stat <- read.csv("C://Desktop//lung cancer.csv",header=T)

x = (2000:2013)

y = (0:12000)

hist(stat$Year,stat$Cases, ylab="Cases",xlab="Year",main="Number of cases of Lung Cancer:2000-2013")

error message:

> hist(ylab="Cases",xlab="Year",main="Number of cases of Lung Cancer:2000-2013")

Error in hist.default(ylab = "Cases", xlab = "Year", main = "Number of cases of Lung Cancer:2000-2013") :

argument "x" is missing, with no default

> stat <- read.csv("C://Heramba//Desktop//lung cancer.csv",header=T)

Error in file(file, "rt") : cannot open the connection

In addition: Warning message:

In file(file, "rt") :

cannot open file 'C://Heramba//Desktop//lung cancer.csv': No such file or directory ]]>

Suppose I have 10 different models as such:

Code:

`fit1.glm <- glm(admit ~ gre , data = dat, family = "binomial")`

fit2.glm <- glm(admit ~ gpa, data = dat, family = "binomial")

fit3.glm <- glm(admit ~ rank, data = dat, family = "binomial")

...

Quote:

variable Odds ratio lower CI upper CI

gre

gpa

rank

...

I have had difficulties plotting results from GLM logit models to show up as odds ratios on a log scale. Ultimately I want to obtain estimates from different models and plot the results on one graph as shown here (https://www.ctspedia.org/do/view/CTS...ClinAEGraph001). Do you have any insight? ]]>