r - 将数据转换为选择模型数据结构
问题描述
我列出了 10 种产品L5num并且我有 PANID`s。我正在尝试为多项逻辑模型创建选择模型，但我正在尝试首先进行一些数据准备。
数据如下所示：
PANID WEEK L5num
1669 3174607 2008-09-08 9
21356 3362137 2011-08-01 7
15540 3174607 2010-10-04 9
5869 3150938 2009-07-13 3
18401 3174607 2011-02-28 9
10616 3174607 2010-03-22 8
我正在尝试构建我的数据，以便 ID3174607选择产品 9 等。我正在尝试将数据以类似于以下的格式放置：
PANID Product
3174607 0
3174607 0
3174607 0
3174607 0
3174607 0
3174607 0
3174607 0
3174607 0
3174607 0
3174607 1
3174607 0
…
Next PANID
所以这个人从 10 种产品中选择了 9 种产品。
希望这是有道理的，任何帮助将不胜感激
编辑：
感谢所有的回复，我真的很感激。我已经使用了你所有的答案，它们都有效。我对我的原始帖子确实有一点“补充”。我希望reshape我的数据。我的数据有 x 个变量，我在原始帖子中没有讨论。
数据看起来像
PANID WEEK L5num X1 X2 X3 X4...
是否可以应用您建议的方法，同时还包括 x 变量？所以输出将与当前建议的相同，但在 RHS EDIT 2 上有 x 变量：
我用“x”变量添加相同的数据
structure(list(PANID = c(3362137L, 3109124L, 3345264L, 3174607L,
3174607L, 3169102L, 3322008L, 3174607L, 3800607L, 3368878L, 3174607L,
3174607L, 3340018L, 3109124L, 3174607L, 3338756L, 3842948L, 3107920L,
3174607L, 3369801L, 3174607L, 3174607L, 3330266L, 3107920L, 3831099L,
3837682L, 3174607L, 3174607L, 3174607L, 3174607L, 3174607L, 3174607L,
3174607L, 3174607L, 3330266L, 3174607L, 3340299L, 3368936L, 3317974L,
3174607L, 3322107L, 3174607L, 3164467L, 3174607L, 3140616L, 3314419L,
3174607L, 3369801L, 3174607L, 3375709L, 3361931L, 3174607L, 3160101L,
3174607L, 3174607L, 3369801L, 3117754L, 3174607L, 3174607L, 3174607L,
3174607L, 3114728L, 3174607L, 3369801L, 3109124L, 3379453L, 3819300L,
3814491L, 3340547L, 3109124L, 3821025L, 3174607L, 3174607L, 3147447L,
3174607L, 3317578L, 3341081L, 3140418L, 3342014L, 3174607L, 3174607L,
3174607L, 3174607L, 3174607L, 3174607L, 3309476L, 3174607L, 3161760L,
3174607L, 3181172L, 3174607L, 3174607L, 3164467L, 3177436L, 3174607L,
3814533L, 3174607L, 3107334L, 3174607L, 3174607L, 3174607L, 3174607L,
3174607L, 3174607L, 3159525L, 3107334L, 3153767L, 3362137L, 3174607L,
3174607L, 3174607L, 3174607L, 3308189L, 3174607L, 3300483L, 3174607L,
3174607L, 3174607L, 3341206L, 3301655L, 3343145L, 3174607L, 3821710L,
3348904L, 3174607L, 3174607L, 3174607L, 3174607L, 3338764L, 3176552L,
3372581L, 3822940L, 3340018L, 3338764L, 3174607L, 3174607L, 3174607L,
3174607L, 3174607L, 3164467L, 3174607L, 3128389L, 3362137L, 3153239L,
3174607L, 3174607L, 3162446L, 3328948L, 3340299L, 3174607L, 3164467L,
3341206L, 3832469L, 3396135L, 3308577L, 3174607L, 3174607L, 3174607L,
3174607L, 3174607L, 3174607L, 3174607L, 3174607L, 3106344L, 3141044L,
3174607L, 3161760L, 3402438L, 3800607L, 3174607L, 3174607L, 3339374L,
3330613L, 3820431L, 3174607L, 3174607L, 3174607L, 3362137L, 3174607L,
3301655L, 3107334L, 3174607L, 3164467L, 3817668L, 3174607L, 3114298L,
3174607L, 3174607L, 3174607L, 3174607L, 3174607L, 3340018L, 3174607L,
3164467L, 3174607L, 3154336L, 3802355L, 3340018L, 3174607L, 3174607L,
3174607L, 3402263L, 3174607L, 3174607L, 3174607L, 3173872L, 3339366L,
3837740L, 3174607L, 3174607L, 3309096L, 3307926L, 3153767L, 3174607L,
3107334L, 3174607L, 3164467L, 3340018L, 3174607L, 3113837L, 3174607L,
3340018L, 3174607L, 3174607L, 3174607L, 3801282L, 3174607L, 3337006L,
3174607L, 3330266L, 3174607L, 3340018L, 3188888L, 3174607L, 3174607L,
3340018L, 3834762L, 3177436L, 3107334L, 3402263L, 3174607L, 3174607L,
3174607L, 3174607L, 3108613L, 3174607L, 3340018L, 3174607L, 3174607L,
3340547L, 3174607L, 3174607L, 3362137L, 3174607L, 3340018L, 3164467L,
3819482L, 3379453L, 3160101L, 3801365L, 3340018L, 3174607L, 3174607L,
3174607L, 3174607L, 3174607L, 3312850L, 3174607L, 3174607L, 3174607L,
3402263L, 3340018L, 3348904L, 3174607L, 3174607L, 3350173L, 3301655L,
3117333L, 3174607L, 3174607L, 3147447L, 3107334L, 3812628L, 3837740L,
3837740L, 3132209L, 3174607L, 3174607L, 3308890L, 3310235L, 3361931L,
3118554L, 3330266L, 3308189L, 3157099L, 3817767L, 3174607L, 3368936L,
3362137L, 3157099L, 3814533L, 3322404L, 3174607L, 3174607L, 3174607L,
3330266L, 3315424L, 3161430L, 3174607L, 3174607L, 3174607L, 3174607L,
3802355L, 3174607L, 3174607L, 3174607L, 3174607L, 3153767L, 3335679L,
3174607L, 3831644L, 3313304L, 3339374L, 3160101L, 3822940L, 3141044L,
3369801L, 3330266L, 3174607L, 3336347L, 3833129L, 3174607L, 3164640L,
3402263L, 3174607L, 3138511L, 3817767L, 3174607L, 3174607L, 3159517L,
3106344L, 3174607L, 3147447L, 3174607L, 3368936L, 3176552L, 3346551L,
3174607L, 3174607L, 3174607L, 3174607L, 3362137L, 3174607L, 3369801L,
3174607L, 3136226L, 3174607L, 3174607L, 3174607L, 3164467L, 3174607L,
3157099L, 3174607L, 3174607L, 3174607L, 3174607L, 3377192L, 3147447L,
3174607L, 3169771L, 3106344L, 3174607L, 3174607L, 3842948L, 3174607L,
3340018L, 3147447L, 3330266L, 3174607L, 3174607L, 3174607L, 3169771L,
3819482L, 3174607L, 3317511L, 3174607L, 3174607L, 3174607L, 3174607L,
3340018L, 3174607L, 3174607L, 3174607L, 3814533L, 3164467L, 3131490L,
3375709L, 3164467L, 3335679L, 3339366L, 3164467L, 3139105L, 3174607L,
3164467L, 3301655L, 3174607L, 3171066L, 3174607L, 3147447L, 3174607L,
3174607L, 3174607L, 3174607L, 3340018L, 3356063L, 3314567L, 3838516L,
3174607L, 3174607L, 3174607L, 3341206L, 3339366L, 3356964L, 3174607L,
3174607L, 3369801L, 3812750L, 3176552L, 3174607L, 3348904L, 3114298L,
3174607L, 3835934L, 3325381L, 3174607L, 3164467L, 3317974L, 3174607L,
3174607L, 3174607L, 3330266L, 3153239L, 3362137L, 3340018L, 3339366L,
3821710L, 3174607L, 3174607L, 3330266L, 3174607L, 3174607L, 3340018L,
3174607L, 3173575L, 3174607L, 3160101L, 3174607L, 3118562L, 3174607L,
3814491L, 3174607L, 3396788L, 3109124L, 3398057L, 3153767L, 3825836L,
3174607L, 3174607L, 3308080L, 3174607L, 3164467L, 3308189L, 3114728L,
3301655L, 3174607L, 3337337L, 3174607L, 3837211L, 3174607L, 3335810L,
3174607L, 3174607L, 3174607L, 3174607L, 3160101L, 3315424L, 3348904L,
3308080L, 3317974L, 3164467L, 3174607L, 3174607L, 3800607L, 3402263L,
3174607L, 3174607L, 3106344L, 3174607L, 3815084L, 3308080L),
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14109, 14571, 14207, 14543, 14956, 14767, 14774, 14795, 15635,
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14816, 14816, 14865, 15061, 14445, 15215, 14879, 14480, 14347,
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14487, 13892, 14606, 14809, 14802, 14879, 13983, 14438, 14739,
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14130, 14578, 15152, 14963, 13955, 14984, 14809, 14312, 14144,
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14795, 14473, 14228, 15061, 14410, 15152, 14683, 14452, 14459,
15138, 15425, 14452, 14571, 15334, 14403, 14816, 14452), class = "Date"),
L5num = c(7, 3, 7, 9, 8, 9, 2, 9, 6, 2, 8, 9, 6, 3, 8, 2,
6, 6, 9, 2, 8, 8, 10, 6, 7, 2, 9, 9, 9, 9, 9, 8, 9, 8, 10,
9, 6, 6, 7, 8, 2, 9, 4, 9, 1, 7, 9, 9, 9, 4, 7, 9, 7, 9,
9, 9, 7, 9, 9, 9, 9, 7, 9, 9, 3, 10, 6, 6, 7, 3, 5, 9, 9,
6, 9, 1, 2, 7, 2, 9, 8, 9, 9, 8, 9, 2, 9, 7, 8, 9, 8, 9,
4, 7, 9, 10, 8, 7, 9, 9, 8, 8, 9, 9, 1, 7, 10, 7, 8, 9, 9,
8, 2, 9, 7, 9, 9, 9, 3, 4, 7, 9, 7, 10, 9, 9, 9, 9, 1, 3,
4, 6, 6, 1, 9, 9, 9, 9, 9, 4, 9, 1, 7, 7, 9, 9, 10, 7, 6,
8, 6, 3, 6, 7, 2, 8, 9, 9, 9, 9, 8, 8, 9, 3, 10, 9, 7, 6,
6, 9, 9, 2, 7, 1, 9, 9, 9, 7, 9, 4, 7, 9, 4, 6, 8, 3, 8,
8, 9, 9, 9, 6, 9, 4, 9, 1, 2, 6, 9, 9, 8, 6, 9, 9, 9, 2,
7, 9, 9, 9, 7, 4, 10, 9, 7, 9, 4, 6, 9, 2, 9, 6, 9, 9, 8,
4, 8, 7, 9, 10, 8, 6, 6, 9, 9, 6, 2, 7, 7, 6, 8, 8, 9, 9,
6, 8, 6, 8, 8, 9, 8, 8, 7, 8, 6, 6, 7, 10, 7, 4, 6, 9, 9,
9, 9, 9, 7, 9, 9, 8, 1, 6, 7, 9, 9, 5, 10, 7, 8, 9, 6, 7,
6, 10, 7, 7, 9, 9, 4, 9, 7, 4, 10, 2, 10, 6, 9, 10, 7, 9,
10, 7, 8, 9, 8, 10, 7, 8, 9, 9, 9, 9, 3, 9, 8, 9, 9, 10,
7, 9, 6, 7, 7, 7, 4, 7, 9, 10, 9, 10, 4, 9, 4, 5, 9, 2, 7,
9, 9, 9, 3, 8, 6, 9, 6, 3, 7, 9, 9, 9, 9, 7, 9, 9, 9, 7,
8, 8, 9, 4, 8, 10, 9, 9, 9, 8, 10, 6, 9, 3, 10, 9, 9, 6,
9, 6, 6, 1, 9, 8, 9, 3, 10, 8, 5, 9, 9, 9, 9, 6, 8, 9, 9,
10, 4, 4, 7, 4, 7, 7, 4, 5, 9, 4, 4, 9, 6, 9, 6, 8, 9, 9,
9, 6, 2, 1, 8, 9, 9, 9, 3, 7, 7, 9, 9, 7, 7, 3, 9, 10, 4,
9, 6, 2, 9, 6, 7, 9, 9, 8, 10, 10, 7, 6, 7, 7, 9, 9, 7, 9,
9, 6, 9, 2, 9, 7, 9, 6, 8, 5, 8, 5, 3, 6, 10, 7, 9, 9, 2,
9, 4, 2, 7, 4, 9, 10, 9, 6, 9, 4, 8, 8, 9, 9, 7, 1, 10, 2,
7, 4, 9, 8, 6, 6, 9, 9, 3, 9, 1, 2), Family.Size = c(2L,
3L, 2L, 4L, 4L, 2L, 2L, 4L, 2L, 3L, 4L, 4L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 2L, 4L, 4L, 2L, 3L, 4L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 2L, 4L, 2L, 1L, 2L, 4L, 2L, 4L, 2L, 4L, 3L, 2L,
4L, 2L, 4L, 5L, 2L, 4L, 2L, 4L, 4L, 2L, 3L, 4L, 4L, 4L, 4L,
2L, 4L, 2L, 3L, 6L, 5L, 2L, 1L, 3L, 1L, 4L, 4L, 1L, 4L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 2L, 4L, 2L, 4L,
4L, 2L, 2L, 4L, 2L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 1L,
2L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 1L, 4L, 4L, 4L, 2L, 3L, 1L,
4L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 1L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 2L, 4L, 2L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 2L,
2L, 6L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L,
2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 2L, 4L, 3L, 1L,
4L, 2L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 2L, 4L, 3L,
2L, 2L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 2L,
2L, 2L, 4L, 1L, 4L, 2L, 2L, 4L, 3L, 4L, 2L, 4L, 4L, 4L, 2L,
4L, 2L, 4L, 2L, 4L, 2L, 4L, 4L, 4L, 2L, 3L, 2L, 1L, 2L, 4L,
4L, 4L, 4L, 2L, 4L, 2L, 4L, 4L, 1L, 4L, 4L, 2L, 4L, 2L, 2L,
2L, 6L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 2L,
2L, 2L, 4L, 4L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 2L, 3L,
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 2L, 2L, 2L,
1L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L,
4L, 2L, 2L, 4L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 2L, 4L,
4L, 3L, 2L, 4L, 3L, 2L, 4L, 4L, 4L, 1L, 4L, 1L, 4L, 1L, 2L,
2L, 4L, 4L, 4L, 4L, 2L, 4L, 2L, 4L, 3L, 4L, 4L, 4L, 2L, 4L,
2L, 4L, 4L, 4L, 4L, 2L, 1L, 4L, 2L, 1L, 4L, 4L, 2L, 4L, 2L,
1L, 2L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 4L,
4L, 4L, 2L, 2L, 4L, 5L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 3L, 4L,
3L, 4L, 1L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 2L,
2L, 2L, 4L, 4L, 2L, 3L, 2L, 4L, 2L, 3L, 4L, 3L, 1L, 4L, 2L,
2L, 4L, 4L, 4L, 2L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 4L, 4L,
2L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 2L, 4L, 2L, 3L, 2L, 2L, 2L,
4L, 4L, 2L, 4L, 2L, 2L, 2L, 3L, 4L, 2L, 4L, 1L, 4L, 2L, 4L,
4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 4L, 4L,
1L, 4L, 2L, 2L)), row.names = c(13036L, 12834L, 8179L, 17887L,
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946L, 13659L, 9496L, 18990L, 23672L, 24721L, 527L, 22688L, 11805L,
12111L, 11691L, 19908L, 14248L, 19843L, 18365L, 16503L, 2017L,
9918L, 874L, 10440L, 827L, 13863L, 967L, 12732L, 1994L, 14707L,
20612L, 18043L, 6827L, 22131L, 9024L, 7828L, 7343L, 3950L, 14063L,
1110L, 3899L, 22080L, 11907L, 5305L, 14319L, 10637L, 21792L,
18845L, 18881L, 20431L, 11169L, 23688L, 20748L, 23301L, 11473L,
5283L, 7652L, 10263L, 13525L, 24187L, 8407L, 14415L, 18256L,
4568L, 2703L, 5858L, 21788L, 12242L, 6657L, 10913L, 21440L, 6220L,
6013L, 12410L, 9253L, 16220L, 2251L, 24751L, 19446L, 2042L, 7895L,
19871L, 15067L, 20311L, 13733L, 11064L, 10699L, 24831L, 14384L,
3668L, 10907L, 10721L, 17641L, 505L, 24340L, 153L, 2317L, 22759L,
16837L, 12497L, 10439L, 5441L, 21121L, 16457L, 20295L, 16668L,
11591L, 22842L, 8801L, 9931L, 14046L, 14792L, 23242L, 24765L,
411L, 21002L, 2562L, 23375L, 24459L, 10865L, 4812L, 16165L, 18617L,
14819L, 236L, 4821L, 15606L, 4896L, 22410L, 3859L, 56L, 3491L,
16158L, 12457L, 19609L, 18955L, 10644L, 18450L, 1106L, 20967L,
24685L, 1632L, 5300L, 121L, 20697L, 14178L, 2431L, 16808L, 21305L,
24995L, 7735L, 23059L, 21441L, 22418L, 12201L, 9991L, 5666L,
18193L, 1839L, 24240L, 16265L, 15011L, 6818L, 8733L, 16602L,
1343L, 2798L, 8000L, 11989L, 1187L, 22516L, 5246L, 6099L, 17059L,
3358L, 21960L, 6742L, 7378L, 16424L, 9194L, 22187L, 12320L, 20656L,
295L, 2166L, 17417L, 7872L, 14536L, 1010L, 6495L, 11245L, 13047L,
6193L, 1336L, 22650L, 11430L, 3642L, 565L, 8633L, 16090L, 7322L,
10444L, 23485L, 23523L, 19310L, 6512L, 1868L, 5248L, 18802L,
2123L, 10045L, 13852L, 17041L, 22224L, 10978L, 13730L, 13770L,
15062L, 19198L, 6026L, 21553L, 15423L, 6839L, 4018L, 4978L, 4870L,
16916L, 498L, 11749L, 12968L, 14404L, 7440L, 11613L, 7059L, 81L,
9306L, 13621L, 13317L, 15421L, 500L, 5987L, 11600L, 10284L, 5096L,
22032L, 12712L, 22102L, 22614L, 11572L, 20781L, 4902L, 1771L,
14573L, 18304L, 22586L, 7154L, 17097L, 4391L, 23305L, 3066L,
9292L, 23270L, 1126L, 14250L, 7625L, 1530L, 7855L, 6085L, 19921L,
10537L, 19031L, 6887L, 23405L, 23776L, 8630L, 22017L, 3156L,
22252L, 24574L, 24172L, 23783L, 9755L, 11180L, 4520L, 24732L,
23985L, 20552L, 15878L, 22799L, 9769L, 10146L, 2155L, 5204L,
23678L, 6665L, 10509L, 1670L, 8797L, 21056L, 16989L, 407L, 17565L,
13666L, 3603L, 1795L, 14158L, 1616L, 14834L, 14521L, 8595L, 18860L,
25015L, 11646L, 8649L, 5601L, 420L, 13286L, 13864L, 16643L, 5285L,
8979L, 24880L, 17801L, 9825L, 4817L, 12218L, 771L, 7662L, 23046L,
19119L, 21016L, 7726L, 2004L, 728L, 20379L, 20958L, 17549L, 283L,
17822L, 17009L, 20008L, 13209L, 6725L, 2685L, 19311L, 5273L,
21046L, 10471L, 6348L, 6464L, 20940L, 23410L, 6335L, 8679L, 22772L,
5089L, 13868L, 6397L), class = "data.frame")
解决方案
如果Data是输入 data.frame 那么：
library(mlogit)
mlogit.data(Data, choice = "L5num", shape = "wide")
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