Aca se analiza como se distribuye la erosion para las piezas 17 15 21 acumulando para cada pieza la cara V,P,I
library(survey)
##
## Attaching package: 'survey'
##
## The following object(s) are masked from 'package:graphics':
##
## dotchart
options(OutDec = ",")
library(car)
## Loading required package: MASS
## Loading required package: nnet
load("~/Dropbox/odontologia/maestria licet/octubre 2013/datos_licet_10112013.RData")
erosion_sin <- read.csv("erosion_sin.csv")
diseniopost1$variables <- cbind(diseniopost1$variables[, 1:455], erosion_sin[,
c(2:29)], diseniopost1$variables[, 484:487])
names(diseniopost1$variables[, 456:483])
## [1] "Eros17" "Eros16" "Eros15" "Eros14" "Eros13" "Eros12" "Eros11"
## [8] "Eros21" "Eros22" "Eros23" "Eros24" "Eros25" "Eros26" "Eros27"
## [15] "Eros37" "Eros36" "Eros35" "Eros34" "Eros33" "Eros32" "Eros31"
## [22] "Eros41" "Eros42" "Eros43" "Eros44" "Eros45" "Eros46" "Eros47"
Recodifica y pasa a Factores
diseniopost1$variables$Eros17 <- as.factor(diseniopost1$variables$Eros17)
diseniopost1$variables$Eros16 <- as.factor(diseniopost1$variables$Eros16)
diseniopost1$variables$Eros15 <- as.factor(diseniopost1$variables$Eros15)
diseniopost1$variables$Eros14 <- as.factor(diseniopost1$variables$Eros14)
diseniopost1$variables$Eros13 <- as.factor(diseniopost1$variables$Eros13)
diseniopost1$variables$Eros12 <- as.factor(diseniopost1$variables$Eros12)
diseniopost1$variables$Eros11 <- as.factor(diseniopost1$variables$Eros11)
diseniopost1$variables$Eros21 <- as.factor(diseniopost1$variables$Eros21)
diseniopost1$variables$Eros22 <- as.factor(diseniopost1$variables$Eros22)
diseniopost1$variables$Eros23 <- as.factor(diseniopost1$variables$Eros23)
diseniopost1$variables$Eros24 <- as.factor(diseniopost1$variables$Eros24)
diseniopost1$variables$Eros25 <- as.factor(diseniopost1$variables$Eros25)
diseniopost1$variables$Eros26 <- as.factor(diseniopost1$variables$Eros26)
diseniopost1$variables$Eros27 <- as.factor(diseniopost1$variables$Eros27)
diseniopost1$variables$Eros37 <- as.factor(diseniopost1$variables$Eros37)
diseniopost1$variables$Eros36 <- as.factor(diseniopost1$variables$Eros36)
diseniopost1$variables$Eros35 <- as.factor(diseniopost1$variables$Eros35)
diseniopost1$variables$Eros34 <- as.factor(diseniopost1$variables$Eros34)
diseniopost1$variables$Eros33 <- as.factor(diseniopost1$variables$Eros33)
diseniopost1$variables$Eros32 <- as.factor(diseniopost1$variables$Eros32)
diseniopost1$variables$Eros31 <- as.factor(diseniopost1$variables$Eros31)
diseniopost1$variables$Eros41 <- as.factor(diseniopost1$variables$Eros41)
diseniopost1$variables$Eros42 <- as.factor(diseniopost1$variables$Eros42)
diseniopost1$variables$Eros43 <- as.factor(diseniopost1$variables$Eros43)
diseniopost1$variables$Eros44 <- as.factor(diseniopost1$variables$Eros44)
diseniopost1$variables$Eros45 <- as.factor(diseniopost1$variables$Eros45)
diseniopost1$variables$Eros46 <- as.factor(diseniopost1$variables$Eros46)
diseniopost1$variables$Eros47 <- as.factor(diseniopost1$variables$Eros47)
save.image("~/Dropbox/odontologia/maestria licet/octubre 2013/datos_licet_10112013.RData")
summary(diseniopost1$variables[, 456:483])
## Eros17 Eros16 Eros15 Eros14 Eros13 Eros12
## 0 :1032 0 :841 0 :996 0 :916 0 :943 0 :632
## 1 : 3 1 :271 1 : 37 1 :112 1 : 87 1 :287
## NA's: 119 2 : 22 2 : 2 2 : 7 2 : 5 2 :217
## 3 : 2 NA's:119 NA's:119 NA's:119 NA's: 18
## NA's: 18
## Eros11 Eros21 Eros22 Eros23 Eros24 Eros25
## 0 :577 0 :579 0 :625 0 :957 0 :943 0 :1007
## 1 :264 1 :274 1 :294 1 : 68 1 : 88 1 : 26
## 2 :295 2 :283 2 :217 2 : 9 2 : 4 2 : 2
## NA's: 18 NA's: 18 NA's: 18 NA's:120 NA's:119 NA's: 119
##
## Eros26 Eros27 Eros37 Eros36 Eros35 Eros34
## 0 :889 0 :1034 0 :1032 0 :911 0 :1011 0 :1009
## 1 :225 1 : 1 1 : 2 1 :208 1 : 24 1 : 25
## 2 : 19 NA's: 119 2 : 1 2 : 16 NA's: 119 2 : 1
## 3 : 3 NA's: 119 3 : 1 NA's: 119
## NA's: 18 NA's: 18
## Eros33 Eros32 Eros31 Eros41 Eros42 Eros43
## 0 :990 0 :911 0 :849 0 :837 0 :883 0 :984
## 1 : 42 1 :174 1 :229 1 :241 1 :190 1 : 46
## 2 : 3 2 : 51 2 : 58 2 : 58 2 : 60 2 : 5
## NA's:119 NA's: 18 NA's: 18 NA's: 18 3 : 3 NA's:119
## NA's: 18
## Eros44 Eros45 Eros46 Eros47
## 0 :1004 0 :1016 0 :884 0 :1029
## 1 : 29 1 : 18 1 :237 1 : 5
## 2 : 2 3 : 1 2 : 15 3 : 1
## NA's: 119 NA's: 119 NA's: 18 NA's: 119
##
Ero17 <- svymean(~Eros17, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero17 * 100, 1)
## mean SE DEff
## Eros170 9,99e+01 6,06e-04 0,43
## Eros171 1,00e-01 6,06e-04 0,43
Ero16 <- svymean(~Eros16, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero16 * 100, 1)
## mean SE DEff
## Eros160 75,50000 0,02623 4,40
## Eros161 22,00000 0,02361 3,85
## Eros162 2,50000 0,00822 3,34
## Eros163 0,10000 0,00104 1,23
Ero15 <- svymean(~Eros15, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero15 * 100, 1)
## mean SE DEff
## Eros150 95,50000 0,00709 1,27
## Eros151 3,90000 0,00740 1,57
## Eros152 0,60000 0,00410 3,17
Ero14 <- svymean(~Eros14, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero14 * 100, 1)
## mean SE DEff
## Eros140 88,50000 0,01471 2,30
## Eros141 10,10000 0,01130 1,52
## Eros142 1,40000 0,00871 5,91
Ero13 <- svymean(~Eros13, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero13 * 100, 1)
## mean SE DEff
## Eros130 90,20000 0,02115 5,44
## Eros131 8,90000 0,02030 5,46
## Eros132 0,90000 0,00347 1,45
Ero12 <- svymean(~Eros12, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero12 * 100, 1)
## mean SE DEff
## Eros120 55,7000 0,0395 7,49
## Eros121 21,8000 0,0237 3,91
## Eros122 22,5000 0,0444 13,39
Ero11 <- svymean(~Eros11, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero11 * 100, 1)
## mean SE DEff
## Eros110 51,1000 0,0389 7,17
## Eros111 19,9000 0,0235 4,12
## Eros112 29,0000 0,0417 9,98
Ero21 <- svymean(~Eros21, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero21 * 100, 1)
## mean SE DEff
## Eros210 51,7000 0,0343 5,58
## Eros211 21,3000 0,0268 5,09
## Eros212 27,0000 0,0384 8,82
Ero22 <- svymean(~Eros22, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero22 * 100, 1)
## mean SE DEff
## Eros220 55,2000 0,0342 5,60
## Eros221 23,2000 0,0259 4,48
## Eros222 21,6000 0,0390 10,62
Ero23 <- svymean(~Eros23, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero23 * 100, 1)
## mean SE DEff
## Eros230 91,90000 0,01886 5,14
## Eros231 6,50000 0,01469 3,82
## Eros232 1,60000 0,00556 2,11
Ero24 <- svymean(~Eros24, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero24 * 100, 1)
## mean SE DEff
## Eros240 92,50000 0,01401 3,04
## Eros241 6,30000 0,01430 3,75
## Eros242 1,30000 0,00986 8,43
Ero25 <- svymean(~Eros25, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero25 * 100, 1)
## mean SE DEff
## Eros250 97,20000 0,00936 3,42
## Eros251 2,30000 0,00587 1,67
## Eros252 0,60000 0,00410 3,17
Ero26 <- svymean(~Eros26, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero26 * 100, 1)
## mean SE DEff
## Eros260 79,70000 0,02580 4,87
## Eros261 18,10000 0,02285 4,18
## Eros262 2,00000 0,00818 3,99
## Eros263 0,20000 0,00136 1,11
Ero27 <- svymean(~Eros27, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero27 * 100, 1)
## mean SE DEff
## Eros270 1,00e+02 2,06e-04 0,23
## Eros271 0,00e+00 2,06e-04 0,23
Ero31 <- svymean(~Eros31, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero31 * 100, 1)
## mean SE DEff
## Eros310 72,8000 0,0385 8,85
## Eros311 18,8000 0,0169 2,23
## Eros312 8,4000 0,0286 12,60
Ero32 <- svymean(~Eros32, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero32 * 100, 1)
## mean SE DEff
## Eros320 77,6000 0,0357 8,70
## Eros321 14,5000 0,0145 1,99
## Eros322 7,9000 0,0285 13,29
Ero33 <- svymean(~Eros33, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero33 * 100, 1)
## mean SE DEff
## Eros330 92,40000 0,02850 12,55
## Eros331 6,80000 0,02601 11,47
## Eros332 0,70000 0,00303 1,37
Ero34 <- svymean(~Eros34, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero34 * 100, 1)
## mean SE DEff
## Eros340 96,40000 0,01475 6,68
## Eros341 3,30000 0,01222 5,04
## Eros342 0,30000 0,00296 2,77
Ero35 <- svymean(~Eros35, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero35 * 100, 1)
## mean SE DEff
## Eros350 96,8000 0,0126 5,45
## Eros351 3,2000 0,0126 5,45
Ero36 <- svymean(~Eros36, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero36 * 100, 1)
## mean SE DEff
## Eros360 8,28e+01 2,06e-02 3,53
## Eros361 1,56e+01 1,93e-02 3,34
## Eros362 1,50e+00 6,42e-03 3,26
## Eros363 1,00e-01 6,59e-04 0,78
Ero37 <- svymean(~Eros37, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero37 * 100, 1)
## mean SE DEff
## Eros370 9,96e+01 2,94e-03 2,29
## Eros371 1,00e-01 4,87e-04 0,39
## Eros372 3,00e-01 2,96e-03 2,77
Ero41 <- svymean(~Eros41, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero41 * 100, 1)
## mean SE DEff
## Eros410 72,7000 0,0367 8,04
## Eros411 18,8000 0,0170 2,24
## Eros412 8,5000 0,0287 12,58
Ero42 <- svymean(~Eros42, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero42 * 100, 1)
## mean SE DEff
## Eros420 7,67e+01 3,90e-02 10,07
## Eros421 1,48e+01 1,59e-02 2,37
## Eros422 8,40e+00 2,84e-02 12,35
## Eros423 1,00e-01 4,31e-04 0,39
Ero43 <- svymean(~Eros43, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero43 * 100, 1)
## mean SE DEff
## Eros430 92,10000 0,02821 11,79
## Eros431 7,10000 0,02956 14,25
## Eros432 0,80000 0,00354 1,74
Ero44 <- svymean(~Eros44, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero44 * 100, 1)
## mean SE DEff
## Eros440 96,00000 0,01332 5,03
## Eros441 3,50000 0,01061 3,62
## Eros442 0,50000 0,00323 2,35
Ero45 <- svymean(~Eros45, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero45 * 100, 1)
## mean SE DEff
## Eros450 97,80000 0,00690 2,40
## Eros451 2,00000 0,00652 2,29
## Eros453 0,10000 0,00133 1,39
Ero46 <- svymean(~Eros46, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero46 * 100, 1)
## mean SE DEff
## Eros460 79,90000 0,02424 4,34
## Eros461 18,20000 0,02303 4,22
## Eros462 1,90000 0,00507 1,65
Ero47 <- svymean(~Eros47, diseniopost1, na.rm = TRUE, deff = TRUE)
round(Ero47 * 100, 1)
## mean SE DEff
## Eros470 99,10000 0,00623 4,78
## Eros471 0,70000 0,00609 5,41
## Eros473 0,10000 0,00133 1,39
svychisq(~Eros21 + Sexo.rec, diseniopost1, statistic = "Wald")
##
## Design-based Wald test of association
##
## data: svychisq(~Eros21 + Sexo.rec, diseniopost1, statistic = "Wald")
## F = 0,5285, ndf = 2, ddf = 39, p-value = 0,5937