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这篇“R语言阈值置信区间怎么计算”文章的知识点大部分人都不太理解,所以小编给大家总结了以下内容,内容详细,步骤清晰,具有一定的借鉴价值,希望大家阅读完这篇文章能有所收获,下面我们一起来看看这篇“R语言阈值置信区间怎么计算”文章吧。
R语言源代码
#################################### Arg<-commandArgs(TRUE) ###########input############################################### individual_analysis<- c(Arg[1]) reprication<-c(Arg[2]) filter_value<-c(Arg[3]) population_structure<-c(Arg[4]) #RIL or F2 depth_analysis<-c(1:300) ###########input############################################### ###########genotype############################# genotype<-function(){ count<-0 if (population_structure=="RIL"){ x<-runif(1) if (x<=0.5){ count<-1 }else{ count<-0 } }else{ for(i in 1:2){ x<-runif(1) if (x<=0.5){ number<-0.5 }else{ number<-0 } if(number == 0.5){ count<- count+0.5 } } } return(count) } ############################################################ ###########caluclate of genotype ratio######################### individuals_genotype<-function(number_of_total_individuals){ ratio_of_genotype<-c() for(i in 1:number_of_total_individuals){ ratio_of_genotype<-c(ratio_of_genotype,genotype()) } return(mean(ratio_of_genotype)) } ############################################################ ###########SNP_index_caluclation######################### snp_index<-function(read_depth,ratio_of_genotype_in_the_population_in_A){ x1<-rbinom(1,read_depth,ratio_of_genotype_in_the_population_in_A) return(x1/read_depth) } ############################################################ #################################### for (key_individual in individual_analysis){ individual_number<-key_individual depth_data<-c() p_l_data_95<-c() p_h_data_95<-c() p_l_data_99<-c() p_h_data_99<-c() for (key_depth in depth_analysis){ depth_data<-c(depth_data,key_depth) depth<-key_depth data_of_delta_snp_index<-c() for(i in 1:reprication){ ##########gene_frequency###################### ratio_of_genotype_in_the_population_in_A<-individuals_genotype(key_individual) Snp_index_of_A<-snp_index(key_depth,ratio_of_genotype_in_the_population_in_A) ratio_of_genotype_in_the_population_in_B<-individuals_genotype(key_individual) Snp_index_of_B<-snp_index(key_depth,ratio_of_genotype_in_the_population_in_B) if(Snp_index_of_A >= filter_value | Snp_index_of_B >=filter_value){ delta_snp_index<-Snp_index_of_A-Snp_index_of_B data_of_delta_snp_index<-c(data_of_delta_snp_index,delta_snp_index) } ##########gene_frequency###################### } order_data_of_delta_snp_index<-sort(data_of_delta_snp_index) length_data_of_delta_snp_index<-length(data_of_delta_snp_index) ##########snp_index_probabirity_0.05###################### snp_cutoff_low_0.025<-order_data_of_delta_snp_index[floor(0.025*length_data_of_delta_snp_index)] snp_cutoff_up_0.975<-order_data_of_delta_snp_index[ceiling(0.975*length_data_of_delta_snp_index)] p_l_data_95<-c(p_l_data_95,snp_cutoff_low_0.025) p_h_data_95<-c(p_h_data_95,snp_cutoff_up_0.975) ##########snp_index_probabirity_0.05###################### ##########snp_index_probabirity_0.01###################### if (floor(0.005*length_data_of_delta_snp_index)>0){ snp_cutoff_low_0.005<-order_data_of_delta_snp_index[floor(0.005*length_data_of_delta_snp_index)] }else{ snp_cutoff_low_0.005<-order_data_of_delta_snp_index[1] } if (ceiling(0.995*length_data_of_delta_snp_index)<length_data_of_delta_snp_index){ snp_cutoff_up_0.995<-order_data_of_delta_snp_index[ceiling(0.995*length_data_of_delta_snp_index)] }else{ snp_cutoff_up_0.995<-order_data_of_delta_snp_index[length_data_of_delta_snp_index] } p_l_data_99<-c(p_l_data_99,snp_cutoff_low_0.005) p_h_data_99<-c(p_h_data_99,snp_cutoff_up_0.995) ##########snp_index_probabirity_0.01###################### } FINAL_DATA<-data.frame(DEPTH=depth_data,P_L_95=p_l_data_95,P_H_95=p_h_data_95,P_L_99=p_l_data_99,P_H_99=p_h_data_99) table_name<-paste("./",population_structure,"_",individual_number,"_individuals.txt",sep="") write.table(FINAL_DATA,table_name,sep="\t", quote=F, append=F,row.name=F) }
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