search for: subset1

...n worked to my problem: I have a data frame that I get from a csv: summarystats<-as.data.frame(read.csv(file=f_summary)); where I have the columns Dataset, Class, Type, Category,.. Problem1: I want to find a subset of this frame, based on values in multiple columns What I do currently is: subset1 <- summarystats subset1<-subset1[subset1$Class == 1,] subset1<-subset1[subset1$Type == 1,] subset1<-subset1[subset1$Category == 1,] Now, this works, but is UGLY! I tried using "&&" or "&" , for isntance : subset1<-subset1[ (subset1$Class == 1)&&a...

Hi, I''m having a dataframe ''Subset1'' with a number of factor variables and 160 numerical variables Now I want to make sums for all rows that have the same values for the different factor variables, except for the factor variables: VAR1,VAR2,VAR3 who may have the same values. With the formula given below this works great, b...

...emingly simple problem with no success. I'm hoping that one or some of you can help. Here is the code I am trying to use: #importing data data.file <-read.csv("/file/location", header=TRUE, sep = ",") #selecting a subset of data based on variable "Sample" data.subset1 <- subset(data.file, subset=(Sample !='sample_x' & Sample !='sample_y')). **This leaves me a data file that has 8 levels of the variable "Sample" and 2 empty levels that correspond to sample_x and sample_y. I need to get rid of these two levels for plotting purpos...

...to give the data.frames descriptive names which I have stored in a character vector. My original thought was that I want the subsets to show up as individual objects, but haveing them stored in a list is fine (maybe better). I can create a list of subsetted data.frames like this: >Lst = list(subset1 = (subset (blablabla)), subset2 = (subset(blabla))) but I have to provide the component names (subset1, subset2) manually. I would like to pull the component names from an existing character vector, but so far my attempts have failed. Any advice is appreciated, even if the advice is "don'...

...st)],CutList,SIMPLIFY=FALSE) CutTable = as.data.frame(table(CutResults)) CutResultsDF = as.data.frame(CutResults) head(CutResultsDF, n=15) dat$aRange = CutResultsDF$a dat$bRange = CutResultsDF$b head(dat, 15) # I don't want to do the following as it doesn't # get managed automatically. Subset1 = subset(subset(dat, aRange==CutTable$a[1]), bRange==CutTable$b[1])[1:2] Subset2 = subset(subset(dat, aRange==CutTable$a[2]), bRange==CutTable$b[2])[1:2] Subset3 = subset(subset(dat, aRange==CutTable$a[3]), bRange==CutTable$b[3])[1:2] Subset4 = subset(subset(dat, aRange==CutTable$a[4]), bRange==Cut...

...value very close to 1. estimates diverging; iteration terminated. In addition: Warning message: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did not converge" The input is the following model: compar.gee(RESPONSE ~ VAR1 + VAR2 + VAR3, data = subset1, family = "binomial", phy = prunedtree1) I have set all of the categorical data as factors and designated the family as "binomial". I don't know what else to do and the error message is not clear to me. If anyone can interpret this error message and/or knows how to run a co...

...ion("par") I was aking myselft what would be the "better" strategy to handle a bunch of options for a package. I ended up with the idea of storing a list, as my options would also be classified, with something like: -- MyPkgOptions = list(set1=list(par1=1,par2=2),set2=list(subset1=list(par1=11,par2=22),subset2=list(par1=111,par2=222))) options(PkgName=MyPkgOptions) -- Then, to make easier the access to an element, I tweaked a little bit getOption, with the following version: -- getOption <- function(x,...) { op = options(x)[[1]] if (length(list(...))>0) op &lt...

...ale Case 0.10 P Female Control 0.41 Q Female Case 0.16 R Male Control 0.72 S Male Case 0.17 T Female Case 0.74 U Male Control 0.35 V Female Control 0.77 W Male Control 0.27 X Male Control 0.98 Y Female Case 0.94 Z Female Case 0.32 # now the sample use > (subset1 =subset(expressionSet,sex="Male",type="Control")) 7 columns selected (C F J R U W X) ExpressionSet (storageMode: lockedEnvironment) assayData: 500 features, 7 samples element names: exprs, se.exprs phenoData sampleNames: C, F, ..., X (7 total) varLabels and varMetadata de...

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