search for: muehliman

Dear R community! I am trying to create a heatmap based on the following data. As you can see the diagonal (0,0 to 10,10) is always 0). If I run the heatmap command like i posted it I get a graph with horizontal lines. What is my mistake? > heatmap(activity.matrix, Rowv = NA, Colv = NA, scale="column", xlab="x Compounds", ylab="y Compounds",

Hi @ all, how do I get the largest or lowest k values of list? It must similar to the min() / max() function, but I just don't get it. Best wishes, Markus

Hello, My question sounds simple, but as I am desperatly searchin for a solution I am asking you all. :) I try to filter out all non-numeric columns of a data frame using a for loop to go through all columns. My if clause looks like this:         for(j in 1:length(data)) {                 ...                 if(!is.numeric(data[j])) skip <- "not numeric")                 ...

Hi! When I use the glm.cv function I get a value called "delta" which is explained as the "raw cross-validation estimate of prediction error". I recently found a formula for that term in literature where it is defined as: alpha = 1 / N * sum over( yi - yi,pred,CV) Well it is somehow similar to the RSS for R2 and the PRESS for Q2. But this delta value increases with

Hello! I have a problem with the multivariante regression function mvr (PLS package) with leave-one-out validation. In the value ...$validation there are the $PRESSs listed. I found in the literature PRESS is the sum of squares of observed minus predicted. Well, I don't understand how PRESS can grow with a better model (which indicated by more the use of more latent variables

Hi community, I want to count the occurrence of values within a dataframe. data$names is a list of many names. With namelist <- unique(data$names) I get all the existing names. But the result is a factor, not a list of strings. I would then like to go trough all the names in a for-loop and count their occurrence. How do I make this? Best regards, Markus

Dear list members, I am using a cross validation of a generalised linear model (glm). The cv.glm function (from boot package) returns an error as so-called ?delta? value. I would like to get to a (cross-validated) squared q, because I want to directly compare it to the squared correlation coefficient r. I tried to find an an equation for the raw and/or adjusted cross-validation estimate of

Hi everyone! I have an array containing the following fields for over hundred compounds: cpd, activity, fixterm, energy1, energy2, energy3, ... I want to run a multiple linear regression on all entries of an array. Therefore I tried to do this with a for loop. (Maybe there is a direct way of calculating it using apply, but I don't know that either.) Actually i tried the following code:

Dear list members. I have problems with the usage of cv.glm from the boot package. Here are some parts of the script I wanted to use: data <- read.table("selected_2D.csv", header=TRUE, sep=",") ? glm.fitted <- glm("ydata$ y ~ 1 + density + vsurf_ID6 + vsurf_S ", data=data) error <- cv.glm(data=data, glm.fitted, K=6) ydata$y is a separate data set, where