在rpy2中使用图和曲线

Question

In R, I can run plot and curve to get the relationship between a predicted probability and the predictor variable by just running:

plot(outcome~survrate, data = d, ylab = "P(outcome = 1 |
survrate)", xlab = "SURVRATE: Probability of Survival after 5
Years", xaxp = c(0, 95, 19))

curve(transform(coef(mod1)[1] + coef(mod1)[2]*x), add = TRUE)

Where transform is a custom R function.

I am trying to do the same thing in rpy2, and so far have the following:

rplot = ro.r('plot')
formula = Formula('outcome~survrate')
formula.getenvironment()['outcome'] = r_analytical_set.rx2('outcome')
formula.getenvironment()['survrate'] = r_analytical_set.rx2('survrate')
ro.r.plot(formula, data=r_analytical_set, ylab = 'P(outcome =  1 | pass)', xlab = 'SURVRATE: Probability of Survival after 5
Years', xaxp = ro.r.c(0, 95, 19))

# read in R function from file
with open('/Users/gregsilverman//development/python/rest_api/rest_api/utils.r', 'r') as f:
    string = f.read()

from rpy2.robjects.packages import STAP
invlogit = STAP(string, "invlogit")

ro.r.curve(transform(ro.r.coef(fit)[0] + ro.r.coef(fit)[1]*ro.r.x), add = True)

In this state, ro.r.curve gives an error that TypeError: unsupported operand type(s) for *: 'float' and 'FloatVector'

So, as per this multiplying all elements of a vector in R , I ran

ro.r.curve(transform(ro.r.coef(fit)[0] + ro.r.prod(ro.r.coef(fit)[1],ro.r.x)), add = True)

But, now I am getting an error TypeError: unsupported operand type(s) for +: 'float' and 'FloatVector'

Before I waste any more time figuring out how to add a scalar to a vector, I was wondering if there was a more efficient way of achieving my end goal.

Answer 1

Use the accessor "R-operator" ( .ro - see http://rpy2.readthedocs.io/en/version_2.8.x/vector.html#operators ):

In [1]: from rpy2.robjects.vectors import FloatVector

In [2]: FloatVector((1,2,3)).ro + 2
Out[2]: 
R object with classes: ('numeric',) mapped to:
<FloatVector - Python:0x7fde744c0308 / R:0x44db740>
[3.000000, 4.000000, 5.000000]