similar to: Factorial regression with multiple features: how to remove non-significant features?

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Dear R users/developers I requested help to solve the problem of formulating Multivariate Sample selection model by using Full Information Maximum Likelihood (FIML)estimation method. I could not get any response. I formulated the following code of FIML to analyse univariate sample selection problem. Would you please advise me where is my problem library (sem) library(nrmlepln) Selection

Dear R users, How do I drop multiplication terms from a formula using update? e.g. forml=as.formula("Surv(time, status) ~ x1+x2+A*x3+A*x4+B*x5+strata(sex)") #I would like to drop all instances of variable A (the main effect and its interactions). The following: updated.forml=update(forml, ~ . -A) #gives me this: #Surv(time, status) ~ x1 + x2 + x3 + x4 + B + x5 + strata(sex) + A:x3 +

Hi, I'd like to apply a 2^k factorial design with k=10 parameters. Obviously this results in a quite long term for the model equation due to the high number of combinations of parameters. How can I specify the equation for the linear model (lm) without writing all combinations explicitly down by hand? Does a R command exist for this problematic? Thanks for your help in advance, Sven

I have a project due in my Linear Regression class re: regression on a data set & my professor gave us a hint that there were *exactly *2 sig interactions. The data set is attached. We have to find which predictors are significant, & which 2 interactions are sig. Also, I nedd some guidance for this & selecting the best model. I tried the `full' model, that being:

I am having great difficulty plotting what should be a simple graph. I have measured 1 'y' and 5 'x' variables in each of two groups. Linear regression shows significant differences in the slopes of the regression for each 'x' variable between the two groups. All that I want to do is to plot one graph that shows the scatterplot for the three groups (each group represented

Dear all,   I did ANOVA for many response variables (Var1, Var2, ....Var75000), and i got the result of p-value like below. Now, I want to select those predictors, which have pvalue less than or equal to 0.05 for each response variable. For example, X1, X2, X3, X4, X5 and X6 in case of Var1, and similarly, X1, X2.......X5 in case of Var2, only X1 in case of Var3 and none of the predictors in case

Dear all, Is there any simple way in R that can I put the all the interactions of the variables in the OLS model? e.g. I have a bunch of variables, x1,x2,.... x20... I expect then to have interaction (e.g. x1*x2, x3*x4*x5... ) with some combinations(2 way or higher dimensions). Is there any way that I can write the model simpler? Thanks! Leaf

Dear R-users I would like to apply classification and regression tree(CART) to the following data. I have some question on using 'tree' package. The data contains one response variable Y and five explanatory variables. The explanatory variable "x2" is categorical and not ordinal. But, the result obtained after running following R code has indicated that x2 is regard as

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

He, I used the "coxph" function, with four covariates. Let's say something like that > model.1 <- coxph(Surv(Time,Event)~X1+X2+X3+X4,data=DATA) So I obtain the 4 coefficients B1,B2,B3,B4 such that h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4). When I use the function on the same data > predict.coxph(model.1,type="lp") how it works in making the prediction?

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear all, I run a linear model with three significant explanatory variabels x1: a factor with 4 levels x2 and x3: factors with two levels each x4: continuous model <- lm(y ~ x1 + x2 * x3 + x4) <> The data is not perfectly balanced between the different factor-combinations and I use treatment contrasts. <> With an aov.object, I assume I could have used model.tables(aov.object,

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

I have a sparse contingency table (most cells are 0): > xtabs(~.,data[,idx:(idx+4)]) , , x3 = 1, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 31 2 0 0 112 3 0 0 94 , , x3 = 2, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 3, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 1, x4

Hi R-helpers, I have a character object called dd that has 32 elements each of which is a model formula contained within quotation marks. Here's what it looks like: > dd [1] "lm(y ~ 1,data=Cement)" "lm(y ~ X,data=Cement)" "lm(y ~ X1,data=Cement)" [4] "lm(y ~ X2,data=Cement)" "lm(y ~

I would like to conduct an extra sum-of -squares test that compares a full MANOVA model (with all 1st order interactions) to a reduced model (no interactions) to determine if I can drop all interactions at the same time. This is analagous to an extra sum-of-squares F-test in ANOVA, but instead using MANOVA. Is there a command in R that does this? If not, is there a command that calculates

I posted this one as an R bug (https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=14767), but Prof. Ripley says I'm premature, and I should raise the question here. Here's the behavior I assert is a bug: The output from delete.response on a terms object alters the formula by removing the dependent variable. It removes the response from the "variables" attribute and it changes