R语言通过factor函数,将数值型的变量转换为因子变量,其作用相当于Stata中的【label define + label value】。factor函数包括两个重要的参数: levels:指定因子变量的取值水平,相当于于省份的数字编码; labels :将数值编码和具体标签对应起来。 比如: auto$rep78 <- factor(auto$rep78, #将rep78转换成为因子型变量 ...
Data = read.table(textConnection(Input),header=TRUE) Data$Treatment = factor(Data$Treatment, levels=unique(Data$Treatment)) Data boxplot(Response ~ Treatment, data = Data, ylab="Response", xlab="Treatment") ### Define linear model model = lm(Response ~ Treatment, data = Data) library(car...
由于使用了中文来标记 label,所以回归结果的显示结果反而变差了。 db$X3f<-factor(db$X3,levels=c("A","B","C","D","E"),labels=c("Excellent","Good","OK","Pass","Fail"))>m3=lm(Y~X1+X2+X3f,data=db)>summary(m3)Call:lm(formula=Y~X1+X2+X3f,data=db)Residuals:Min1QMedian3QMax...
In general, you should not specify profile names within single quotation marks because most classes will not allow this, and the RDEFINE command will fail. Classes such as FACILITY (or others whose class definition allowsanycharacter as the first character) will allow RDEFINE to work, but this...
plot_cm <- function(preds, refs, title) { library(caret) cm <- confusionMatrix(factor(refs), factor(preds)) cm_table <- as.data.frame(cm$table) cm_table$Prediction <- factor(cm_table$Prediction, levels=rev(levels(cm_table$Prediction))) ggplot(cm_table, aes(Reference, Prediction, fill...
> tmp <- stats[,c("id_met","coverage")] %>% setkey(coverage) %>% .[,id_met:=factor(id_met,levels=id_met)] > tmp$cellcolor <- c("black","red")[as.numeric(tmp$coverage < opts$met_coverage_threshold)+1] > p1 <- ggplot(tmp, aes(x=id_met, y=coverage)) + ...
Data=read.table(textConnection(Input),header=TRUE)Data$Treatment=factor(Data$Treatment,levels=unique(Data$Treatment))Databoxplot(Response~Treatment,data=Data,ylab="Response",xlab="Treatment")### Define linear modelmodel=lm(Response~Treatment,data=Data)library(car)Anova(model,type="II")summary(model...
理想中的线性模型各个自变量应该是线性无关的,若自变量间存在共线性,则会降低回归系数的准确性。一般用方差膨胀因子VIF(Variance Inflation Factor)来衡量共线性,《统计学习》中认为VIF超过5或10就存在共线性,《R语言实战》中认为VIF大于4则存在共线性。理想中的线性模型VIF=1,表完全不存在共线性。
理想中的线性模型各个自变量应该是线性无关的,若自变量间存在共线性,则会降低回归系数的准确性。一般用方差膨胀因子VIF(Variance Inflation Factor)来衡量共线性,《统计学习》中认为VIF超过5或10就存在共线性,《R语言实战》中认为VIF大于4则存在共线性。理想中的线性模型VIF=1,表完全不存在共线性。
因子使用整数进行内部实施。The levels attribute maps each integer to a factor level 通过设置类属性,可以将这个转变成一个因子。 数据框 数据框是一种代表表格数据的有用方式。A data frame represents a table of data. Each column may be a different type, but each row in the data frame must have ...