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Luciane Maria Pilotto
2015-Jul-10 12:47 UTC
[R] clm funtion and CI

I am using the commands bellow. 

##########################################
load(file.choose())#dataframe:"id3.rda"
attach(id3)

dput(head(id3,10))

structure(list(regiao = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("Norte", "Nordeste",
"Sudeste", "Sul",
"Centro-Oeste"), class = "factor"), estado = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Rondonia",
"Acre",
"Amazonas", "Roraima", "Para", "Amapa",
"Tocantins", "Maranhao",
"Piaui", "Ceara", "Rio Grande Do Norte",
"Paraiba", "Pernambuco",
"Alagoas", "Sergipe", "Bahia", "Minas
Gerais", "Espirito Santo",
"Rio De Janeiro", "Sao Paulo", "Parana",
"Santa Catarina", "Rio Grande Do Sul",
"Mato Grosso Do Sul", "Mato Grosso", "Goias",
"Distrito Federal"
), class = "factor"), cod_mun = c(1200401L, 1200401L, 1200401L, 
1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L
), setor = c("05000056", "05000056", "05000056",
"05000056",
"05000056", "05000056", "05000056",
"05000056", "05000062", "05000062"
), cap_int = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L), .Label = c("Interior", "Capital"), class =
"factor"), idade = c(15L,
15L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 15L), getario = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("5 anos", "12
anos",
"15 a 19 anos", "35 a 44 anos", "65 a 74 anos"),
class = "factor"),
? ? sexo = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L
? ? ), .Label = c("Male", "Female"), class =
"factor"), grp_etni = structure(c(1L,
? ? 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L), .Label = c("Branca", 
? ? "Preta", "Amarela", "Parda",
"Indigena", "Sem Informacao"
? ? ), class = "factor"), quest_01 = c(8L, 2L, 4L, 4L, 3L, 4L, 
? ? 3L, 3L, 3L, 4L), quest_02 = c(4L, 4L, 2L, 2L, 4L, 1L, 3L, 
? ? 1L, 3L, 2L), density = c(2, 0.5, 2, 2, 0.75, 4, 1, 3, 1, 
? ? 2), quest_03 = c(3L, 4L, 6L, 5L, 5L, 3L, 3L, 3L, 6L, 5L), 
? ? quest_04 = structure(c(2L, 3L, 3L, 4L, NA, 2L, 1L, NA, 3L, 
? ? 3L), .Label = c("Ate 250", "251 a 500", "501 a
1.500", "1.501 a 2.500",
? ? "2.501 a 4.500", "4.501 a 9.500", "Mais de
9.500", "Nao sabe/Nao respondeu"
? ? ), class = "factor"), inc_percapita1 = c(46.875, 500, 250, 
? ? 500, NA, 93.75, 41.6666679382324, NA, 333.333343505859, 250
? ? ), inc_percapita2 = c(46.875, 500, 250, 500, NA, 93.75, 41.6666679382324, 
? ? NA, 333.333343505859, 250), inc_sqrt1 = c(132.58251953125, 
? ? 707.106811523438, 500, 1000, NA, 187.5, 72.1687850952148, 
? ? NA, 577.350280761719, 500), inc_sqrt2 = c(132.58251953125, 
? ? 707.106811523438, 500, 1000, NA, 187.5, 72.1687850952148, 
? ? NA, 577.350280761719, 500), q05 = c(11L, 4L, 12L, 6L, 11L, 
? ? 10L, 7L, 12L, 8L, 7L), quest_06 = structure(c(2L, 2L, 2L, 
? ? 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Nao", "Sim",
"Nao se aplica",
? ? "Nao sabe"), class = "factor"), quest_07 =
structure(c(1L,
? ? 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("Nao",
"Sim",
? ? "Nao se aplica", "Nao sabe"), class =
"factor"), quest_08 = c(9L,
? ? 9L, 9L, 9L, 9L, 9L, 9L, 9L, 3L, 9L), quest_09 = structure(c(1L,
? ? 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Nao",
"Sim",
? ? "Nao se aplica", "Nao sabe"), class =
"factor"), quest_10 = structure(c(5L,
? ? 5L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), .Label = c("Menos de 1 ano",
? ? "1 a 2 anos", ">3 anos", "Outros",
"N?a se aplica", "Nao sabe"
? ? ), class = "factor"), quest_11 = structure(c(5L, 5L, 1L, 
? ? 1L, 2L, 1L, 1L, 1L, 1L, 1L), .Label = c("Publico",
"Particular",
? ? "Plano de Saude/Convenios", "Outros", "Nao se
aplica", "Nao sabe"
? ? ), class = "factor"), quest_12 = structure(c(6L, 6L, 2L, 
? ? 4L, 1L, 3L, 1L, 4L, 2L, 1L), .Label = c("Revisao/Prevencao", 
? ? "Dor", "Extracao", "Tratamento",
"Outros", "Nao se aplica",
? ? "Nao sabe"), class = "factor"), quest_13 =
structure(c(NA,
? ? NA, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L), .Label = c("Muito bom", 
? ? "Bom", "Regular", "Ruim", "Muito
Ruim", "Nao se aplica",
? ? "Nao sabe"), class = "factor"), quest_14 =
structure(c(3L,
? ? 2L, 3L, 4L, 2L, 2L, 4L, 3L, 4L, 2L), .Label = c("Muito
satisfeito",
? ? "Satisfeito", "Nem satisfeito/insatisfeito",
"Insatisfeito",
? ? "Muito Insatisfeito", "Nao sabe"), class =
"factor"), quest_15 = structure(c(1L,
? ? 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Nao",
"Sim",
? ? "Nao se aplica", "Nao sabe"), class =
"factor"), exame = c("1",
? ? "1", "1", "1", "1", "1",
"1", "1", "1", "1"), cpod = c(3L,
? ? 0L, 8L, 3L, 3L, 12L, 1L, 6L, 6L, 3L), p_sang = c(0L, 1L, 
? ? 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L), p_calc = c(0L, 1L, 1L, 0L, 
? ? 0L, 1L, 0L, 1L, 0L, 0L), cpi_max = structure(c(1L, 3L, 3L, 
? ? 1L, 1L, 3L, 1L, 3L, 1L, 2L), .Label = c("Higido",
"Sangramento",
? ? "Calculo", "Bolsa 4-5 mm", "Bolsa 6 mm ou +",
"4", "A", "X"
? ? ), class = "factor"), dai = c(21L, 32L, 23L, 21L, 19L, 18L, 
? ? 17L, 23L, 34L, 25L), trauma = c(NA_integer_, NA_integer_, 
? ? NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
? ? NA_integer_, NA_integer_, NA_integer_), n_higido = c(26L, 
? ? 28L, 24L, 24L, 25L, 17L, 27L, 25L, 24L, 25L), n_cariado = c(3L,
? ? 0L, 6L, 2L, 0L, 2L, 1L, 2L, 4L, 3L), n_restcar = c(0L, 0L, 
? ? 0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L), n_restaur = c(0L, 0L, 2L, 
? ? 0L, 3L, 6L, 0L, 2L, 0L, 0L), n_perdcar = c(0L, 0L, 0L, 1L, 
? ? 0L, 4L, 0L, 0L, 1L, 0L), n_perdout = c(0L, 0L, 0L, 0L, 0L, 
? ? 0L, 0L, 0L, 0L, 0L), perdidos = c(0, 0, 0, 1, 0, 4, 0, 0, 
? ? 1, 0), usaprots = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), usaproti =
structure(c(1L,
? ? 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("N?o Usa", 
? ? "Uma Ponte Fixa", "Mais de 1 PF", "Pr?tese Parcial
Remov?vel",
? ? "Pr?tese Fixa + Remov?vel", "Pr?tese Total", "Sem
Informa??o"
? ? ), class = "factor"), necprots = structure(c(1L, 1L, 3L, 
? ? 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("N?o necessita", 
? ? "Pr?tese 1 elemento", "Mais de 1 elemento",
"Combina??o de pr?teses",
? ? "Pr?tese Total", "Sem Informa??o"), class =
"factor"), necproti = structure(c(1L,
? ? 1L, 4L, 2L, 1L, 3L, 1L, 1L, 2L, 1L), .Label = c("N?o necessita", 
? ? "Pr?tese 1 elemento", "Mais de 1 elemento",
"Combina??o de pr?teses",
? ? "Pr?tese Total", "Sem Informa??o"), class =
"factor"), necprot = structure(c(1L,
? ? 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L), .Label = c("N?o necessita", 
? ? "Parcial 1 maxilar", "Parcial 2 maxilar", "Total 1
maxilar",
? ? "Parcial + Total", "Total 2 maxilar", "Sem
Informa??o"), class = "factor"),
? ? oidp = c(0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 1L), f1 = c(1, 
? ? 1, 1, 1, 1, 1, 1, 1, 1, 1), f2 = c(0.0518900007009506, 0.0518900007009506, 
? ? 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 
? ? 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 
? ? 0.106059998273849, 0.106059998273849), f3 = c(0.0662299990653992,
? ? 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 
? ? 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 
? ? 0.0662299990653992, 0.138889998197556, 0.138889998197556), 
? ? f = c(0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 
? ? 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 
? ? 0.00343999988399446, 0.00343999988399446, 0.0147299999371171, 
? ? 0.0147299999371171), bwgr_et = c(291.019989013672, 291.019989013672, 
? ? 291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 
? ? 291.019989013672, 291.019989013672, 67.879997253418, 67.879997253418
? ? ), cluster = c(120040105000056, 120040105000056, 120040105000056,
? ? 120040105000056, 120040105000056, 120040105000056, 120040105000056,
? ? 120040105000056, 120040105000062, 120040105000062), cluster2 =
c("01120308",
? ? "01120308", "01120308", "01120308",
"01120308", "01120308",
? ? "01120308", "01120308", "01120309",
"01120309"), getni = c(1L,
? ? 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L), q04 = c(2L, 3L, 3L, 
? ? 4L, NA, 2L, 1L, NA, 3L, 3L), q06 = c(2L, 2L, 2L, 2L, 2L, 
? ? 2L, 2L, 2L, 2L, 2L), q07 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
? ? 1L, 2L, 1L), q08 = c(0, 0, 0, 0, 0, 0, 0, 0, 3, 0), q10 = c(NA,
? ? NA, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), q11 = c(NA, NA, 1L, 
? ? 1L, 2L, 1L, 1L, 1L, 1L, 1L), q12 = c(NA, NA, 2L, 4L, 1L, 
? ? 3L, 1L, 4L, 2L, 1L), q13 = structure(c(NA, NA, 2L, 3L, 2L, 
? ? 3L, 2L, 2L, 2L, 2L), .Label = c("1", "2", "3",
"4", "5"), class = "factor"),
? ? q14 = c(3L, 2L, 3L, 4L, 2L, 2L, 4L, 3L, 4L, 2L), q15 = c(1L, 
? ? 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), edcat = c(3, 1, 4, 2, 
? ? 3, 3, 2, 4, 2, 2), peso = c(291, 291, 291, 291, 291, 291, 
? ? 291, 291, 68, 68), cariado = c(3L, 0L, 6L, 2L, 0L, 2L, 1L, 
? ? 4L, 5L, 3L), getar = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), .Names =
c("regiao",
"estado", "cod_mun", "setor", "cap_int",
"idade", "getario",
"sexo", "grp_etni", "quest_01",
"quest_02", "density", "quest_03",
"quest_04", "inc_percapita1", "inc_percapita2",
"inc_sqrt1",
"inc_sqrt2", "q05", "quest_06",
"quest_07", "quest_08", "quest_09",
"quest_10", "quest_11", "quest_12",
"quest_13", "quest_14", "quest_15",
"exame", "cpod", "p_sang", "p_calc",
"cpi_max", "dai", "trauma",
"n_higido", "n_cariado", "n_restcar",
"n_restaur", "n_perdcar",
"n_perdout", "perdidos", "usaprots",
"usaproti", "necprots",
"necproti", "necprot", "oidp", "f1",
"f2", "f3", "f", "bwgr_et",
"cluster", "cluster2", "getni", "q04",
"q06", "q07", "q08", "q10",
"q11", "q12", "q13", "q14",
"q15", "edcat", "peso", "cariado",
"getar"), row.names = c("1241", "1242",
"1243", "1244", "1245",
"1246", "1247", "1248", "1256",
"1268"), class = "data.frame")
################################################################################

id3$q13<-as.factor(id3$q13)
m1 <- polr(q13 ~ q11 + q10 + q12 + edcat + q08 + q06 + q14, data=id3,
Hess=TRUE)
summary(m1)
ordinal.or.display(m1)
####################################################################################

fm1 <- clm(q13 ~ q11 + q10 + q12 + edcat + q08 + q06 + q14, data=id3) 

summary(fm1)
exp(coef(fm1)) 

[[elided Yahoo spam]]
(ci <- confint(fm1))
exp(cbind(OR = coef(fm1), ci))

############################################################################################
nominal_test(fm1)# test partial proportional odds assumption

##clm2 - partial proprotional odds

fm.nom <- clm2(q13 ~ q11 + q10 + q12 + q08 + q06 + q14, data=id3, nominal=~
edcat)
summary(fm.nom)
exp(coef(fm.nom))
[[elided Yahoo spam]]
exp(cbind(OR = coef(fm.nom), ci))

Thanks,
Luciane
--------------------------------------------
Em qui, 9/7/15, Kevin Wright <kw.stat at gmail.com> escreveu:

 Assunto: Re: [R] clm funtion and CI

 Data: Quinta-feira, 9 de Julho de 2015, 11:44

 You need a reproducible
 example.

 On Wed, Jul 8, 2015 at 7:43
 PM, Luciane Maria Pilotto

 wrote:
 > Hi,
 >
 > I'm working with ordinal logistic
 regression and fitting the model with the "clm"
 funtion of the ordinal package and would like to get the CI.
 According to the Tutorial on fitting Cumulative Link Models
 with the ordinal Package, Rune Haubo B Christensen (21
 January 2015) you can run the OR, but not CI. The same
 happens with the "clm2" for partial proportional
 odds.
 >
 > I appreciate
 any help !!
 >
 >
 > Luciane
 >
 >
 ______________________________________________
 > R-help at r-project.org
 mailing list -- To UNSUBSCRIBE and more, see
 > https://stat.ethz.ch/mailman/listinfo/r-help
 > PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
 > and provide commented, minimal,
 self-contained, reproducible code.



 -- 
 Kevin Wright
R help - Jul 2015 - clm funtion and CI

[R] clm funtion and CI
