Getting started

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 `%>%`
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=925)    1 (N=897)        OR (multivariable) 
——————————————————————————————————————————————————————————————————————————————————
rx                         Obs  282 (30.5%)  342 (38.1%)                           
                           Lev  285 (30.8%)    323 (36%)  0.93 (0.74-1.18, p=.571) 
                       Lev+5FU  358 (38.7%)  232 (25.9%)  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  60.1 ± 11.5  59.5 ± 12.3  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.

autoReg(fit) %>% myft()

Dependent: status		0 (N=925)	1 (N=897)	OR (multivariable)
rx	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	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	60.1 ± 11.5	59.5 ± 12.3	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=925)	Died (N=897)	OR (multivariable)
rx	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.42-0.68, p<.001)
sex.factor	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.78-1.15, p=.589)
age	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.01, p=.586)
obstruct.factor	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.33 (1.04-1.70, p=.023)
perfor.factor	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	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=925)	Died (N=897)	OR (multivariable)
Treatment	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.42-0.68, p<.001)
Sex	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.78-1.15, p=.589)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.01, p=.586)
Obstruction	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.33 (1.04-1.70, p=.023)
Perforation	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	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=925)	Died (N=897)	OR (multivariable)
Treatment	Lev	285 (30.8%)	323 (36%)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.42-0.68, p<.001)
Sex	Male	489 (52.9%)	463 (51.6%)	0.95 (0.78-1.15, p=.589)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.01, p=.586)
Obstruction	Yes	161 (17.4%)	191 (21.3%)	1.33 (1.04-1.70, p=.023)
Perforation	Yes	22 (2.4%)	32 (3.6%)	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=925)	Died (N=897)	OR (univariable)	OR (multivariable)
Treatment	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.75-1.17, p=.554)	0.93 (0.74-1.18, p=.570)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.43-0.67, p<.001)	0.54 (0.42-0.68, p<.001)
Sex	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.79-1.14, p=.594)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.00, p=.305)
Obstruction	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.28 (1.02-1.62, p=.036)	1.32 (1.04-1.69, p=.025)
Perforation	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	1.52 (0.88-2.63, p=.137)	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=925)	Died (N=897)	OR (univariable)	OR (multivariable)
Treatment	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.75-1.17, p=.554)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.43-0.67, p<.001)	0.53 (0.42-0.68, p<.001)
Sex	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.79-1.14, p=.594)	0.95 (0.78-1.15, p=.589)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.00, p=.305)	1.00 (0.99-1.01, p=.586)
Obstruction	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.28 (1.02-1.62, p=.036)	1.33 (1.04-1.70, p=.023)
Perforation	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	1.52 (0.88-2.63, p=.137)	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=925)	Died (N=897)	OR (univariable)	OR (multivariable)	OR (final)
Treatment	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.75-1.17, p=.554)	0.93 (0.74-1.18, p=.571)	0.94 (0.74-1.18, p=.575)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.43-0.67, p<.001)	0.53 (0.42-0.68, p<.001)	0.54 (0.42-0.68, p<.001)
Sex	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.79-1.14, p=.594)	0.95 (0.78-1.15, p=.589)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.00, p=.305)	1.00 (0.99-1.01, p=.586)
Obstruction	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.28 (1.02-1.62, p=.036)	1.33 (1.04-1.70, p=.023)	1.34 (1.05-1.71, p=.019)
Perforation	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	1.52 (0.88-2.63, p=.137)	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()

Dependent: Mortality		Alive (N=925)	Died (N=897)	OR (multivariable)	OR (imputed)
Treatment	Obs	282 (30.5%)	342 (38.1%)
	Lev	285 (30.8%)	323 (36%)	0.93 (0.74-1.18, p=.571)	0.93 (0.74-1.18, p=.571)
	Lev+5FU	358 (38.7%)	232 (25.9%)	0.53 (0.42-0.68, p<.001)	0.53 (0.42-0.68, p<.001)
Sex	Female	436 (47.1%)	434 (48.4%)
	Male	489 (52.9%)	463 (51.6%)	0.95 (0.78-1.15, p=.589)	0.95 (0.78-1.15, p=.589)
Age(Years)	Mean ± SD	60.1 ± 11.5	59.5 ± 12.3	1.00 (0.99-1.01, p=.586)	1.00 (0.99-1.01, p=.586)
Obstruction	No	764 (82.6%)	706 (78.7%)
	Yes	161 (17.4%)	191 (21.3%)	1.33 (1.04-1.70, p=.023)	1.33 (1.04-1.70, p=.024)
Perforation	No	903 (97.6%)	865 (96.4%)
	Yes	22 (2.4%)	32 (3.6%)	1.38 (0.78-2.44, p=.274)	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)

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)