By running the following function, the output would be

library(pmsampsize)

pmsampsize(type = "s", csrsquared = 0.5, parameters = 10, rate = 0.065,
timepoint = 2, meanfup = 2.07)

NB: Assuming 0.05 acceptable difference in apparent & adjusted R-squared 
NB: Assuming 0.05 margin of error in estimation of overall risk at time point = 2  
NB: Events per Predictor Parameter (EPP) assumes overall event rate = 0.065  
 
             Samp_size Shrinkage Parameter CS_Rsq Max_Rsq Nag_Rsq   EPP
Criteria 1        5143     0.900        30  0.051   0.555   0.092 23.07
Criteria 2        1039     0.648        30  0.051   0.555   0.092  4.66
Criteria 3 *      5143     0.900        30  0.051   0.555   0.092 23.07
Final SS          5143     0.900        30  0.051   0.555   0.092 23.07
 
 Minimum sample size required for new model development based on user inputs = 5143, 
 corresponding to 10646 person-time** of follow-up, with 692 outcome events 
 assuming an overall event rate = 0.065 and therefore an EPP = 23.07  

I am looking for a way (loop function) to run the following function for multiple values of csrsquared and parameters and brining all resullts including the value of r, value of parameter, minimum sample size, and outcome events into a table. Suppose

R=seq (from=0, to=0.5, by=0.1)

PA=seq(from=1, to=10, by=1)

pmsampsize(type = "s", csrsquared = R, parameters = PA, rate = 0.065, timepoint = 2, meanfup = 2.07)

the outcome should be sth like a table including different values of csrsquared and parameters, and their corresponding estimation for sample size and outcome events.

csrsquared parameters Minimum sample size outcome events