nls

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

问题 *Note - I have read several of the posts on how to find starting values for NLS - however, I have not found one with an equation of this form (i.e. 4 parameters, exponent raised to a power) I am struggling tremendously to find suitable starting values for the Chapman Richards equation, which is commonly used in forestry to model tree growth. y(t) = α * (1 - β * exp(-k * t)^{1/(1-m)}) I typically try to find initial values by plotting a line with set parameters, and then tweaking it to fit the

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

问题 I am dealing with the relationship: y = h * R + x * v * h where: x = (N - M) * exp(-Q * u) + M which gives the principal equation: y = h * R + v * h * (N - M) * exp(-Q * u) + v * h * M All uppercase letters are constants, and all lowercase letters are variables. I have real data for all the variables, but I either do not know the values of the constants (R and Q), or want to check the ability of the data to fit the values of the constants (N and M). I want to use nls() to fit the equation

问题 I can run a nls regression at R if I explicitly define the parameters ("a" and "b" in the example below). However, how could I code the nls with a generic number of variables/higher degress in the poly function? df <- data.frame(var1 = rnorm(100), var2 = rnorm(100)) p <- as.data.frame(poly(df$var2, degree = 2)) names(p) <- paste0("poly", names(p)) df <- cbind(df, p) nls(var1 ~ a*poly1 + b*poly2, data = df, start = list(a = 1, b = 2)) Trying code, as is done with the lm function, is not