lm

With the linear model function lm() polynomial formulas can contain a shortcut notation like this: m <- lm(y ~ poly(x,3)) this is a shortcut that keeps the user from having to create x^2 and x^3 variables or typing them in the formula like I(x^2) + I(x^3) . Is there comparable notation for the nonlinear function nls() ? poly(x, 3) is rather more than just a shortcut for x + I(x ^ 2) + I(x ^ 3) - it actually produces legendre polynomials which have the nice property of being uncorrelated: options(digits = 2) x <- runif(100) var(cbind(x, x ^ 2, x ^ 3)) # x # x 0.074 0.073 0.064 # 0.073 0.077 0

How does plot.lm() determine what points are outliers (that is, what points to label) for residual vs fitted plot? The only thing I found in the documentation is this: Details sub.caption—by default the function call—is shown as a subtitle (under the x-axis title) on each plot when plots are on separate pages, or as a subtitle in the outer margin (if any) when there are multiple plots per page. The ‘Scale-Location’ plot, also called ‘Spread-Location’ or ‘S-L’ plot, takes the square root of the absolute residuals in order to diminish skewness (sqrt(|E|)) is much less skewed than | E | for

The problem: I cannot remove a lower order parameter (e.g., a main effects parameter) in a model as long as the higher order parameters (i.e., interactions) remain in the model. Even when doing so, the model is refactored and the new model is not nested in the higher model. See the following example (as I am coming from ANOVAs I use contr.sum ): d <- data.frame(A = rep(c("a1", "a2"), each = 50), B = c("b1", "b2"), value = rnorm(100)) options(contrasts=c('contr.sum','contr.poly')) m1 <- lm(value ~ A * B, data = d) m1 ## Call: ## lm(formula = value ~ A * B, data = d) ## ## Coefficients: ##

I have a few datapoints (x and y) that seem to have a logarithmic relationship. > mydata x y 1 0 123 2 2 116 3 4 113 4 15 100 5 48 87 6 75 84 7 122 77 > qplot(x, y, data=mydata, geom="line") Now I would like to find an underlying function that fits the graph and allows me to infer other datapoints (i.e. 3 or 82 ). I read about lm and nls but I'm not getting anywhere really. At first, I created a function of which I thought it resembled the plot the most: f <- function(x, a, b) { a * exp(b *-x) } x <- seq(0:100) y <- f(seq(0:100), 1,1) qplot(x,y, geom="line") Afterwards, I tried to generate a

Is it possible to set a stepwise linear model to use the BIC criteria rather than AIC? I've been trying this but it still calculates each step using AIC values rather than BIC null = lm(data[,1] ~ 1) full = lm(data[,1] ~ age + bmi + gender + group) step(null, scope = list(lower=null,upper=full), direction="both", criterion = "BIC") Add the argument k=log(n) to the step function ( n number of samples in the model matrix) From ?step : Arguments: ... k the multiple of the number of degrees of freedom used for the penalty. Only k = 2 gives the genuine AIC; k = log(n) is sometimes referred to as