forked from winterwang/LSHTMlearningnote
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Graphtest etc.R
204 lines (155 loc) · 6.79 KB
/
Graphtest etc.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
library(haven)
library(ggplot2)
library(ggthemes)
library(RYoudaoTranslate)
library(RCurl)
library(rjson)
library(ggsci)
library(epicalc)
apikey = "498375134"
keyfrom = "JustForTestYouDao"
pkg <- c("knitr", "kfigr", "tidyverse", "survival", "ggfortify", "survminer", "plotly",
"gridExtra", "Epi", "KMsurv", "gnm", "cmprsk", "mstate", "flexsurv", "splines",
"epitools", "eha", "shiny")
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg)) install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
growgam1 <- read_dta("backupfiles/growgam1.dta")
ggplot(growgam1, aes(x=age, y=wt)) + geom_point(shape=20, colour="grey40") +
stat_smooth(method = lm, size = 0.3) +
scale_x_continuous(breaks=seq(0, 38, 4),limits = c(0,36.5))+
scale_y_continuous(breaks = seq(0, 20, 5),limits = c(0,20.5)) +
theme_stata() +labs(x = "Age (Months)", y = "Weight (kg)")
Model <- lm(wt~age, data=growgam1)
plot(growgam1$age, growgam1$wt, main="Regression")
abline(Model, col="lightblue")
range(growgam1$age)
newage <- seq(5,36, by=0.05)
pred_interval <- predict(Model, newdata=data.frame(age=newage), interval="prediction",
level = 0.95)
lines(newage, pred_interval[,2], col="orange", lty=2)
lines(newage, pred_interval[,3], col="orange", lty=2)
library(knitr)
library(kableExtra)
dt <- read.csv("backupfiles/anova2.csv", header = T)
kable(dt, "html", align = "c",caption = "One-way ANOVA table") %>%
kable_styling(bootstrap_options = c("striped", "bordered"))
par(mfrow=c(1,2))
a <- rnorm(10000,50, 5)
b <- rnorm(10000, 50, 10)
hist(a, xlim=c(0,100), main = "")
hist(b, xlim=c(0,100), main = "")
x <- rbeta(10000,1.2,1.3)
hist(x, breaks = 30,
xlim=c(min(x),max(x)), probability=T,
col='lightblue', xlab='Kurtosis < 3', ylab=' ', axes=F,
main='Light-tailed')
curve(dnorm(x, mean = 0.5), xlim = c(0,1), add = T, lwd=2)
y <- rt(10000, 8)
hist(y, breaks = 30,
xlim=c(min(y),max(y)), probability=T, ylim = c(0,0.4),
col='lightblue', xlab='Kurtosis > 3', ylab=' ', axes=F,
main='Heavy-tailed')
curve(dnorm(x), xlim = c(-5,5), add = T, lwd=2)
lines(dn, col='red', lwd=2)
set.seed(1234)
#hist(x, breaks = 30, probability = T)
curve(dt(x, 10), xlim = c(-5,5), frame=F, type = "l", lty=2)
curve(dt(x, 5), xlim = c(-5,5), add = T, col="red", type = "l", lty=3)
curve(dnorm(x), xlim = c(-5,5), add = T, col="blue")
# design a function that helps to calculate the binomial distribution
# and draw the discrete graph
graph.binom <- function(n, p) {
x <- (dbinom(0:n, size = n, prob = p))
barplot(x, yaxt="n",
col = "lightblue",
ylim = c(0, 0.4),
names.arg = 0:n, ylab = "Probability",
main = sprintf(paste('Binomial Distribution (n,p)' , n, p, sep = ', ')))
axis(2, at=c(seq(0, 0.4, 0.1), 0.025), las=2)
abline(h=0.025, col = "Red", lty=2, lwd = 2)
}
graph.binom(20, 0.0866)
graph.binom(20, 0.4910)
library(mvtnorm) #多変量正規分布パッケージ
library(scatterplot3d) #三次元描画パッケージ
sigma.zero <- matrix(c(1,0,0,1), ncol=2) #分散共分散行列（無相関）
x1 <- seq(-3, 3, length=50) # 変量x1の定義域 -3≦x1≦3
x2 <- seq(-3, 3, length=50) # 変量x2の定義域 -3≦x1≦3
f.zero <- function(x1,x2) {
dmvnorm(matrix(c(x1,x2), ncol=2),
mean=c(0,0), sigma=sigma.zero) }
# 分散共分散行列 sigma.zero の密度関数
z <- outer(x1, x2, f.zero)
# x1とx2の定義域の外積に対する密度関数f.zeroの値域
z[is.na(z)] <- 1 # z に関する条件
op <- par(bg = "white") #グラフィクスの環境設定
persp(x1, x2, z, theta = -30, phi = 15, expand = 0.7, col = "lightblue", xlab = "X",
ylab = "Y", zlab = "Probability Density")
# interactive 3d bivariate normal distribution
library(rgl)
x10000 <- rmvnorm(n=10000, mean=c(0,0), sigma=sigma.zero)#乱数1000個
plot3d(x10000[,1], x10000[,2],col = "lightblue", xlab = "X",
ylab = "Y", zlab = "Probability Density",
dmvnorm(x10000, mean=c(0,0), sigma=sigma.zero), type="s", size=1, lit=TRUE, main = "",sub="3-D Plot")
library(car)
x10000 <- rmvnorm(n=10000, mean=c(0,0), sigma=sigma.zero)#乱数10000個
data.ellipse(x10000[1], x10000[2], levels=c(0.5, 0.975))
contour(x.points,y.points,z)
library(knitr)
library(kableExtra)
dt <- read.csv("backupfiles/walkingAT.csv", header = T)
names(dt) <- c("", "Active exercise group (i=1)", "Eight week control group (i=2)")
kable(dt, "html", align = "c",caption = "表 22.1: Children's ages at time of first walking alone by randomisation group") %>%
kable_styling(bootstrap_options = c("striped", "bordered")) %>%
add_header_above(c("", "Age in months for walking alone" = 2))
data <- data.frame(x^3,x^2,x,sqrt(x),log(x),1/sqrt(x),1/x,1/(x^2),1/(x^3))
names(data) <- c("cubic","square","identity","square root","log","1/(square root)",
"inverse","1/square","1/cubic")
test <- shapiro.test(data$cubic)
W.statistic <- as.numeric(test$statistic)
p.value <- test$p.value
Transformation <- "cubic"
Formula <- "square"
(results <- data.frame(Transformation, Formula, W.statistic, p.value))
Ladder.x <- function(x){
data <- data.frame(x^3,x^2,x,sqrt(x),log(x),1/sqrt(x),1/x,1/(x^2),1/(x^3))
names(data) <- c("cubic","square","identity","square root","log","1/(square root)",
"inverse","1/square","1/cubic")
# options(scipen=3, digits = 3)
test1 <- shapiro.test(data$cubic)
test2 <- shapiro.test(data$square)
test3 <- shapiro.test(data$identity)
test4 <- shapiro.test(data$`square root`)
test5 <- shapiro.test(data$log)
test6 <- shapiro.test(data$`1/(square root)`)
test7 <- shapiro.test(data$inverse)
test8 <- shapiro.test(data$`1/square`)
test9 <- shapiro.test(data$`1/cubic`)
W.statistic <- c(test1$statistic,
test2$statistic,
test3$statistic,
test4$statistic,
test5$statistic,
test6$statistic,
test7$statistic,
test8$statistic,
test9$statistic)
p.value <- c(test1$p.value,
test2$p.value,
test3$p.value,
test4$p.value,
test5$p.value,
test6$p.value,
test7$p.value,
test8$p.value,
test9$p.value)
Transformation <- c("cubic","square","identity","square root","log","1/(square root)",
"inverse","1/square","1/cubic")
Formula <- c("x^3","x^2","x","sqrt(x)","log(x)","1/sqrt(x)","1/x","1/(x^2)","1/(x^3)")
(results <- data.frame(Transformation, Formula, W.statistic, p.value))
}
library(gvlma)
gvmodel <- gvlma(Model1)
summary(gvmodel)
plot.gvlma(gvmodel)