mapply                 package:base                 R Documentation

_A_p_p_l_y _a _f_u_n_c_t_i_o_n _t_o _m_u_l_t_i_p_l_e _l_i_s_t _o_r _v_e_c_t_o_r _a_r_g_u_m_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     'mapply' is a multivariate version of 'sapply'. 'mapply' applies
     'FUN' to the first elements of each ... argument, the second
     elements, the third elements, and so on. Arguments are recycled if
     necessary. 

     'Vectorize' returns a new function that acts as if 'mapply' was
     called.

_U_s_a_g_e:

     mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE,
           USE.NAMES = TRUE)

     Vectorize(FUN, vectorize.args = arg.names, SIMPLIFY = TRUE,
               USE.NAMES = TRUE)

_A_r_g_u_m_e_n_t_s:

     FUN: Function to apply, found via 'match.fun'.

     ...: Arguments to vectorise over (list or vector).

MoreArgs: A list of other arguments to 'FUN'.

SIMPLIFY: Attempt to reduce the result to a vector or matrix?

USE.NAMES: If the first ... argument is character and the result does
          not already have names, use it as the names.

vectorize.args: A character vector of arguments which should be
          vectorized.  Defaults to all arguments to 'FUN'.

_D_e_t_a_i_l_s:

     The arguments named in the 'vectorize.args' argument to
     'Vectorize' correspond to the arguments passed in the '...' list
     to 'mapply'.  However, only those that are actually passed will be
     vectorized; default values will not. See the example below.

_V_a_l_u_e:

     'mapply' returns a list, vector, or matrix.

     'Vectorize' returns a function with the same arguments as 'FUN',
     but wrapping a call to 'mapply'.

_S_e_e _A_l_s_o:

     'sapply', 'outer'

_E_x_a_m_p_l_e_s:

     mapply(rep, 1:4, 4:1)

     mapply(rep, times=1:4, x=4:1)

     mapply(rep, times=1:4, MoreArgs=list(x=42))

     # Repeat the same using Vectorize
     vrep <- Vectorize(rep)
     vrep(1:4, 4:1)
     vrep(times=1:4, x=4:1)

     vrep <- Vectorize(rep, "times")
     vrep(times=1:4, x=42)

     f <- function(x=1:3, y) c(x,y)
     vf <- Vectorize(f, SIMPLIFY = FALSE)
     f(1:3,1:3)
     vf(1:3,1:3)
     vf(y=1:3) # Only vectorizes y, not x

     # Nonlinear regression contour plot, based on nls() example

     SS <- function(Vm, K, resp, conc) {
         pred <- (Vm * conc)/(K + conc)
         sum((resp - pred)^2 / pred)
     }
     vSS <- Vectorize(SS, c("Vm", "K"))
     Treated <- subset(Puromycin, state == "treated")

     Vm <- seq(140, 310, len=50)
     K <- seq(0, 0.15, len=40)
     SSvals <- outer(Vm, K, vSS, Treated$rate, Treated$conc)
     contour(Vm, K, SSvals, levels=(1:10)^2, xlab="Vm", ylab="K")

