Compte tenu des exemples de données sampleDT ci-dessous, qui contiennent les moyens mean1 à mean10 et l'écart type sd2 , J'apprécierais toute aide pour créer une fonction qui:
pour chaque colonne de moyennes
mean1àmean10estimer, extraire et ajouter à la trame de données les valeurs de la fonction de densité pour un conditionnel distribution normale évaluée au niveau observé de la variabledollar.wage_1en utilisant l'écart typesd2.
En utilisant le code ci-dessous, je peux calculer avec succès pour une seule colonne de moyennes, mais je ne vois pas comment spécifier une fonction à calculer simultanément pour chaque colonne de moyennes.
#sample data
sampleDT$dens_test <- dnorm(sampleDT$dollar.wage_1,
mean = sampleDT$mean1,sd = sampleDT$sd2)
#my approach
sampleDT<-structure(list(id = 1:10, N = c(10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L), A = c(62L, 96L, 17L, 41L, 212L, 143L, 143L,
143L, 73L, 73L), B = c(3L, 1L, 0L, 2L, 170L, 21L, 0L, 33L, 62L,
17L), C = c(0.05, 0.01, 0, 0.05, 0.8, 0.15, 0, 0.23, 0.85, 0.23
), employer = c(1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), F = c(0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L), G = c(1.94, 1.19, 1.16,
1.16, 1.13, 1.13, 1.13, 1.13, 1.12, 1.12), H = c(0.14, 0.24,
0.28, 0.28, 0.21, 0.12, 0.17, 0.07, 0.14, 0.12), dollar.wage_1 = c(1.94,
1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), mean1 = c(1.936652081,
3.688171386, 3.160993574, 3.768485048, 1.311370546, 0.313760016,
-1.621000294, 1.13182676, 1.114458025, 1.119315775), mean2 = c(1.946806222,
3.688885811, 3.15903495, 3.767778705, 1.309663497, 0.316394741,
-1.618552806, 1.134088181, 1.117600968, 1.120688482), mean3 = c(1.893627954,
3.689341572, 3.157622975, 3.771231512, 1.324985578, 0.318026311,
-1.620565712, 1.13301769, 1.120760085, 1.119426932), mean4 = c(1.887509366,
3.660243949, 3.160911994, 3.738992465, 1.331637143, 0.284716279,
-1.655368774, 1.137338962, 1.122096234, 1.120837428), mean5 = c(7.071170501,
3.458558276, 3.156676637, 3.160692822, 1.131841192, 1.126997224,
1.028924299, 1.219378155, 0.118097115, 1.118108075), mean6 = c(7.010141264,
3.434098438, 3.160978044, 3.161388054, 1.131706507, 1.131073576,
1.044957033, 1.202376831, 0.088502176, 1.120101488), mean7 = c(6.918631396,
3.455412441, 3.064840549, 3.158657611, 1.134281965, 1.131677907,
1.035688483, 1.181551066, 0.542276222, 1.121549931), mean8 = c(6.980214117,
3.513440689, 3.175191087, 3.158919334, 1.130088008, 1.131692248,
1.12222788, 1.235102249, 0.281700405, 1.118473791), mean9 = c(6.708505027,
3.504542699, 3.173629275, 3.158457814, 1.134560107, 1.129357587,
1.151489857, 1.219991269, 0.364343124, 1.120228667), mean10 = c(6.883206883,
3.467216323, 3.174805298, 3.160917024, 1.128835398, 1.128265912,
1.084046983, 1.214981489, 0.160046133, 1.118496504), sd1 = c(2.6334129999306,
2.6334129999306, 2.6334129999306, 2.6334129999306, 2.6334129999306,
2.6334129999306, 2.6334129999306, 2.6334129999306, 2.6334129999306,
2.6334129999306), sd2 = c(514.02608349227, 101.976862386691,
8.70627514696715, 4.79710442214283, 2.45930925299156e+49, 2.01406038865916e+30,
1.8980055884822e+34, 1.65244344266379e+28, 26.9398910547703,
1.74978644797635)), row.names = c(NA, -10L), spec = structure(list(
cols = list(id = structure(list(), class = c("collector_integer",
"collector")), N = structure(list(), class = c("collector_integer",
"collector")), A = structure(list(), class = c("collector_integer",
"collector")), B = structure(list(), class = c("collector_integer",
"collector")), C = structure(list(), class = c("collector_double",
"collector")), employer = structure(list(), class = c("collector_integer",
"collector")), F = structure(list(), class = c("collector_integer",
"collector")), G = structure(list(), class = c("collector_double",
"collector")), H = structure(list(), class = c("collector_double",
"collector")), dollar.wage_1 = structure(list(), class = c("collector_double",
"collector")), mean1 = structure(list(), class = c("collector_double",
"collector")), mean2 = structure(list(), class = c("collector_double",
"collector")), mean3 = structure(list(), class = c("collector_double",
"collector")), mean4 = structure(list(), class = c("collector_double",
"collector")), mean5 = structure(list(), class = c("collector_double",
"collector")), mean6 = structure(list(), class = c("collector_double",
"collector")), mean7 = structure(list(), class = c("collector_double",
"collector")), mean8 = structure(list(), class = c("collector_double",
"collector")), mean9 = structure(list(), class = c("collector_double",
"collector")), mean10 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector"))), class = "col_spec"), class = c("tbl_df",
"tbl", "data.frame"))
Merci à avance pour toute aide.
4 Réponses :
Nous pouvons aller plus loin avec votre approche:
means <- as.matrix(sampleDT[, grep("mean", names(sampleDT))])
dnorm(sampleDT$dollar.wage_1, mean = means, sd = sampleDT$sd2)
De cette façon, nous passons une matrice de moyens, tandis que dollar.wage_1 et sd2 code> sont également utilisés correctement en raison du recyclage.
Ensuite, vous pouvez ajouter ce résultat à sampleDT simplement avec cbind .
p >
voici mon approche en utilisant data.table:
sampleDT <- as.data.table(sampleDT)
for(i in c(1:10)){
sampleDT[, eval(paste0("dnorm",i)):=mapply(dnorm,dollar.wage_1,get(paste0("mean",i)),sd2)] }
Ou mettre dans un sapply ().
sapply(grep("^mean", names(sampleDT)), function(x)
dnorm(sampleDT$dollar.wage_1, sampleDT[[x]], sampleDT$sd2))
C'est un cas typique de dplyr::mutate_at():
df %>% mutate_at(vars(matches("mean")),
funs(dens = dnorm(dollar.wage_1, mean = ., sd = sd2)))
La sortie sera un jeu de données complet, et vous n'avez pas besoin de lier quoi que ce soit .