nycrefugee - 1 year ago 148

R Question

I'm relatively new to survival analysis and have been used some standard telco churn data example with a sample below called 'telco':

`telco <- read.csv(text = "State,Account_Length,Area_Code,Intl_Plan,Day_Mins,Day_Calls,Day_Charge,Eve_Mins,Eve_Calls,Eve_Charge,Night_Mins,Night_Calls,Night_Charge,Intl_Mins,Intl_Calls,Intl_Charge,CustServ_Calls,Churn`

IN,65,415,no,129.1,137,21.95,228.5,83,19.42,208.8,111,9.4,12.7,6,3.43,4,TRUE

RI,74,415,no,187.7,127,31.91,163.4,148,13.89,196,94,8.82,9.1,5,2.46,0,FALSE

IA,168,408,no,128.8,96,21.9,104.9,71,8.92,141.1,128,6.35,11.2,2,3.02,1,FALSE

MT,95,510,no,156.6,88,26.62,247.6,75,21.05,192.3,115,8.65,12.3,5,3.32,3,FALSE

IA,62,415,no,120.7,70,20.52,307.2,76,26.11,203,99,9.14,13.1,6,3.54,4,FALSE

NY,161,415,no,332.9,67,56.59,317.8,97,27.01,160.6,128,7.23,5.4,9,1.46,4,TRUE")

I've run:

`library(survival)`

dependentvars = Surv(telco$Account_Length, telco$Churn)

telcosurvreg = survreg(dependentvars ~ -Churn -Account_Length, dist="gaussian",data=telco)

telcopred = predict(telcosurvreg, newdata=telco, type="quantile", p=.5)

...to get the predicted lifetime of each customer.

What I'm struggling with is how to visualise a survival curve for this. Is there a way (preferably in ggplot2) to do this from the data I have?

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Answer Source

Here is a `base`

R version that plots the predicted survival curves. I have changed the `formula`

so the curves differ for each row

```
> # change setup so we have one covariate
> telcosurvreg = survreg(
+ Surv(Account_Length, Churn) ~ Eve_Charge, dist = "gaussian", data = telco)
> telcosurvreg # has more than an intercept
Call:
survreg(formula = Surv(Account_Length, Churn) ~ Eve_Charge, data = telco,
dist = "gaussian")
Coefficients:
(Intercept) Eve_Charge
227.274695 -3.586121
Scale= 56.9418
Loglik(model)= -12.1 Loglik(intercept only)= -12.4
Chisq= 0.54 on 1 degrees of freedom, p= 0.46
n= 6
>
> # find linear predictors
> vals <- predict(telcosurvreg, newdata = telco, type = "lp")
>
> # use the survreg.distributions object. See ?survreg.distributions
> x_grid <- 1:400
> sur_curves <- sapply(
+ vals, function(x)
+ survreg.distributions[[telcosurvreg$dist]]$density(
+ (x - x_grid) / telcosurvreg$scale)[, 1])
>
> # plot with base R
> matplot(x_grid, sur_curves, type = "l", lty = 1)
```

Here is the result

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