mcGraph3(rockchalk)
mcGraph3()所属R语言包：rockchalk
mcGraph3 draws a 3-dimensional scatter and a regression plane
mcGraph3绘制一个3 - 维的散点图和回归平面
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The observations are scattered in 3-dimensions, the fitted values are represented by a mesh, and their shadows in the x1-x2 plane are also represented.
观测散落在3 - 维，拟合值表示的网状结构，以及他们的x1-x2中平面中的阴影表示。
用法----------Usage----------
mcGraph3(x1, x2, y, interaction = FALSE,
drawArrows = TRUE, x1lab, x2lab, ylab, ...)
参数----------Arguments----------
参数：x1
a predictor vector
一个预测向量
参数：x2
a predictor vector
一个预测向量
参数：y
the dependent variable
因变量
参数：interaction
a TRUE or FALSE request for inclusion of the x1-x2 interaction in the regression calculation
在回归计算中的X1-X2互动的列入一个TRUE或FALSE要求的
参数：drawArrows
TRUE or FALSE, do you want arrows from the plane to observed y?
TRUE或FALSE，你想不想箭头从飞机上观察到的Ÿ？
参数：x1lab
label for the x1 axis, (the one called "xlab" inside persp)
标签X1轴，（称为“xlab”内persp一个）
参数：x2lab
label for the x2 axis, (the one called "ylab" inside persp)
标签X2轴，（称为“ylab”内persp一个）
参数：ylab
label for the y (vertical) axis (the one called "zlab" inside persp)
标签在y（垂直）轴（1名为“zlab”里面persp的）
参数：...
optional arguments passed to persp
可选参数传递给persp
值----------Value----------
a list of 2 objects, the fitted regression model and the perspective matrix used with persp to draw the image.
一个列表对象，拟合回归模型和透视矩阵使用persp绘制图像。
（作者）----------Author(s)----------
Paul E. Johnson <pauljohn@ku.edu>
实例----------Examples----------
set.seed(12345)
## Create data with x1 and x2 correlated at 0.10[＃创建x1和x2的相关数据，在0.10]
dat <- genCorrelatedData(rho=.1, stde=7)
mcGraph3(dat$x1, dat$x2, dat$y, theta = 0)
dat2 <- genCorrelatedData(rho = 0, stde = 7)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = 0, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = 30, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = -10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = -15)
## Run regressions with not-strongly correlated data[＃密切相关的数据进行回归分析，]
modset1 <- list()
for(i in 1:20){
dat2 <- genCorrelatedData(rho = .1, stde = 7)
summary(lm( y ~ x1 + x2 , data = dat2))
modset1[[i]] <- mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30)
}
## Run regressions with strongly correlated data[＃密切相关的数据进行回归分析]
modset2 <- list()
for(i in 1:20){
dat2 <- genCorrelatedData(rho = .981, stde = 7)
summary(lm( y ~ x1 + x2 , data = dat2))
modset2[[i]] <- mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30)
}
dat3 <- genCorrelatedData(rho = .981, stde = 100, beta=c(0.1, 0.2, 0.3, -0.1))
mcGraph3(dat3$x1, dat3$x2, dat3$y, theta=-10, interaction = TRUE)
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。
|
发表于 2012-9-27 22:42:42
