[英]How to use a loop to work out a model through several variables in R
这是我的提升数据
new=structure(list(B1 = c(6914L, 6914L, 6914L, 6958L, 6958L, 6958L,
6958L, 6914L, 6914L, 6914L, 6914L, 5672L, 6014L, 6014L, 6014L,
6014L, 6014L, 6958L, 6958L, 6958L, 6958L, 6958L, 6958L, 6914L,
6914L, 6914L, 6914L, 6092L, 6092L, 6092L, 6092L, 6092L, 6239L,
6239L, 6239L, 6239L, 6239L, 6239L, 6615L, 6615L, 6615L, 6615L,
6615L, 7038L, 7038L, 7038L, 7038L, 7038L, 7038L, 6602L, 8136L,
8136L, 7369L, 8136L, 8136L, 7369L, 8136L, 8136L, 8136L, 7369L,
7369L, 8136L, 8136L, 8136L, 8136L, 7369L, 7369L, 8136L, 8136L,
8136L, 8136L, 8317L, 8317L, 8317L, 8821L, 8821L, 8821L, 8821L,
8821L, 8317L, 8317L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L,
8317L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L, 8821L,
8821L, 8821L, 8821L, 9245L), B2 = c(5560L, 5380L, 5644L, 5088L,
5280L, 5200L, 5472L, 5568L, 5560L, 5424L, 5404L, 4784L, 4696L,
4820L, 4588L, 4544L, 4452L, 4716L, 5048L, 5236L, 5416L, 5584L,
5824L, 5800L, 5932L, 5980L, 6112L, 4796L, 4860L, 5396L, 5900L,
5968L, 5968L, 5776L, 5440L, 5248L, 4884L, 4760L, 4796L, 4860L,
4776L, 4664L, 4716L, 4952L, 5168L, 5316L, 5548L, 5768L, 5900L,
5948L, 5796L, 5756L, 5912L, 5956L, 6000L, 6196L, 6044L, 6164L,
6268L, 6588L, 6508L, 6460L, 6608L, 6592L, 6600L, 7100L, 7016L,
6988L, 6952L, 6800L, 6644L, 7308L, 7340L, 7528L, 7492L, 7304L,
6928L, 6748L, 6764L, 7492L, 7648L, 7580L, 7416L, 7108L, 6864L,
7056L, 7164L, 7744L, 7720L, 7360L, 7188L, 7204L, 7280L, 7236L,
7520L, 7352L, 7352L, 7376L, 7320L, 7428L), B3 = c(4768L, 4840L,
4936L, 4320L, 4388L, 4572L, 4640L, 4704L, 4696L, 4488L, 4396L,
4002L, 4030L, 3960L, 3684L, 3680L, 3896L, 4212L, 4364L, 4508L,
4732L, 4896L, 4848L, 4872L, 4960L, 5052L, 4848L, 4308L, 4800L,
5216L, 5224L, 5248L, 5136L, 4720L, 4428L, 4120L, 3918L, 4052L,
4058L, 3806L, 3802L, 3930L, 4092L, 4324L, 4476L, 4604L, 4768L,
4980L, 5080L, 5288L, 4840L, 4936L, 5096L, 5040L, 5096L, 5292L,
5280L, 5360L, 5480L, 5584L, 5528L, 5524L, 5700L, 5736L, 5732L,
6136L, 5980L, 5884L, 5904L, 5820L, 5864L, 6488L, 6572L, 6476L,
6256L, 6024L, 5844L, 5884L, 6100L, 6684L, 6596L, 6376L, 6188L,
5952L, 6044L, 6212L, 6268L, 6668L, 6484L, 6336L, 6164L, 6332L,
6432L, 6396L, 6592L, 6548L, 6500L, 6464L, 6460L, 7008L), B4 = c(4960L,
4964L, 4540L, 4164L, 4412L, 4608L, 4628L, 4588L, 4416L, 4312L,
4372L, 3806L, 3652L, 3570L, 3480L, 3708L, 3886L, 4188L, 4284L,
4344L, 4704L, 4776L, 4772L, 4980L, 5000L, 4852L, 4508L, 4916L,
5356L, 5400L, 5268L, 5156L, 4620L, 4324L, 4016L, 3884L, 3854L,
3854L, 3770L, 3562L, 3708L, 3854L, 4084L, 4228L, 4440L, 4532L,
4784L, 5008L, 5292L, 5464L, 4868L, 4996L, 4908L, 4932L, 5060L,
5136L, 5280L, 5444L, 5492L, 5500L, 5560L, 5604L, 5704L, 5660L,
5716L, 5892L, 5844L, 5796L, 5752L, 5816L, 5892L, 6500L, 6488L,
6212L, 5928L, 5796L, 5876L, 6084L, 6284L, 6660L, 6424L, 6088L,
6004L, 6044L, 6268L, 6336L, 6664L, 6500L, 6308L, 6152L, 6288L,
6424L, 6376L, 6860L, 6464L, 6500L, 6508L, 6468L, 7144L, 7652L
), B5 = c(5554L, 5554L, 4782L, 4736L, 4736L, 5018L, 5018L, 4968L,
4968L, 4677L, 4677L, 3814L, 3667L, 3667L, 3594L, 3975L, 3975L,
4348L, 4348L, 4736L, 4736L, 5018L, 5018L, 4968L, 4968L, 4677L,
4677L, 4930L, 5524L, 5524L, 5229L, 5229L, 4424L, 4424L, 4113L,
4113L, 4069L, 4069L, 3857L, 3932L, 3932L, 4228L, 4228L, 4591L,
4591L, 4918L, 4918L, 5324L, 5324L, 5543L, 5327L, 5327L, 5301L,
5471L, 5471L, 5301L, 5471L, 5471L, 5846L, 5977L, 5977L, 5899L,
5899L, 6099L, 6099L, 5977L, 5977L, 5899L, 5899L, 6099L, 6099L,
6857L, 6517L, 6517L, 6220L, 6220L, 6418L, 6418L, 6969L, 6517L,
6517L, 6220L, 6220L, 6418L, 6418L, 6969L, 6969L, 6861L, 6581L,
6581L, 6729L, 6729L, 7265L, 7265L, 6581L, 6729L, 6729L, 7265L,
7265L, 8025L), B6 = c(5249L, 5249L, 4428L, 4553L, 4553L, 4832L,
4832L, 4741L, 4741L, 4428L, 4428L, 3736L, 3464L, 3464L, 3509L,
3894L, 3894L, 4270L, 4270L, 4553L, 4553L, 4832L, 4832L, 4741L,
4741L, 4428L, 4428L, 5030L, 5441L, 5441L, 4926L, 4926L, 4146L,
4146L, 3907L, 3907L, 3910L, 3910L, 3721L, 3831L, 3831L, 4201L,
4201L, 4509L, 4509L, 4871L, 4871L, 5235L, 5235L, 5217L, 5207L,
5207L, 5087L, 5290L, 5290L, 5087L, 5290L, 5290L, 5777L, 5721L,
5721L, 5746L, 5746L, 5982L, 5982L, 5721L, 5721L, 5746L, 5746L,
5982L, 5982L, 6504L, 6116L, 6116L, 5946L, 5946L, 6257L, 6257L,
6916L, 6116L, 6116L, 5946L, 5946L, 6257L, 6257L, 6916L, 6916L,
6407L, 6293L, 6293L, 6545L, 6545L, 7197L, 7197L, 6293L, 6545L,
6545L, 7197L, 7197L, 7998L), B7 = c(4893L, 4893L, 4138L, 4527L,
4527L, 4681L, 4681L, 4505L, 4505L, 4170L, 4170L, 3629L, 3388L,
3388L, 3545L, 3982L, 3982L, 4288L, 4288L, 4527L, 4527L, 4681L,
4681L, 4505L, 4505L, 4170L, 4170L, 5127L, 5268L, 5268L, 4703L,
4703L, 3996L, 3996L, 3775L, 3775L, 3713L, 3713L, 3594L, 3813L,
3813L, 4166L, 4166L, 4462L, 4462L, 4836L, 4836L, 5277L, 5277L,
4910L, 5235L, 5235L, 5001L, 5241L, 5241L, 5001L, 5241L, 5241L,
5688L, 5539L, 5539L, 5599L, 5599L, 5988L, 5988L, 5539L, 5539L,
5599L, 5599L, 5988L, 5988L, 6278L, 5864L, 5864L, 5931L, 5931L,
6177L, 6177L, 6896L, 5864L, 5864L, 5931L, 5931L, 6177L, 6177L,
6896L, 6896L, 6212L, 6159L, 6159L, 6382L, 6382L, 7383L, 7383L,
6159L, 6382L, 6382L, 7383L, 7383L, 7856L), B8 = c(4836L, 4840L,
5044L, 4074L, 4236L, 4404L, 4592L, 4668L, 4796L, 4628L, 4632L,
3914L, 3896L, 3796L, 3580L, 3598L, 3596L, 3830L, 4096L, 4320L,
4460L, 4648L, 4904L, 4980L, 4940L, 5148L, 5180L, 4164L, 4628L,
5304L, 5512L, 5592L, 5500L, 5216L, 4732L, 4380L, 4036L, 4008L,
3994L, 3784L, 3660L, 3650L, 3794L, 4018L, 4212L, 4296L, 4516L,
4648L, 4908L, 5148L, 4876L, 4828L, 4936L, 4992L, 5052L, 5236L,
5236L, 5336L, 5380L, 5708L, 5648L, 5624L, 5580L, 5724L, 5796L,
6280L, 6156L, 6012L, 5876L, 5864L, 5868L, 6488L, 6636L, 6752L,
6612L, 6268L, 5924L, 5968L, 6092L, 6752L, 6876L, 6764L, 6484L,
6176L, 6108L, 6300L, 6460L, 6924L, 6724L, 6528L, 6484L, 6352L,
6500L, 6600L, 6784L, 6876L, 6676L, 6616L, 6732L, 6896L), B8A = c(4679L,
4679L, 4098L, 4524L, 4524L, 4643L, 4643L, 4460L, 4460L, 3987L,
3987L, 3413L, 3294L, 3294L, 3490L, 3840L, 3840L, 4140L, 4140L,
4524L, 4524L, 4643L, 4643L, 4460L, 4460L, 3987L, 3987L, 5232L,
5152L, 5152L, 4421L, 4421L, 3863L, 3863L, 3697L, 3697L, 3561L,
3561L, 3558L, 3788L, 3788L, 4110L, 4110L, 4493L, 4493L, 4894L,
4894L, 5032L, 5032L, 4606L, 5209L, 5209L, 4889L, 5233L, 5233L,
4889L, 5233L, 5233L, 5787L, 5324L, 5324L, 5492L, 5492L, 6018L,
6018L, 5324L, 5324L, 5492L, 5492L, 6018L, 6018L, 5872L, 5544L,
5544L, 5876L, 5876L, 6279L, 6279L, 6963L, 5544L, 5544L, 5876L,
5876L, 6279L, 6279L, 6963L, 6963L, 6134L, 6187L, 6187L, 6547L,
6547L, 7280L, 7280L, 6187L, 6547L, 6547L, 7280L, 7280L, 7968L
), B9 = c(6752L, 6752L, 6752L, 7098L, 7098L, 7098L, 7098L, 6752L,
6752L, 6752L, 6752L, 4997L, 5626L, 5626L, 5626L, 5626L, 5626L,
7098L, 7098L, 7098L, 7098L, 7098L, 7098L, 6752L, 6752L, 6752L,
6752L, 5698L, 5698L, 5698L, 5698L, 5698L, 5408L, 5408L, 5408L,
5408L, 5408L, 5408L, 5685L, 5685L, 5685L, 5685L, 5685L, 6352L,
6352L, 6352L, 6352L, 6352L, 6352L, 5794L, 8407L, 8407L, 7048L,
8407L, 8407L, 7048L, 8407L, 8407L, 8407L, 7048L, 7048L, 8407L,
8407L, 8407L, 8407L, 7048L, 7048L, 8407L, 8407L, 8407L, 8407L,
8487L, 8487L, 8487L, 9610L, 9610L, 9610L, 9610L, 9610L, 8487L,
8487L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 8487L, 9610L,
9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L, 9610L,
9610L, 10128L), B10 = c(4170L, 4170L, 3407L, 3301L, 3301L, 3612L,
3612L, 3600L, 3600L, 3352L, 3352L, 2502L, 2388L, 2388L, 2403L,
2659L, 2659L, 2979L, 2979L, 3301L, 3301L, 3612L, 3612L, 3600L,
3600L, 3352L, 3352L, 3856L, 4192L, 4192L, 3840L, 3840L, 3187L,
3187L, 2782L, 2782L, 2634L, 2634L, 2501L, 2562L, 2562L, 2792L,
2792L, 3068L, 3068L, 3405L, 3405L, 3816L, 3816L, 3858L, 3343L,
3343L, 3188L, 3438L, 3438L, 3188L, 3438L, 3438L, 3774L, 3543L,
3543L, 3653L, 3653L, 3934L, 3934L, 3543L, 3543L, 3653L, 3653L,
3934L, 3934L, 4224L, 3999L, 3999L, 3881L, 3881L, 4162L, 4162L,
4724L, 3999L, 3999L, 3881L, 3881L, 4162L, 4162L, 4724L, 4724L,
4293L, 4161L, 4161L, 4380L, 4380L, 5052L, 5052L, 4161L, 4380L,
4380L, 5052L, 5052L, 5756L), B11 = c(3124L, 3124L, 2514L, 2969L,
2969L, 3137L, 3137L, 2922L, 2922L, 2487L, 2487L, 1850L, 1822L,
1822L, 2014L, 2309L, 2309L, 2600L, 2600L, 2969L, 2969L, 3137L,
3137L, 2922L, 2922L, 2487L, 2487L, 3753L, 3535L, 3535L, 2823L,
2823L, 2296L, 2296L, 2151L, 2151L, 1974L, 1974L, 1924L, 2100L,
2100L, 2386L, 2386L, 2731L, 2731L, 3087L, 3087L, 3380L, 3380L,
3006L, 2740L, 2740L, 2443L, 2800L, 2800L, 2443L, 2800L, 2800L,
3228L, 2669L, 2669L, 2935L, 2935L, 3338L, 3338L, 2669L, 2669L,
2935L, 2935L, 3338L, 3338L, 3049L, 2893L, 2893L, 3087L, 3087L,
3550L, 3550L, 4235L, 2893L, 2893L, 3087L, 3087L, 3550L, 3550L,
4235L, 4235L, 3184L, 3242L, 3242L, 3774L, 3774L, 4487L, 4487L,
3242L, 3774L, 3774L, 4487L, 4487L, 5217L), B = c(20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L), E = c(20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L), C = c(20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L), OC = c(30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L
), OLS = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA, -100L
))
具有相关变量的五列（B、E、C、OC、OLS）。 我必须执行 5 个增强模型。 简单我可以做到。 拿这个脚本，只需更改目标变量 5 次（任何 dep var ~.）
# train GBM model
gbm.fit.final109 <- gbm(
formula = Value ~ .,
distribution = "gaussian",
data = new,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
但是，是否可以创建某种循环，将列表中带有依赖变量的列替换到脚本中？ 例如(mylistwithdepended_vars=c((B,E,C,OC,OLS) )，IEwe 取每个因变量，一旦它在循环中计算出来，我们就从列表中替换另一个，直到通过这个循环我们计算出所有列。
我该怎么做。 作为 output 我只想要 5 个型号
gbm.fit.finalB
gbm.fit.finalE
gbm.fit.finalC
gbm.fit.finalOC
gbm.fit.finalOLS
始终感谢您的帮助，谢谢。
您是否正在寻找类似以下内容的内容？
library(gbm)
#> Loaded gbm 2.1.8
dependend <- c("B", "E", "C", "OC", "OLS")
mylistwithdepended_vars <- mget(dependend, envir = as.environment(new))
fun <- function(Value){
gbm.fit.final109 <- gbm(
formula = Value ~ .,
distribution = "gaussian",
data = new,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
}
gbm_models <- Map(fun, mylistwithdepended_vars)
old_par <- par(mfrow = c(1, 5))
mapply(summary, gbm_models, main = names(gbm_models))
#> B E C OC OLS
#> var character,17 character,17 character,17 character,17 character,17
#> rel.inf numeric,17 numeric,17 numeric,17 numeric,17 numeric,17
par(old_par)
由reprex package (v2.0.1) 创建于 2022-03-11
接受的解决方案适合一组神秘的 5 个模型（而不是 OP 考虑的模型），因为不清楚公式Value ~. .
最好明确说明模型、响应和预测变量是什么。
library("gbm")
#> Loaded gbm 2.1.8
library("tidyverse")
data <- tibble(new)
# Let's define a function to fit a model ...
model_fn <- function(formula, data) {
# Generalized Boosted Regression
gbm(
formula = formula,
distribution = "gaussian",
data = data,
n.trees = 483,
interaction.depth = 5,
shrinkage = 0.1,
n.minobsinnode = 5,
bag.fraction = .65,
train.fraction = 1,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
}
# ... as well as a function to extract information from the fitted model.
summary_fn <- function(fit) {
# Generalized Boosted Regression
summary(fit, plotit = FALSE) %>%
as_tibble()
}
model_summaries <-
data %>%
pivot_longer(
# We want to fit 5 models, one for each of the following responses:
c(B, C, E, OC, OLS),
names_to = "response",
values_to = "y"
) %>%
nest_by(response) %>%
mutate(
fit = list(model_fn(
# For each response we want to use variables B1 through B11 as predictors.
y ~ B1 + B2 + B3 + B4 + B5 + B6 + B7 + B8 + B9 + B10 + B11,
data = data
))
) %>%
summarise(
summary_fn(fit)
)
#> `summarise()` has grouped output by 'response'. You can override using the
#> `.groups` argument.
model_summaries
#> # A tibble: 55 × 3
#> # Groups: response [5]
#> response var rel.inf
#> <chr> <chr> <dbl>
#> 1 B B1 1 e+ 2
#> 2 B B2 4.11e-29
#> 3 B B8 1.17e-29
#> 4 B B3 8.67e-30
#> 5 B B4 6.88e-30
#> 6 B B11 5.43e-30
#> 7 B B5 2.85e-30
#> 8 B B7 6.14e-31
#> 9 B B10 3.82e-31
#> 10 B B6 1.84e-31
#> # … with 45 more rows
附言。 gbm 模型非常适合您的数据。 您确定响应是高斯响应吗？ 很容易检查每个响应是否采用两个唯一值，并且每个值都被恰好观察了 50 次。
data %>%
pivot_longer(
# We want to fit 5 models, one for each of the following responses:
c(B, C, E, OC, OLS),
names_to = "response",
values_to = "y"
) %>%
count(
response, y
)
#> # A tibble: 10 × 3
#> response y n
#> <chr> <int> <int>
#> 1 B 20 50
#> 2 B 50 50
#> 3 C 10 50
#> 4 C 20 50
#> 5 E 20 50
#> 6 E 30 50
#> 7 OC 10 50
#> 8 OC 30 50
#> 9 OLS 0 50
#> 10 OLS 10 50
由reprex package (v2.0.1) 创建于 2022-03-12
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