[R] clm funtion and CI
Luciane Maria Pilotto
lutipilotto at yahoo.com.br
Fri Jul 10 14:47:49 CEST 2015
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", "Nïa 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("Não Usa",
"Uma Ponte Fixa", "Mais de 1 PF", "Prótese Parcial Removível",
"Prótese Fixa + Removível", "Prótese Total", "Sem Informação"
), class = "factor"), necprots = structure(c(1L, 1L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Não necessita",
"Prótese 1 elemento", "Mais de 1 elemento", "Combinação de próteses",
"Prótese Total", "Sem Informação"), class = "factor"), necproti = structure(c(1L,
1L, 4L, 2L, 1L, 3L, 1L, 1L, 2L, 1L), .Label = c("Não necessita",
"Prótese 1 elemento", "Mais de 1 elemento", "Combinação de próteses",
"Prótese Total", "Sem Informação"), class = "factor"), necprot = structure(c(1L,
1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L), .Label = c("Não necessita",
"Parcial 1 maxilar", "Parcial 2 maxilar", "Total 1 maxilar",
"Parcial + Total", "Total 2 maxilar", "Sem Informação"), 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 em 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
>
>
______________________________________________
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> and provide commented, minimal,
self-contained, reproducible code.
--
Kevin Wright
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