diffusion(simecol)
diffusion()所属R语言包：simecol
A Random Walk Particle Diffusion Model
随机游走粒子扩散模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
simecol example: This is a random walk (basic particle
simecol例如：这是一个随机游走（基本粒子
用法----------Usage----------
data(diffusion)
格式----------Format----------
An S4 object according to the rwalkModel specification. The object contains the following slots:
S4的对象根据rwalkModel规格的。该对象包含以下插槽：
main A function with the movement rules for the particles.
mainA功能的粒子的运动规律。
parms A list with the following components:
parmsA以下组件列表：
ninds number of simulated particles,
ninds模拟颗粒，
speed speed of the particles,
speed的颗粒速度
area vector with 4 elements giving the coordinates (left,
area矢量与提供的坐标的4个元素（左
times Simulation time (discrete time steps, by-argument
times模拟时间（离散时间步长，by参数
init Data frame holding the start properties (Cartesian coordinates x and y and movement angle a) of the
init的数据框持有的开始的属性（直角坐标系x和y和运动的角度a）
Details
详细信息----------Details----------
To see all details, please have a look into the implementation.
要看到所有的详细信息，请，看看进入实施。
参见----------See Also----------
sim, parms, init, times.
sim，parms，init，times。
实例----------Examples----------
##============================================[＃============================================]
## Basic Usage:[＃的基本用法：]
## explore the example[＃探索的例子]
##============================================[＃============================================]
## Not run: [＃不运行：]
data(diffusion)
## (1) minimal example[＃（1）最小的例子]
plot(sim(diffusion))
## show "grid of environmental conditions"[＃“的环境条件下的电网”]
image(inputs(diffusion))
## (2) scenario[＃（2）方案]
## with homogeneous environment (no "refuge" in the middle)[＃均匀环境（没有在中间的“避难所”）]
no_refuge <- diffusion # Cloning of the whole model object[整个模型对象克隆]
inputs(no_refuge) <- matrix(1, 100, 100)
plot(sim(no_refuge))
##============================================[＃============================================]
## Advanced Usage:[＃高级用法：]
## Assign a function to the observer-slot.[＃配置功能的观察员插槽。]
##============================================[＃============================================]
observer(diffusion) <- function(state, ...) {
## numerical output to the screen[＃数值输出到屏幕上]
cat("mean x=", mean(state$x),
", mean y=", mean(state$y),
", sd x=", sd(state$x),
", sd y=", sd(state$y), "\n")
## animation[＃动画]
par(mfrow=c(2,2))
plot(state$x, state$y, xlab="x", ylab="y", pch=16, col="red", xlim=c(0, 100))
hist(state$y)
hist(state$x)
## default case: return the state --> iteration stores it in "out"[＃默认情况下返回状态 - >迭代并将其存储在“出”]
state
}
sim(diffusion)
## remove the observer and restore original behavior[＃删除观察员，并恢复原来的行为]
observer(diffusion) <- NULL
diffusion <- sim(diffusion)
## End(Not run)[＃（不执行）]
##============================================[＃============================================]
## Implementation:[＃实现：]
## The code of the diffusion model.[＃扩散模型的代码。]
## Note the use of the "initfunc"-slot.[＃注意使用的“initfunc”型槽。]
##============================================[＃============================================]
diffusion <- rwalkModel(
main = function(time, init, parms, inputs = NULL) {
speed <- parms$speed
xleft <- parms$area[1]
xright <- parms$area[2]
ybottom <- parms$area[3]
ytop <- parms$area[4]
x <- init$x # x coordinate[x坐标]
y <- init$y # y coordinate[y坐标]
a <- init$a # angle (in radians)[角度（弧度）]
n <- length(a)
## Rule 1: respect environment (grid as given in "inputs")[＃第1条：尊重环境（网格，在“输入”）]
## 1a) identify location on "environmental 2D grid" for each individual[＃1A）确定“环境二维网格”每个人的位置]
i.j <- array(c(pmax(1, ceiling(x)), pmax(1, ceiling(y))), dim=c(n, 2))
## 1b) speed dependend on "environmental conditions"[＃1B）的速度时效关系，“环境条件”]
speed <- speed * inputs[i.j]
## Rule 2: Random Walk[＃规则2：随机漫步]
a <- (a + 2 * pi / runif(a))
dx <- speed * cos(a)
dy <- speed * sin(a)
x <- x + dx
y <- y + dy
## Rule 3: Wrap Around[＃第3条规定：回绕]
x <- ifelse(x > xright, xleft, x)
y <- ifelse(y > ytop, ybottom, y)
x <- ifelse(x < xleft, xright, x)
y <- ifelse(y < ybottom, ytop, y)
data.frame(x=x, y=y, a=a)
},
times = c(from=0, to=100, by=1),
parms = list(ninds=50, speed = 1, area = c(0, 100, 0, 100)),
solver = "iteration",
initfunc = function(obj) {
ninds <- obj@parms$ninds
xleft <- obj@parms$area[1]
xright <- obj@parms$area[2]
ybottom <- obj@parms$area[3]
ytop <- obj@parms$area[4]
obj@init <- data.frame(x = runif(ninds) * (xright - xleft) + xleft,
y = runif(ninds) * (ytop - ybottom) + ybottom,
a = runif(ninds) * 2 * pi)
inp <- matrix(1, nrow=100, ncol=100)
inp[, 45:55] <- 0.2
inputs(obj) <- inp
obj
}
)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 02:36:35
