anteneh asmare
2022-Jun-14 20:01 UTC
[R] Extracting only some coefficients for the logistic regression model and its plot
sample_data =
read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";")
head(sample_data)
model = glm(Y~0+X1+X2+X3,family=binomial,data=sample_data)
summary(model)
exp(coef(model ))
exp(cbind(OR = coef(model ), confint(model )))
I have the aove sample data on logistic regression with categorical predictor
I try the above code i get the follwing out put,
OR 2.5 % 97.5 %
X1 1.67639337 1.352583976 2.09856514
X2 1.23377720 1.071959330 1.42496949
X3A 0.01157565 0.001429430 0.08726854
X3B 0.06627849 0.008011818 0.54419759
X3C 0.01118084 0.001339984 0.08721028
X3D 0.01254032 0.001545240 0.09539880
X3E 0.10654454 0.013141540 0.87369972
but i am wondering i want to extract OR and CI only for factors, My
desire out put will be
OR 2.5 % 97.5 %
X3A 0.01157565 0.001429430 0.08726854
X3B 0.06627849 0.008011818 0.54419759
X3C 0.01118084 0.001339984 0.08721028
X3D 0.01254032 0.001545240 0.09539880
X3E 0.10654454 0.013141540 0.87369972
Can any one help me the code to extact it?
additionally I want to plot the above OR with confidence interval for
the extracted one
can you also help me the code with plot,or box plot
Kind Regards,
Hana
Rui Barradas
2022-Jun-15 05:06 UTC
[R] Extracting only some coefficients for the logistic regression model and its plot
Hello,
To extract all but the first 2 rows, use a negative index on the rows.
I will also coerce to data.frame and add a id column, it will be needed
to plot the confidence intervals.
ORCI <- exp(cbind(OR = coef(model ), confint(model )))[-(1:2), ]
ORCI <- cbind.data.frame(ORCI, id = row.names(ORCI))
Now the base R and ggplot plots. In both cases plot the bars first, then
the points.
1. Base R
ymin <- min(apply(ORCI[2:3], 1, range)[1,])
ymax <- max(apply(ORCI[2:3], 1, range)[2,])
plot((ymin + ymax)/2,
type = "n",
xaxt = "n",
xlim = c(0.5, 5.5),
ylim = c(ymin, ymax),
xlab = "X3",
ylab = "Odds Ratio")
with(ORCI, arrows(x0 = seq_along(id),
y0 = `2.5 %`,
y1 = `97.5 %`,
code = 3,
angle = 90))
points(OR ~ seq_along(id), ORCI, pch = 16)
axis(1, at = seq_along(ORCI$id), labels = ORCI$id)
2. Package ggplot2
library(ggplot2)
ggplot(ORCI, aes(id, OR)) +
geom_errorbar(aes(ymin = `2.5 %`, max = `97.5 %`)) +
geom_point() +
theme_bw()
Hope this helps,
Rui Barradas
?s 21:01 de 14/06/2022, anteneh asmare escreveu:> sample_data =
read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";")
> head(sample_data)
> model = glm(Y~0+X1+X2+X3,family=binomial,data=sample_data)
> summary(model)
> exp(coef(model ))
> exp(cbind(OR = coef(model ), confint(model )))
> I have the aove sample data on logistic regression with categorical
predictor
> I try the above code i get the follwing out put,
> OR 2.5 % 97.5 %
> X1 1.67639337 1.352583976 2.09856514
> X2 1.23377720 1.071959330 1.42496949
> X3A 0.01157565 0.001429430 0.08726854
> X3B 0.06627849 0.008011818 0.54419759
> X3C 0.01118084 0.001339984 0.08721028
> X3D 0.01254032 0.001545240 0.09539880
> X3E 0.10654454 0.013141540 0.87369972
> but i am wondering i want to extract OR and CI only for factors, My
> desire out put will be
> OR 2.5 % 97.5 %
> X3A 0.01157565 0.001429430 0.08726854
> X3B 0.06627849 0.008011818 0.54419759
> X3C 0.01118084 0.001339984 0.08721028
> X3D 0.01254032 0.001545240 0.09539880
> X3E 0.10654454 0.013141540 0.87369972
> Can any one help me the code to extact it?
> additionally I want to plot the above OR with confidence interval for
> the extracted one
> can you also help me the code with plot,or box plot
> Kind Regards,
> Hana
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Jim Lemon
2022-Jun-15 05:07 UTC
[R] Extracting only some coefficients for the logistic regression model and its plot
Hi Hana, str(model) will display the structure of the returned object. However, you want the summary of that object, so: As you have already constructed the estimate and confidence interval matrix, you only have to extract rows 3 to 7 and columns 1:3:> exp(cbind(OR = coef(model ), confint(model )))[3:7,1:3]Waiting for profiling to be done... OR 2.5 % 97.5 % X3A 0.01157565 0.001429430 0.08726854 X3B 0.06627849 0.008011818 0.54419759 X3C 0.01118084 0.001339984 0.08721028 X3D 0.01254032 0.001545240 0.09539880 X3E 0.10654454 0.013141540 0.87369972 Jim On Wed, Jun 15, 2022 at 1:38 PM anteneh asmare <hanatezera at gmail.com> wrote:> > sample_data = read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";") > head(sample_data) > model = glm(Y~0+X1+X2+X3,family=binomial,data=sample_data) > summary(model) > exp(coef(model )) > exp(cbind(OR = coef(model ), confint(model ))) > I have the aove sample data on logistic regression with categorical predictor > I try the above code i get the follwing out put, > OR 2.5 % 97.5 % > X1 1.67639337 1.352583976 2.09856514 > X2 1.23377720 1.071959330 1.42496949 > X3A 0.01157565 0.001429430 0.08726854 > X3B 0.06627849 0.008011818 0.54419759 > X3C 0.01118084 0.001339984 0.08721028 > X3D 0.01254032 0.001545240 0.09539880 > X3E 0.10654454 0.013141540 0.87369972 > but i am wondering i want to extract OR and CI only for factors, My > desire out put will be > OR 2.5 % 97.5 % > X3A 0.01157565 0.001429430 0.08726854 > X3B 0.06627849 0.008011818 0.54419759 > X3C 0.01118084 0.001339984 0.08721028 > X3D 0.01254032 0.001545240 0.09539880 > X3E 0.10654454 0.013141540 0.87369972 > Can any one help me the code to extact it? > additionally I want to plot the above OR with confidence interval for > the extracted one > can you also help me the code with plot,or box plot > Kind Regards, > Hana > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.