84 lines
2.5 KiB
R
84 lines
2.5 KiB
R
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## Not run:
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# import and format a mixed-type data set
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library(kamila)
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data(Byar, package='clustMD')
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ds <- readr::read_csv("2 Longterm/assigndata.csv",na = c("","NA")) |> na.omit()
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ds <- ds |> dplyr::mutate(mrs_1 = mrs_1>2)
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cat_i <- lapply(ds,is.character) |> purrr::list_c()
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con_i <- lapply(ds,is.double) |> purrr::list_c()
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xor(cat_i,con_i)
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clin_clust=2
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# Byar$logSpap <- log(Byar$Serum.prostatic.acid.phosphatase)
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# conInd <- c(5,6,8:10,16)
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# conVars <- Byar[,conInd]
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# conVars <- data.frame(scale(conVars))
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conVars <- ds[,con_i]
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conVars <- data.frame(scale(conVars))
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# catVarsFac <- Byar[,-c(1:2,conInd,11,14,15)]
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# catVarsFac[] <- lapply(catVarsFac, factor)
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# catVarsDum <- dummyCodeFactorDf(catVarsFac)
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catVarsFac <- ds[,cat_i]
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catVarsFac <- lapply(catVarsFac, factor) |> dplyr::bind_cols() |> as.data.frame()
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catVarsDum <- dummyCodeFactorDf(catVarsFac)
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# Modha-Spangler clustering with kmeans default Hartigan-Wong algorithm
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gmsResHw <- gmsClust(conVars, catVarsDum, nclust = clin_clust)
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# Modha-Spangler clustering with kmeans Forgy-Lloyd algorithm
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# NOTE searchDensity should be >= 10 for optimal performance:
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# this is just a syntax demo
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gmsResLloyd <- gmsClust(conVars, catVarsDum, nclust = clin_clust,
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algorithm = "Lloyd", searchDensity = 15)
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# KAMILA clustering
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kamRes <- kamila(conVars, catVarsFac, numClust=2:7, numInit=10, calcNumClust="ps")
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# Plot results
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# ternarySurvival <- factor(Byar$SurvStat)
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# levels(ternarySurvival) <- c('Alive','DeadProst','DeadOther')[c(1,2,rep(3,8))]
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plottingData <- cbind(
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conVars,
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catVarsFac,
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KamilaCluster = factor(kamRes$finalMemb))
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# plottingData$Bone.metastases <- ifelse(
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# plottingData$Bone.metastases == '1', yes='Yes',no='No')
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#
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# # Plot Modha-Spangler/Hartigan-Wong results
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# msPlot <- ggplot(
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# plottingData,
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# aes(
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# x=logSpap,
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# y=Index.of.tumour.stage.and.histolic.grade,
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# color=ternarySurvival,
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# shape=MSCluster))
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# plotOpts <- function(pl) (pl + geom_point() +
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# scale_shape_manual(values=c(2,3,7)) + geom_jitter())
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# plotOpts(msPlot)
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# Plot KAMILA results
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kamPlot <- ggplot(
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plottingData,
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aes(
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x=pase_0,
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y=pase_6,
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color=KamilaCluster,
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shape=KamilaCluster))
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plotOpts(kamPlot)
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plotting_ls <- tibble(KamilaCluster = factor(kamRes$finalMemb),
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MSCluster = factor(gmsResHw$results$cluster)) |>
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purrr::map(\(x) cbind(x, ds))
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plotting_ls |> purrr::map(\(y) {
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y |> gtsummary::tbl_summary(by=x) |> gtsummary::add_p() |> gtsummary::add_overall()}) |>
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gtsummary::tbl_merge()
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