See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the … Read more
For fitting y = A + B log x, just fit y against (log x). >>> x = numpy.array([1, 7, 20, 50, 79]) >>> y = numpy.array([10, 19, 30, 35, 51]) >>> numpy.polyfit(numpy.log(x), y, 1) array([ 8.46295607, 6.61867463]) # y ≈ 8.46 log(x) + 6.62 For fitting y = AeBx, take the logarithm of both … Read more
If I don’t get you wrong, you are working with a dataset like this: set.seed(0) dat <- data.frame(y1 = rnorm(30), y2 = rnorm(30), y3 = rnorm(30), x1 = rnorm(30), x2 = rnorm(30), x3 = rnorm(30)) x1, x2 and x3 are covariates, and y1, y2, y3 are three independent response. You are trying to fit three … Read more
Since 2009, dplyr has been released which actually provides a very nice way to do this kind of grouping, closely resembling what SAS does. library(dplyr) d <- data.frame(state=rep(c(‘NY’, ‘CA’), c(10, 10)), year=rep(1:10, 2), response=c(rnorm(10), rnorm(10))) fitted_models = d %>% group_by(state) %>% do(model = lm(response ~ year, data = .)) # Source: local data frame [2 … Read more
Here is one solution # GET EQUATION AND R-SQUARED AS STRING # SOURCE: https://groups.google.com/forum/#!topic/ggplot2/1TgH-kG5XMA lm_eqn <- function(df){ m <- lm(y ~ x, df); eq <- substitute(italic(y) == a + b %.% italic(x)*”,”~~italic(r)^2~”=”~r2, list(a = format(unname(coef(m)[1]), digits = 2), b = format(unname(coef(m)[2]), digits = 2), r2 = format(summary(m)$r.squared, digits = 3))) as.character(as.expression(eq)); } p1 <- p … Read more