个体发育
2020-10-16
2020-10-16
个体发育分析
个体发育,指的是一种生物从其出生到死亡的整个过程中身体出现的形态变化。从幼体到成年,生物的形态变化通常比较明显。在古生物学中研究个体发育,一方面有助于理解不同化石物种的生长变化;另一方面,也有助于在后续研究辅助化石的鉴定。在古生物的个体发育研究中,通常会总结一种生物不同身体部位随年龄增长的变化趋势、这种趋势或是某些器官的增大,或是某些结构数量的增加。由于生物的种类千差万别,因此不同古生物的个体发育过程也存在很大差异,需要根据实际情况选用适宜的衡量标准。
1 数据描述
#先安装包:install.packages("PerformanceAnalytics")
setwd("C:\\Users\\Lenovo\\Desktop\\data_3")
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
## # A tibble: 6 x 6
## Specimen_No. thecal_height stem_length `minimun N_brac~ circlets_plates~
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 10 0.828 0.666 6 10
## 2 15 0.625 1.16 5 7
## 3 16 0.93 0.871 7 6
## 4 23 0.936 0.892 7 7
## 5 25 1.74 1 12 10
## 6 27 1.73 1.12 11 11
## # ... with 1 more variable: number_part <dbl>
## Classes 'tbl_df', 'tbl' and 'data.frame': 18 obs. of 6 variables:
## $ Specimen_No. : num 10 15 16 23 25 27 32 33 39 40 ...
## $ thecal_height : num 0.828 0.625 0.93 0.936 1.743 ...
## $ stem_length : num 0.666 1.16 0.871 0.892 1 ...
## $ minimun N_brachioles: num 6 5 7 7 12 11 4 6 7 7 ...
## $ circlets_plates_No : num 10 7 6 7 10 11 8 9 7 7 ...
## $ number_part : num 7 6 7 6 7 7 6 6 6 6 ...
数据共有18条记录,6个变量,数据类型都为数值型数据。为描述各变量之间的相关性情况,对数据进行形态相关分析如下:
2 个体变量相关性分析
选择生物的个体发育指标,得到形态相关的相关性值,可以了解到stem_length和thecal_height相关性为0.8,二者相关性强度较高,其次是thecal_height和minimun N_brachioles的相关性最好。
3 个体发育趋势分析
利用贝叶斯信息准则(BIC)作出的频率分布图利用贝叶斯信息准则(BIC)作出的频率分布图,探究各变量之间的正太性和核密度分布,如下图所示:
setwd("C:\\Users\\Lenovo\\Desktop\\data_3")
data1=read_excel("Ontogeny_echinoderms_1.xlsx")
str(data1)
## Classes 'tbl_df', 'tbl' and 'data.frame': 9 obs. of 7 variables:
## $ X : num 0.2 2.3 3.7 4 5 6 7 8 9.8
## $ A1: num -54 -46 -58 -69 -68 -80 -98 -108 -118
## $ A2: num -54 -46 -55 -62 -65 -72 -80 -90 -93
## $ B1: num -153 -146 NA NA NA NA NA NA NA
## $ B2: num -153 -142 -155 -158 -155 -158 -167 -173 -180
## $ C1: num -30 -41 -49 NA NA NA NA NA NA
## $ C2: num -30 -37 -43 -48 -56 -65 -66 -69 -74
layout(matrix(c(1,2,3,4,5,6),2,3,byrow = TRUE))
plot(data1$X,data1$A1,type="b",yaxt="n",ylab="Precent surviviing",xlab="Length (mm)",pch=1,lwd=2,lty=1)
par(new=TRUE) # 是否叠加新图形,没叠加一次运行一次该命令
plot(data1$X,data1$A2,type="b",ylab="Precent surviviing",xlab="Length (mm)",pch=4,lwd=1,lty=6)
text(8, -50,"A")
plot(data1$X,data1$B1,type="b",yaxt="n",ylab="Precent surviviing",xlab="Length (mm)",pch=1,lwd=2,lty=1)
par(new=TRUE) # 是否叠加新图形,没叠加一次运行一次该命令
plot(data1$X,data1$B2,type="b",ylab="Precent surviviing",xlab="Length (mm)",pch=4,lwd=1,lty=6)
text(8,-146, "B")
plot(data1$X,data1$C1,type="b",ylab="Precent surviviing",xlab="Length (mm)",pch=1,lwd=2,lty=1)
par(new=TRUE) # 是否叠加新图形,没叠加一次运行一次该命令
plot(data1$X,data1$C2,type="b",ylab="Precent surviviing",xlab="Length (mm)",pch=4,lwd=1,lty=6)
text(8, -33, "C")
hist(data$thecal_height, prob=T, xlab='',main='Histogram of maximum pH value')#画直方图
lines(density(data$thecal_height,na.rm=T))#画概率密度曲线
text(1.5, 1.5, "D")
hist(data$circlets_plates_No, prob=T, xlab='',main='Histogram of maximum pH value',ylim=0:1)#画直方图
lines(density(data$circlets_plates_No,na.rm=T))#画概率密度曲线
text(10, 0.9, "E")
hist(data$ stem_length, prob=T, xlab='',main='Histogram of maximum pH value')#画直方图
lines(density(data$ stem_length,na.rm=T))#画概率密度曲线
text(1.5, 1.5, "F")
五组测量变量间的一一对应关系如上所示,说明长度在5到11的呈现右偏分布,0.5到1.5的基本呈现正态分布。
3.1 皮尔逊相关性检验分析
## Warning: package 'basicTrendline' was built under R version 3.5.3
trendline(data$thecal_height,data$stem_length, ,model="line3P",Pvalue.corrected = TRUE,
linecolor = "yellow", lty = 0, lwd = 1, show.equation = FALSE,
show.Rpvalue = FALSE, Rname = 1, Pname = 0, xname = "x", yname = "y",
yhat = FALSE, summary = FALSE, ePos.x = NULL, ePos.y = NULL,
text.col = "black", eDigit = 5, eSize = 1, CI.fill = TRUE,
CI.level = 0.95, CI.color = "white", CI.alpha = 1, CI.lty = 1,
CI.lwd = 1, las = 1, xlab = NULL, ylab = NULL,xaxt="n",yaxt="n")
由上图知,皮尔相关系数为0.67609,P值为0.000212.说明变量之间存在很强的显著性差异,变量之间的相关性较好。
3.2 回归系数分析结果
library(basicTrendline)
trendline(log(data$thecal_height),log(data$stem_length), Pvalue.corrected = TRUE,
linecolor = "black", lty = 1, lwd = 2, show.equation = FALSE,
show.Rpvalue = TRUE, Rname = 1, Pname = 0, xname = "x", yname = "y",
yhat = FALSE, summary = FALSE, ePos.x = NULL, ePos.y = NULL,
eDigit = 5, eSize = 1, CI.fill = TRUE,
CI.level = 0.95, CI.color = "white", CI.alpha = 1, CI.lty = 1,
CI.lwd = 1, las = 1, xlab ="", ylab ="",xaxt="n",yaxt="n")
par(new=TRUE)
##95%的结果
mode=lm(log(data$thecal_height)~log(data$stem_length))
trendline(fitted(mode),log(data$stem_length),CI.fill = FALSE,CI.color = "red",CI.lty = 2,linecolor = "red",summary =FALSE,Pvalue.corrected = FALSE, show.equation = FALSE,xlab = "",text.col = "white")
## Warning in summary.lm(fit): essentially perfect fit: summary may be
## unreliable
par(new=TRUE)
trendline(log(data$thecal_height),log(data$stem_length),model="line3P",ePos.x = "topleft",summary =FALSE,eDigit = 5,CI.fill = FALSE,text.col = "white",ylab =NULL,xaxt="n",yaxt="n",Pvalue.corrected = FALSE,show.Rpvalue =FALSE,show.equation = FALSE)