search for: ncoefficients

...lm1$model[,2]^2) * sum(lm2$model[,2]^2) F.val <-(as.numeric(coefficients(lm1)[2]) - as.numeric(coefficients(lm2) [2]))^2/((SSall/SSprod)*sigma) p.val <-1-pf(F.val, 1, (lm1$df.residual + lm2$df.residual-4)) cat("\n\nTest for equality between two regression slopes\n\n") cat("\nCoefficients model 1:\n\n") print(coef1) cat("\nCoefficients model 2:\n\n") print(coef2) cat("\nF-value on 1 and", lm1$df.residual + lm2$df.residual-4, "degrees of freedom:" ,F.val, "\n") cat("p =", ifelse(p.val>=0.0001, p.val, "< 0.0001"...

...ercept) {x <- cbind(1,x); dn <- c("(Intercept)", dn)} if(is.factor(y)) y <- (unclass(y) !=1) fit <- nlminb(start, fmin, gmin, X=x, y=y, w=wt, method = "BFGS", ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) # R: use fit$value and fit$convergence cat("\nResidual Deviance:", format(fit$objective), "\n") cat("\nConvergence message:", fit$message, "\n") invisible(fit)...

...ject$df pval <- 2 * pt(abs(tval), df, lower.tail = FALSE) coefmat <- cbind(est, se, tval, pval) colnames(coefmat) <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)") cat("\nCoefficients:\n") printCoefmat( coefmat, P.values=T, has.Pvalue=T, signif.legend=T ) cat("\nFraction of information about the coefficients missing due to nonresponse:","\n") print(object$f) ans <- list( coefficients=coef...

My question is about the "Signif. codes" , the output when I run matcoef =cbind(fit$par, se.coef,tval,2*(1-pnorm(abs(tval)))) dimnames(matcoef)=list(names(tval),c("Estimate","Std.Error","t value","pr(>|t|)")) cat("\nCoefficient(s):\n") printCoefmat(matcoef, digits=4, signif.stars = TRUE) Coefficient(s): Estimate

Hello, I am looking for a routine to do a logistic regression. In the book "Modern Applied Statistics with S-PLUS" a function is described which uses the 'ms' command. Is there an analogue for R, or an alternative approach that can accomplish the same thing? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia

...t;Max") } print(rq, digits = digits, ...) } else if (rdf > 0) { cat("Residuals:\n") print(resid, digits = digits, ...) } # if (nsingular <- df[3] - df[1]) # cat("\nCoefficients: (", nsingular, " not defined because of singularities)\n", # sep = "") # else cat("\nCoefficients:\n") print(roundfun(x$coefficients, digits = digits), quote = FALSE, ...) cat("\nResidua...

...= wt, method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) #fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------...

...y = y, w = wt, method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------------...

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

I apologize for my earlier posting that, unbeknownst to me before, apparently was not in the correct format for this list. Hopefully this attempt will go through, and no-one will hold the newbie mistake against me. I could really use some help in writing a new glm link function in order to run an analysis of daily nest survival rates. I've struggled with this for weeks now, and can at least

...y = y, w = wt, method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------------...