standardized

问题 Is it possible to normalize this table in R based on the last column(samples) samples = number of sequenced genomes. So I want to get a normalised distribution of all the genes in all the conditions. Simplified example of my data: I tried: dat1 <- read.table(text = " gene1 gene2 gene3 samples condition1 1 1 8 120 condition2 18 4 1 118 condition3 0 0 1 75 condition4 32 1 1 130", header = TRUE) dat1<-normalize(dat1, method = "standardize", range = c(0, 1), margin = 1L, on.constant = "quiet")

问题 Is it possible to normalize this table in R based on the last column(samples) samples = number of sequenced genomes. So I want to get a normalised distribution of all the genes in all the conditions. Simplified example of my data: I tried: dat1 <- read.table(text = " gene1 gene2 gene3 samples condition1 1 1 8 120 condition2 18 4 1 118 condition3 0 0 1 75 condition4 32 1 1 130", header = TRUE) dat1<-normalize(dat1, method = "standardize", range = c(0, 1), margin = 1L, on.constant = "quiet")

问题 I came across a puzzling difference in standardized (beta) coefficients with linear regression model computed with R and SPSS using dummy coded variables. I have used the hsb2 data set and created a contrast (dummy coding), so that the third category is the reference. Here is the R code: # Read the data hsb2 <- read.table('https://stats.idre.ucla.edu/stat/data/hsb2.csv', header = TRUE, sep = ",") # Create a factor variable with respondents' race hsb2$race.f <- factor(hsb2$race, labels = c(

问题 Let's say I want to use a LinearSVC to perform k-fold-cross-validation on a dataset. How would I perform standardization on the data? The best practice I have read is to build your standardization model on your training data then apply this model to the testing data. When one uses a simple train_test_split(), this is easy as we can just do: X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) clf = svm.LinearSVC() scalar = StandardScaler() X_train = scalar.fit_transform(X

问题 Let's say I want to use a LinearSVC to perform k-fold-cross-validation on a dataset. How would I perform standardization on the data? The best practice I have read is to build your standardization model on your training data then apply this model to the testing data. When one uses a simple train_test_split(), this is easy as we can just do: X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) clf = svm.LinearSVC() scalar = StandardScaler() X_train = scalar.fit_transform(X

问题 I would really appreciate your help in this question. I have the following dataset and I would like to create a new variable which would contain the standardized values (z distribution) per level of a given factor variable. x <- data.frame(gender = c("boy","boy","boy","girl","girl","girl"), values=c(1,2,3,6,7,8)) x gender values 1 boy 1 2 boy 2 3 boy 3 4 girl 6 5 girl 7 6 girl 8 My aim is to create one new variable which will contain the z-values calculated separately for each factor level

问题 My apologies for the dumb question...but I can't seem to find a simple solution I want to extract the standardized coefficients from a fitted linear model (in R) there must be a simple way or function that does that. can you tell me what is it? EDIT (following some of the comments below): I should have probably provided more contextual information about my question. I was teaching an introductory R workshop for a bunch of psychologists. For them, a linear model without the ability to get

问题 I am using the 'MuMIn' package in R to select models and calculate effect sizes of the input variables (rain, brk, onset, wid). To make my effect size comparable between variables, I standardised them using standardize function in arm package. Here is the code that I am following: For reference, please refer to the appendix of this paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02210.x/full Grueber et al. 2011: Multimodel inference in ecology and evolution: challenges and

问题 I am using the 'MuMIn' package in R to select models and calculate effect sizes of the input variables (rain, brk, onset, wid). To make my effect size comparable between variables, I standardised them using standardize function in arm package. Here is the code that I am following: For reference, please refer to the appendix of this paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02210.x/full Grueber et al. 2011: Multimodel inference in ecology and evolution: challenges and

问题 I'm trying to do a simple transformation. I've used the following code and it worked fine: data_stdz <- transform(data_header, z.v1 = v1+2) But, I can't get the following code to work: data_stdz <- transform(data_header, z.v1 = (v1 - mean(v1))/(2*sd(v1)) I've also tried to get just the mean function to work: data_stdz <- transform(data_header, z.v1 = mean(v1) But, I keep getting the following error: Error: unexpected symbol in: "data_std2 <- transform(data_header, z.v1 = mean(v1) data_std2"