similar to: extract data from lm object and then use again?

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

Given a data set with a group factor, I want to translate the numeric variables to their centroid, by subtracting out the group means (adding back the grand means). The following gives what I want, but there must be an easier way using sweep or apply or some such. iris2 <- iris[,c(1,2,5)] means <- colMeans(iris2[,1:2]) pooled <- lm(cbind(Sepal.Length, Sepal.Width) ~ Species,

Hello! I'm a newcomer to R hoping to replace some convoluted database code with an R script. Unfortunately, I haven't been able to figure out how to implement the following logic. Essentially, we have a database of transactions that are coded with a geographic locale and a type. These are being loaded into a data.frame with named variables city, type, and price. E.g., trans$city

I'm confused, hope someone can point out what is not obvious to me. I thought I was creating a new data frame by 'deleting' rows from an existing dataframe - I've tried 2 methods. But this new data frame seems to remember values from its parent - even though there are no occurences. Where does it get the values versicolor and virginica from and give then a count of 0? What

Probably a stupidly simple question, but I wouldn't know how to google it: xyplot(neuro ~ time | UserID, data=data_sub) creates a proper plot. However, if I add type = "l" the lines do not go first through time1, then time2, then time3 etc but in about 50% of all subjects the lines go through points seemingly random (e.g. from 1 to 4 to 2 to 5 to 3). The lines always start at time

> data(iris) # iris3 is first 3 rows of iris > iris3 <- iris[1:3,] # z compares row 1 to each row of iris3 and is correctly computed > z <- c(F,F,F) > for(i in seq(z)) z[i] <- identical(iris3[1,],iris3[i,]) > z [1] TRUE FALSE FALSE # this should do the same but is incorrect > apply(iris3,1,function(x)identical(x,iris3[1,])) 1 2 3 FALSE FALSE FALSE

Hi all, I'd be grateful for your help. I am a new user struggling with a barplot issue. I am plotting categories (X axis) and their mean count (Y axies) with barplot(). The first call to barplot works fine. I remove records from the dataframe using final=[!final$varname == "some value",] I echo the dataframe and the records are no longer in the dataframe. When I call plot again

Hi, there: Can anyone here kindly point some good reference or links on this topic? Esp. some solutions from BioConductor or R, when dealing with microarray-like, "fat" data? thanks, -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[alternative HTML version deleted]]

Hi there: I have a question about generating mean value of a data.frame. Take iris data for example, if I have a data.frame looking like the following: --------------------- Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2

In the examples below, the first loses the name attached by foo(), the second retains names attached by bar(). Is this an intentional difference? I?d prefer that the names be retained in both cases. foo <- function(x) { c(mean = base::mean(x)) } bar <- function(x) { c(mean = base::mean(x), sd = stats::sd(x))} aggregate(iris$Sepal.Length, by = list(iris$Species), FUN = foo) #>

Dear all, I'd like to get a percentage variable based on a group, but without creating a new data frame. For example: data(iris) iris$percent <-unlist(tapply(iris$Sepal.Length,iris$Species,function(x) x/sum(x, na.rm=TRUE))) This does not work, I should have only three standard values, respectively for setosa, versicolor, and virginica. How can I do this? MANY THANKS, Karine

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Hopefully I will not be flamed for this on the list, but I am starting out with R and having some trouble with combining plots. I am playing with the famous iris dataset (checking out example dataset in R while reading through Introduction to datamining) What I would like to do is create three graphs (combined boxplots) besides each other for each of the three species (Setosa, Versicolour and

Hi, I was wondering if it's possible to get the row numbers from a filtering. Here's an example: # give me the rows with sepal.length == 6.2 iris[(iris[,1]==6.2),] # output Sepal.Length Sepal.Width Petal.Length Petal.Width Species 69 6.2 2.2 4.5 1.5 versicolor 98 6.2 2.9 4.3 1.3 versicolor 127 6.2

The anti_join from the package dplyr might also be handy. install.package("dplyr") library(dplyr) anti_join (x1, x2) You can get help on the different functions by ?function.name(), so ?anti_join() will bring you help - and examples - on the anti_join function. It might be worth testing your approach on a small subset of the data. That makes it easier for you to follow what happens

The following command produces red axis line in a pairs plot: pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species", pch = "+", col = c("red", "green3", "blue")[unclass(iris$Species)]) Trying to fool pairs in the following way produces the same plot as above: pairs(iris[1:4], main = "Anderson's Iris Data -- 3

Hi R People: Several of you pointed out that using "tapply" on a data frame will work on the iris data frame. I'm still having a problem. The iris data frame has 150 rows, 5 variables. The first 4 are numeric, while the last is a factor, which has the Species names. I can use tapply for 1 variable at a time: >tapply(iris[,1],iris[,5],mean) setosa versicolor virginica

Hi folk, I tried this and it works just perfectly tapply(iris[,1],iris[5],mean) but, how to obtain a single table from multiple variables? In tapply x is an atomic object so this code doesn't work tapply(iris[,1:4],iris[5],mean) Thanx and great summer holidays Gianandrea -- View this message in context: http://www.nabble.com/multiple-tapply-tp18868063p18868063.html Sent from the R help