lm

working with a data frame x Date Val 1/1/2012 7 2/1/2012 9 3/1/2012 20 4/1/2012 24 5/1/2012 50 a <- seq(as.Date(tail(x, 1)$Date), by="month", length=5) a <- data.frame(a) x.lm <- lm(x$Val ~ x$Date) x.pre<-predict(x.lm, newdata=a) I am getting this erro: Warning message: 'newdata' had 5 rows but variable(s) found have 29 rows what am I doing wrong? here is the dput output: dput(x) structure(list(Date = structure(c(14610, 14641, 14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883, 14914, 14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156, 15187, 15218, 15248, 15279, 15309, 15340, 15371,

Is there a single function, similar to "runif", "rnorm" and the like which will produce simulated predictions for a linear model? I can code it on my own, but the code is ugly and I assume that this is something someone has done before. slope = 1.5 intercept = 0 x = as.numeric(1:10) e = rnorm(10, mean=0, sd = 1) y = slope * x + intercept + e fit = lm(y ~ x, data = df) newX = data.frame(x = as.numeric(11:15)) What I'm interested in is a function that looks like the line below: sims = rlm(1000, fit, newX) That function would return 1000 simulations of y values, based on the new x variables.

I want to use aggregate with this custom function: #linear regression f-n CalculateLinRegrDiff = function (sample){ fit <- lm(value~ date, data = sample) diff(range(fit$fitted)) } dataset2 = aggregate(value ~ id + col, dataset, CalculateLinRegrDiff(dataset)) I receive the error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'FUN' of mode 'function' was not found What is wrong? Your syntax on using aggregate is wrong in the first place. Pass function CalculateLinRegrDiff not an evaluated one CalculateLinRegrDiff(dataset) to FUN argument. Secondly, you've chosen the

I have three datasets: response - matrix of 5(samples) x 10(dependent variables) predictors - matrix of 5(samples) x 2(independent variables) test_set - matrix of 10(samples) x 10(dependent variables defined in response) response <- matrix(sample.int(15, size = 5*10, replace = TRUE), nrow = 5, ncol = 10) colnames(response) <- c("1_DV","2_DV","3_DV","4_DV","5_DV","6_DV","7_DV","8_DV","9_DV","10_DV") predictors <- matrix(sample.int(15, size = 7*2, replace = TRUE), nrow = 5, ncol = 2) colnames(predictors) <- c("1_IV","2_IV") test_set <- matrix(sample.int(15, size = 10*2, replace = TRUE), nrow =

I am trying to fit two nested models and then test those against each other using anova function. The commands used are: probit <- glm(grad ~ afqt1 + fhgc + mhgc + hisp + black + male, data=dt, family=binomial(link = "probit")) nprobit <- update(probit, . ~ . - afqt1) anova(nprobit, probit, test="Rao") However, the variable afqt1 apparently contains NA s and because the update call does not take the same subset of data, anova() returns error Error in anova.glmlist(c(list(object), dotargs), dispersion = dispersion, : models were not all fitted to the same size of dataset Is there a simple way

I was working on some code and I noticed something peculiar. When I run LM on a subset of some panel data I have it works fine, something like this: library('plm') data(Cigar) lm(log(price) ~ log(pop) + log(ndi), data=Cigar, subset=Cigar$state==1) Call: lm(formula = log(price) ~ log(pop) + log(ndi), data = Cigar, subset = Cigar$state == 1) Coefficients: (Intercept) log(pop) log(ndi) -26.4919 3.2749 0.4265 but when I try to wrap this in a function I get: myfunction <- function(formula, data, subset){ return(lm(formula, data, subset)) } myfunction(formula = log(price) ~ log(pop) + log(ndi), data

I'd like to run 10 regressions against the same regressor, then pull all the standard errors without using a loop . depVars <- as.matrix(data[,1:10]) # multiple dependent variables regressor <- as.matrix([,11]) # independent variable allModels <- lm(depVars ~ regressor) # multiple, single variable regressions summary(allModels)[1] # Can "view" the standard error for 1st regression, but can't extract... allModels is stored as an "mlm" object, which is really tough to work with. It'd be great if I could store a list of lm objects or a matrix with statistics of interest. Again, the objective is