| Title: | Visualization of Restricted Cubic Splines |
|---|---|
| Description: | Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U, L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures. rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta". The Restricted Cubic Splines method were based on Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>, and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)). |
| Authors: | Zhiqiang Nie [aut, cre, cph] (ORCID = 0000-0001-7642-3286, wechat = Biostatistics-SCI), JunZhang [ctb], Chaolei Chen [ctb] |
| Maintainer: | Zhiqiang Nie <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 0.4.0 |
| Built: | 2024-12-23 06:46:14 UTC |
| Source: | CRAN |
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
time |
censor time |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
Cox models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_cox.lshap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.lshap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")library(rcssci) rcs_cox.lshap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.lshap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
time |
censor time |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
Cox models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_cox.nshap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.nshap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")library(rcssci) rcs_cox.nshap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.nshap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
time |
censor time |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
Cox models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_cox.prob(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.prob(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")library(rcssci) rcs_cox.prob(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.prob(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
time |
censor time |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
Cox models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_cox.ushap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.ushap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")library(rcssci) rcs_cox.ushap(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_cox.ushap(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
linear models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_linear.lshap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.lshap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_linear.lshap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.lshap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
linear models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_linear.nshap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci # rcs_linear.nshap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_linear.nshap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci # rcs_linear.nshap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
linear models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_linear.prob(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.prob(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_linear.prob(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.prob(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
linear models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_linear.ushap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.ushap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_linear.ushap(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_linear.ushap(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
logistic models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_logistic.lshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.lshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_logistic.lshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.lshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
logistic models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_logistic.nshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.nshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_logistic.nshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.nshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
logistic models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_logistic.prob(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.prob(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_logistic.prob(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.prob(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
logistic models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_logistic.ushap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.ushap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_logistic.ushap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_logistic.ushap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
quasipoisson models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_quasipoisson.lshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.lshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_quasipoisson.lshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.lshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
quasipoisson models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_quasipoisson.nshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.nshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_quasipoisson.nshap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.nshap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
quasipoisson models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_quasipoisson.prob(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.prob(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_quasipoisson.prob(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.prob(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
quasipoisson models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcs_quasipoisson.ushap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.ushap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcs_quasipoisson.ushap(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcs_quasipoisson.ushap(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
time |
censor time |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
Cox models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcssci_cox(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_cox(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")library(rcssci) rcssci_cox(data=sbpdata, y = "status",x = "sbp",time = "time", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_cox(knot=4,data=sbpdata, y = "status",x = "sbp",covs=c("age"), # time = "time", prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
linear models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcssci_linear(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_linear(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcssci_linear(data=sbpdata, y = "sbp",x = "age", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_linear(knot=4,data=sbpdata, y = "sbp",x = "age", # covs=c("gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
logistic models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcssci_logistic(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_logistic(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcssci_logistic(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_logistic(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
restricted cubic splines (RCS) published in SCI.
data |
data.frame.Rdata |
knot |
knot=3-7 or automatic calculate by AIC min |
y |
outcome=0,1 |
covs |
covariables, univariate analysis without "covs" command, multivariable analysis with "covs" command |
prob |
position parameter,range from 0-1 |
x |
main exposure and X-axis when plotting |
filepath |
path of plots output. |
quasipoisson models with RCS splines were performed to explore the shape linear or nonlinear(U, inverted U,J,S,L,log,-log,temporary plateau shape)
message.print PH assumption and other message
Zhiqiang Nie, [email protected]
library(rcssci) rcssci_quasipoisson(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_quasipoisson(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")library(rcssci) rcssci_quasipoisson(data=sbpdata, y = "status",x = "sbp", prob=0.1,filepath=tempdir()) # library(rcssci) # rcssci_quasipoisson(knot=4,data=sbpdata, y = "status",x = "sbp", # covs=c("age","gender"),prob=0.1,filepath="D:/temp")
A data on sbp and status.
data(sbpdata)data(sbpdata)
An object of class tbl_df (inherits from tbl, data.frame) with 3621 rows and 5 columns.
data(sbpdata)data(sbpdata)