Question

我无法正确设置需要传递给函数的行。我有＆＃34;计算功能＆＃34;和＆＃34;行选择功能＆＃34;。我用来计算的功能正在做我想要的。该函数必须首先是子集estado=3（该部分正常）然后将正确的行号子集化并将它们传递给＆＃34;计算函数＆＃34;。

正如我所说，我选择的代码应该选择特定的行号（在本例中为1：11,23：23,46：56和68:78）。问题是我试图为任何数据帧的长度自动进行此操作。我应该得到这样的模式：rep（11,12）我的意思是我把前两个dfs之间的第一个11放到我传递给计算函数并且在前两个和下一个块之间丢弃12并循环直到数据帧是结束（见照片以获得更好的不受欢迎）。

enter image description here

行选择基于时间（您可以看到＆＃39; sa tiempo列，因此根据时间（t_muestreo，t_pasaje，t__entre）计算行数.I＆＃ 39;我试图根据这个时间设置行号，以防万一我们必须在实验中更改它们。

pendientes_ensayo <- function(df){

  pendientes = data.frame()

  t_muestreo = 0.2
  t_pasaje = 2
  t_entre = 2.5

  nro_filas_salto = (t_pasaje / t_muestreo)
  nro_filas_entre = (t_entre/t_muestreo)


  df_a_medir_completo = df[df$estado == 3,] # Me quedo solo con la parte que mido

  tamanodf = dim(df_a_medir_completo)[1]
  df$tiempo = as.numeric( df$tiempo)
  cant_animales = length(names(df)) - 2 # tiempo y estado

  pos_en_filas = 1
  for(nro_ensayo in 1:(tamanodf/ (nro_filas_salto + nro_filas_entre)   )) {

    print( pos_en_filas )
    print( pos_en_filas + nro_filas_salto )

    df_a_medir = df_a_medir_completo[pos_en_filas:(pos_en_filas+nro_filas_salto),]
    pendientes = rbind(pendientes,obtener_pendientes(df_a_medir))  

    pos_en_filas = pos_en_filas + nro_filas_salto + nro_filas_entre  - 0.5

  }
  return(pendientes)
}

我得到的回报（打印行）就是这个

[1] 1
[1] 11
[1] 23
[1] 33
[1] 45
[1] 55
[1] 67
[1] 77

请注意，前2个子集（1:11和23:33是正确的）但45:55和67:77是一行异相。

要运行pendientes_ensayo功能，您需要

iti = 153
t_adaptacion = 600
t_pre_estimulo = 18
t_muestreo = 0.2
t_pasaje = 2
t_entre = 2.5
defasaje = 1
nro_ensayos = 2


regresion <- function(formula){ 
  regresion = lm( formula )
  ss = summary(regresion)
  return(c( coef(regresion)[2] , ss$adj.r.squared))
}

sacar_columnas <- function (dataframe,columnas){
  return(dataframe[,!(names(dataframe) %in% columnas)])
}

desplazar<- function(tiempo,dataframe){
  matriz = as.matrix(dataframe)
  matriz_dif = abs(diff(matriz )) # derivar
  matriz_dif_inv = diffinv(matriz_dif) # integrar

  return(data.frame(cbind(tiempo, matriz_dif_inv )))
}

obtener_pendientes <- function(df_a_medir){
  df_a_medir2 =sacar_columnas(df_a_medir,  c("estado","tiempo"))
  mat_desplazamiento = desplazar(df_a_medir$tiempo,df_a_medir2)
  pend_muestreo = apply(mat_desplazamiento,2,function(x) regresion(x~df_a_medir$tiempo))  
  return(pend_muestreo)
}

这是一个已经由df $ estado = 3

提交的数据框的示例

dput(d)
structure(list(estado = c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3), tiempo = c(618.2, 
618.4, 618.6, 618.8, 619, 619.2, 619.4, 619.6, 619.8, 620, 620.2, 
620.4, 620.6, 620.8, 621, 621.2, 621.4, 621.6, 621.8, 622, 622.2, 
622.4, 622.6, 622.8, 623, 623.2, 623.4, 623.6, 623.8, 624, 624.2, 
624.4, 624.6, 624.8, 625, 625.2, 625.4, 625.6, 625.8, 626, 626.2, 
626.4, 626.6, 626.8, 627, 3138.2, 3138.4, 3138.6, 3138.8, 3139, 
3139.2, 3139.4, 3139.6, 3139.8, 3140, 3140.2, 3140.4, 3140.6, 
3140.8, 3141, 3141.2, 3141.4, 3141.6, 3141.8, 3142, 3142.2, 3142.4, 
3142.6, 3142.8, 3143, 3143.2, 3143.4, 3143.6, 3143.8, 3144, 3144.2, 
3144.4, 3144.6, 3144.8, 3145, 3145.2, 3145.4, 3145.6, 3145.8, 
3146, 3146.2, 3146.4, 3146.6, 3146.8, 3147), UT2x45 = c(14663, 
14662, 14661, 14659, 14657, 14654, 14652, 14650, 14651, 14651, 
14651, 14650, 14648, 14647, 14645, 14644, 14641, 14640, 14640, 
14640, 14640, 14640, 14639, 14639, 14638, 14638, 14637, 14637, 
14637, 14636, 14636, 14635, 14634, 14632, 14631, 14629, 14628, 
14628, 14626, 14625, 14625, 14625, 14624, 14624, 14624, 10888, 
10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 
10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 
10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 
10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888, 
10888, 10888, 10888, 10888, 10888, 10888, 10888, 10888), TR2x45 = c(14663, 
14663, 14655, 14634, 14632, 14634, 14634, 14632, 14633, 14605, 
14573, 14555, 14548, 14540, 14525, 14523, 14517, 14510, 14506, 
14499, 14497, 14494, 14492, 14487, 14453, 14426, 14414, 14416, 
14413, 14413, 14416, 14409, 14387, 14380, 14367, 14364, 14356, 
14347, 14347, 14343, 14337, 14332, 14330, 14329, 14327, 13432, 
13432, 13427, 13424, 13426, 13427, 13427, 13427, 13428, 13428, 
13426, 13424, 13423, 13423, 13423, 13423, 13423, 13422, 13421, 
13421, 13420, 13419, 13418, 13417, 13410, 13410, 13410, 13411, 
13411, 13411, 13411, 13411, 13410, 13410, 13410, 13410, 13410, 
13411, 13411, 13411, 13411, 13411, 13411, 13411, 13411), UT2x45.2 = c(15510, 
15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 
15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 
15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 
15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 
15510, 15510, 15510, 15510, 15510, 15510, 15510, 15510, 15282, 
15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 
15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 
15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 
15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282, 
15282, 15282, 15282, 15282, 15282, 15282, 15282, 15282), TR2x45.2 = c(17295, 
17295, 17293, 17266, 17248, 17250, 17268, 17277, 17252, 17195, 
17141, 17115, 17108, 17105, 17102, 17099, 17098, 17097, 17093, 
17089, 17086, 17084, 17084, 17083, 17080, 17036, 16975, 16919, 
16884, 16869, 16836, 16788, 16738, 16700, 16675, 16661, 16653, 
16648, 16643, 16638, 16634, 16633, 16631, 16629, 16626, 24047, 
24047, 24047, 24047, 24047, 24062, 24085, 24119, 24163, 24191, 
24181, 24182, 24181, 24183, 24183, 24183, 24183, 24183, 24183, 
24183, 24183, 24184, 24184, 24184, 24182, 24137, 24070, 24011, 
23978, 23965, 23920, 23868, 23826, 23806, 23799, 23796, 23788, 
23782, 23776, 23774, 23772, 23769, 23766, 23764, 23761), TR2x45.3 = c(14702, 
14702, 14702, 14702, 14702, 14702, 14702, 14704, 14720, 14733, 
14739, 14741, 14743, 14744, 14746, 14750, 14753, 14754, 14755, 
14756, 14756, 14758, 14759, 14760, 14760, 14760, 14763, 14767, 
14770, 14774, 14773, 14774, 14775, 14775, 14776, 14776, 14776, 
14776, 14776, 14776, 14776, 14776, 14776, 14776, 14776, 14818, 
14818, 14818, 14818, 14818, 14820, 14819, 14818, 14819, 14819, 
14819, 14819, 14819, 14819, 14819, 14819, 14819, 14819, 14819, 
14819, 14819, 14819, 14819, 14819, 14817, 14816, 14816, 14816, 
14816, 14816, 14817, 14817, 14817, 14817, 14817, 14817, 14817, 
14817, 14817, 14817, 14817, 14817, 14817, 14817, 14817), UT2x45.3 = c(18521, 
18518, 18517, 18518, 18517, 18515, 18513, 18512, 18510, 18507, 
18505, 18503, 18501, 18499, 18499, 18497, 18496, 18495, 18493, 
18492, 18490, 18487, 18483, 18481, 18479, 18476, 18474, 18472, 
18471, 18470, 18467, 18464, 18461, 18457, 18455, 18453, 18451, 
18449, 18447, 18445, 18442, 18438, 18435, 18432, 18430, 18204, 
18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 
18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 
18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 
18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204, 
18204, 18204, 18204, 18204, 18204, 18204, 18204, 18204), TR2x45.1 = c(14052, 
14049, 14013, 13961, 13907, 13874, 13861, 13844, 13801, 13742, 
13680, 13627, 13585, 13563, 13547, 13536, 13524, 13511, 13497, 
13487, 13477, 13466, 13457, 13446, 13406, 13352, 13295, 13255, 
13248, 13237, 13211, 13163, 13110, 13066, 13034, 13015, 13000, 
12980, 12969, 12958, 12944, 12931, 12927, 12919, 12910, 11396, 
11394, 11355, 11301, 11247, 11210, 11198, 11195, 11164, 11130, 
11081, 11038, 11011, 11003, 10991, 10982, 10974, 10966, 10960, 
10954, 10949, 10944, 10941, 10934, 10892, 10839, 10789, 10748, 
10729, 10722, 10697, 10656, 10611, 10574, 10547, 10534, 10525, 
10516, 10509, 10502, 10494, 10489, 10485, 10482, 10476), UT2x45.1 = c(17050, 
17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 
17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 
17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 
17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 
17050, 17050, 17050, 17050, 17050, 17050, 17050, 17050, 17077, 
17077, 17077, 17077, 17077, 17077, 17077, 17077, 17077, 17077, 
17077, 17077, 17077, 17077, 17077, 17077, 17077, 17077, 17077, 
17077, 17077, 17077, 17078, 17078, 17080, 17083, 17084, 17084, 
17084, 17085, 17087, 17089, 17090, 17093, 17095, 17095, 17096, 
17098, 17100, 17101, 17103, 17106, 17107, 17109, 17111), UT2x45.4 = c(16434, 
16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 
16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 
16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 
16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 
16434, 16434, 16434, 16434, 16434, 16434, 16434, 16434, 16823, 
16824, 16825, 16825, 16825, 16825, 16825, 16826, 16826, 16826, 
16826, 16826, 16826, 16826, 16824, 16824, 16824, 16824, 16826, 
16827, 16828, 16828, 16828, 16828, 16829, 16829, 16830, 16831, 
16832, 16832, 16832, 16832, 16833, 16833, 16834, 16835, 16834, 
16833, 16833, 16833, 16833, 16834, 16834, 16835, 16836), TR2x45.4 = c(12488, 
12488, 12486, 12471, 12448, 12435, 12430, 12423, 12418, 12370, 
12307, 12247, 12190, 12136, 12095, 12069, 12045, 12025, 12015, 
12001, 11994, 11985, 11978, 11967, 11931, 11885, 11817, 11761, 
11714, 11667, 11624, 11574, 11519, 11475, 11438, 11405, 11375, 
11354, 11330, 11313, 11303, 11284, 11272, 11265, 11255, 9056, 
9056, 9056, 9056, 9056, 9056, 9056, 9056, 9054, 9039, 9006, 8994, 
8983, 8969, 8968, 8964, 8957, 8950, 8951, 8949, 8943, 8936, 8935, 
8934, 8920, 8885, 8841, 8808, 8778, 8752, 8715, 8654, 8595, 8549, 
8524, 8500, 8488, 8477, 8461, 8458, 8447, 8441, 8436, 8426, 8421
), UT2x45.1.1 = c(12004, 12004, 12004, 12004, 12004, 12004, 12004, 
12004, 12003, 12003, 12002, 12002, 12002, 12002, 12002, 12002, 
12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 
12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 
12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 12002, 
12002, 12002, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 
11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 
11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 
11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 
11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 11015, 
11015, 11015), TR2x45.1.1 = c(20832, 20832, 20828, 20818, 20822, 
20855, 20873, 20895, 20930, 20915, 20904, 20904, 20904, 20904, 
20904, 20904, 20904, 20904, 20905, 20906, 20907, 20908, 20911, 
20916, 20918, 20887, 20857, 20861, 20879, 20892, 20910, 20897, 
20893, 20894, 20893, 20893, 20893, 20893, 20893, 20893, 20893, 
20893, 20893, 20893, 20893, 22371, 22371, 22370, 22369, 22383, 
22410, 22440, 22459, 22470, 22426, 22414, 22406, 22404, 22404, 
22404, 22403, 22402, 22402, 22402, 22402, 22402, 22402, 22402, 
22402, 22405, 22387, 22371, 22373, 22387, 22390, 22364, 22327, 
22303, 22295, 22283, 22272, 22264, 22253, 22250, 22240, 22234, 
22232, 22230, 22223, 22214), TR2x45.5 = c(11994, 11992, 11991, 
11992, 11994, 11994, 11992, 11989, 11991, 11988, 11981, 11978, 
11978, 11977, 11974, 11971, 11971, 11969, 11962, 11955, 11952, 
11948, 11945, 11943, 11942, 11942, 11942, 11942, 11941, 11939, 
11938, 11938, 11931, 11928, 11927, 11925, 11920, 11917, 11915, 
11914, 11909, 11906, 11904, 11901, 11897, 3997, 3997, 3997, 3997, 
3997, 3997, 3997, 3997, 3997, 3997, 3998, 3998, 3998, 3998, 3998, 
3998, 3998, 3998, 3998, 3998, 3998, 3998, 3998, 3998, 3998, 3998, 
3998, 3998, 3998, 3999, 3999, 3999, 3999, 3999, 3999, 3999, 3999, 
3999, 3999, 3999, 3999, 3999, 3999, 3999, 3999), TR2x45.1.2 = c(12363, 
12363, 12352, 12332, 12333, 12330, 12329, 12329, 12329, 12318, 
12298, 12296, 12289, 12286, 12277, 12264, 12264, 12261, 12254, 
12252, 12249, 12248, 12246, 12244, 12244, 12240, 12227, 12224, 
12220, 12219, 12218, 12215, 12206, 12200, 12197, 12197, 12196, 
12196, 12195, 12191, 12187, 12183, 12182, 12179, 12177, 11923, 
11922, 11921, 11914, 11913, 11913, 11913, 11913, 11913, 11905, 
11904, 11903, 11903, 11903, 11902, 11902, 11901, 11901, 11901, 
11901, 11901, 11900, 11900, 11899, 11898, 11891, 11880, 11878, 
11875, 11873, 11871, 11870, 11866, 11864, 11861, 11859, 11857, 
11856, 11855, 11854, 11853, 11852, 11851, 11849, 11848), UT2x45.5 = c(12040, 
12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 
12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 
12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 
12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 
12040, 12040, 12040, 12040, 12040, 12040, 12040, 12040, 10462, 
10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 
10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 
10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 
10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462, 
10462, 10462, 10462, 10462, 10462, 10462, 10462, 10462), UT2x45.1.2 = c(12349, 
12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 
12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 
12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 
12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 
12349, 12349, 12349, 12349, 12349, 12349, 12349, 12349, 9580, 
9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 
9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 
9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 
9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580, 9580
), UT2x45.6 = c(15564, 15563, 15562, 15562, 15563, 15563, 15563, 
15563, 15562, 15562, 15560, 15554, 15544, 15542, 15541, 15540, 
15539, 15538, 15538, 15537, 15536, 15535, 15533, 15529, 15528, 
15525, 15524, 15521, 15519, 15517, 15516, 15516, 15515, 15515, 
15514, 15514, 15513, 15510, 15508, 15505, 15502, 15501, 15500, 
15498, 15496, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 
12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 
12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 
12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 
12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 12438, 
12438, 12438), UT2x45.1.3 = c(18966, 18966, 18966, 18966, 18966, 
18966, 18966, 18966, 18966, 18966, 18966, 18966, 18966, 18966, 
18966, 18966, 18966, 18966, 18966, 18966, 18967, 18968, 18968, 
18969, 18970, 18970, 18971, 18971, 18971, 18972, 18973, 18974, 
18974, 18975, 18976, 18977, 18977, 18978, 18978, 18978, 18978, 
18979, 18980, 18980, 18981, 20189, 20189, 20189, 20189, 20189, 
20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 
20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 
20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 
20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 20189, 
20189, 20189, 20189, 20189), TR2x45.6 = c(15834, 15834, 15834, 
15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 
15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 
15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 15834, 
15834, 15834, 15834, 15835, 15835, 15835, 15835, 15835, 15835, 
15835, 15835, 15835, 15835, 15835, 15835, 16775, 16775, 16775, 
16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 
16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 
16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 
16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 16775, 
16775, 16775, 16775, 16775, 16775, 16775), TR2x45.1.3 = c(19716, 
19716, 19716, 19716, 19716, 19718, 19718, 19718, 19720, 19722, 
19723, 19723, 19723, 19723, 19723, 19723, 19722, 19722, 19722, 
19723, 19723, 19723, 19724, 19724, 19725, 19726, 19728, 19730, 
19732, 19733, 19736, 19737, 19737, 19737, 19737, 19738, 19738, 
19738, 19738, 19738, 19738, 19738, 19738, 19738, 19738, 27720, 
27720, 27720, 27720, 27720, 27720, 27720, 27720, 27720, 27720, 
27720, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 
27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 
27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719, 
27719, 27719, 27719, 27719, 27719, 27719, 27719, 27719)), .Names = c("estado", 
"tiempo", "UT2x45", "TR2x45", "UT2x45.2", "TR2x45.2", "TR2x45.3", 
"UT2x45.3", "TR2x45.1", "UT2x45.1", "UT2x45.4", "TR2x45.4", "UT2x45.1.1", 
"TR2x45.1.1", "TR2x45.5", "TR2x45.1.2", "UT2x45.5", "UT2x45.1.2", 
"UT2x45.6", "UT2x45.1.3", "TR2x45.6", "TR2x45.1.3"), row.names = c("3087", 
"3088", "3089", "3090", "3091", "3092", "3093", "3094", "3095", 
"3096", "3097", "3098", "3099", "3100", "3101", "3102", "3103", 
"3104", "3105", "3106", "3107", "3108", "3109", "3110", "3111", 
"3112", "3113", "3114", "3115", "3116", "3117", "3118", "3119", 
"3120", "3121", "3122", "3123", "3124", "3125", "3126", "3127", 
"3128", "3129", "3130", "3131", "15687", "15688", "15689", "15690", 
"15691", "15692", "15693", "15694", "15695", "15696", "15697", 
"15698", "15699", "15700", "15701", "15702", "15703", "15704", 
"15705", "15706", "15707", "15708", "15709", "15710", "15711", 
"15712", "15713", "15714", "15715", "15716", "15717", "15718", 
"15719", "15720", "15721", "15722", "15723", "15724", "15725", 
"15726", "15727", "15728", "15729", "15730", "15731"), class = "data.frame")

我希望我已经明确表示我认为必须有一种更简单的方法来做到这一点。谢谢。

Answer 1

我认为for循环只是对函数ibtener_pendientes进行子集化和应用。如果是这种情况，您可以使用：

## create list of dfs based on row idx
idx <- list(1:11, 23:33, 45:56, 67:78)
## or write a function to automate the rows
# idx <- lapply(seq(1, nrow(d), by = 10 + 12), function(x) seq(x, x + 11))
l <- lapply(idx, function(x) d[x, ])
do.call('rbind', Map('obtener_pendientes', l))


#                   tiempo        V2         V3  V4          V5         V6
# df_a_medir$tiempo      1 8.2272727 37.9090909   0  95.5909091 16.6818182
#                        1 0.9495273  0.7936207 NaN   0.8618870  0.5851504
# df_a_medir$tiempo      1 2.3181818 50.3181818   0 184.8636364 10.6363636
#                        1 0.9436334  0.8228418 NaN   0.9712757  0.9353285
# df_a_medir$tiempo      1 0.0000000  4.4220779   0  51.0649351  1.4675325
#                        1       NaN  0.8718034 NaN   0.7925802  0.6962431
# df_a_medir$tiempo      1 0.0000000  4.0000000   0 194.3636364  1.9090909
#                        1       NaN  0.5621631 NaN   0.9699970  0.7561455
#                           V7          V8        V9       V10         V11
# df_a_medir$tiempo  8.4090909 182.0000000 0.0000000 0.0000000  77.0454545
#                    0.9803865   0.9748662       NaN       NaN   0.8338448
# df_a_medir$tiempo 10.4090909 169.5000000 0.0000000 0.0000000 242.1363636
#                    0.9862105   0.9684383       NaN       NaN   0.9918088
# df_a_medir$tiempo  0.0000000 120.2272727 0.0000000 1.6818182  36.2532468
#                          NaN   0.9357959       NaN 0.8381040   0.8932908
# df_a_medir$tiempo  0.0000000 165.5000000 5.8636364 2.6363636 171.0909091
#                          NaN   0.9730404 0.9418502 0.9271605   0.9658712
#                         V12        V13       V14        V15 V16 V17       V18
# df_a_medir$tiempo 0.7727273 86.1363636 9.8636364 27.8181818   0   0 2.2727273
#                   0.5311111  0.9337161 0.8997295  0.8450071 NaN NaN 0.8739165
# df_a_medir$tiempo 0.0000000 77.2727273 4.9545455 20.1363636   0   0 9.0454545
#                         NaN  0.9727717 0.7449921  0.9362205 NaN NaN 0.9558411
# df_a_medir$tiempo 0.0000000 52.7532468 0.3571429  5.7337662   0   0 0.0000000
#                         NaN  0.8280456 0.7368421  0.8605300 NaN NaN       NaN
# df_a_medir$tiempo 0.0000000 68.8181818 0.6363636 18.5454545   0   0 0.0000000
#                         NaN  0.9035839 0.6666667  0.9262417 NaN NaN       NaN
#                         V19 V20       V21
# df_a_medir$tiempo 0.0000000   0 3.5000000
#                         NaN NaN 0.8115468
# df_a_medir$tiempo 2.8636364   0 7.6363636
#                   0.9541063 NaN 0.9702682
# df_a_medir$tiempo 0.0000000   0 0.3571429
#                         NaN NaN 0.7368421
# df_a_medir$tiempo 0.0000000   0 0.0000000
#                         NaN NaN       NaN

看起来类似于我从循环函数得到的输出

辅助函数：（没有足够的空间在这里添加数据d）

regresion <- function(formula){ 
  regresion = lm( formula )
  ss = summary(regresion)
  return(c( coef(regresion)[2] , ss$adj.r.squared))
}

sacar_columnas <- function (dataframe,columnas){
  return(dataframe[,!(names(dataframe) %in% columnas)])
}

desplazar<- function(tiempo,dataframe){
  matriz = as.matrix(dataframe)
  matriz_dif = abs(diff(matriz )) # derivar
  matriz_dif_inv = diffinv(matriz_dif) # integrar

  return(data.frame(cbind(tiempo, matriz_dif_inv )))
}

obtener_pendientes <- function(df_a_medir){
  df_a_medir2 =sacar_columnas(df_a_medir,  c("estado","tiempo"))
  mat_desplazamiento = desplazar(df_a_medir$tiempo,df_a_medir2)
  pend_muestreo = apply(mat_desplazamiento,2,function(x) regresion(x~df_a_medir$tiempo))  
  return(pend_muestreo)
}

使用for循环R将函数应用于特定行

1 个答案: