par(no.readonly=TRUE) 修改当前图形参数，会话结束前一直有效。
符号与线条：pch 绘制点时使用的符号；cex 符号大小；lty 线条类型；ldy 线条宽度
legend 图例标签
barplot(height, width = 1, space = NULL,
names.arg = NULL, legend.text = NULL, beside = FALSE,
horiz = FALSE, density = NULL, angle = 45,
col = NULL, border = par("fg"),
main = NULL, sub = NULL, xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL, xpd = TRUE, log = "",
axes = TRUE, axisnames = TRUE,
cex.axis = par("cex.axis"), cex.names = par("cex.axis"),
inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,
add = FALSE, args.legend = NULL, ...)
beside为F，则为堆砌条形图，为T，则为分组条形图
library(vcd)
attach(Arthritis)
counts<-table(Treatment,Improved)
spine(counts,main="Spinogram")
hist(mtcars$mpg,freq=F,breaks=12,col="red",xlab="Miles perGallon",main="Histogram,rug plot,density curve")
rug(jitter(mtcars$mpg))
lines(density(mtcars$mpg),col="blue",lwd=2)#density为核密度图
freq
logical; if TRUE, the histogramgraphic is a representation of frequencies,thecounts component of the result;if FALSE, probability densities, componentdensity,are plotted (so that the histogram has a total area of one).Defaults to TRUE if and onlyif breaks are equidistant(and probability is notspecified).
par(lwd=2) #双倍线条宽度
library(sm)
attach(mtcars)
cyl.f<-factor(cyl,levels=c(4,6,8),labels=c("4cylinders","6cylinders","8cylinders"))#创建分组因子
sm.density.compare(mpg,cyl,xlab="miles per gallon")#绘制密度图
title(main="MPG Distribution by Car Cylinders")
colfill<-c(2:(1+length(levels(cyl.f)))) #通过鼠标点击添加图例
legend(locator(1),levels(cyl.f),fill=colfill)
detach(mtcars)
boxplot(mpg~cyl,data=mtcars,main="Car Mileage Data",xlab="Numberof Cylinders",ylab="Miles perGallon")
mtcars$cyl.f<-factor(mtcars$cyl,levels=c(4,6,8),labels=c("4","6","8"))
mtcars$am.f<-factor(mtcars$am,levels=c(0,1),labels=c("auto","standard"))
boxplot(mpg~am.f*cyl.f,data=mtcars,varwidth=T,col=c("gold","green"),main="MPGDistribution by Auto Type",xlab="Auto Type")
dotchart(x,labels=)
x<-mtcars[order(mtcars$mpg),]
x$cyl<-factor(x$cyl)
x$color[x$cyl==4]<-"red"
x$color[x$cyl==6]<-"blue"
x$color[x$cyl==8]<-"darkgreen"
dotchart(x$mpg,labels=row.names(x),cex=.7,groups=x$cyl,gcolor="black",color=x$color,pch=19,main="GasMileage for Car Models",xlab="Miles per Gallon")
scatterplot(weight~height,data=women,spread=F,lty.smooth=2,pch=19,main="WomenAge 30-39",xlab="Height",ylab="Weight")
scatterplotMatrix(x, var.labels=colnames(x),
diagonal=c("density", "boxplot", "histogram", "oned", "qqplot","none"),
adjust=1,nclass,
plot.points=TRUE, smoother=loessLine, smoother.args=list(), smooth,span,
spread =!by.groups, reg.line=lm,
transform=FALSE, family=c("bcPower", "yjPower"),
ellipse=FALSE, levels=c(.5, .95), robust=TRUE,
groups=NULL,by.groups=FALSE,
use=c("complete.obs", "pairwise.complete.obs"),
labels,id.method="mahal", id.n=0, id.cex=1, id.col=palette()[1],
col=if(n.groups == 1) palette()[3:1] else rep(palette(),length=n.groups),
pch=1:n.groups, lwd=1, lty=1,
cex=par("cex"), cex.axis=par("cex.axis"), cex.labels=NULL,
cex.main=par("cex.main"),
legend.plot=length(levels(groups)) > 1,legend.pos=NULL, row1attop=TRUE, ...)
symbols（x,y,circle=radius）
corrgram(x, type=NULL, order = FALSE, labels,panel=panel.shade,
lower.panel=panel,upper.panel=panel, diag.panel=NULL,
text.panel=textPanel,label.pos=c(0.5, 0.5), label.srt=0, cex.labels=NULL,
font.labels=1, row1attop=TRUE,dir="", gap=0, abs=FALSE,
col.regions=colorRampPalette(c("red","salmon","white","royalblue","navy")),
cor.method="pearson",
...)
library(corrgram)
corrgram(mtcars,order=T,lower.panel=panel.shade,upper.panel=panel.pie,text.panel=panel.txt,main="Correlogram of mtcars intercorrelations")
正斜杠为正相关，反斜杠为负相关。颜色越深，相关性越大。正相关从12点处开始顺时针填充饼图，负相关逆时针填充饼图。
aggregate(mtcars[vars],by=list(am=mtcars$am),mean)
am mpg hp wt
1 0 17.14737 160.2632 3.768895
2 1 24.39231 126.8462 2.411000
aggregate()仅允许在每次调用中使用平均数、标准差这样的单返回值函数。
列联表：
library(vcd) #use data Arthritis
library(gmodels)
CrossTable(Arthritis$Treatment,Arthritis$Improved)
Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
Total Observations in Table: 84
|Arthritis$Improved
Arthritis$Treatment| None| Some | Marked| Row Total |
--------------------|-----------|-----------|-----------|-----------|
Placebo| 29| 7| 7| 43 |
| 2.616| 0.004| 3.752| |
| 0.674| 0.163| 0.163| 0.512 |
| 0.690| 0.500| 0.250| |
| 0.345| 0.083| 0.083| |
--------------------|-----------|-----------|-----------|-----------|
Treated| 13| 7| 21| 41 |
| 2.744| 0.004 | 3.935| |
| 0.317| 0.171| 0.512| 0.488 |
| 0.310| 0.500| 0.750| |
| 0.155| 0.083| 0.250| |
--------------------|-----------|-----------|-----------|-----------|
Column Total| 42| 14| 28| 84 |
| 0.500| 0.167| 0.333| |
--------------------|-----------|-----------|-----------|-----------|
lm(formula, data, subset, weights, na.action,
method = "qr", model =TRUE, x = FALSE, y = FALSE, qr = TRUE,
singular.ok = TRUE,contrasts = NULL, offset, ...)
states<-as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
cor(states)
Murder PopulationIlliteracy Income Frost
Murder 1.0000000 0.3436428 0.7029752-0.2300776 -0.5388834
Population 0.3436428 1.0000000 0.1076224 0.2082276-0.3321525
Illiteracy 0.7029752 0.1076224 1.0000000 -0.4370752 -0.6719470
Income -0.2300776 0.2082276 -0.4370752 1.0000000 0.2262822
Frost -0.5388834 -0.3321525 -0.6719470 0.2262822 1.0000000
library(car)
scatterplotMatrix(states,spread=F,lty.smooth=2,main="Scatter PlotMatrix")
lm(formular,data=mydata)
For example：
fit<- lm(weight~height+I(height^2),data=women)
coef(fit) #output the result ofcoefficient
(Intercept) height I(height^2)
261.87818358 -7.34831933 0.08306399
confint(fit)
2.5% 97.5 %
(Intercept) 206.97913605 316.77723111
height -9.04276525 -5.65387341
I(height^2) 0.07003547 0.09609252
95%的置信区间从2.5%~97.5%。如果置信区间包含0，则该变量对方程无影响。
par(mfrow=c(2,2))
plot(fit)
library(MASS)
fit1<-lm(Murder~Population+Illiteracy+Income+Frost,data=states)
stepAIC(fit1,direction="backward")
Start: AIC=97.75
Murder ~ Population + Illiteracy + Income + Frost
Df Sum of Sq RSS AIC
-Frost 1 0.021 289.19 95.753
-Income 1 0.057 289.22 95.759
289.17 97.749
- Population 1 39.238328.41 102.111
- Illiteracy 1 144.264 433.43 115.986
Step: AIC=95.75
Murder ~ Population + Illiteracy + Income
Df Sum of Sq RSS AIC
-Income 1 0.057 289.25 93.763
289.19 95.753
- Population 1 43.658332.85 100.783
- Illiteracy 1 236.196 525.38 123.605
Step: AIC=93.76
Murder ~ Population + Illiteracy
Df Sum of Sq RSS AIC
289.25 93.763
- Population 1 48.517337.76 99.516
- Illiteracy 1 299.646 588.89 127.311
Call:
lm(formula = Murder ~ Population + Illiteracy, data =states)
Coefficients:
(Intercept) Population Illiteracy
1.6515497 0.0002242 4.0807366
regsubsets(x=, data=, weights=NULL, nbest=1,nvmax=8, force.in=NULL, force.out=NULL, intercept=TRUE,method=c("exhaustive", "backward", "forward", "seqrep"),really.big=FALSE,...)
x
design matrix or model formula for full model,or biglm object
data
Optional data frame
y
response vector
weights
weight vector
nbest
number of subsets of each size to record
nvmax
maximum size of subsets to examine
force.in
index to columns of design matrix that should be in allmodels
force.out
index to columns of design matrix that should be in nomodels
intercept
Add an intercept?
method
Use exhaustive search, forward selection, backward selection orsequential replacement to search.
really.big
Must be TRUE to perform exhaustive search on more than 50variables.
object
regsubsets object
all.best
Show all the best subsets or just one of each size
matrix
Show a matrix of the variables in each model or just summarystatistics
matrix.logical
With matrix=TRUE, the matrix islogical TRUE/FALSE orstring "*"/" "
df
Specify a number of degrees of freedom for the summarystatistics. The default is n-1
id
Which model or models (ordered as in the summary output) toreturn coefficients and variance matrix for
vcov
If TRUE, return the variance-covariance matrixas an attribute
leaps<-regsubsets(Murder~Population+Illiteracy+Income+Frost,data=states,nbest=4)
plot(leaps,scale="adjr2")
adjr2为调整的R^2
用到的安装包“AER”
glm ( formular, data = mydata, family = binomial () ).其中formular为回归式，因变量为二元值。
泊松回归：
glm ( formular, data= mydata, family = poisson()).Formular为回归式。
主成分分析：
用到的安装包“psych”
Principal() 含多种方差旋转方法的主成分分析；fa()可用主轴、最小残差、最大似然估计的因子分析；fa.parallel() 含平行分析的碎石图；factor()绘制因子分析或主成分分析的结果。
1. 判断主成分个数
fa.parallel(Harman23.cor$cov,n.obs=302,fa="pc",n.iter=100,show.legend=F,main="ScreePlot")
2. 提取主成分及主成分旋转
rc<-principal(Harman23.cor$cov,nfactors=2,rotate="varimax")#nfators mean 2 facors
rc
Principal Components Analysis
Call: principal(r = Harman23.cor$cov, nfactors = 2, rotate ="varimax")
Standardized loadings (pattern matrix) based upon correlationmatrix
PC1 PC2 h2 u2 com
height 0.90 0.25 0.88 0.123 1.2
arm.span 0.93 0.19 0.90 0.097 1.1
forearm 0.92 0.16 0.87 0.128 1.1
lower.leg 0.90 0.22 0.86 0.139 1.1
weight 0.26 0.88 0.85 0.150 1.2
bitro.diameter 0.19 0.84 0.74 0.261 1.1
chest.girth 0.11 0.84 0.72 0.283 1.0
chest.width 0.26 0.75 0.62 0.375 1.2
PC1 PC2
SSloadings 3.52 2.92
ProportionVar 0.44 0.37
CumulativeVar 0.44 0.81
两个主成分PC1，PC2，h2为成分公因子方差——主成分对每个变量的解释度，u2（1-h2）为无法解释的比例。ssloadings包含与主成分相关联的特征值；proportion var 是每个主成分对整个数据集的解释度；cumulativevar是主成分累计可解释度。
3. 获取主成分得分系数
round(unclass(rc$weights),2) #have already standardized
PC1 PC2
height 0.28 -0.05
arm.span 0.30 -0.08
forearm 0.30 -0.09
lower.leg 0.28 -0.06
weight -0.06 0.33
bitro.diameter -0.08 0.32
chest.girth -0.10 0.34
chest.width -0.04 0.27
