search for: regressor

The model matrix for the code at the end the email is shown below. Since the model matrix doesn't have -1, I think that it is made of dummy regressors rather than deviation regressors. I'm wondering how to make a model matrix using deviation regressors. Could somebody let me know? > model.matrix(aaov) (Intercept) A2 B2 B3 A2:B2 A2:B3 1 1 0 0 0 0 0 2 1 0 0 0 0 0 3 1 0 0 0 0...

To make a long story short I was doing some in-sample testing in which some dynamically created regressors would end up either all true or all false based on the validation portion. In my case a new mainframe configuration (this is a crappy way to handle a level shift but I do what I can.) So here is the code snippet that finally let me pre-check my regressors and drop any of them that were all true or...

dear R users, I am performing a linear regression with lm, and I would like to plot the regressor in dashed lines. I know that the lty=2 option is the way out, but it has a very strange behaviour: the line starts dashed but then the spaces between each dash becomes very tiny and so the line become somehow continuous for the human eye. Do you know how to fix that problem, in order to have a...

I am reading regressors from an excel file (I have no control over the file) and some of the element names have spaces: i.e. "Small Bank Aquired" but I have found that lm(SourceData ~ . - "Small Bank Aquired", mcReg) doesn't work (mcReg = modelCurrentRegressors) As they are toggles I have ran t...

...explanatory var y<-sample(0:1,50,replace=T)#response # first script I've tried mod <- list() for (i in 1:3) { mod[[i]] <- nls(y~exp(c+(a1*x1)+(a2*x2) )/ (1+exp(c+(a1*x1)+(a2*x2))) ,start=list(a1=-0.2,a2=-0.2,c=0.2)) print(summary(mod[[i]])) } #second script I've tried regressors<-c("x1","x2") regMat<-expand.grid(c(T,F),c(T,F)) regMat allModelsList <- apply(regMat,1, function(x)as.formula(nls(y~exp(c+(a1*x1)+(a2*x2) )/ (1+exp(c+(a1*x1)+(a2*x2))) ,start=list(a1=-0.2,a2=-0.2,c=0.2)))) allModelsList allModelsResults <- lapply(allModelsList,...

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0, 0), period = NA), xreg = NULL, include.mean = TRUE, transform.pars = TRUE, fixed = N...

dear r-experts: there is probably a very easy way to do it, but it eludes me right now. I have a large data frame with, say, 26 columns named "a" through "z". I would like to define "sets of regressors" from this data frame. something like myregressors=c("b", "j", "x") lm( l ~ myregressors, data=... ) is the best way to create new data frames that contain all the variables I want, then use ".", and then destroy them again? or am I overlooking...

...last coefficient to be NA, which makes sense given the perfect collinearity. without getting in lengthy details about my code, let me just say that to avoid the colinearity problem,. I do not want to omit G1 from the regression. Instead, I want to fix the regression coefficient of one of the regressors, G1, to 1. I have read the R manual section on formulae but I have not found how to do fix a regression coefficient. Conceptually speaking it seems to me that it should be simple, and I am sure that someone explained it somewhere, but I did not find the proper keywords to find it! So, does so...

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and anything else that I can see (**except** for the value of the $J$-test) regardless of the weighting matrix. I do not think this is...

Hi all, I've experienced this problem using step() and stepAIC() when a name of a regressors has blanks in between (R:R1.7.0, os: w2ksp4). Please look at the following code: "x" <- c(14.122739306734, 14.4831100207131, 14.5556459667089, 14.5777151911177, 14.5285815352327, 14.0217803203846, 14.0732571632964, 14.7801310180502, 14.7839362960477, 14.7862217992577) &...

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645610 I am estimating a system of seemingly unrelated regressions (SUR) with `gmm::sysGmm` in R. Each of the equations has one unique regressor and one common regressor. The common regressor is a dummy variable indicating the last observation (n-1 zeros followed by 1). I impose a restriction that the coefficients on the common regressor are equal across equations. See a reproducible example below. The fitting routine yields the following...

...M Richard Hardy <brukalinis at gmail.com> wrote: > > A copy of this question can be found on Cross Validated: > https://stats.stackexchange.com/questions/645362 > > I am estimating a system of seemingly unrelated regressions (SUR) in R. > Each of the equations has one unique regressor and one common regressor. I > am using `gmm::sysGmm` and am experimenting with different weighting > matrices. I get the same results (point estimates, standard errors and > anything else that I can see (**except** for the value of the $J$-test) > regardless of the weighting matrix. I d...

Hi, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] How do I find the estimates of the coefficients in R? And also I would like to know what technique R employs to find the estimates? Any help is appreciated. Thanks, Preetam -- Preetam Pal (+91)-9432212774 M-Stat 2nd Year,...

...lyses consist often in a the interaction effect of treatment and usual training effect, with typically arma type of error, resulting in the above model. Typically, all the needed data are in a data.frame . An additional advantage concerns the names of the coefficient in the output: if only one regressor: >arima(anxiete$symptome, order=c(1,0,0), xreg=anxiete[, c("trait01")]) [...] Coefficients: ar1 intercept anxiete[, c("trait01")] 0.5649 33.8623 -8.1225 s.e. 0.1073 0.5969 0.8052 but the name convention changes w...

Hello, Does anybody know if the function arima with an external regressor (xreg) applies the auto correlation on the dependant variable or on the residuals. In comparison with SAS (proc autoreg), it seems that the auto correlation applies on the residuals but i'd like to have the confirmation. I want to estimate: Y[t] = a[1]*X[t] + a[2] + E[t] with E[t]=b[1]*E[t-1]...

Hi, All. I have a logistic regression model that I have run. The question came up: which of these regressors is more important than another? (I'm using Design) Logistic Regression Model lrm(formula = iconicgesture ~ ST + SSP + magnitude + Condition + Expertise, data = d) Coef S.E. Wald Z P Intercept -3.2688 0.2854 -11.45 0.0000 ST 2.0871 0.2730 7.64 0.0000 SSP...

Hello, I need to perform a robust regression on data which contains errors in BOTH observations and regressors. Right now I am using rlm from the MASS package with 'method="MM"' and get visually very nice results. MASS is quite clear, however, that the described methodologies are only applicable to observation-error only data (p. 157, 4th Ed.). So here's the questions now: a) is ther...

...e built a model that includes subject ID as a random effect, and has a continous variable (time) and I want to test whether the slope of this line differs between treatments (this is tested with the interaction between treatment and "time"). My question now is that I also want to include regressors that might explain variation in this slope between subjects (and of which I have this one estimate for the whole time period). How do I do this? If I just code it as the same number for all the time points I have measured I am overestimating my sample size don't I? (because I have not measured...

...0) lm.yx<-lm(y~x1) To predict from a new data source, one can use: # works as expected dum<-data.frame(x1=rnorm(200)) predict(lm.yx, newdata=dum) Suppose lm.yx has been run and we have the lm object. And we have a dataframe that has columns that don't correspond by name to the original regressors. I very! naively assumed that doing this (below) would work. It does not. # does not work lm.yx$coefficients<-c("Intercept", "n.x1") dum2<-data.frame(Int=rep(1,200), n.x1=rnorm(200)) predict(lm.yx, newdata=dum2) I know that a simple alternative is to do: # because we m...

Does anyone know if there is a way to specify regressors dynamically rather than explicitly? More specifically, I have a data set in "long format" that details a number of individuals and their responses to a question (which can be positive, negative, or no answer). Each individual answers as many questions as they want, so there are a diffe...