similar to: nested unbalanced regression analysis

Hi, I'm a biologist, so please forgive me if my question sounds absurd! I have 3 parameters x1, x2, x3 and a response variable y.The sample size is 75. I tried to do the following: mylm<-lm(y~ x1 + x2 + x3, data="mydata") but i can only get stats from anova for the first 2 variables. The third comes up as NA. The degrees of freedom for the third variable are 0. Is there

Dear all, I have a question about using categorical predictors for SVM, using "svm" from library(e1071). If I have multiple categorical predictors, should they just be included as factors? Take a simple artificial data example: x1<-rnorm(500) x2<-rnorm(500) #Categorical Predictor 1, with 5 levels x3<-as.factor(rep(c(1,2,3,4,5),c(50,150,130,70,100))) #Catgegorical Predictor

Hello all, I'm trying to plot the effects of variables estimated by a regression model fit individually, and for categorical predictors, the independent variable shows up on the y-axis, with the dependent variable on the x-axis. Is there a way to prevent this reversal? Sample code with dummy data: # make dummy data set.seed(1) x1 <- runif(200) x2 <- sample(c(1,2),200, TRUE) x3 <-

Dear R people: I wish to switch from SPSS to R, but there is one particular type of ANOVA design that cannot be done in R. Or more likely, it can be done, but it is nowhere documented. The problem is typical for psychologists: You have a repeated measures design with different groups of subjects. Now, this can be done with the aov command, but the number of subjects in both groups must be

Hi! This is my first time posting. I've read the general rules and guidelines, but please bear with me if I make some fatal error in posting. Anyway, I have a continuous response and 29 predictors made up of continuous variables and nominal and ordinal categorical variables. I'd like to do lasso on these, but I get an error. The way I am using "lars" doesn't allow for the

I need to fit a number of models with different number of predictors in each model. Say for example, I have three predictors: x1, x2, x3 and I want to fit three models: lm(y~x1+x2) lm(y~x2+x3) lm(y~x1+x2+x3) Instead of typing all models, what I want is to create a variable which can take the right hand side of the models. I tried this with paste function.

I have a function that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~

Dear R-ers, I have a data frame data with predictors x1 through x5 and the response variable y. I am running a simple regression: reg<-lm(y~x1, data=data) I would like to loop through all predictors. Something like: predictors<-c("x1","x2",... "x10) for(i in predictors){ reg<-lm(y~i) etc. } But it's not working. I am getting an error: Error in

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 All, I would like to perform linear regressions based on Y and all of the combinations of the five predictors, i.e.,(y,x1,x2),(y,x1,x3),....,(y,x1,x2,x4,x5),....,(y,x1,x2,x3,x4,x5). Is there any quick way to do it instead of repeat performing regressions for 31 times? Or, is there any method to manipulate the dataset into the 31 combinations? Thanks for your help!

Hi, Fitting all possible models (GLM) with 10 predictors will result in loads of (2^10 - 1) models. I want to do that in order to get the importance of variables (having an unbalanced variable design) by summing the up the AIC-weights of models including the same variable, for every variable separately. It's time consuming and annoying to define all possible models by hand. Is there a

Hello, helpeRs, I recently used a linear mixed effects model followed by ANOVA to assess the relationship between a categorical predictor variable with 2 levels (and random effects) and a numeric response variable. As I was concerned about the lack of a power analysis prior to data collection, it was suggested that I use an equivalence test to complement the conventional hypothesis test.

Hi folks, I have a dataset from a trial measuring the subjects' pupils. There are many measurements, all of which must be analysed in a similar fashion; so if I get the analysis right for one of them, I've got them all. For simplicity, let us call any measurement we may be interested as "response". The study design is an unbalanced latin square, with 5 periods, 5 treatments and

Hello, I wanted to do the multiple regression on categorical predictor data there's variable x1,x2,x3 and x3 is categorical one. so i just used as.factor(x3) and then ran multiple regression is it a good way to do the multiple regression on categorical predictor data? and how can I interpret the estimates? also if using as.factor is a good way, is there any difference with doing dummy coding

Hello, I'm trying to plot a set of 3 dependant variables (y) against 4 predictors (x) in a matrix-like plot, sharing x- an y-axis for all the plot on the same column/line : y1/x1 y1/x2 y1/x3 y1/x4 y2/x1 y2/x2 y2/x3 y2/x4 y3/x1 y3/x2 y3/x3 y3/x4 In fact, this plot is a rectangular selection of the result of pairs(), limited to the relations between x's and y's

I've been using lme4 to fit Poisson GLMMs with crossed random effects. The data are counts(y) sampled at 55 sites over 4 (n=12) or 5 (n=43) years. Most models use three fixed effects: x1 is a two level factor; x2 and x3 are continuous. We are including random intercepts for YEAR and SITE. On subject-matter considerations, we are also including a random coefficient for x3 within YEAR.

Hi, can anybody tell me how the Hausman test for endogenty works? I have a simulated model with three correlated predictors (X1-X3). I also have an instrument W for X1 Now I want to test for endogeneity of X1 (i.e., when I omit X2 and X3 from the equation). My current approach: library(systemfit) fit2sls <- systemfit(Y~X1,data=data,method="2SLS",inst=~W) fitOLS <-

I want to use the grplasso package on a data set where I want to fit a linear model.? My interest is in identifying significant?beta coefficients.? The documentation is a bit cryptic so I'd appreciate some help. ? I know this is a strategy for large numbers of variables but consider a simple case for pedagogical puposes.? Say I have?two 3 category predictors (2 dummies each), a binary

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

Hello, I have spent considerable time trying to figure out that which I am about to describe. This included searching Help, consulting my various R books, and trail and (always) error. I have been assuming I would need to use a loop (looping over columns) but perhaps and apply function would do the trick. I have unsuccessfully tried both. A scaled down version of my situation is as follows: