densityplot
library(moonBook)
densityplot(Height~sex,data=acs)##
## ' Height ' is an invalid column name: Instead ' height ' is used
densityplot(age~Dx,data=acs)
library(moonBook)
densityplot(Height~sex,data=acs)##
## ' Height ' is an invalid column name: Instead ' height ' is used
densityplot(age~Dx,data=acs)
require(survival)## Loading required package: survival
## Loading required package: splinesdata(colon)
out1=glm(status~sex+age+rx+obstruct+node4,data=colon)
out2=glm(status~rx+node4,data=colon)
ORplot(out1,type=2,show.CI=TRUE,xlab="This is xlab",main="Odds Ratio")
ORplot(out2,type=1)
ORplot(out1,type=1,show.CI=TRUE,col=c("blue","red"))
ORplot(out1,type=4,show.CI=TRUE,sig.level=0.05)
ORplot(out1,type=1,show.CI=TRUE,main="Odds Ratio",sig.level=0.05,
pch=1,cex=2,lwd=4,col=c("red","blue"))
attach(colon)
colon$TS=Surv(time,status==1)
out=mycph(TS~.,data=colon)##
## mycph : perform coxph of individual expecting variables
##
## Call: TS ~ ., data= colon
##
## study was excluded : NaN
## status was excluded : infiniteout## HR lcl ucl p
## id 1.00 1.00 1.00 0.317
## rxLev 0.98 0.84 1.14 0.786
## rxLev+5FU 0.64 0.55 0.76 0.000
## sex 0.97 0.85 1.10 0.610
## age 1.00 0.99 1.00 0.382
## obstruct 1.27 1.09 1.49 0.003
## perfor 1.30 0.92 1.85 0.142
## adhere 1.37 1.16 1.62 0.000
## nodes 1.09 1.08 1.10 0.000
## differ 1.36 1.19 1.55 0.000
## extent 1.78 1.53 2.07 0.000
## surg 1.28 1.11 1.47 0.001
## node4 2.47 2.17 2.83 0.000
## time 0.98 0.98 0.98 0.000
## etype 0.81 0.71 0.92 0.001HRplot(out,type=2,show.CI=TRUE,pch=2,cex=2,
main="Hazard ratios of all individual variables")
Function “mytable”" produce table for descriptive analysis easily. It is most useful to make table to describe baseline charateristics common in medical research papers.
data(acs)
mytable(Dx~.,data=acs)
Descriptive Statistics by 'Dx'
_________________________________________________________________
NSTEMI STEMI Unstable Angina p
(N=153) (N=304) (N=400)
-----------------------------------------------------------------
age 64.3 ± 12.3 62.1 ± 12.1 63.8 ± 11.0 0.073
sex 0.012
- Female 50 (32.7%) 84 (27.6%) 153 (38.2%)
- Male 103 (67.3%) 220 (72.4%) 247 (61.8%)
cardiogenicShock 0.000
- No 149 (97.4%) 256 (84.2%) 400 (100.0%)
- Yes 4 ( 2.6%) 48 (15.8%) 0 ( 0.0%)
entry 0.001
- Femoral 58 (37.9%) 133 (43.8%) 121 (30.2%)
- Radial 95 (62.1%) 171 (56.2%) 279 (69.8%)
EF 55.0 ± 9.3 52.4 ± 9.5 59.2 ± 8.7 0.000
height 163.3 ± 8.2 165.1 ± 8.2 161.7 ± 9.7 0.000
weight 64.3 ± 10.2 65.7 ± 11.6 64.5 ± 11.6 0.361
BMI 24.1 ± 3.2 24.0 ± 3.3 24.6 ± 3.4 0.064
obesity 0.186
- No 106 (69.3%) 209 (68.8%) 252 (63.0%)
- Yes 47 (30.7%) 95 (31.2%) 148 (37.0%)
TC 193.7 ± 53.6 183.2 ± 43.4 183.5 ± 48.3 0.057
LDLC 126.1 ± 44.7 116.7 ± 39.5 112.9 ± 40.4 0.004
HDLC 38.9 ± 11.9 38.5 ± 11.0 37.8 ± 10.9 0.501
TG 130.1 ± 88.5 106.5 ± 72.0 137.4 ± 101.6 0.000
DM 0.209
- No 96 (62.7%) 208 (68.4%) 249 (62.2%)
- Yes 57 (37.3%) 96 (31.6%) 151 (37.8%)
HBP 0.002
- No 62 (40.5%) 150 (49.3%) 144 (36.0%)
- Yes 91 (59.5%) 154 (50.7%) 256 (64.0%)
smoking 0.000
- Ex-smoker 42 (27.5%) 66 (21.7%) 96 (24.0%)
- Never 50 (32.7%) 97 (31.9%) 185 (46.2%)
- Smoker 61 (39.9%) 141 (46.4%) 119 (29.8%)
----------------------------------------------------------------- The first argument of function mytable is 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. . is allowed on the right side of formula which means all variables in the data.frame specified by the 2nd argument data. The sample data ‘acs’ containing demographic data and laboratory data of 857 pateints with acute coronary syndrome(ACS). For more information about the data acs, type ?acs in your R console.
str(acs)'data.frame': 857 obs. of 17 variables:
$ age : int 62 78 76 89 56 73 58 62 59 71 ...
$ sex : chr "Male" "Female" "Female" "Female" ...
$ cardiogenicShock: chr "No" "No" "Yes" "No" ...
$ entry : chr "Femoral" "Femoral" "Femoral" "Femoral" ...
$ Dx : chr "STEMI" "STEMI" "STEMI" "STEMI" ...
$ EF : num 18 18.4 20 21.8 21.8 22 24.7 26.6 28.5 31.1 ...
$ height : num 168 148 NA 165 162 153 167 160 152 168 ...
$ weight : num 72 48 NA 50 64 59 78 50 67 60 ...
$ BMI : num 25.5 21.9 NA 18.4 24.4 ...
$ obesity : chr "Yes" "No" "No" "No" ...
$ TC : num 215 NA NA 121 195 184 161 136 239 169 ...
$ LDLC : int 154 NA NA 73 151 112 91 88 161 88 ...
$ HDLC : int 35 NA NA 20 36 38 34 33 34 54 ...
$ TG : int 155 166 NA 89 63 137 196 30 118 141 ...
$ DM : chr "Yes" "No" "No" "No" ...
$ HBP : chr "No" "Yes" "Yes" "No" ...
$ smoking : chr "Smoker" "Never" "Never" "Never" ...You can choose the grouping variable(s) and row-variable(s) with the formula.
mytable(sex~age+Dx,data=acs)
Descriptive Statistics by 'sex'
__________________________________________________
Female Male p
(N=287) (N=570)
--------------------------------------------------
age 68.7 ± 10.7 60.6 ± 11.2 0.000
Dx 0.012
- NSTEMI 50 (17.4%) 103 (18.1%)
- STEMI 84 (29.3%) 220 (38.6%)
- Unstable Angina 153 (53.3%) 247 (43.3%)
-------------------------------------------------- You can choose row-variable(s) with . and + and - and variable name in an additive way.
mytable(am~.-hp-disp-cyl-carb-gear,data=mtcars)
Descriptive Statistics by 'am'
____________________________________
0 1 p
(N=19) (N=13)
------------------------------------
mpg 17.1 ± 3.8 24.4 ± 6.2 0.001
drat 3.3 ± 0.4 4.0 ± 0.4 0.000
wt 3.8 ± 0.8 2.4 ± 0.6 0.000
qsec 18.2 ± 1.8 17.4 ± 1.8 0.209
vs 0.556
- 0 12 (63.2%) 6 (46.2%)
- 1 7 (36.8%) 7 (53.8%)
------------------------------------ By default continuous variables are analyzed as normal-distributed and are described with mean and standard deviation. To change default options, you can use the method argument. Possible values of method argument are:
When continuous variables are analyzed as non-normal, they are described with median and interquantile range.
mytable(sex~height+weight+BMI,data=acs,method=3)
Descriptive Statistics by 'sex'
_______________________________________________
Female Male p
(N=287) (N=570)
-----------------------------------------------
height 153.8 ± 6.2 167.9 ± 6.1 0.000
weight 58.0 [50.0;63.0] 68.0 [62.0;75.0] 0.000
BMI 24.2 ± 3.6 24.3 ± 3.2 0.623
----------------------------------------------- Because the method argument is selected as 3, a Shapiro-Wilk test normality test is used to decide if the variable is normal or non-normal distributed. Note that height and BMI was described as mean \(\pm\) sd, whereas the weight was described as median and interquatile range.
In many cases, categorical variables are usually coded as numeric. For example, many people usually code 0 and 1 instead of “No” and “Yes”. Similarly, factor variables with three or four levels are coded 0/1/2 or 0/1/2/3. In many cases, if we analyze these variables as continuous variables, we are not able to get the right result. In mytable, variables with less than five unique values are treated as a categorical variables.
mytable(am~.,data=mtcars)
Descriptive Statistics by 'am'
_______________________________________
0 1 p
(N=19) (N=13)
---------------------------------------
mpg 17.1 ± 3.8 24.4 ± 6.2 0.001
cyl 0.013
- 4 3 (15.8%) 8 (61.5%)
- 6 4 (21.1%) 3 (23.1%)
- 8 12 (63.2%) 2 (15.4%)
disp 290.4 ± 110.2 143.5 ± 87.2 0.000
hp 160.3 ± 53.9 126.8 ± 84.1 0.221
drat 3.3 ± 0.4 4.0 ± 0.4 0.000
wt 3.8 ± 0.8 2.4 ± 0.6 0.000
qsec 18.2 ± 1.8 17.4 ± 1.8 0.209
vs 0.556
- 0 12 (63.2%) 6 (46.2%)
- 1 7 (36.8%) 7 (53.8%)
gear 0.000
- 3 15 (78.9%) 0 ( 0.0%)
- 4 4 (21.1%) 8 (61.5%)
- 5 0 ( 0.0%) 5 (38.5%)
carb 2.7 ± 1.1 2.9 ± 2.2 0.781
--------------------------------------- In mtcars data, all variables are expressed as numeric. But as you can see, cyl, vs and gear is treated as categorical variables. The carb variables has six unique values and treated as continuous variables. If you wanted the carb variable to be treated as categorical variable, you can changed the max.ylev argument.
mytable(am~carb,data=mtcars,max.ylev=6)
Descriptive Statistics by 'am'
________________________________
0 1 p
(N=19) (N=13)
--------------------------------
carb 0.284
- 1 3 (15.8%) 4 (30.8%)
- 2 6 (31.6%) 4 (30.8%)
- 3 3 (15.8%) 0 ( 0.0%)
- 4 7 (36.8%) 3 (23.1%)
- 6 0 ( 0.0%) 1 ( 7.7%)
- 8 0 ( 0.0%) 1 ( 7.7%)
-------------------------------- If you wanted to make two separate tables and combine into one table, mytable is the function of choice. For example, if you wanted to build seperate table for female and male patients stratified by presence or absence of DM and combine it,
mytable(sex+DM~.,data=acs)
Descriptive Statistics stratified by 'sex' and 'DM'
_____________________________________________________________________________________
Male Female
-------------------------------- -------------------------------
No Yes p No Yes p
(N=380) (N=190) (N=173) (N=114)
-------------------------------------------------------------------------------------
age 60.9 ± 11.5 60.1 ± 10.6 0.459 69.3 ± 11.4 67.8 ± 9.7 0.241
cardiogenicShock 0.685 0.296
- No 355 (93.4%) 175 (92.1%) 168 (97.1%) 107 (93.9%)
- Yes 25 ( 6.6%) 15 ( 7.9%) 5 ( 2.9%) 7 ( 6.1%)
entry 0.552 0.665
- Femoral 125 (32.9%) 68 (35.8%) 74 (42.8%) 45 (39.5%)
- Radial 255 (67.1%) 122 (64.2%) 99 (57.2%) 69 (60.5%)
Dx 0.219 0.240
- NSTEMI 71 (18.7%) 32 (16.8%) 25 (14.5%) 25 (21.9%)
- STEMI 154 (40.5%) 66 (34.7%) 54 (31.2%) 30 (26.3%)
- Unstable Angina 155 (40.8%) 92 (48.4%) 94 (54.3%) 59 (51.8%)
EF 56.5 ± 8.3 53.9 ± 11.0 0.007 56.0 ± 10.1 56.6 ± 10.0 0.655
height 168.1 ± 5.8 167.5 ± 6.7 0.386 153.9 ± 6.5 153.6 ± 5.8 0.700
weight 68.1 ± 10.4 69.8 ± 10.2 0.069 56.5 ± 8.7 58.4 ± 10.0 0.117
BMI 24.0 ± 3.1 24.9 ± 3.5 0.007 23.8 ± 3.2 24.8 ± 4.0 0.046
obesity 0.027 0.359
- No 261 (68.7%) 112 (58.9%) 121 (69.9%) 73 (64.0%)
- Yes 119 (31.3%) 78 (41.1%) 52 (30.1%) 41 (36.0%)
TC 184.1 ± 46.7 181.8 ± 44.5 0.566 186.0 ± 43.1 193.3 ± 60.8 0.274
LDLC 117.9 ± 41.8 112.1 ± 39.4 0.108 116.3 ± 35.2 119.8 ± 48.6 0.519
HDLC 38.4 ± 11.4 36.8 ± 9.6 0.083 39.2 ± 10.9 38.8 ± 12.2 0.825
TG 115.2 ± 72.2 153.4 ± 130.7 0.000 114.2 ± 82.4 128.4 ± 65.5 0.112
HBP 0.000 0.356
- No 205 (53.9%) 68 (35.8%) 54 (31.2%) 29 (25.4%)
- Yes 175 (46.1%) 122 (64.2%) 119 (68.8%) 85 (74.6%)
smoking 0.386 0.093
- Ex-smoker 101 (26.6%) 54 (28.4%) 34 (19.7%) 15 (13.2%)
- Never 77 (20.3%) 46 (24.2%) 118 (68.2%) 91 (79.8%)
- Smoker 202 (53.2%) 90 (47.4%) 21 (12.1%) 8 ( 7.0%)
------------------------------------------------------------------------------------- If you want more beautiful table, you can use mylatex function.
out=mytable(Dx~.,data=acs)
mylatex(out)out1=mytable(sex+DM~.,data=acs)
mylatex(out1)