=T, sep="\t") trait <- phe$Trait line <- phe$Line loc <- phe$Loc TRAIT <- as.numeric(trait) LINE <- as.factor (line) LOC <- as.factor(loc) ## 建模 blup <- lmer(TRAIT~(1|LINE)+(1|LOC)) summary(blup) ? trait <- phe$Trait line <- phe$Line loc <- phe$Loc rep <- phe$Rep TRAIT <- as.numeric(trait) LINE <- as.factor (line) LOC <- as.factor(loc) REP <- as.factor(rep) ## 建模 blup <- lmer(TRAIT~(1|LINE)+(1|LOC)+(1|REP%in (line) LOC <- as.factor(loc) REP <- as.factor(rep) YEAR <- as.factor(year) ## 建模 blup <-lmer(TRAIT~(1
0.06440 0.02090 3.081 0.00206 ** as.factor(ai)2002 0.20242 0.02025 9.995 < 2e-16 *** as.factor -16 *** as.factor(ai)2005 0.50271 0.02079 24.179 < 2e-16 *** as.factor(bj)1 -0.96513 0.01359 -70.994 < 2e-16 *** as.factor(bj)2 -4.14853 0.06613 -62.729 < 2e-16 *** as.factor(bj)3 -5.10499 0.12632 -40.413 < 2e-16 *** as.factor(bj)4 -5.94962 0.24279 -24.505 < 2e-16 *** as.factor 0.22548 -22.641 6.36e-10 *** as.factor(bj)4 -5.94962 0.43338 -13.728 8.17e-08 *** as.factor
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0.06440 0.02090 3.081 0.00206 ** as.factor(ai)2002 0.20242 0.02025 9.995 < 2e-16 ***as.factor 16 ***as.factor(ai)2005 0.50271 0.02079 24.179 < 2e-16 ***as.factor(bj)1 -0.96513 0.01359 -70.994 < 2e-16 ***as.factor(bj)2 -4.14853 0.06613 -62.729 < 2e-16 ***as.factor(bj)3 -5.10499 0.12632 -40.413 < 2e-16 ***as.factor(bj)4 -5.94962 0.24279 -24.505 < 2e-16 ***as.factor(bj 07 ***as.factor(ai)2005 0.50271 0.03711 13.546 9.28e-08 ***as.factor(bj)1 -0.96513 0.02427
** as.factor(ai)2005 0.6126 0.2070 2.959 0.01431 * as.factor(bj)1 -0.9674 0.1109 -8.726 5.46e-06 ***as.factor(bj)2 -4.2329 0.1208 -35.038 8.50e-12 ***as.factor(bj)3 -5.0571 0.1342 -37.684 4.13e-12 ***as.factor(bj)4 -5.9031 0.1562 -37.783 4.02e-12 ***as.factor(bj 16 ***as.factor(ai)2005 0.50271 0.02079 24.179 < 2e-16 ***as.factor(bj)1 -0.96513 0.01359 0.12632 -40.413 < 2e-16 ***as.factor(bj)4 -5.94962 0.24279 -24.505 < 2e-16 ***as.factor(bj
0.1604 0.1109 1.447 0.17849 as.factor(ai)2002 0.2718 0.1208 2.250 0.04819 * as.factor ** as.factor(ai)2005 0.6126 0.2070 2.959 0.01431 * as.factor(bj)1 -0.9674 0.1109 - 8.726 5.46e-06 *** as.factor(bj)2 -4.2329 0.1208 -35.038 8.50e-12 *** as.factor(bj)3 -5.0571 0.1342 -37.684 4.13e-12 *** as.factor(bj)4 -5.9031 0.1562 -37.783 4.02e-12 *** as.factor 0.12632 -40.413 < 2e-16 *** as.factor(bj)4 -5.94962 0.24279 -24.505 < 2e-16 *** as.factor
# accuracy table table(as.numeric(as.factor(tweets[11:15,2])), results[,"FORESTS_LABEL"]) table(as.numeric (as.factor(tweets[11:15,2])), results[,"MAXENTROPY_LABEL"]) # recall accuracy recall_accuracy(as.numeric (as.factor(tweets[11:15,2])), results[,"FORESTS_LABEL"]) recall_accuracy(as.numeric(as.factor(tweets[ 11:15,2])), results[,"MAXENTROPY_LABEL"]) recall_accuracy(as.numeric(as.factor(tweets[11:15,2])), results (as.numeric(as.factor(tweets[11:15,2])), results[,"SVM_LABEL"]) 得到模型的结果摘要（特别是结果的有效性）： # model summary
(as.factor(tweets[11:15, 2])), results[,"MAXENTROPY_LABEL"]) # recall accuracy recall_accuracy(as.numeric (as.factor(tweets[11:15, 2])), results[,"FORESTS_LABEL"]) recall_accuracy(as.numeric(as.factor(tweets [11:15, 2])), results[,"MAXENTROPY_LABEL"]) recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"TREE_LABEL"]) recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"BAGGING_LABEL "]) recall_accuracy(as.numeric(as.factor(tweets[11:15, 2])), results[,"SVM_LABEL"]) 得到模型的结果摘要（特别是结果的有效性
F0E442", "#0072B2", "#D55E00", "#CC79A7") plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor image.png 更改点的大小 plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor(HMP$country) image.png 更改x轴和y轴的标签 plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor(HMP$country)], image.png 更改坐标轴的范围 plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor(HMP$country details/52004182 par(fig=c(0,1,0,0.75)) plot(log10(rel_res)~log10(rel_crAss), data=HMP, bg=cols[as.factor
(rstatix) library(ggprism) library(ggpubr) library(ggsci) 数据清洗 df <- ToothGrowth %>% mutate(dose=as.factor theme_niwot()+ scale_fill_brewer(palette="Blues") 统计分析2 stat.test2 <- ToothGrowth %>% mutate(dose=as.factor ) %>% add_significance("p.adj") %>% add_xy_position() 方差分析 res.aov <- ToothGrowth %>% mutate(dose=as.factor (dose)) %>% anova_test(len ~ dose) 方差分析事后检验 stat.test4 <- ToothGrowth %>% mutate(dose=as.factor(dose )) %>% tukey_hsd(len ~ dose) %>% add_xy_position("dose") ToothGrowth %>% mutate(dose=as.factor(dose
(mtcars$gear))) ################## Df Sum Sq Mean Sq F value Pr(>F) as.factor 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # aov函数生成的模型也可以以表的形式输入摘要 model.tables(aov(mtcars$mpg~as.factor (mtcars$gear))) # ############ Tables of effects as.factor(mtcars$gear) 3 4 5 -3.984 4.443 1.289 rep 15.000 12.000 5.000 # TukeyHSD事后比较检验（多重比较检验） TukeyHSD(aov(mtcars$mpg~as.factor (mtcars$gear)) $`as.factor(mtcars$gear)` diff lwr upr p adj 4-3 8.426667
绘图函数要求这些设置颜色的数据是factor，所以我们要把加到图上的 ## 临床信息转变为因子 laml@clinical.data$neoplasm_histologic_grade <- as.factor ) = levels(laml@clinical.data$neoplasm_histologic_grade) laml@clinical.data$race.demographic <- as.factor Racecolors) = levels(laml@clinical.data$race.demographic) laml@clinical.data$gender.demographic <- as.factor ) names(Gendercolors) = levels(laml@clinical.data$gender.demographic) laml@clinical.data$HBV <- as.factor ffffb3", "#e31a1c") names(HBVcolors) = levels(laml@clinical.data$HBV) laml@clinical.data$HCV <- as.factor
格栅 qplot() qplot(Sepal.Length,Sepal.Width,data=iris,col=as.factor(Species),size=as.factor(Species),shape =as.factor(Species)) ?
> Y = tapply(base$X,as.factor(base$AM),mean)> Z = ts(as.numeric(Y[1:(146-24)]), start=c(2004,1),frequency 然后，我们可以使用此模型对初始序列进行预测 > Y2=tapply(base$X,as.factor(base$AM),mean) > lines(futur,obs_reel,col="blue 我们可以对原始系列进行预测， > Yp=predict(model3,n.ahead=24) ++ predict(trend,newdata=data.frame(T=futur) > Y2=tapply( X,as.factor
margin(t = 5)), legend.position = "non") } 统计分析 stat.test2 <- ToothGrowth %>% mutate(dose=as.factor adjust_pvalue() %>% add_significance("p.adj") %>% add_xy_position() res.aov <- ToothGrowth %>% mutate(dose=as.factor (dose)) %>% anova_test(len ~ dose) 数据可视化 ToothGrowth %>% mutate(dose=as.factor(dose)) %>% ggplot(
rnorm(10) + 15 dd = data.frame(Group = rep(c("A","B"),each=10),y = c(y1,y2)) dd str(dd) dd$Group = as.factor 10) + 15 dd = data.frame(Group = rep(c("A","B","C"),each=10),y = c(y1,y2,y3)) dd str(dd) dd$Group = as.factor (dd$Group1) dd$Group2 = as.factor(dd$Group2) str(dd) 「数据预览：」 > dd Group1 Group2 y 1 rnorm(10) + 15 dd = data.frame(Group = rep(c("A","B"),each=10),y = c(y1,y2)) dd str(dd) dd$Group = as.factor (dd$Group1) dd$Group2 = as.factor(dd$Group2) str(dd) ## 分组查看 p = ggboxplot(dd,x = "Group1",y="y",color
Manhattan图 1 纵坐标为P值转-log10（） ggplot(Snp_pos, aes(x=BPcum, y=-log10(P))) + geom_point( aes(color=as.factor ”Manhattan图 p <- ggplot(Snp_pos, aes(x=BPcum, y=-log10(P))) + #设置点的大小，透明度 geom_point( aes(color=as.factor log10(P)>6, "yes", "no")) #绘图 p1 <- ggplot(data, aes(x=BPcum, y=-log10(P))) + geom_point( aes(color=as.factor = "rs4064"] <- "yes" # 绘图 p2 <- ggplot(data, aes(x=BPcum, y=-log10(P))) + geom_point( aes(color=as.factor know", sep="") p4 <- ggplot(data, aes(x=BPcum, y=-log10(P), text=text)) + geom_point( aes(color=as.factor
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