HI everyone, I try to get some bootstrap CIs for coefficients obtained by quantile regression. I have influencial values and thus switched to quantreg.. The data is clustered and within clusters the variance of my DV = 0.. Is this sensible for the below data? And what about the warnings? Thanks in advance for any guidance, Kay> dput(d)structure(list(Porenfläche = c(4990L, 7002L, 7558L, 7352L, 7943L, 7979L, 9333L, 8209L, 8393L, 6425L, 9364L, 8624L, 10651L, 8868L, 9417L, 8874L, 10962L, 10743L, 11878L, 9867L, 7838L, 11876L, 12212L, 8233L, 6360L, 4193L, 7416L, 5246L, 6509L, 4895L, 6775L, 7894L, 5980L, 5318L, 7392L, 7894L, 3469L, 1468L, 3524L, 5267L, 5048L, 1016L, 5605L, 8793L, 3475L, 1651L, 5514L, 9718L), P.Perimeter = c(2791.9, 3892.6, 3930.66, 3869.32, 3948.54, 4010.15, 4345.75, 4344.75, 3682.04, 3098.65, 4480.05, 3986.24, 4036.54, 3518.04, 3999.37, 3629.07, 4608.66, 4787.62, 4864.22, 4479.41, 3428.74, 4353.14, 4697.65, 3518.44, 1977.39, 1379.35, 1916.24, 1585.42, 1851.21, 1239.66, 1728.14, 1461.06, 1426.76, 990.388, 1350.76, 1461.06, 1376.7, 476.322, 1189.46, 1644.96, 941.543, 308.642, 1145.69, 2280.49, 1174.11, 597.808, 1455.88, 1485.58), P.Form = c(0.0903296, 0.148622, 0.183312, 0.117063, 0.122417, 0.167045, 0.189651, 0.164127, 0.203654, 0.162394, 0.150944, 0.148141, 0.228595, 0.231623, 0.172567, 0.153481, 0.204314, 0.262727, 0.200071, 0.14481, 0.113852, 0.291029, 0.240077, 0.161865, 0.280887, 0.179455, 0.191802, 0.133083, 0.225214, 0.341273, 0.311646, 0.276016, 0.197653, 0.326635, 0.154192, 0.276016, 0.176969, 0.438712, 0.163586, 0.253832, 0.328641, 0.230081, 0.464125, 0.420477, 0.200744, 0.262651, 0.182453, 0.200447), Durchlässigkeit = c(6.3, 6.3, 6.3, 6.3, 17.1, 17.1, 17.1, 17.1, 119, 119, 119, 119, 82.4, 82.4, 82.4, 82.4, 58.6, 58.6, 58.6, 58.6, 142, 142, 142, 142, 740, 740, 740, 740, 890, 890, 890, 890, 950, 950, 950, 950, 100, 100, 100, 100, 1300, 1300, 1300, 1300, 580, 580, 580, 580), Gebiete structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 5L, 5L, 5L, 5L, 12L, 12L, 12L, 12L, 8L, 8L, 8L, 8L), .Label = c("6.3", "17.1", "58.6", "82.4", "100", "119", "142", "580", "740", "890", "950", "1300"), class = "factor")), .Names c("Porenfläche", "P.Perimeter", "P.Form", "Durchlässigkeit", "Gebiete"), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48"), class = "data.frame") ## do quantile regression and bootstrap the coefficients, allowing for clustered data ## by putting "Gebiet" as strata argument (?), ## dv variation within clusters/Gebiet = 0! bs <- function(formula, data, indices) { d <- data[indices, ] # allows boot to select sample fit <- rlm(formula, data = d) return(coef(fit)) } results <- boot(data = d, statistic = bs, strata = d$Gebiete, R = 199, formula = Durchlässigkeit ~ P.Perimeter + P.Form) # get 99% confidence intervals boot.ci(results, type="bca", index=1, conf = .99) # intercept boot.ci(results, type="bca", index=2, conf = .99) # P.Perimeter boot.ci(results, type="bca", index=3, conf = .99) # P.Form -- Kay Cichini, MSc Biol Grubenweg 22, 6071 Aldrans E-Mail: kay.cichini@gmail.com -- [[alternative HTML version deleted]]
sry, I forgot to replace rlm() - but actually I tried both and the question applies to both approaches.. Am 31.10.2012 00:19 schrieb "Kay Cichini" <kay.cichini@gmail.com>:> > HI everyone, > > I try to get some bootstrap CIs for coefficients obtained by quantileregression. I have influencial values and thus switched to quantreg..> The data is clustered and within clusters the variance of my DV = 0.. > > Is this sensible for the below data? And what about the warnings? > > Thanks in advance for any guidance, > Kay > > > dput(d) > structure(list(Porenfläche = c(4990L, 7002L, 7558L, 7352L, 7943L, > 7979L, 9333L, 8209L, 8393L, 6425L, 9364L, 8624L, 10651L, 8868L, > 9417L, 8874L, 10962L, 10743L, 11878L, 9867L, 7838L, 11876L, 12212L, > 8233L, 6360L, 4193L, 7416L, 5246L, 6509L, 4895L, 6775L, 7894L, > 5980L, 5318L, 7392L, 7894L, 3469L, 1468L, 3524L, 5267L, 5048L, > 1016L, 5605L, 8793L, 3475L, 1651L, 5514L, 9718L), P.Perimeter = c(2791.9, > 3892.6, 3930.66, 3869.32, 3948.54, 4010.15, 4345.75, 4344.75, > 3682.04, 3098.65, 4480.05, 3986.24, 4036.54, 3518.04, 3999.37, > 3629.07, 4608.66, 4787.62, 4864.22, 4479.41, 3428.74, 4353.14, > 4697.65, 3518.44, 1977.39, 1379.35, 1916.24, 1585.42, 1851.21, > 1239.66, 1728.14, 1461.06, 1426.76, 990.388, 1350.76, 1461.06, > 1376.7, 476.322, 1189.46, 1644.96, 941.543, 308.642, 1145.69, > 2280.49, 1174.11, 597.808, 1455.88, 1485.58), P.Form = c(0.0903296, > 0.148622, 0.183312, 0.117063, 0.122417, 0.167045, 0.189651, 0.164127, > 0.203654, 0.162394, 0.150944, 0.148141, 0.228595, 0.231623, 0.172567, > 0.153481, 0.204314, 0.262727, 0.200071, 0.14481, 0.113852, 0.291029, > 0.240077, 0.161865, 0.280887, 0.179455, 0.191802, 0.133083, 0.225214, > 0.341273, 0.311646, 0.276016, 0.197653, 0.326635, 0.154192, 0.276016, > 0.176969, 0.438712, 0.163586, 0.253832, 0.328641, 0.230081, 0.464125, > 0.420477, 0.200744, 0.262651, 0.182453, 0.200447), Durchlässigkeit c(6.3, > 6.3, 6.3, 6.3, 17.1, 17.1, 17.1, 17.1, 119, 119, 119, 119, 82.4, > 82.4, 82.4, 82.4, 58.6, 58.6, 58.6, 58.6, 142, 142, 142, 142, > 740, 740, 740, 740, 890, 890, 890, 890, 950, 950, 950, 950, 100, > 100, 100, 100, 1300, 1300, 1300, 1300, 580, 580, 580, 580), Gebiete structure(c(1L, > 1L, 1L, 1L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 3L, > 3L, 3L, 3L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, > 11L, 11L, 11L, 11L, 5L, 5L, 5L, 5L, 12L, 12L, 12L, 12L, 8L, 8L, > 8L, 8L), .Label = c("6.3", "17.1", "58.6", "82.4", "100", "119", > "142", "580", "740", "890", "950", "1300"), class = "factor")), .Names c("Porenfläche", > "P.Perimeter", "P.Form", "Durchlässigkeit", "Gebiete"), row.names c("1", > "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", > "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", > "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", > "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", > "47", "48"), class = "data.frame") > > ## do quantile regression and bootstrap the coefficients, allowing forclustered data> ## by putting "Gebiet" as strata argument (?), > ## dv variation within clusters/Gebiet = 0! > bs <- function(formula, data, indices) { > d <- data[indices, ] # allows boot to select sample > fit <- rlm(formula, data = d) > return(coef(fit)) > } > > results <- boot(data = d, statistic = bs, strata = d$Gebiete, > R = 199, formula = Durchlässigkeit ~ P.Perimeter + P.Form) > > # get 99% confidence intervals > boot.ci(results, type="bca", index=1, conf = .99) # intercept > boot.ci(results, type="bca", index=2, conf = .99) # P.Perimeter > boot.ci(results, type="bca", index=3, conf = .99) # P.Form > > -- > > Kay Cichini, MSc Biol > > Grubenweg 22, 6071 Aldrans > > E-Mail: kay.cichini@gmail.com > -- > >[[alternative HTML version deleted]]
There is no automatic "clustering" option for QR bootstrapping. You will have to roll your own. url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Urbana, IL 61801 On Oct 31, 2012, at 1:38 AM, Kay Cichini wrote:> sry, I forgot to replace rlm() - but actually I tried both and the question > applies to both approaches.. > > Am 31.10.2012 00:19 schrieb "Kay Cichini" <kay.cichini at gmail.com>: >> >> HI everyone, >> >> I try to get some bootstrap CIs for coefficients obtained by quantile > regression. I have influencial values and thus switched to quantreg.. >> The data is clustered and within clusters the variance of my DV = 0.. >> >> Is this sensible for the below data? And what about the warnings? >> >> Thanks in advance for any guidance, >> Kay >> >>> dput(d) >> structure(list(Porenfl?che = c(4990L, 7002L, 7558L, 7352L, 7943L, >> 7979L, 9333L, 8209L, 8393L, 6425L, 9364L, 8624L, 10651L, 8868L, >> 9417L, 8874L, 10962L, 10743L, 11878L, 9867L, 7838L, 11876L, 12212L, >> 8233L, 6360L, 4193L, 7416L, 5246L, 6509L, 4895L, 6775L, 7894L, >> 5980L, 5318L, 7392L, 7894L, 3469L, 1468L, 3524L, 5267L, 5048L, >> 1016L, 5605L, 8793L, 3475L, 1651L, 5514L, 9718L), P.Perimeter = c(2791.9, >> 3892.6, 3930.66, 3869.32, 3948.54, 4010.15, 4345.75, 4344.75, >> 3682.04, 3098.65, 4480.05, 3986.24, 4036.54, 3518.04, 3999.37, >> 3629.07, 4608.66, 4787.62, 4864.22, 4479.41, 3428.74, 4353.14, >> 4697.65, 3518.44, 1977.39, 1379.35, 1916.24, 1585.42, 1851.21, >> 1239.66, 1728.14, 1461.06, 1426.76, 990.388, 1350.76, 1461.06, >> 1376.7, 476.322, 1189.46, 1644.96, 941.543, 308.642, 1145.69, >> 2280.49, 1174.11, 597.808, 1455.88, 1485.58), P.Form = c(0.0903296, >> 0.148622, 0.183312, 0.117063, 0.122417, 0.167045, 0.189651, 0.164127, >> 0.203654, 0.162394, 0.150944, 0.148141, 0.228595, 0.231623, 0.172567, >> 0.153481, 0.204314, 0.262727, 0.200071, 0.14481, 0.113852, 0.291029, >> 0.240077, 0.161865, 0.280887, 0.179455, 0.191802, 0.133083, 0.225214, >> 0.341273, 0.311646, 0.276016, 0.197653, 0.326635, 0.154192, 0.276016, >> 0.176969, 0.438712, 0.163586, 0.253832, 0.328641, 0.230081, 0.464125, >> 0.420477, 0.200744, 0.262651, 0.182453, 0.200447), Durchl?ssigkeit > c(6.3, >> 6.3, 6.3, 6.3, 17.1, 17.1, 17.1, 17.1, 119, 119, 119, 119, 82.4, >> 82.4, 82.4, 82.4, 58.6, 58.6, 58.6, 58.6, 142, 142, 142, 142, >> 740, 740, 740, 740, 890, 890, 890, 890, 950, 950, 950, 950, 100, >> 100, 100, 100, 1300, 1300, 1300, 1300, 580, 580, 580, 580), Gebiete > structure(c(1L, >> 1L, 1L, 1L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 3L, >> 3L, 3L, 3L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, >> 11L, 11L, 11L, 11L, 5L, 5L, 5L, 5L, 12L, 12L, 12L, 12L, 8L, 8L, >> 8L, 8L), .Label = c("6.3", "17.1", "58.6", "82.4", "100", "119", >> "142", "580", "740", "890", "950", "1300"), class = "factor")), .Names > c("Porenfl?che", >> "P.Perimeter", "P.Form", "Durchl?ssigkeit", "Gebiete"), row.names > c("1", >> "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", >> "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", >> "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", >> "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", >> "47", "48"), class = "data.frame") >> >> ## do quantile regression and bootstrap the coefficients, allowing for > clustered data >> ## by putting "Gebiet" as strata argument (?), >> ## dv variation within clusters/Gebiet = 0! >> bs <- function(formula, data, indices) { >> d <- data[indices, ] # allows boot to select sample >> fit <- rlm(formula, data = d) >> return(coef(fit)) >> } >> >> results <- boot(data = d, statistic = bs, strata = d$Gebiete, >> R = 199, formula = Durchl?ssigkeit ~ P.Perimeter + P.Form) >> >> # get 99% confidence intervals >> boot.ci(results, type="bca", index=1, conf = .99) # intercept >> boot.ci(results, type="bca", index=2, conf = .99) # P.Perimeter >> boot.ci(results, type="bca", index=3, conf = .99) # P.Form >> >> -- >> >> Kay Cichini, MSc Biol >> >> Grubenweg 22, 6071 Aldrans >> >> E-Mail: kay.cichini at gmail.com >> -- >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > 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.
Maybe Matching Threads
- problem with interaction in lmer even after creating an "interaction variable"
- Separating columns, and sorting by rows
- Bland Altman summary stats for all column combinations
- Problem with understanding output of Cox model
- convert.times in chron, error when 59 < seconds < 60 (PR#6878)