基于重叠分布均衡两个数据子集中的试验次数
Equalize number of trials in two data subsets based on overlapping distributions
我做了一个实验，其中受试者 (n = 14) 必须在键盘上响应屏幕上显示的刺激。在两种不同的情况下，他们可能会因为不正确的回答而受到罚款 --> 这两种情况在下文中称为处罚 4 和处罚 14。我在其他变量中测量了这些主题在任务中的决策时间 (DT)。
所有数据都存在于名为 'OutputTable_Online' 的 table 中。这是 OutputTable_Online 的样子（顶部）：
OutputTable_Online（底部）：
我想要做的是对每个 'Subjectnbr' 和每个 'Penalty' 的名为 "ampl_RFDI_sb"、"ampl_RAPB_sb"、"ampl_RADM_sb" 的变量求平均值作为函数的 'StimType'。如上图所示，我需要的所有信息都在 OutputTable_Online 中。这是我为此使用的代码：
Melt_OutputTable_Online <- melt(OutputTable_Online,
id.var = c('Subjectnbr', 'Penalty','Trial_Nbr',
'StimType'), measure.var = c('ampl_RFDI_sb', 'ampl_RAPB_sb', 'ampl_RADM_sb',
'ampl_LFDI_sb', 'ampl_LAPB_sb', 'ampl_LADM_sb', 'ampl_RFDI_ss',
'ampl_RAPB_ss', 'ampl_RADM_ss', 'ampl_LFDI_ss', 'ampl_LAPB_ss',
'ampl_LADM_ss', 'ampl_RFDI_sm', 'ampl_RAPB_sm', 'ampl_RADM_sm',
'ampl_LFDI_sm', 'ampl_LAPB_sm', 'ampl_LADM_sm', 'ampl_RFDI_sl',
'ampl_RAPB_sl', 'ampl_RADM_sl', 'ampl_LFDI_sl', 'ampl_LAPB_sl',
'ampl_LADM_sl'))
Cast_Melt_OutputTable_Online <- cast(Melt_OutputTable_Online,
Subjectnbr * Penalty ~ StimType * variable, mean)
这是这个过程的输出：
然而，正如预期的那样，当惩罚为 14 时，DT 分布向右移动，因为受试者等待响应的时间更长（他们更加谨慎）。因此，在 Penalty 14 条件下的平均 DT 比在 Penalty 4 条件下更长。
惩罚 4（黑色）和惩罚 14（绿色）的组级密度分布如图所示；垂直线表示组级平均值。 这是我用来绘制此图的代码：
OutputTable_Online_DT <- ddply(OutputTable_Online, "Penalty", summarise,
grp.mean=mean(DT))
Density_OutputTable_Online <- ggplot(OutputTable_Online, aes(x = DT,
fill=Penalty))
Density_OutputTable_Online <- Density_OutputTable_Online +
geom_density(aes(y = ..count.., group=Penalty), alpha=0.2)+
geom_vline(data=OutputTable_Online_DT,aes(xintercept=grp.mean,
color=Penalty),linetype="dashed", size=1)+ ggtitle("Density distributions
for both penalty conditions") + scale_color_manual(labels = c("P4", "P14"),
values = c("black", "green"))+ scale_fill_manual(labels = c("P4", "P14"),
values = c("black", "green"))+ labs(x = "DT (ms)", y = "Density of trials
(a.u.)")+ coord_cartesian(ylim=c(0, 3.5), xlim=c(0, 3000))
Density_OutputTable_Online
这是我的问题：当我如上所述对变量 "ampl_RFDI_sb"、"ampl_RAPB_sb"、"ampl_RADM_sb" 等进行平均时，得到的平均值实际上可能取决于 DT （即，因为 DT 在 2 个惩罚条件中是不同的）。我想摆脱这个混杂因素。为此，**我想在每个主题的两个惩罚条件下对平均 DT 进行均质化。我在想这样做的一种方法是 select 在每个受试者中，试验出现在上面绘制的相互重叠的分布部分中（即，绿色分布与黑色分布重叠的地方）。换句话说，当我在对变量 "ampl_RFDI_sb"、"ampl_RAPB_sb" 进行平均程序之前绘制 DT 的密度分布时，我希望在 OutputTable_Online 中的每个惩罚条件下都有相同的试验分布, "ampl_RADM_sb", 等等
实现此目的的一种方法是在每个 DT bin 中平衡惩罚 4 和惩罚 14 条件下的试验次数。但是，我不知道如何根据上述 OutputTable_Online 中存在的数据执行此操作。**
非常欢迎任何提示。
提前感谢您的帮助，
杰拉德
为了能够在上述两种惩罚条件下获得相同的 DT，我根据 DT 的 bins（使用子集函数）对 table 进行了子集化，并对每个 bin 中的试验次数进行了均质化每个条件都基于试验次数最少的条件。为此，我使用了 "sample" 函数。我使用 for 循环为 table 的每个主题都这样做了。这是代码：
# Loop for each Subject.
for (s in c(unique(DF_ampl_sb$Subjectnbr)))
{
tmp1<- subset(DF_ampl_sb,subset=Subjectnbr==s)
tmp2<- subset(tmp1,subset=DT>1&DT<=250)
tmp3<- subset(tmp1,subset=DT>250&DT<=500)
tmp4<- subset(tmp1,subset=DT>500&DT<=750)
tmp5<- subset(tmp1,subset=DT>750&DT<=1000)
tmp6<- subset(tmp1,subset=DT>1000&DT<=1250)
tmp7<- subset(tmp1,subset=DT>1250&DT<=1500)
tmp8<- subset(tmp1,subset=DT>1500&DT<=1750)
tmp9<- subset(tmp1,subset=DT>1750&DT<=2000)
tmp10<- subset(tmp1,subset=DT>2000&DT<=2250)
tmp11<- subset(tmp1,subset=DT>2250&DT<=2500)
tmp12<- subset(tmp1,subset=DT>2500&DT<=2750)
tmp13<- subset(tmp1,subset=DT>2750&DT<=3000)
tmp2_Penalty1<- subset(tmp2,subset=Penalty==1)
tmp2_Penalty2<- subset(tmp2,subset=Penalty==2)
tmp2_Penalty1<- tmp2_Penalty1[sample(nrow(tmp2_Penalty1), min(dim(tmp2_Penalty2)
[1],dim(tmp2_Penalty1)[1])), ]
tmp2_Penalty2<- tmp2_Penalty2[sample(nrow(tmp2_Penalty2), min(dim(tmp2_Penalty2)
[1],dim(tmp2_Penalty1)[1])), ]
tmp3_Penalty1<- subset(tmp3,subset=Penalty==1)
tmp3_Penalty2<- subset(tmp3,subset=Penalty==2)
tmp3_Penalty1<- tmp3_Penalty1[sample(nrow(tmp3_Penalty1), min(dim(tmp3_Penalty2)[1],dim(tmp3_Penalty1)[1])), ]
tmp3_Penalty2<- tmp3_Penalty2[sample(nrow(tmp3_Penalty2), min(dim(tmp3_Penalty2)[1],dim(tmp3_Penalty1)[1])), ]
tmp4_Penalty1<- subset(tmp4,subset=Penalty==1)
tmp4_Penalty2<- subset(tmp4,subset=Penalty==2)
tmp4_Penalty1<- tmp4_Penalty1[sample(nrow(tmp4_Penalty1), min(dim(tmp4_Penalty2)
[1],dim(tmp4_Penalty1)[1])), ]
tmp4_Penalty2<- tmp4_Penalty2[sample(nrow(tmp4_Penalty2), min(dim(tmp4_Penalty2)
[1],dim(tmp4_Penalty1)[1])), ]
tmp5_Penalty1<- subset(tmp5,subset=Penalty==1)
tmp5_Penalty2<- subset(tmp5,subset=Penalty==2)
tmp5_Penalty1<- tmp5_Penalty1[sample(nrow(tmp5_Penalty1), min(dim(tmp5_Penalty2)
[1],dim(tmp5_Penalty1)[1])), ]
tmp5_Penalty2<- tmp5_Penalty2[sample(nrow(tmp5_Penalty2), min(dim(tmp5_Penalty2)
[1],dim(tmp5_Penalty1)[1])), ]
tmp6_Penalty1<- subset(tmp6,subset=Penalty==1)
tmp6_Penalty2<- subset(tmp6,subset=Penalty==2)
tmp6_Penalty1<- tmp6_Penalty1[sample(nrow(tmp6_Penalty1), min(dim(tmp6_Penalty2)[1],dim(tmp6_Penalty1)[1])), ]
tmp6_Penalty2<- tmp6_Penalty2[sample(nrow(tmp6_Penalty2), min(dim(tmp6_Penalty2)[1],dim(tmp6_Penalty1)[1])), ]
tmp7_Penalty1<- subset(tmp7,subset=Penalty==1)
tmp7_Penalty2<- subset(tmp7,subset=Penalty==2)
tmp7_Penalty1<- tmp7_Penalty1[sample(nrow(tmp7_Penalty1), min(dim(tmp7_Penalty2)[1],dim(tmp7_Penalty1)[1])), ]
tmp7_Penalty2<- tmp7_Penalty2[sample(nrow(tmp7_Penalty2), min(dim(tmp7_Penalty2)[1],dim(tmp7_Penalty1)[1])), ]
tmp8_Penalty1<- subset(tmp8,subset=Penalty==1)
tmp8_Penalty2<- subset(tmp8,subset=Penalty==2)
tmp8_Penalty1<- tmp8_Penalty1[sample(nrow(tmp8_Penalty1), min(dim(tmp8_Penalty2)
[1],dim(tmp8_Penalty1)[1])), ]
tmp8_Penalty2<- tmp8_Penalty2[sample(nrow(tmp8_Penalty2), min(dim(tmp8_Penalty2)
[1],dim(tmp8_Penalty1)[1])), ]
tmp9_Penalty1<- subset(tmp9,subset=Penalty==1)
tmp9_Penalty2<- subset(tmp9,subset=Penalty==2)
tmp9_Penalty1<- tmp9_Penalty1[sample(nrow(tmp9_Penalty1), min(dim(tmp9_Penalty2)
[1],dim(tmp9_Penalty1)[1])), ]
tmp9_Penalty2<- tmp9_Penalty2[sample(nrow(tmp9_Penalty2), min(dim(tmp9_Penalty2)
[1],dim(tmp9_Penalty1)[1])), ]
tmp10_Penalty1<- subset(tmp10,subset=Penalty==1)
tmp10_Penalty2<- subset(tmp10,subset=Penalty==2)
tmp10_Penalty1<- tmp10_Penalty1[sample(nrow(tmp10_Penalty1), min(dim(tmp10_Penalty2)
[1],dim(tmp10_Penalty1)[1])), ]
tmp10_Penalty2<- tmp10_Penalty2[sample(nrow(tmp10_Penalty2), min(dim(tmp10_Penalty2)
[1],dim(tmp10_Penalty1)[1])), ]
tmp11_Penalty1<- subset(tmp11,subset=Penalty==1)
tmp11_Penalty2<- subset(tmp11,subset=Penalty==2)
tmp11_Penalty1<- tmp11_Penalty1[sample(nrow(tmp11_Penalty1), min(dim(tmp11_Penalty2)
[1],dim(tmp11_Penalty1)[1])), ]
tmp11_Penalty2<- tmp11_Penalty2[sample(nrow(tmp11_Penalty2), min(dim(tmp11_Penalty2)
[1],dim(tmp11_Penalty1)[1])), ]
tmp12_Penalty1<- subset(tmp12,subset=Penalty==1)
tmp12_Penalty2<- subset(tmp12,subset=Penalty==2)
tmp12_Penalty1<- tmp12_Penalty1[sample(nrow(tmp12_Penalty1), min(dim(tmp12_Penalty2)
[1],dim(tmp12_Penalty1)[1])), ]
tmp12_Penalty2<- tmp12_Penalty2[sample(nrow(tmp12_Penalty2), min(dim(tmp12_Penalty2)
[1],dim(tmp12_Penalty1)[1])), ]
tmp13_Penalty1<- subset(tmp13,subset=Penalty==1)
tmp13_Penalty2<- subset(tmp13,subset=Penalty==2)
tmp13_Penalty1<- tmp13_Penalty1[sample(nrow(tmp13_Penalty1), min(dim(tmp13_Penalty2)
[1],dim(tmp13_Penalty1)[1])), ]
tmp13_Penalty2<- tmp13_Penalty2[sample(nrow(tmp13_Penalty2), min(dim(tmp13_Penalty2)
[1],dim(tmp13_Penalty1)[1])), ]
# Add the content to the data frame (DF_rms_sb) by binding the data (row-binding).
DF_ampl_sb_tmp <- rbind (DF_ampl_sb_tmp,tmp2_Penalty1, tmp2_Penalty2, tmp3_Penalty1,
tmp3_Penalty2, tmp4_Penalty1, tmp4_Penalty2, tmp5_Penalty1, tmp5_Penalty2,
tmp6_Penalty1, tmp6_Penalty2, tmp7_Penalty1, tmp7_Penalty2, tmp8_Penalty1,
tmp8_Penalty2, tmp9_Penalty1, tmp9_Penalty2, tmp10_Penalty1, tmp10_Penalty2,
tmp11_Penalty1, tmp11_Penalty2, tmp12_Penalty1, tmp12_Penalty2,tmp13_Penalty1,
tmp13_Penalty2)
# Remove objects from a specified environment.
rm(tmp1, tmp2_Penalty1, tmp2_Penalty2, tmp3_Penalty1, tmp3_Penalty2, tmp4_Penalty1,
tmp4_Penalty2, tmp5_Penalty1, tmp5_Penalty2, tmp6_Penalty1, tmp6_Penalty2,
tmp7_Penalty1, tmp7_Penalty2, tmp8_Penalty1, tmp8_Penalty2, tmp9_Penalty1,
tmp9_Penalty2, tmp10_Penalty1, tmp10_Penalty2, tmp11_Penalty1, tmp11_Penalty2,
tmp12_Penalty1, tmp12_Penalty2, tmp13_Penalty1, tmp13_Penalty2)
}
}
dim(DF_ampl_sb_tmp)
DF_ampl_sb <- DF_ampl_sb_tmp
可能还有另一种子集 table 的方法，这里我在循环中手动定义了 bin（即从 tmp2 到 tmp13）。但是，它已经运行良好。这是我在使用代码之前获得的分布类型：enter image description here
之后，使用它：
enter image description here
杰拉德
我做了一个实验，其中受试者 (n = 14) 必须在键盘上响应屏幕上显示的刺激。在两种不同的情况下，他们可能会因为不正确的回答而受到罚款 --> 这两种情况在下文中称为处罚 4 和处罚 14。我在其他变量中测量了这些主题在任务中的决策时间 (DT)。
所有数据都存在于名为 'OutputTable_Online' 的 table 中。这是 OutputTable_Online 的样子（顶部）：
我想要做的是对每个 'Subjectnbr' 和每个 'Penalty' 的名为 "ampl_RFDI_sb"、"ampl_RAPB_sb"、"ampl_RADM_sb" 的变量求平均值作为函数的 'StimType'。如上图所示，我需要的所有信息都在 OutputTable_Online 中。这是我为此使用的代码：
Melt_OutputTable_Online <- melt(OutputTable_Online,
id.var = c('Subjectnbr', 'Penalty','Trial_Nbr',
'StimType'), measure.var = c('ampl_RFDI_sb', 'ampl_RAPB_sb', 'ampl_RADM_sb',
'ampl_LFDI_sb', 'ampl_LAPB_sb', 'ampl_LADM_sb', 'ampl_RFDI_ss',
'ampl_RAPB_ss', 'ampl_RADM_ss', 'ampl_LFDI_ss', 'ampl_LAPB_ss',
'ampl_LADM_ss', 'ampl_RFDI_sm', 'ampl_RAPB_sm', 'ampl_RADM_sm',
'ampl_LFDI_sm', 'ampl_LAPB_sm', 'ampl_LADM_sm', 'ampl_RFDI_sl',
'ampl_RAPB_sl', 'ampl_RADM_sl', 'ampl_LFDI_sl', 'ampl_LAPB_sl',
'ampl_LADM_sl'))
Cast_Melt_OutputTable_Online <- cast(Melt_OutputTable_Online,
Subjectnbr * Penalty ~ StimType * variable, mean)
这是这个过程的输出：
然而，正如预期的那样，当惩罚为 14 时，DT 分布向右移动，因为受试者等待响应的时间更长（他们更加谨慎）。因此，在 Penalty 14 条件下的平均 DT 比在 Penalty 4 条件下更长。
惩罚 4（黑色）和惩罚 14（绿色）的组级密度分布如图所示；垂直线表示组级平均值。
OutputTable_Online_DT <- ddply(OutputTable_Online, "Penalty", summarise,
grp.mean=mean(DT))
Density_OutputTable_Online <- ggplot(OutputTable_Online, aes(x = DT,
fill=Penalty))
Density_OutputTable_Online <- Density_OutputTable_Online +
geom_density(aes(y = ..count.., group=Penalty), alpha=0.2)+
geom_vline(data=OutputTable_Online_DT,aes(xintercept=grp.mean,
color=Penalty),linetype="dashed", size=1)+ ggtitle("Density distributions
for both penalty conditions") + scale_color_manual(labels = c("P4", "P14"),
values = c("black", "green"))+ scale_fill_manual(labels = c("P4", "P14"),
values = c("black", "green"))+ labs(x = "DT (ms)", y = "Density of trials
(a.u.)")+ coord_cartesian(ylim=c(0, 3.5), xlim=c(0, 3000))
Density_OutputTable_Online
这是我的问题：当我如上所述对变量 "ampl_RFDI_sb"、"ampl_RAPB_sb"、"ampl_RADM_sb" 等进行平均时，得到的平均值实际上可能取决于 DT （即，因为 DT 在 2 个惩罚条件中是不同的）。我想摆脱这个混杂因素。为此，**我想在每个主题的两个惩罚条件下对平均 DT 进行均质化。我在想这样做的一种方法是 select 在每个受试者中，试验出现在上面绘制的相互重叠的分布部分中（即，绿色分布与黑色分布重叠的地方）。换句话说，当我在对变量 "ampl_RFDI_sb"、"ampl_RAPB_sb" 进行平均程序之前绘制 DT 的密度分布时，我希望在 OutputTable_Online 中的每个惩罚条件下都有相同的试验分布, "ampl_RADM_sb", 等等
实现此目的的一种方法是在每个 DT bin 中平衡惩罚 4 和惩罚 14 条件下的试验次数。但是，我不知道如何根据上述 OutputTable_Online 中存在的数据执行此操作。**
非常欢迎任何提示。
提前感谢您的帮助，
杰拉德
为了能够在上述两种惩罚条件下获得相同的 DT，我根据 DT 的 bins（使用子集函数）对 table 进行了子集化，并对每个 bin 中的试验次数进行了均质化每个条件都基于试验次数最少的条件。为此，我使用了 "sample" 函数。我使用 for 循环为 table 的每个主题都这样做了。这是代码：
# Loop for each Subject.
for (s in c(unique(DF_ampl_sb$Subjectnbr)))
{
tmp1<- subset(DF_ampl_sb,subset=Subjectnbr==s)
tmp2<- subset(tmp1,subset=DT>1&DT<=250)
tmp3<- subset(tmp1,subset=DT>250&DT<=500)
tmp4<- subset(tmp1,subset=DT>500&DT<=750)
tmp5<- subset(tmp1,subset=DT>750&DT<=1000)
tmp6<- subset(tmp1,subset=DT>1000&DT<=1250)
tmp7<- subset(tmp1,subset=DT>1250&DT<=1500)
tmp8<- subset(tmp1,subset=DT>1500&DT<=1750)
tmp9<- subset(tmp1,subset=DT>1750&DT<=2000)
tmp10<- subset(tmp1,subset=DT>2000&DT<=2250)
tmp11<- subset(tmp1,subset=DT>2250&DT<=2500)
tmp12<- subset(tmp1,subset=DT>2500&DT<=2750)
tmp13<- subset(tmp1,subset=DT>2750&DT<=3000)
tmp2_Penalty1<- subset(tmp2,subset=Penalty==1)
tmp2_Penalty2<- subset(tmp2,subset=Penalty==2)
tmp2_Penalty1<- tmp2_Penalty1[sample(nrow(tmp2_Penalty1), min(dim(tmp2_Penalty2)
[1],dim(tmp2_Penalty1)[1])), ]
tmp2_Penalty2<- tmp2_Penalty2[sample(nrow(tmp2_Penalty2), min(dim(tmp2_Penalty2)
[1],dim(tmp2_Penalty1)[1])), ]
tmp3_Penalty1<- subset(tmp3,subset=Penalty==1)
tmp3_Penalty2<- subset(tmp3,subset=Penalty==2)
tmp3_Penalty1<- tmp3_Penalty1[sample(nrow(tmp3_Penalty1), min(dim(tmp3_Penalty2)[1],dim(tmp3_Penalty1)[1])), ]
tmp3_Penalty2<- tmp3_Penalty2[sample(nrow(tmp3_Penalty2), min(dim(tmp3_Penalty2)[1],dim(tmp3_Penalty1)[1])), ]
tmp4_Penalty1<- subset(tmp4,subset=Penalty==1)
tmp4_Penalty2<- subset(tmp4,subset=Penalty==2)
tmp4_Penalty1<- tmp4_Penalty1[sample(nrow(tmp4_Penalty1), min(dim(tmp4_Penalty2)
[1],dim(tmp4_Penalty1)[1])), ]
tmp4_Penalty2<- tmp4_Penalty2[sample(nrow(tmp4_Penalty2), min(dim(tmp4_Penalty2)
[1],dim(tmp4_Penalty1)[1])), ]
tmp5_Penalty1<- subset(tmp5,subset=Penalty==1)
tmp5_Penalty2<- subset(tmp5,subset=Penalty==2)
tmp5_Penalty1<- tmp5_Penalty1[sample(nrow(tmp5_Penalty1), min(dim(tmp5_Penalty2)
[1],dim(tmp5_Penalty1)[1])), ]
tmp5_Penalty2<- tmp5_Penalty2[sample(nrow(tmp5_Penalty2), min(dim(tmp5_Penalty2)
[1],dim(tmp5_Penalty1)[1])), ]
tmp6_Penalty1<- subset(tmp6,subset=Penalty==1)
tmp6_Penalty2<- subset(tmp6,subset=Penalty==2)
tmp6_Penalty1<- tmp6_Penalty1[sample(nrow(tmp6_Penalty1), min(dim(tmp6_Penalty2)[1],dim(tmp6_Penalty1)[1])), ]
tmp6_Penalty2<- tmp6_Penalty2[sample(nrow(tmp6_Penalty2), min(dim(tmp6_Penalty2)[1],dim(tmp6_Penalty1)[1])), ]
tmp7_Penalty1<- subset(tmp7,subset=Penalty==1)
tmp7_Penalty2<- subset(tmp7,subset=Penalty==2)
tmp7_Penalty1<- tmp7_Penalty1[sample(nrow(tmp7_Penalty1), min(dim(tmp7_Penalty2)[1],dim(tmp7_Penalty1)[1])), ]
tmp7_Penalty2<- tmp7_Penalty2[sample(nrow(tmp7_Penalty2), min(dim(tmp7_Penalty2)[1],dim(tmp7_Penalty1)[1])), ]
tmp8_Penalty1<- subset(tmp8,subset=Penalty==1)
tmp8_Penalty2<- subset(tmp8,subset=Penalty==2)
tmp8_Penalty1<- tmp8_Penalty1[sample(nrow(tmp8_Penalty1), min(dim(tmp8_Penalty2)
[1],dim(tmp8_Penalty1)[1])), ]
tmp8_Penalty2<- tmp8_Penalty2[sample(nrow(tmp8_Penalty2), min(dim(tmp8_Penalty2)
[1],dim(tmp8_Penalty1)[1])), ]
tmp9_Penalty1<- subset(tmp9,subset=Penalty==1)
tmp9_Penalty2<- subset(tmp9,subset=Penalty==2)
tmp9_Penalty1<- tmp9_Penalty1[sample(nrow(tmp9_Penalty1), min(dim(tmp9_Penalty2)
[1],dim(tmp9_Penalty1)[1])), ]
tmp9_Penalty2<- tmp9_Penalty2[sample(nrow(tmp9_Penalty2), min(dim(tmp9_Penalty2)
[1],dim(tmp9_Penalty1)[1])), ]
tmp10_Penalty1<- subset(tmp10,subset=Penalty==1)
tmp10_Penalty2<- subset(tmp10,subset=Penalty==2)
tmp10_Penalty1<- tmp10_Penalty1[sample(nrow(tmp10_Penalty1), min(dim(tmp10_Penalty2)
[1],dim(tmp10_Penalty1)[1])), ]
tmp10_Penalty2<- tmp10_Penalty2[sample(nrow(tmp10_Penalty2), min(dim(tmp10_Penalty2)
[1],dim(tmp10_Penalty1)[1])), ]
tmp11_Penalty1<- subset(tmp11,subset=Penalty==1)
tmp11_Penalty2<- subset(tmp11,subset=Penalty==2)
tmp11_Penalty1<- tmp11_Penalty1[sample(nrow(tmp11_Penalty1), min(dim(tmp11_Penalty2)
[1],dim(tmp11_Penalty1)[1])), ]
tmp11_Penalty2<- tmp11_Penalty2[sample(nrow(tmp11_Penalty2), min(dim(tmp11_Penalty2)
[1],dim(tmp11_Penalty1)[1])), ]
tmp12_Penalty1<- subset(tmp12,subset=Penalty==1)
tmp12_Penalty2<- subset(tmp12,subset=Penalty==2)
tmp12_Penalty1<- tmp12_Penalty1[sample(nrow(tmp12_Penalty1), min(dim(tmp12_Penalty2)
[1],dim(tmp12_Penalty1)[1])), ]
tmp12_Penalty2<- tmp12_Penalty2[sample(nrow(tmp12_Penalty2), min(dim(tmp12_Penalty2)
[1],dim(tmp12_Penalty1)[1])), ]
tmp13_Penalty1<- subset(tmp13,subset=Penalty==1)
tmp13_Penalty2<- subset(tmp13,subset=Penalty==2)
tmp13_Penalty1<- tmp13_Penalty1[sample(nrow(tmp13_Penalty1), min(dim(tmp13_Penalty2)
[1],dim(tmp13_Penalty1)[1])), ]
tmp13_Penalty2<- tmp13_Penalty2[sample(nrow(tmp13_Penalty2), min(dim(tmp13_Penalty2)
[1],dim(tmp13_Penalty1)[1])), ]
# Add the content to the data frame (DF_rms_sb) by binding the data (row-binding).
DF_ampl_sb_tmp <- rbind (DF_ampl_sb_tmp,tmp2_Penalty1, tmp2_Penalty2, tmp3_Penalty1,
tmp3_Penalty2, tmp4_Penalty1, tmp4_Penalty2, tmp5_Penalty1, tmp5_Penalty2,
tmp6_Penalty1, tmp6_Penalty2, tmp7_Penalty1, tmp7_Penalty2, tmp8_Penalty1,
tmp8_Penalty2, tmp9_Penalty1, tmp9_Penalty2, tmp10_Penalty1, tmp10_Penalty2,
tmp11_Penalty1, tmp11_Penalty2, tmp12_Penalty1, tmp12_Penalty2,tmp13_Penalty1,
tmp13_Penalty2)
# Remove objects from a specified environment.
rm(tmp1, tmp2_Penalty1, tmp2_Penalty2, tmp3_Penalty1, tmp3_Penalty2, tmp4_Penalty1,
tmp4_Penalty2, tmp5_Penalty1, tmp5_Penalty2, tmp6_Penalty1, tmp6_Penalty2,
tmp7_Penalty1, tmp7_Penalty2, tmp8_Penalty1, tmp8_Penalty2, tmp9_Penalty1,
tmp9_Penalty2, tmp10_Penalty1, tmp10_Penalty2, tmp11_Penalty1, tmp11_Penalty2,
tmp12_Penalty1, tmp12_Penalty2, tmp13_Penalty1, tmp13_Penalty2)
}
}
dim(DF_ampl_sb_tmp)
DF_ampl_sb <- DF_ampl_sb_tmp
可能还有另一种子集 table 的方法，这里我在循环中手动定义了 bin（即从 tmp2 到 tmp13）。但是，它已经运行良好。这是我在使用代码之前获得的分布类型：enter image description here
之后，使用它： enter image description here
杰拉德