Yes Thanks! that works, but I loose the \n when I would like to save or edit it.
getTextInWindows is a function that edits any text in editor. getTextInWindows(summary): without "\n" save (file= "junk.txt", junk):without "\n" getTextInWindow(capture.output(cat(junk, sep = "\n"))) :No works Thanks Ô__ c/ /'_;~~~~kmezhoud (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ http://bioinformatics.tn/ On Thu, Nov 13, 2014 at 9:49 PM, William Dunlap <wdun...@tibco.com> wrote: > Use capture.output(), as in > > junk <- capture.output(summary(1:10)) > > junk > [1] " Min. 1st Qu. Median Mean 3rd Qu. Max. " > [2] " 1.00 3.25 5.50 5.50 7.75 10.00 " > > cat(junk, sep="\n") > Min. 1st Qu. Median Mean 3rd Qu. Max. > 1.00 3.25 5.50 5.50 7.75 10.00 > > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > On Thu, Nov 13, 2014 at 12:35 PM, Karim Mezhoud <kmezh...@gmail.com> > wrote: > >> Hi, >> >> the print of rpart fitting gives the summary of tree >> I would like to save the console text of: >> fit <- rpart(formula, data) >> summary <- print(fit) >> >> when I look in "summary" I did not find the same thing as in >> >> >> "print(rpart)" >> >> >> [1] "Clinical Data exists" >> [1] "merging samples from Clinical and Profile Data" >> [1] "Selected formula: DFS_STATUS~." >> n= 236 >> >> node), split, n, loss, yval, (yprob) >> * denotes terminal node >> >> 1) root 236 58 DiseaseFree (0.21610169 0.75423729 0.02966102) >> 2) PIK3CA< 302.7615 105 42 DiseaseFree (0.39047619 0.60000000 >> 0.00952381) >> 4) FGFR1< 941.6309 41 16 (0.60975610 0.36585366 0.02439024) >> 8) ANXA1>=2148.882 19 3 (0.84210526 0.10526316 0.05263158) * >> 9) ANXA1< 2148.882 22 9 DiseaseFree (0.40909091 0.59090909 >> 0.00000000) >> 18) RAF1< 2315.279 13 4 (0.69230769 0.30769231 0.00000000) * >> 19) RAF1>=2315.279 9 0 DiseaseFree (0.00000000 1.00000000 >> 0.00000000) * >> 5) FGFR1>=941.6309 64 16 DiseaseFree (0.25000000 0.75000000 >> 0.00000000) >> 10) CDH2>=153.6887 10 2 (0.80000000 0.20000000 0.00000000) * >> 11) CDH2< 153.6887 54 8 DiseaseFree (0.14814815 0.85185185 >> 0.00000000) >> 22) PCNA< 696.389 7 3 (0.57142857 0.42857143 0.00000000) * >> 23) PCNA>=696.389 47 4 DiseaseFree (0.08510638 0.91489362 >> 0.00000000) * >> 3) PIK3CA>=302.7615 131 16 DiseaseFree (0.07633588 0.87786260 >> 0.04580153) * >> > >> class(summary) >> #rpart >> >> summary >> {list(var = c(6, 3, 1, 4, 7, 4, 4, 2, 4, 5, 4, 4, 4), n = c(236, 105, 41, >> 19, 22, 13, 9, 64, 10, 54, 7, 47, 131), wt = c(236, 105, 41, 19, 22, 13, >> 9, >> 64, 10, 54, 7, 47, 131), dev = c(58, 42, 16, 3, 9, 4, 0, 16, 2, 8, 3, 4, >> 16), yval = c(2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2), complexity = >> c(0.0862068965517241, 0.0862068965517241, 0.0775862068965517, 0.01, >> 0.0775862068965517, 0.01, 0.01, 0.0862068965517241, 0.01, >> 0.0172413793103448, 0.01, 0, 0), ncompete = c(4, 4, 4, 0, 4, 0, 0, 4, 0, >> 4, >> 0, 0, 0), >> nsurrogate = c(5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0), yval2 = c(2, 2, >> 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 51, 41, 25, 16, 9, 9, 0, 16, 8, 8, 4, 4, >> 10, 178, 63, 15, 2, 13, 4, 9, 48, 2, 46, 3, 43, 115, 7, 1, 1, 1, 0, 0, 0, >> 0, 0, 0, 0, 0, 6, 0.216101694915254, 0.39047619047619, 0.609756097560976, >> 0.842105263157895, 0.409090909090909, 0.692307692307692, 0, 0.25, 0.8, >> 0.148148148148148, 0.571428571428571, 0.0851063829787234, >> 0.0763358778625954, 0.754237288135593, 0.6, 0.365853658536585, >> 0.105263157894737, >> 0.590909090909091, 0.307692307692308, 1, 0.75, 0.2, 0.851851851851852, >> 0.428571428571429, 0.914893617021277, 0.877862595419847, >> 0.0296610169491525, 0.00952380952380952, 0.024390243902439, >> 0.0526315789473684, 0, 0, 0, 0, 0, 0, 0, 0, 0.0458015267175573, 1, >> 0.444915254237288, 0.173728813559322, 0.0805084745762712, >> 0.0932203389830508, 0.0550847457627119, 0.038135593220339, >> 0.271186440677966, 0.0423728813559322, 0.228813559322034, >> 0.0296610169491525, 0.199152542372881, 0.555084745762712))} {c(13, 13, 13, >> 12, 7, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, >> 13, 13, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, >> 13, >> 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, >> 13, >> 13, 13, 13, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, >> 13, >> 13, 13, 13, 13, 13, 13, 13, 13, 13, 6, 12, 12, 12, 6, 6, 13, 6, 13, 4, 13, >> 13, 13, 7, 12, 6, 12, 12, 12, 9, 4, 12, 11, 12, 12, 12, 11, 12, 12, 13, 7, >> 4, 12, 12, 4, 9, 7, 13, 12, 7, 12, >> 12, 13, 12, 13, 12, 11, 12, 13, 12, 13, 13, 13, 12, 13, 12, 13, 7, 13, 13, >> 12, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 13, >> 13, >> 13, 7, 13, 11, 13, 4, 7, 4, 9, 9, 13, 4, 12, 13, 13, 13, 13, 4, 13, 7, 4, >> 6, 12, 12, 12, 12, 12, 13, 11, 13, 6, 13, 13, 4, 12, 12, 4, 11, 12, 12, >> 12, >> 13, 13, 13, 12, 9, 9, 9, 4, 9, 4, 4, 4, 11, 13, 4, 4, 9, 12, 6, 4, 9, 4, >> 6, >> 6, 6, 6, 6, 12)} {rpart(formula = frmla, data = ProfData, method = >> "class")} {DFS_STATUS ~ A1CF + ACACA + ACKR2 + AGXT + AHCYL2 + AHSA1 + >> AIMP2 + AKR1B1 + AKT1 + AKT1S1 + ANO3 + ANXA1 + APOBR + AQP7 + AR + >> ARHGEF26 + ARID1A + ATM + BAK1 + BAX + BCL2 + BCL2L1 + BCL2L11 + BECN1 + >> BID + BIRC2 + BRAF + CASP3 + CASP7 + CASP8 + CASP9 + CAT + CAV1 + CCL15 + >> CCNB1 + CCND1 + CCNE1 + CCNE2 + CDH1 + CDH2 + CDHR2 + CDHR5 + CDK1 + >> CDKN1A >> + CHEK1 + CHEK2 + CHST5 + CLDN7 + CLIC6 + CNTN1 + COL6A2 + COX2 + CTNNB1 + >> DDR2 + DEFA6 + DIABLO + DIRAS1 + DKK3 + DNAJC22 + DVL3 + EDAR + EEF2 + >> EEF2K + >> EGFR + EIF4E + EIF4EBP1 + ENGASE + ERBB2 + ERBB3 + ERC2 + ERCC1 + >> ERRFI1 + ESR1 + FA2H + FAM153A + FAM184A + FGFR1 + FGGY + FN1 + FOXO3 + >> GAB2 + GARS + GATA3 + GRID1 + GSK3A + GSK3B + GUCY2C + H3F3AP6 + HOMER2 + >> HPGDS + HSPA1A + HSPA1B + HSPB8 + IFI27 + IGF1R + IGFBP2 + INF2 + INPP4B + >> IRS1 + ITGA2 + JUN + KCNJ5 + KDR + KIAA0226L + KIT + KLK1 + KRAS + LCK + >> LPAR1 + LPAR3 + MAP2K1 + MAPK1 + MAPK14 + MAPK4 + MAPK6 + MAPK8 + MAPK9 + >> MAPT + MET + MOGAT3 + MRE11A + MS4A1 + MS4A2 + MSH2 + MSH6 + MTOR + >> MYC + MYH7B + NANOS3 + NCOA3 + NDRG1 + NEURL1 + NF2 + NKX2.1 + NOTCH1 >> + >> NOTCH3 + NPPC + PARK7 + PARP1 + PCDHB11 + PCNA + PDK1 + PDPK1 + PEA15 + >> PECAM1 + PGR + PIK3CA + PIK3CB + PIK3CD + PNMAL1 + PRH2 + PRKAA1 + PRKAA2 >> + >> PRKCA + PRKCD + PSMC4 + PSMD9 + PTCH1 + PTEN + PTK2 + PXN + RAB11A + RAB25 >> + RAD50 + RAD51 + RAF1 + RB1 + REG1B + RORA + RPS6 + SETD2 + SHC1 + >> SLC18A1 >> + SLC7A8 + SMAD1 + SMAD3 + SMAD4 + SNAI1 + SRC + SSSCA1 + SSUH2 + STAT3 + >> STAT5A + STK11 + STMN4 + SYK + TAZ + TCEAL1 + TGM1 + >> TGM2 + TGM3 + TGM4 + TMEM37 + TNFRSF11A + TONSL + TP53 + TP53AIP1 + >> TP53BP1 + TRIL + TSC2 + TSPO2 + VASP + WWTR1 + XBP1 + XBP1P1 + XIAP + >> XRCC1 >> + XRCC5 + YBX1 + YWHAE + YY1AP1} {c(0.0862068965517241, >> 0.0775862068965517, >> 0.0172413793103448, 0.01, 0, 3, 5, 6, 1, 0.724137931034483, >> 0.568965517241379, 0.551724137931034, 1, 1.22413793103448, >> 1.3448275862069, >> 1.41379310344828, 0.114035482086724, 0.121475068159872, 0.124592485529312, >> 0.12611980528159)} class {list(prior = c(0.216101694915254, >> 0.754237288135593, 0.0296610169491525), loss = c(0, 1, 1, 1, 0, 1, 1, 1, >> 0), split = 1)} {list(minsplit = 20, minbucket = 7, cp = 0.01, maxcompete >> = >> 4, maxsurrogate = 5, usesurrogate = 2, surrogatestyle = 0, maxdepth = 30, >> xval = 10)} {list(summary = function (yval, dev, wt, ylevel, digits) >> { >> nclass <- (ncol(yval) - 2)/2 >> group <- yval[, 1] >> counts <- yval[, 1 + (1:nclass)] >> yprob <- yval[, 1 + nclass + 1:nclass] >> nodeprob <- yval[, 2 * nclass + 2] >> if (!is.null(ylevel)) >> group <- ylevel[group] >> temp1 <- formatg(counts, format = "%5g") >> temp2 <- formatg(yprob, format = "%5.3f") >> if (nclass > 1) { >> temp1 <- apply(matrix(temp1, ncol = nclass), 1, paste, collapse = >> " >> ") >> temp2 <- apply(matrix(temp2, ncol = nclass), 1, paste, collapse = >> " >> ") >> } >> dev <- dev/(wt[1] * nodeprob) >> paste0(" predicted class=", format(group, justify = "left"), " >> expected loss=", formatg(dev, digits), " P(node) =", formatg(nodeprob, >> digits), "\n", " class counts: ", temp1, "\n", " probabilities: ", >> temp2) >> }, print = function (yval, ylevel, digits) >> { >> temp <- if (is.null(ylevel)) >> as.character(yval[, 1]) >> else ylevel[yval[, 1]] >> nclass <- (ncol(yval) - 2)/2 >> yprob <- if (nclass < 5) >> format(yval[, 1 + nclass + 1:nclass], digits = digits, nsmall = >> digits) >> else formatg(yval[, 1 + nclass + 1:nclass], digits = 2) >> if (!is.matrix(yprob)) >> yprob <- matrix(yprob, nrow = 1) >> temp <- paste0(temp, " (", yprob[, 1]) >> for (i in 2:ncol(yprob)) temp <- paste(temp, yprob[, i], sep = " ") >> temp <- paste0(temp, ")") >> temp >> }, text = function (yval, dev, wt, ylevel, digits, n, use.n) >> { >> nclass <- (ncol(yval) - 2)/2 >> group <- yval[, 1] >> counts <- yval[, 1 + (1:nclass)] >> if (!is.null(ylevel)) >> group <- ylevel[group] >> temp1 <- formatg(counts, digits) >> if (nclass > 1) >> temp1 <- apply(matrix(temp1, ncol = nclass), 1, paste, collapse = >> "/") >> >> ....................... >> >> How can I save print(fit)? >> Thank? >> >> Ô__ >> c/ /'_;~~~~kmezhoud >> (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ >> http://bioinformatics.tn/ >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@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. >> > > [[alternative HTML version deleted]] ______________________________________________ R-help@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.
