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Installation

You can install autoReg package on github.

#install.packages("devtools")
devtools::install_github("cardiomoon/autoReg")

Load package

To load the package, use library() function.

Main features

1.Summarizing baseline characteristics : gaze()

You can make a table summarizing baseline characteristics easily.

library(moonBook) # For use of example data acs
gaze(sex~.,data=acs)
————————————————————————————————————————————————————————————————————————
  Dependent:sex        levels           Female          Male        p   
       (N)                             (N=287)        (N=570)           
————————————————————————————————————————————————————————————————————————
age               Mean ± SD             68.7 ± 10.7   60.6 ± 11.2  <.001 
cardiogenicShock  No                    275 (95.8%)     530 (93%)   .136 
                  Yes                     12 (4.2%)       40 (7%)        
entry             Femoral               119 (41.5%)   193 (33.9%)   .035 
                  Radial                168 (58.5%)   377 (66.1%)        
Dx                NSTEMI                 50 (17.4%)   103 (18.1%)   .012 
                  STEMI                  84 (29.3%)   220 (38.6%)        
                  Unstable Angina       153 (53.3%)   247 (43.3%)        
EF                Mean ± SD             56.3 ± 10.1    55.6 ± 9.4   .387 
height            Mean ± SD             153.8 ± 6.2   167.9 ± 6.1  <.001 
weight            Mean ± SD              57.2 ± 9.3   68.7 ± 10.3  <.001 
BMI               Mean ± SD              24.2 ± 3.6    24.3 ± 3.2   .611 
obesity           No                    194 (67.6%)   373 (65.4%)   .580 
                  Yes                    93 (32.4%)   197 (34.6%)        
TC                Mean ± SD            188.9 ± 51.1  183.3 ± 45.9   .124 
LDLC              Mean ± SD            117.8 ± 41.2  116.0 ± 41.1   .561 
HDLC              Mean ± SD             39.0 ± 11.5   37.8 ± 10.9   .145 
TG                Mean ± SD            119.9 ± 76.2  127.9 ± 97.3   .195 
DM                No                    173 (60.3%)   380 (66.7%)   .077 
                  Yes                   114 (39.7%)   190 (33.3%)        
HBP               No                     83 (28.9%)   273 (47.9%)  <.001 
                  Yes                   204 (71.1%)   297 (52.1%)        
smoking           Ex-smoker              49 (17.1%)   155 (27.2%)  <.001 
                  Never                 209 (72.8%)   123 (21.6%)        
                  Smoker                 29 (10.1%)   292 (51.2%)        
————————————————————————————————————————————————————————————————————————

For easy reproducible research : myft()

You can make a publication-ready table easily using myft(). It makes a flextable object which can use in either HTML and PDF format.

library(dplyr) # for use of `%>%`

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
ft=gaze(sex~.,data=acs) %>% myft()
ft

name

levels

Female (N=287)

Male (N=570)

p

age

Mean ± SD

68.7 ± 10.7

60.6 ± 11.2

<.001

cardiogenicShock

No

275 (95.8%)

530 (93%)

.136

Yes

12 (4.2%)

40 (7%)

entry

Femoral

119 (41.5%)

193 (33.9%)

.035

Radial

168 (58.5%)

377 (66.1%)

Dx

NSTEMI

50 (17.4%)

103 (18.1%)

.012

STEMI

84 (29.3%)

220 (38.6%)

Unstable Angina

153 (53.3%)

247 (43.3%)

EF

Mean ± SD

56.3 ± 10.1

55.6 ± 9.4

.387

height

Mean ± SD

153.8 ± 6.2

167.9 ± 6.1

<.001

weight

Mean ± SD

57.2 ± 9.3

68.7 ± 10.3

<.001

BMI

Mean ± SD

24.2 ± 3.6

24.3 ± 3.2

.611

obesity

No

194 (67.6%)

373 (65.4%)

.580

Yes

93 (32.4%)

197 (34.6%)

TC

Mean ± SD

188.9 ± 51.1

183.3 ± 45.9

.124

LDLC

Mean ± SD

117.8 ± 41.2

116.0 ± 41.1

.561

HDLC

Mean ± SD

39.0 ± 11.5

37.8 ± 10.9

.145

TG

Mean ± SD

119.9 ± 76.2

127.9 ± 97.3

.195

DM

No

173 (60.3%)

380 (66.7%)

.077

Yes

114 (39.7%)

190 (33.3%)

HBP

No

83 (28.9%)

273 (47.9%)

<.001

Yes

204 (71.1%)

297 (52.1%)

smoking

Ex-smoker

49 (17.1%)

155 (27.2%)

<.001

Never

209 (72.8%)

123 (21.6%)

Smoker

29 (10.1%)

292 (51.2%)

You can also make a powerpoint file using rrtable::table2pptx() function.

library(rrtable)

table2pptx(ft)
Exported table as Report.pptx

You can make a microsoft word file using rrtable::table2docx() function.

table2docx(ft)
Exported table as Report.docx

Summarizing baseline characteristics with two or more grouping variables

You can get a table summarizing baseline characteristics with two or more grouping variables.

gaze(sex+Dx~.,data=acs) %>% myft()

sex (N)

Female (N=287)

Male (N=570)

name

levels

NSTEMI (N=50)

STEMI (N=84)

Unstable Angina (N=153)

p

NSTEMI (N=103)

STEMI (N=220)

Unstable Angina (N=247)

p

age

Mean ± SD

70.9 ± 11.4

69.1 ± 10.4

67.7 ± 10.7

.177

61.1 ± 11.6

59.4 ± 11.7

61.4 ± 10.6

.133

cardiogenicShock

No

49 (98%)

73 (86.9%)

153 (100%)

<.001

100 (97.1%)

183 (83.2%)

247 (100%)

<.001

Yes

1 (2%)

11 (13.1%)

0 (0%)

3 (2.9%)

37 (16.8%)

0 (0%)

entry

Femoral

22 (44%)

45 (53.6%)

52 (34%)

.013

36 (35%)

88 (40%)

69 (27.9%)

.022

Radial

28 (56%)

39 (46.4%)

101 (66%)

67 (65%)

132 (60%)

178 (72.1%)

EF

Mean ± SD

54.8 ± 9.1

52.3 ± 10.9

59.4 ± 8.8

<.001

55.1 ± 9.4

52.4 ± 8.9

59.1 ± 8.7

<.001

height

Mean ± SD

154.2 ± 5.1

155.7 ± 5.4

152.6 ± 6.7

.002

167.5 ± 5.7

168.7 ± 6.0

167.3 ± 6.4

.055

weight

Mean ± SD

57.2 ± 10.3

57.4 ± 9.0

57.1 ± 9.1

.978

67.5 ± 8.4

68.8 ± 10.9

69.0 ± 10.6

.479

BMI

Mean ± SD

24.1 ± 4.3

23.6 ± 3.2

24.5 ± 3.5

.215

24.1 ± 2.6

24.1 ± 3.4

24.6 ± 3.4

.205

obesity

No

35 (70%)

60 (71.4%)

99 (64.7%)

.528

71 (68.9%)

149 (67.7%)

153 (61.9%)

.301

Yes

15 (30%)

24 (28.6%)

54 (35.3%)

32 (31.1%)

71 (32.3%)

94 (38.1%)

TC

Mean ± SD

196.3 ± 52.7

180.7 ± 45.7

191.1 ± 53.1

.192

192.6 ± 54.3

184.1 ± 42.6

178.7 ± 44.6

.036

LDLC

Mean ± SD

127.7 ± 39.5

111.0 ± 40.0

118.3 ± 41.8

.088

125.4 ± 47.1

118.9 ± 39.1

109.5 ± 39.2

.002

HDLC

Mean ± SD

40.1 ± 13.8

39.5 ± 11.2

38.5 ± 10.8

.627

38.4 ± 10.9

38.1 ± 10.9

37.4 ± 10.9

.655

TG

Mean ± SD

112.5 ± 51.1

112.3 ± 87.2

126.3 ± 76.0

.316

138.0 ± 100.2

104.3 ± 65.5

144.3 ± 114.2

<.001

DM

No

25 (50%)

54 (64.3%)

94 (61.4%)

.240

71 (68.9%)

154 (70%)

155 (62.8%)

.219

Yes

25 (50%)

30 (35.7%)

59 (38.6%)

32 (31.1%)

66 (30%)

92 (37.2%)

HBP

No

19 (38%)

28 (33.3%)

36 (23.5%)

.084

43 (41.7%)

122 (55.5%)

108 (43.7%)

.016

Yes

31 (62%)

56 (66.7%)

117 (76.5%)

60 (58.3%)

98 (44.5%)

139 (56.3%)

smoking

Ex-smoker

8 (16%)

13 (15.5%)

28 (18.3%)

.184

34 (33%)

53 (24.1%)

68 (27.5%)

.002

Never

37 (74%)

57 (67.9%)

115 (75.2%)

13 (12.6%)

40 (18.2%)

70 (28.3%)

Smoker

5 (10%)

14 (16.7%)

10 (6.5%)

56 (54.4%)

127 (57.7%)

109 (44.1%)

You can also use three or more grouping variables.The resultant table will be too long to review, but you can try.

gaze(sex+DM+HBP~age,data=acs) %>% myft()

sex
DM
(N)

Female
No
(N=173)

Female
Yes
(N=114)

Male
No
(N=380)

Male
Yes
(N=190)

name

levels

No (N=54)

Yes (N=119)

p

No (N=29)

Yes (N=85)

p

No (N=205)

Yes (N=175)

p

No (N=68)

Yes (N=122)

p

age

Mean ± SD

68.5 ± 14.2

69.6 ± 9.9

.589

67.1 ± 7.8

68.0 ± 10.3

.660

57.7 ± 11.5

64.5 ± 10.4

<.001

56.9 ± 10.4

61.9 ± 10.3

.002

2. For automatic selection of explanatory variables : autoReg()

You can make a table summarizing results of regression analysis. For example, let us perform a logistic regression with the colon cancer data.

library(survival)   # For use of data colon
data(cancer)  

fit=glm(status~rx+sex+age+obstruct+perfor+nodes,data=colon,family="binomial")
summary(fit)

Call:
glm(formula = status ~ rx + sex + age + obstruct + perfor + nodes, 
    family = "binomial", data = colon)

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.645417   0.285558  -2.260   0.0238 *  
rxLev       -0.067422   0.118907  -0.567   0.5707    
rxLev+5FU   -0.627480   0.121684  -5.157 2.51e-07 ***
sex         -0.053541   0.098975  -0.541   0.5885    
age          0.002307   0.004234   0.545   0.5859    
obstruct     0.283703   0.125194   2.266   0.0234 *  
perfor       0.319281   0.292034   1.093   0.2743    
nodes        0.190563   0.018255  10.439  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2525.4  on 1821  degrees of freedom
Residual deviance: 2342.4  on 1814  degrees of freedom
  (36 observations deleted due to missingness)
AIC: 2358.4

Number of Fisher Scoring iterations: 4

You can make table with above result.

autoReg(fit)
——————————————————————————————————————————————————————————————————————————————————
Dependent: status                 0 (N=938)    1 (N=920)        OR (multivariable) 
——————————————————————————————————————————————————————————————————————————————————
rx                         Obs  285 (30.4%)  345 (37.5%)                           
                           Lev  287 (30.6%)  333 (36.2%)  0.93 (0.74-1.18, p=.571) 
                       Lev+5FU    366 (39%)  242 (26.3%)  0.53 (0.42-0.68, p<.001) 
sex                  Mean ± SD    0.5 ± 0.5    0.5 ± 0.5  0.95 (0.78-1.15, p=.589) 
age                  Mean ± SD  60.0 ± 11.5  59.5 ± 12.4  1.00 (0.99-1.01, p=.586) 
obstruct             Mean ± SD    0.2 ± 0.4    0.2 ± 0.4  1.33 (1.04-1.70, p=.023) 
perfor               Mean ± SD    0.0 ± 0.2    0.0 ± 0.2  1.38 (0.78-2.44, p=.274) 
nodes                Mean ± SD    2.7 ± 2.4    4.6 ± 4.2  1.21 (1.17-1.25, p<.001) 
——————————————————————————————————————————————————————————————————————————————————

Or you can make a publication-ready table.

Dependent: status

0 (N=938)

1 (N=920)

OR (multivariable)

rx

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.93 (0.74-1.18, p=.571)

Lev+5FU

366 (39%)

242 (26.3%)

0.53 (0.42-0.68, p<.001)

sex

Mean ± SD

0.5 ± 0.5

0.5 ± 0.5

0.95 (0.78-1.15, p=.589)

age

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.01, p=.586)

obstruct

Mean ± SD

0.2 ± 0.4

0.2 ± 0.4

1.33 (1.04-1.70, p=.023)

perfor

Mean ± SD

0.0 ± 0.2

0.0 ± 0.2

1.38 (0.78-2.44, p=.274)

nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

If you want make a table with more explanation, you can make categorical variables with numeric variables. For example, the explanatory variables obstruct(obstruction of colon by tumor) and perfor(perforation of colon) is coded as 0 or 1, but it is “No” or “Yes” actually. Also the dependent variable status is coded as 0 or 1, it is “Alive” or “Died”.

colon$status.factor=factor(colon$status,labels=c("Alive","Died"))
colon$obstruct.factor=factor(colon$obstruct,labels=c("No","Yes"))
colon$perfor.factor=factor(colon$perfor,labels=c("No","Yes"))
colon$sex.factor=factor(colon$sex,labels=c("Female","Male"))

fit=glm(status.factor~rx+sex.factor+age+obstruct.factor+perfor.factor+nodes,data=colon,family="binomial")
result=autoReg(fit) 
result %>% myft()

Dependent: status.factor

Alive (N=938)

Died (N=920)

OR (multivariable)

rx

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.93 (0.74-1.18, p=.571)

Lev+5FU

366 (39%)

242 (26.3%)

0.53 (0.42-0.68, p<.001)

sex.factor

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.95 (0.78-1.15, p=.589)

age

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.01, p=.586)

obstruct.factor

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.33 (1.04-1.70, p=.023)

perfor.factor

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.38 (0.78-2.44, p=.274)

nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

You can add labels to the names of variables with setLabel() function.

colon$status.factor=setLabel(colon$status.factor,"Mortality")
colon$rx=setLabel(colon$rx,"Treatment")
colon$age=setLabel(colon$age,"Age(Years)")
colon$sex.factor=setLabel(colon$sex.factor,"Sex")
colon$obstruct.factor=setLabel(colon$obstruct.factor,"Obstruction")
colon$perfor.factor=setLabel(colon$perfor.factor,"Perforation")
colon$nodes=setLabel(colon$nodes,"Positive nodes")

fit=glm(status.factor~rx+sex.factor+age+obstruct.factor+perfor.factor+nodes,data=colon,family="binomial")
result=autoReg(fit) 
result %>% myft()

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (multivariable)

Treatment

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.93 (0.74-1.18, p=.571)

Lev+5FU

366 (39%)

242 (26.3%)

0.53 (0.42-0.68, p<.001)

Sex

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.95 (0.78-1.15, p=.589)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.01, p=.586)

Obstruction

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.33 (1.04-1.70, p=.023)

Perforation

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.38 (0.78-2.44, p=.274)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

If you do not want to show the reference values in table, you can shorten the table.

shorten(result) %>% myft()

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (multivariable)

Treatment

Lev

287 (30.6%)

333 (36.2%)

0.93 (0.74-1.18, p=.571)

Lev+5FU

366 (39%)

242 (26.3%)

0.53 (0.42-0.68, p<.001)

Sex

Male

492 (52.5%)

476 (51.7%)

0.95 (0.78-1.15, p=.589)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.01, p=.586)

Obstruction

Yes

163 (17.4%)

197 (21.4%)

1.33 (1.04-1.70, p=.023)

Perforation

Yes

22 (2.3%)

32 (3.5%)

1.38 (0.78-2.44, p=.274)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

Add univariate models to table and automatic selection of explanatory variables

You can add the results of univariate analyses to the table. At this time, the autoReg() function automatically select explanatory variables below the threshold(default value 0.2) and perform multivariate analysis. In this table, the p values of explanatory variables sex.factor and age is above the default threshold(0.2), they are excluded in multivariate model.

autoReg(fit, uni=TRUE) %>% myft()

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (univariable)

OR (multivariable)

Treatment

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.96 (0.77-1.20, p=.709)

0.93 (0.74-1.18, p=.570)

Lev+5FU

366 (39%)

242 (26.3%)

0.55 (0.44-0.68, p<.001)

0.54 (0.42-0.68, p<.001)

Sex

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.97 (0.81-1.17, p=.758)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.00, p=.296)

Obstruction

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.30 (1.03-1.63, p=.028)

1.32 (1.04-1.69, p=.025)

Perforation

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.50 (0.87-2.60, p=.149)

1.38 (0.78-2.44, p=.273)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

1.21 (1.17-1.25, p<.001)

If you want to include all explanatory variables in the multivariate model, just set the threshold 1.

autoReg(fit, uni=TRUE,threshold=1) %>% myft()

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (univariable)

OR (multivariable)

Treatment

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.96 (0.77-1.20, p=.709)

0.93 (0.74-1.18, p=.571)

Lev+5FU

366 (39%)

242 (26.3%)

0.55 (0.44-0.68, p<.001)

0.53 (0.42-0.68, p<.001)

Sex

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.97 (0.81-1.17, p=.758)

0.95 (0.78-1.15, p=.589)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.00, p=.296)

1.00 (0.99-1.01, p=.586)

Obstruction

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.30 (1.03-1.63, p=.028)

1.33 (1.04-1.70, p=.023)

Perforation

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.50 (0.87-2.60, p=.149)

1.38 (0.78-2.44, p=.274)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

1.21 (1.17-1.25, p<.001)

You can perform stepwise backward elimination to select variables and make a final model. Just set final=TRUE.

autoReg(fit, uni=TRUE,threshold=1, final=TRUE) %>% myft()

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (univariable)

OR (multivariable)

OR (final)

Treatment

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.96 (0.77-1.20, p=.709)

0.93 (0.74-1.18, p=.571)

0.94 (0.74-1.18, p=.575)

Lev+5FU

366 (39%)

242 (26.3%)

0.55 (0.44-0.68, p<.001)

0.53 (0.42-0.68, p<.001)

0.54 (0.42-0.68, p<.001)

Sex

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.97 (0.81-1.17, p=.758)

0.95 (0.78-1.15, p=.589)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.00, p=.296)

1.00 (0.99-1.01, p=.586)

Obstruction

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.30 (1.03-1.63, p=.028)

1.33 (1.04-1.70, p=.023)

1.34 (1.05-1.71, p=.019)

Perforation

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.50 (0.87-2.60, p=.149)

1.38 (0.78-2.44, p=.274)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

1.21 (1.17-1.25, p<.001)

1.21 (1.17-1.25, p<.001)

Multiple imputation with mice()

When the argument imputed=TRUE, autoReg() function make a multiple imputed model using mice::mice() function. By default, 20 imputations performed. If you want, you can change the number of imputations with m argument.

autoReg(fit, imputed=TRUE) %>% myft()
Warning: Number of logged events: 5

Dependent: Mortality

Alive (N=938)

Died (N=920)

OR (multivariable)

OR (imputed)

Treatment

Obs

285 (30.4%)

345 (37.5%)

Lev

287 (30.6%)

333 (36.2%)

0.93 (0.74-1.18, p=.571)

0.95 (0.76-1.20, p=.688)

Lev+5FU

366 (39%)

242 (26.3%)

0.53 (0.42-0.68, p<.001)

0.54 (0.43-0.68, p<.001)

Sex

Female

446 (47.5%)

444 (48.3%)

Male

492 (52.5%)

476 (51.7%)

0.95 (0.78-1.15, p=.589)

0.97 (0.80-1.17, p=.736)

Age(Years)

Mean ± SD

60.0 ± 11.5

59.5 ± 12.4

1.00 (0.99-1.01, p=.586)

1.00 (0.99-1.01, p=.646)

Obstruction

No

775 (82.6%)

723 (78.6%)

Yes

163 (17.4%)

197 (21.4%)

1.33 (1.04-1.70, p=.023)

1.34 (1.05-1.71, p=.018)

Perforation

No

916 (97.7%)

888 (96.5%)

Yes

22 (2.3%)

32 (3.5%)

1.38 (0.78-2.44, p=.274)

1.37 (0.77-2.43, p=.278)

Positive nodes

Mean ± SD

2.7 ± 2.4

4.6 ± 4.2

1.21 (1.17-1.25, p<.001)

1.21 (1.17-1.25, p<.001)

Summarize regression model results in a plot : modelPlot()

You can draw the plot summarizing the model with modelPlot()

x=modelPlot(fit)
x

You can make powerpoint file with this plot using rrtable::plot2pptx().

plot2pptx(print(x))
Exported plot as Report.pptx

You can summarize models in a plot. If you want to summarize univariate and multivariate model in a plot, just set the uni=TRUE and adjust the threshold. You can decide whether or not show the reference by show.ref argument.

modelPlot(fit,uni=TRUE,threshold=1,show.ref=FALSE)