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我試圖修改一些現有的代碼,原來是在這裏(https://stats.stackexchange.com/questions/76999/simulating-longitudinal-lognormal-data-in-r)中發現的問題,並用在以下網站展示R中的散點圖:https://hopstat.wordpress.com/2014/10/30/my-commonly-done-ggplot2-graphs/在GGPLOT2散點圖更改色彩
這是一個簡單和愚蠢的問題,但我整個上午一直在努力。下面的代碼給出了一個很好的黑白分佈圖。我想修改代碼以使線條非常淺灰色。
library(MASS)
library(nlme)
library(plyr)
library(ggplot2)
### set number of individuals
n <- 200
### average intercept and slope
beta0 <- 1.0
beta1 <- 6.0
### true autocorrelation
ar.val <- .4
### true error SD, intercept SD, slope SD, and intercept-slope cor
sigma <- 1.5
tau0 <- 2.5
tau1 <- 2.0
tau01 <- 0.3
### maximum number of possible observations
m <- 10
### simulate number of observations for each individual
p <- round(runif(n,4,m))
### simulate observation moments (assume everybody has 1st obs)
obs <- unlist(sapply(p, function(x) c(1, sort(sample(2:m, x-1,
replace=FALSE)))))
### set up data frame
dat <- data.frame(id=rep(1:n, times=p), obs=obs)
### simulate (correlated) random effects for intercepts and slopes
mu <- c(0,0)
S <- matrix(c(1, tau01, tau01, 1), nrow=2)
tau <- c(tau0, tau1)
S <- diag(tau) %*% S %*% diag(tau)
U <- mvrnorm(n, mu=mu, Sigma=S)
### simulate AR(1) errors and then the actual outcomes
dat$eij <- unlist(sapply(p, function(x) arima.sim(model=list(ar=ar.val),
n=x) * sqrt(1-ar.val^2) * sigma))
dat$yij <- (beta0 + rep(U[,1], times=p)) + (beta1 + rep(U[,2], times=p)) *
log(dat$obs) + dat$eij
dat = ddply(dat, .(id), function(x){
x$alpha = ifelse(runif(n = 1) > 0.9, 1, 0.1)
x$grouper = factor(rbinom(n=1, size =3 ,prob=0.5), levels=0:3)
x
})
tspag = ggplot(dat, aes(x=obs, y=yij)) +
geom_line() + guides(colour=FALSE) + xlab("Observation Time Point") +
ylab("Y")
spag = tspag + aes(colour = factor(id))
spag
bwspag = tspag + aes(group=factor(id))
bwspag
我已經試過scale_colour_manual,我已經試過定義在bwspag線...沒有運氣的AES語句中的顏色。我對R相對缺乏經驗。我很感激任何幫助!
完美,正是我需要的。謝謝! –