similar to: nls: how do you know if the model is significant?

Hi, I have a question about repeating something for several columns. I calculated a lot of values for different objects. The values are normally calculated step-wise for every day for each object. With this obtained data frame, I make further calculations. Here I have to calculate some things and I have to take into account the years because I want a number for each year. I do these first for one

Hi all, Like a lot of people I noticed that I get different results when I use nls in R compared to the exponential fit in excel. A bit annoying because often the R^2 is higher in excel but when I'm reading the different topics on this forum I kind of understand that using R is better than excel? (I don't really understand how the difference occurs, but I understand that there is a

Dear all, I have a question about the knowing for which row I have the max value of one of my variables. I calculated the Rsquared for different columns and made a list to gather them. I unlisted this list to create a vector with this values. I want to know for which column I have the max value of Rsquared. The columns were always named in the same way. They always start with results4$depth_

Dear all, I have a question concerning manipulating data of several columns of a dataframe at the same time. I manage to do it for one column (with the use of the specific name for this column). In each columns, I have 60 values. But I should reorganize the values (because I created this as an output before and I want to compare it with an other dataset). I want that the value on row 2 becomes

Hi all, I have a basic question concerning graphs in R. I?m using the par() function and I?m working with multiple figures by row (mfrow) but my the hight of my figures become compressed. I have 4 rows and 2 columns (because I want to plot 8 histograms (freq = FALSE ) on it. I know I can adapt my margins with for example ?oma? and ?mai? but I don?t know how to choose the size of the figure? I

Hi all, I have a question concerning using several conditions in an ifelse function used as the function in apply. I want to create a new value with the function ifelse ? object which can be coerced to logical mode ?test[n,] >1 & test[n-1,]==0? With n I mean the row. I don?t know how I could do this without a loop. I want to avoid the usage of loops and was thinking about apply. This

Hi all, I was wondering if there is a function kind of similar that splits a dataframe, applies a function to each row and returns in a data frame. I know ddply but this one isn?t useful in this situation. I have a dataframe with values for each day (rows) for different objects (columns). I have values for several years. Now, I want to do calculations on only the data of that year. With the

I tried to bootstrap the correlation between two variables x1 and x2. The resulting distribution has two distinct peaks, how should I interprete it? The original code is attached. Y. C. Tao ---------------- library(boot); my.correl<-function(d, i) cor(d[i,1], d[i,2]) x1<-c(-2.612,-0.7859,-0.5229,-1.246,1.647,1.647,0.1811,-0.07097,0.8711,0.4323,0.1721,2.143,

Hello everybody, I am a new R & Grass user. I am interested in doing a kriging of the Temperature in a basin, based on pixel elevation (DTM). I use Grass 6.2.1 and R 2.5.1 and have installed the spgrass6 package. The DTM is called "DTM_trentino_100m" and is like this: north:5158000.0 south:5058500.0 east:1729700.0 west:1611600.0 rows:995 cols:1181 -9999.0 -9999.0 -9999.0

Hi r-helpers. Please forgive my ignorance, but I would like to plot a smoothing spline (smooth.spline) from package "stats", and show the knots in the plot, and I can't seem to figure out where smooth.spline has located the knots (when I use nknots). Unfortunately, I don't know a lot about splines, but I know that they provide me an easy way to estimate the location of local

I'm trying to analyze the following data set (sample): "ID" "adj.P.Val" "logFC" "Gene.symbol" "1419156_at" "5.32e-12" "2.6462565" "Sox4" "1433575_at" "5.32e-12" "3.9417089" "Sox4" "1428942_at" "2.64e-11"

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

Dear all, quite a naive question: I have a data frame and I computed "the kernel density estimate" with density on each column. Now I'd like to export in a txt file the density function output for each column, but, when if I use write.table, I get a message "Error in as.data.frame.default(x[[i]], optional = TRUE) : can't coerce class "density" into a

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

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I have the following: Time A1 A1 B1 B1 C1 C2 x y x y x y 0 5 6 6 7 7 9 1 3 4 4 3 9 9 2 5 2 6 4 7 4 I want to change it to the following: 0 1 2 x y x y x y A1 5 6 3 4 5 2 B1

Hello, I have a question concerning ?for loops? on multiple columns. I made 91 columns with results (all made together with a for loop) and I want to us lm to fit the model. I want to compare the results of all these calculated columns (91) with one column with observed values. I use the function lm to fit the model and calculate r.squared. I manage to do this for each column separately: For

Dear R users I have a data set which has five variables. One depenedent variable y, and 4 Independent variables (education-level, householdincome, countrygdp and countrygdpsquare). The first two are data corresponding to the individual and the next two coorespond to the country to which the individual belongs to. My data set does not make this distinction between individual level and country

Dear list, I want to retrieve the p-value of a two-polynomial regression. For a one-polynomial lm I can easily do this with: summary(lm(b~a, data=c)[[4]][[8]]. But how do I find the final p-value in the two-polynomial regression? Under $coefficients I don't find it Any suggestions? Patrick alt <-(2260,2183,2189,1930,2435, 2000,2100,2050,2020,2470, 1700,2310,2090,1560,2060,

OK, what is the trick to extracting the overall p value from an lm object? It shows up in the summary(lm(model)) output but I can't seem to extract it: > test2 = apply(aa, 1, function(x) summary(lm(x[,1] ~ 0 + x[,3] + x[,6]))) > test2[[1]] Call: lm(formula = x[, 1] ~ 0 + x[, 3] + x[, 6]) [omitted summary output] F-statistic: 40.94 on 2 and 7 DF, p-value: 0.0001371 It does not seem