similar to: merge function to combine two tables

Dear R-help members I would be grateful if anyone could help me with the following problem: I would like to combine two matrices (SCH_15 and SCH_16, they are attached) which have a species presence/absence x sampling plot structure. The aim would be to have in the end only one matrix which shows all existing species and their presence/absence on all the different plots(an_1, an_2 etc.) To do

Dear r-help members. I would like to compare species numbers of moths between eight different forests (each sampled for six nights). I would like to do a nested anova to compare species numbers between forests and nights. For more site specific details I wanted to do a Tukey test (TukeyHSD). Unfortunately this test is not working (message: "nicht anwendbare Methode f?r 'TukeyHSD' auf

Hi, I am trying to read the "credit.lisp" file of the Japanese credit database in UCI repository, but it is in lisp format which I do not know how to read. I have not found how to do that in the foreign library http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening <http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening> Could anyone help me? Best

Hello, for my project I wanted to create a side-by-side barplot. For each of 143 treatments there are always two measurement. This two measurements (a pair) are represented by a red resp. a yellow bar. I was able to write the code for the side-by-side barplot. Unfortunately however I am not able to vary the distance between the measurement pairs ( the distance from one red-yellow bar pair to the

I have some data 'myData' in wide form (attached at the end), and would like to convert it to long form. I wish to have five variables in the result: 1) Subj: factor 2) Group: between-subjects factor (2 levels: s / w) 3) Reference: within-subject factor (2 levels: Me / She) 4) F: within-subject factor (2 levels: F1 / F2) 5) J: within-subject factor (2 levels: J1 / J2) As this is the

Hi, May be you can try this: obj_name<- load("arun.RData") Pred1<- get(obj_name[1]) Actual1<- get(obj_name[2]) library(reshape2) dat<-cbind(melt(Pred1,id.vars="S1"),value2=melt(Actual1,id.vars="S1")[,3])? # to reshape to long form colnames(dat)[3:4]<- c("Predict","Actual") dat$variable<- as.character(dat$variable) #not that

Hi R User, I am new in R and trying to create tables with selecting rows randomly (but with replacement) for each group but each group should have same number as original. Is it possible to create it using the following example data set? Your help is highly appreciated. dat1<-structure(list(RegionA = structure(c(1L, 1L, 2L, 3L, 3L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L),

Hi R Users, I do have very big data sets and wanted to run some of the analyses many times with randomization (1000 times). I have done the analysis using an example data but it need to be done with randomized data (1000 times). I am doing manually for 10000 times but taking so much time, I wonder whether it is possible to perform the analysis with creating a loop for many replicated datasets?

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") I try to do a PLSDA to illustrate the impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get the warning

Hi maybe there is another more elegant solution but something like this > idx <- 1:nrow(dat1) > lll <- split(idx, dat1$group) > dat1[unlist(lapply(lll, sample, rep=TRUE)),] gives you selected rows. You could use for cycle or save those data frames manually Cheers Petr > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Marna

Dear R-help members, I did the following pie chart: R:Code= plants<-c(11,5,4,4,4,3,3,3,3,3,3,2,2,2,2,2,2,43) pie(plants,labels =plant_labels,cex=0.7, col=colors) par(mar=c(0,0,4,0),oma=c(0,0,0,0)) legend(1,1,cex=0.7,pt.cex=25,

Dear R-help members. Maybe this is not the right platform to ask this, but I'm looking desperately for a test which is calculating confidence intervals from diversity measurements (non-normaly distributed) (fishers alpha diversity). I was checking the package "vegan" but there seems to be nothing useful. Does anyone of you know with what package I easily could calculate such a

The implementation of an float ordered <= looks buggy, but maybe I'm not reading the assembly right. This is lesf2vfp.S in compiler-rt, and it has this code: // extern int __lesf2vfp(float a, float b); // // Returns one iff a <= b and neither is NaN. // Uses Darwin calling convention where single precision arguments are passsed // like 32-bit ints //

Hi, ABCRegister.td : def SGPR32 : RegisterClass<"ABC", [i32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; def SFGPR32 : RegisterClass<"ABC", [f32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; ===== Instruction selection ends: ... t8: i32 = ADDrr t37, t32

Building on Petr's suggestion, you could modify his code to get all 10 samples at once in a compact format: > Samples <- lapply(lll, function(x) replicate(10, sample(x, rep=TRUE))) # Samples is a list containing 3 matrices, one for each group # Each column gives the index (row) numbers for a particular sample > str(Samples) List of 3 $ A: int [1:3, 1:10] 2 1 3 1 1 3 2 3 1 2 ... $

Folding for conversions: F32->(U{16/32}, S{16/32}) and (U{16/32}, {S16/32})->F32 Signed-off-by: Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> --- .../drivers/nouveau/codegen/nv50_ir_peephole.cpp | 109 +++++++++++++++++++++ 1 file changed, 109 insertions(+) diff --git a/src/gallium/drivers/nouveau/codegen/nv50_ir_peephole.cpp

Problem: I have a Set of samples each with a list of observed species (presence only). Data is stored in a excel spreadsheet and the columns (spl) have different numbers of observations (spcs). Now I want to organize the data in a species by sample matrix with presence/absence style in R. data style (in excel): spl_A spl_B spl_C spcs1 spcs1 spcs2 spcs2 spcs3 spcs3 spcs4 spcs5 spcs5 desired

Folding for conversions: F32/64->(U16/32, S16/32) and (U16/32, S16/32)->F32 No piglit regressions observed on nv50 and nvc0! Signed-off-by: Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> --- V2: fix usage of wrong variable V3: enable F64 support V4: - disable F64 support again - handle saturate flag: clamp to min/max if needed

Folding for conversions: F32->(U{16/32}, S{16/32}) and (U{16/32}, {S16/32})->F32 Signed-off-by: Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> --- V2: beat me, whip me, split out F64 .../drivers/nouveau/codegen/nv50_ir_peephole.cpp | 81 ++++++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/src/gallium/drivers/nouveau/codegen/nv50_ir_peephole.cpp

Dear r-help members. I would like to: a) control the margin around my legend box. Unfortunately I did not find an appropriate command under "?legend". The margin around the actual legend is way too wide. There is a lot of unnecessary "empty space" on the right side. b) increase the width of the individual barplots. I saw that this can be obtained with the command