all = FALSE)

Arguments

y

a categorical vector

x

a numeric vector

method

method An integer indicating methods for continuous variables. Possible values in methods are 1 forces analysis as normal-distributed 2 forces analysis as continuous non-normal 3 performs a Shapiro-Wilk test or nortest::ad.test to decide between normal or non-normal Default value is 1.

all

A logical

Value

A numeric vector of length 1

Examples

library(moonBook)
y=acs$sex
x=acs$height
my.t.test2(y,x)

Fit Simple Proportional Hazards Regression Model

Description

Fit Simple Proportional Hazards Regression Model

Usage

mycphSimple(fit, threshold = 0.2, digits = 2)

Arguments

fit

An object of class coxph

threshold

numeric p-value threshold to enter multiple model

digits

integer indicating the position decimal place

Value

An object of class "data.frame"

Examples

require(survival)
data(cancer)
fit=coxph(Surv(time,status)~age+sex+obstruct+perfor,data=colon)
mycphSimple(fit)

Convert data.frame to printable format

Description

Convert data.frame to printable format

Usage

myformat(x, showid = FALSE, digits = 3)

Arguments

x

A data.frame

showid

logical if TRUE, show id

digits

Integer indicating the number of decimal places

Value

A data.frame

Examples

fit=lm(mpg~wt*hp,data=mtcars)
gaze(fit) %>% myformat()

Convert data.frame into flextable

Description

Convert data.frame into flextable

Usage

myft(x, vanilla = TRUE, fontsize = 10, digits, showid = FALSE, ...)

Arguments

x

A data.frame

vanilla

logical

fontsize

Numeric

digits

integer indicating the position of decimal place

showid

logical if TRUE, show id

...

Further arguments to be passed to df2flextable()

Value

An object of class flextable

Examples

data(acs,package="moonBook")
library(dplyr)
gaze(acs) %>% myft()
gaze(sex~.,acs) %>% myft()
fit=lm(mpg~hp*wt,data=mtcars)
gaze(fit) %>% myft()
library(survival)
fit=coxph(Surv(time,status) ~rx,data=anderson1)
gaze(fit) %>% myft()

gaze(sex+Dx~.,data=acs,show.p=TRUE,show.total=TRUE,show.n=TRUE,shiw.missing=TRUE) %>% myft()
gaze(Dx+sex~cardiogenicShock,data=acs,show.p=TRUE) %>% myft()
gaze(Dx+sex+HBP~cardiogenicShock,data=acs,show.p=TRUE) %>% myft()

Fit Simple AFT Model

Description

Fit Simple AFT Model

Usage

mysurvregSimple(fit, threshold = 0.2, digits = 2, mode = 1)

Arguments

fit

An object of class survreg

threshold

numeric p-value threshold to enter multiple model

digits

integer indicating the position decimal place

mode

integer

Value

An object of class "data.frame"

Examples

require(survival)
data(cancer)
fit=survreg(Surv(time,status)~rx+age+strata(sex)+obstruct+perfor,data=colon)
mysurvregSimple(fit)

Convert a numeric column in a data.frame to a factor

Description

Convert a numeric column in a data.frame to a factor

Usage

num2factor(data, call, name, no = 3)

Arguments

data

A data.frame

call

a function call

name

character Name of numeric column

no

numeric

Value

A data.frame

Examples

num2factor(anderson,name="logWBC")
library(survival)
fit=coxph(Surv(time,status)~logWBC+rx,data=anderson)
num2factor(anderson,call=fit$call,name="logWBC",no=2)

Summarize numeric vector to statistical summary

Description

Summarize numeric vector to statistical summary

Usage

num2stat(x, digits = 1, method = 1, p = NULL)

Arguments

x

A numeric vector

digits

integer indicating the number of decimal places

method

An integer indicating methods for continuous variables. Possible values in methods are 1 forces analysis as normal-distributed 2 forces analysis as continuous non-normal 3 performs a Shapiro-Wilk test or nortest::ad.test to decide between normal or non-normal Default value is 1.

p

A numeric

Value

A character vector of length 1

Examples

library(moonBook)
num2stat(acs$age)
num2stat(acs$age,method=2)

Change p value to string

Description

Change p value to string

Usage

p2character2(x, digits = 3, add.p = TRUE)

Arguments

x

a numeric

digits

integer indicating decimal place

add.p

logical

Value

A character vector

S3 method print for an object of class autoReg

Description

S3 method print for an object of class autoReg

Usage

## S3 method for class 'autoReg'
print(x, ...)

Arguments

x

An object of class autoReg

...

Further arguments

Value

No return value, called for side effects

Examples

data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
autoReg(fit)

S3 method print for an object of class gaze

Description

S3 method print for an object of class gaze

Usage

## S3 method for class 'gaze'
print(x, ...)

Arguments

x

An object of class gaze

...

Further arguments

Value

No return value, called for side effects

Examples

data(acs,package="moonBook")
x=gaze(acs,show.n=TRUE,show.missing=TRUE)
gaze(sex~.,acs,show.p=TRUE,show.n=TRUE,show.missing=TRUE,show.total=TRUE)

gaze(Dx+sex~.,acs,show.p=TRUE)
gaze(sex+Dx+HBP~.,acs,show.p=TRUE)

S3 method for an class modelPlot

Description

S3 method for an class modelPlot

Usage

## S3 method for class 'modelPlot'
print(x, ...)

Arguments

x

An object of class modelPlot

...

Further arguments to be passed to plot()

Print function for data.frame

Description

Print function for data.frame

Usage

printdf(x)

Arguments

x

A data.frame

Value

No return value, called for side effects

Examples

x=mtcars[1:5,1:5]
printdf(x)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%, select

Remove duplicated term

Description

Remove duplicated term

Usage

removeDup(x, replacement = "")

Arguments

x

A vector

replacement

A character to be replaced or NA

Value

A vector with the same class as x

Examples

x=rep(1:5,each=3)
removeDup(x)

Make a residual plot of NULL model

Description

Make a residual plot of NULL model

Usage

residualNull(x, add.log = TRUE, type = "martingale")

Arguments

x

An object of calss coxph

add.log

logical If true, log of predictor varaibles are added

type

character type of residuals

Examples

library(survival)
data(pharmacoSmoking,package="asaur")
pharmacoSmoking$priorAttemptsT=pharmacoSmoking$priorAttempts
pharmacoSmoking$priorAttemptsT[pharmacoSmoking$priorAttemptsT>20]=20
x=coxph(Surv(ttr,relapse)~age+priorAttemptsT+longestNoSmoke,data=pharmacoSmoking)
residualNull(x)

Draw a residual plot with an object of class coxph

Description

Draw a residual plot with an object of class coxph

Usage

residualPlot(
  fit,
  type = "martingale",
  vars = NULL,
  ncol = 2,
  show.point = TRUE,
  se = TRUE,
  topn = 5,
  labelsize = 4
)

Arguments

fit

An object of class coxph or survreg

type

character One of the c("martingale","deviance","score","schoenfeld", "dfbeta","dfbetas","scaledsch","partial"). Default value is "martingale".

vars

character Names of variables to plot. default value is NULL

ncol

numeric number of columns

show.point

logical Whether or not show point

se

logical Whether or not show se

topn

numeric number of data to be labelled

labelsize

numeric size of label

Value

A patchwork object

Examples

require(survival)
data(cancer)
fit=coxph(Surv(time,status==2)~log(bili)+age+cluster(edema),data=pbc)
residualPlot(fit)
residualPlot(fit,vars="age")
fit=coxph(Surv(time,status==2)~age,data=pbc)
residualPlot(fit)
residualPlot(fit,"partial")
fit=coxph(Surv(time,status)~rx+sex+logWBC,data=anderson)
residualPlot(fit,ncol=3)
## Not run: 
data(pharmacoSmoking,package="asaur")
fit=coxph(Surv(ttr,relapse)~grp+employment+age,data=pharmacoSmoking)
residualPlot(fit)
residualPlot(fit,var="age")
residualPlot(fit,type="dfbeta")
residualPlot(fit,type="dfbeta",var="age")
residualPlot(fit,type="dfbeta",var="employment")
residualPlot(fit,type="dfbeta",var="employmentother")
pharmacoSmoking$ttr[pharmacoSmoking$ttr==0]=0.5
fit=survreg(Surv(ttr,relapse)~grp+age+employment,data=pharmacoSmoking,dist="weibull")
residualPlot(fit,type="response")
residualPlot(fit,type="deviance")
residualPlot(fit,type="dfbeta",vars="age")
fit=survreg(Surv(time,status)~ph.ecog+sex*age,data=lung,dist="weibull")
residualPlot(fit,"dfbeta")
residualPlot(fit,"deviance")

## End(Not run)

restore data with factor in column name

Description

restore data with factor in column name

Usage

restoreData(data)

Arguments

data

An object of class "data.frame"

Value

An object of class "data.frame"

restore data with I() in column name

Description

restore data with I() in column name

Usage

restoreData2(df)

Arguments

df

An object of class "data.frame"

Value

An object of class "data.frame"

restore data with operator in column name

Description

restore data with operator in column name

Usage

restoreData3(df, changeLabel = FALSE)

Arguments

df

An object of class "data.frame"

changeLabel

logical

Value

An object of class "data.frame"

get opposite arithmetic operator

Description

get opposite arithmetic operator

Usage

revOperator(operator)

Arguments

operator

A character

Value

A character

Convert numeric columns of data.frame to character

Description

Convert numeric columns of data.frame to character

Usage

roundDf(df, digits = 2)

Arguments

df

a data.frame

digits

integer indicating the number of decimal places

Value

An object of class "data.frame"

Add label to a vector

Description

Add label to a vector

Usage

setLabel(x, label = "")

Arguments

x

a vector

label

string

Value

a labelled vector

Shorten an object of class gaze

Description

Shorten an object of class gaze

Usage

shorten(x, xname = NULL, ref = 1)

Arguments

x

an object of class gaze

xname

A variable name

ref

Numeric Th number to be used as reference

Value

An object of class "gaze" which is described in gaze

Examples

data(acs,package="moonBook")
x=gaze(sex~.,data=acs)
shorten(x)

Show effects of covariates

Description

Show effects of covariates

Usage

showEffect(
  fit,
  x = NULL,
  color = NULL,
  facet = NULL,
  autovar = TRUE,
  pred.values = list(),
  se = TRUE,
  logy = TRUE,
  collabel = label_both,
  rowlabel = label_both
)

Arguments

fit

An object of class survreg

x

character name of x-axis variable

color

character name of color variable

facet

character name of facet variable

autovar

logical Whether or not select color and facet variable automatically

pred.values

list list of values of predictor variables

se

logical whether or not show se

logy

logical WHether or not draw y-axis on log scale

collabel

labeller for column

rowlabel

labeller for row

Value

A ggplot

Examples

library(survival)
library(ggplot2)
fit=survreg(Surv(time,status)~ph.ecog+sex*age,data=lung,dist="weibull")
showEffect(fit)
fit=survreg(Surv(time,status)~rx+sex+age+obstruct+adhere,data=colon,dist="weibull")
showEffect(fit)
showEffect(fit,rowlabel=label_value)
fit=survreg(Surv(time,status)~ph.ecog+sex,data=lung,dist="weibull")
showEffect(fit)
fit=survreg(Surv(time,status)~ph.ecog+age,data=lung,dist="weibull")
showEffect(fit)
fit=survreg(Surv(time,status)~ph.ecog+sex*age,data=lung,dist="weibull")
showEffect(fit,x="age",color="sex",facet="ph.ecog")
showEffect(fit,pred.values=list(age=c(50,60,70),ph.ecog=c(0,3),sex=c(1,2)),
  x="ph.ecog",color="sex",facet="age",autovar=FALSE)
fit=survreg(Surv(time,status)~age,data=lung,dist="weibull")
showEffect(fit)

Convert a character vector to a data.frame

Description

Convert a character vector to a data.frame

Usage

strata2df(strata)

Arguments

strata

A character vector

Value

A data.frame

Extract survival data from an object of class "survfit"

Description

Extract survival data from an object of class "survfit"

Usage

survfit2df(fit, labels = NULL)

Arguments

fit

An object of class "survfit"

labels

Character

Value

A data.frame

Examples

library(survival)
data(cancer,package="survival")
fit=survfit(coxph(Surv(time,status)~sex+age+strata(rx),data=colon))
survfit2df(fit)
## Not run: 
fit=coxph(Surv(time,status)~sex+age+strata(rx),data=colon)
fit=survfit(as.formula(deparse(fit$terms)),data=fit2model(fit))
survfit2df(fit)
fit=survfit(Surv(time,status)~rx+sex+age,data=colon)
survfit2df(fit)
fit=survfit(Surv(time,status)~1,data=colon)
survfit2df(fit)

## End(Not run)

Make final model using stepwise backward elimination

Description

Make final model using stepwise backward elimination

Usage

survreg2final(fit, threshold = 0.2)

Arguments

fit

An object of class "survreg"

threshold

Numeric

Value

An object of class "survreg" which is described in survreg

Examples

require(survival)
data(cancer)
fit=survreg(Surv(time,status)~rx+age+sex+obstruct+perfor,data=colon)
survreg2final(fit)

Make multivariable regression model by selecting univariable models with p.value below threshold

Description

Make multivariable regression model by selecting univariable models with p.value below threshold

Usage

survreg2multi(fit, threshold = 0.2)

Arguments

fit

An object of class "survreg"

threshold

Numeric

Value

An object of class "survreg"

Examples

require(survival)
data(cancer)
fit=survreg(Surv(time,status)~rx+age+sex+obstruct+perfor,data=colon)
survreg2multi(fit)