similar to: split-apply question

Hi, I would like to operate on certain columns in a dataframe, but not others. My data looks like this: x1 x2 x3 1 2 3 4 5 6 7 8 9 I want to create a new column named x4 that is the sum of x1 and x2, but NOT x3. I looked at colSums and apply, but those functions seem to use all the columns in a dataframe. How do I only use select columns? If it helps, in Stata this would be gen x4

I have some data fit to a smooth.spline object as follows: (x=vector of data for the predictor variable, y=vector of data for the response variable) fit <- smooth.spline(x,y) Now, given a spline fit point y_new, I want to be able to find out what value of x_new yielded this fit value. How to do so? (This problem is the inverse of the predict.smooth.spline function, which takes x_new as input

Hi, I would like to do the following operations: variable.df is a character string that contains the name of the data frame that I want to do the following operations on: variable.df <- data.frame(); # I can do the above command using assign( variable.df, data.frame() ) How can I perform the assignment statements below ? colnames(variable.df) = colnames(some.other.df) variable.df =

Dear List, I have a data frame called trait with roughly 800 species in, each species have 15 columns of information: Species 1 2 3 etc.. a t y h b f j u c r y u etc.. I then have another data frame called com with the composition of species in each region, there are 506 different communities: community species NA1102 a NA1102 c NA0402 b NA0402 c AT1302 a AT1302 b etc.. What

Hi R users, I have a data frame that contains 10K obs and 200 variables where I am trying to format the numeric columns to look like the output table below (format to 2 decimal places) but I am having no luck.. Can someone tell me the best way to accomplist this? Thanks in advance for any help! str(ad.test) 'data.frame': 10,000 obs. of 200 variables: $ ID : Factor

Hi all, I'm trying to improve my R skills and make my programming more efficient and succinct. I can solve the following question, but wonder if there's a better way to do it: I'm trying to calculate mean by several variables and then put this back into the original data set as a new variable. For example, if I were measuring weight, I might want to have each individual's

Hi, I have a set of (x,y) coordinate pairs that are stored as a list > my_list $x [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 $y [1] -8.0866819 -7.3876052 -6.6849311 -5.9837693 -5.2967432 -4.6525466 [7] -4.0999453 -3.6556190 -3.3076102 -3.0360780 -2.8220465 -2.6532085 [13] -2.5192816 -2.4086241 -2.3072977 -2.1969611 -2.0574250 -1.8737694 [19] -1.6357864

Hello all R-users! In Jun-2005, I find the follow discussion about using of LME function ( in NLME library ) for fitting non-replicate data The thread: ANOVA vs REML approach to variance component estimation http://tolstoy.newcastle.edu.au/R/help/05/06/6498.html Someone expose the follow problem: # non-replicate data y <- c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3, 0.5, -1.4,

Dear list-subscriber, in the process of writing a general code snippet to extract coefficients in an expression (in the example below: 0.5 and -0.7), I stumbled over the following peculiar (at least peculiar to me:-) ) sorting behaviour of the function all.names(): > expr1 <- expression(x3 = 0.5 * x1 - 0.7 * x2) > all.names(expr1) [1] "-" "*" "x1"

Hi, I'd like to normalize a dataset by dividing each row by the first row. Very simple, right? I tried this: > expt.fluor X1 X2 X3 1 124 120 134 2 165 163 174 3 52 51 43 4 179 171 166 5 239 238 235 >first.row <- expt.fluor[1,] > normed <- apply(expt.fluor, 1, function(r) {r / first.row}) >normed [[1]] X1 X2 X3 1 1 1 1 [[2]] X1 X2 X3 1

Dear r users, I have 4 variables x1,x2,x3,x4 and each one has two levels, for example Y and N. For x1: prob(Y)=0.6, prob(N)=0.4; For x2: prob(Y)=0.5, prob(N)=0.5; For x3: prob(Y)=0.8, prob(N)=0.2; For x4: prob(Y)=0.9, prob(N)=0.1; Therefore, the sample space for (x1, x2, x3, x4)={YYYY, YYYN, YYNY,......} (16 possible combination) and the corresponding probabilities are {(0.6)(0.5)(0.8)(0.9),

Hi all, I would like to invoke a function that takes multiple arguments (some of which are specified columns in the data frame, and others that are independent of the data frame) on split parts of a data frame, how do I do this? For example, let's say I have a data frame >fitness_data name height weight country rob 5.8 200 usa nancy 5.5 140 germany jen

Hi there, In SPSS, small piece of data can be input as following: DATA LIST LIST /x1 x2 x3 x4 x5 . BEGIN DATA 5700 12.8 2500 270 25000 1000 10.9 600 10 10000 3400 8.8 1000 10 9000 3800 13.6 1700 140 25000 4000 12.8 1600 140 25000 8200 8.3 2600 60 12000 1200 11.4 400 10 16000 9100 11.5 3300 60 14000 9900 12.5 3400 180 18000 9600 13.7 3600 390 25000

I am using gam in mgcv (1.3-22) and trying to use gcv to help with model selection. However, I'm a little confused by the process of assessing GCV scores based on their magnitude (or on relative changes in magnitude). Differences in GCV scores often seem "obvious" with my poisson gams but with negative binomial, the decision seems less clear. My data represent a similar pattern as

Greetings, I am not familiar with processing text in R. Can someone tell me how to read each line of words as separate elements in a list? FE, I would like to turn: word1 word2 word3 word2 word4 into a list of length two with three character elements in the first list and two elements in the second. I know that this should be easy, but I am a little confused by the text functions. Thanks in

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

Dears, I'm trying to find the parameters (a,b, ... l) that optimize the function (Model) described below. 1) How can I set some constraints with MLE2 function? I want to set p1>0, p2>0, p3>0, p1>p3. 2) The code is giving the following warning. Warning: optimization did not converge (code 1) How can I solve this problem? Can someone help me? M <- 14 Y = c(0, 1, 0, 0, 0,

Dear helpers. I often need to make dichotomous variables out of continuous ones (yes, I realize the problems with throwing away much of the information), but I then like to check the min and max of each category. I have the following simple code to do this that cuts each variable (x1,x2,x3) at the 90th percentile, and then prints the min and max of each category:

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

I'm fitting data from a mixture experiment, and I'd like to present the results in a ternary graph with contours. I found this code by Walmes Zeviani http://n4.nabble.com/Triangular-filled-contour-plot-td1557386.html which is just what I want--except I would like the axis titles and labels to be proportionately larger than the ternary graph itself, for legibility in publication. When I