Does anyone know how to visualize the smooth component of gam models in R very well? I would really like to visualize something like the output of the function visreg. This code below illustrates my problem
library(gam)
f=function(v){exp(v)}
n=100
x=runif(n)
t=runif(n)
y=x+f(t)+rnorm(n, sd=0.1)
fit=gam(y~x+s(t))
plot(t,y)
lines(t,as.numeric(fit$smooth))
#want something more like
library(visreg)
visreg(fit)
You could use the plotting method for gam objects, but you'd have to use the data parameter of gam:
library(gam)
f <- function(v){exp(v)}
n <- 100
x <- runif(n)
t <- runif(n)
y <- x+f(t)+rnorm(n, sd=0.1)
DF <- data.frame(y, x, t)
fit <- gam(y~x+s(t), data = DF)
layout(t(1:2))
plot(fit, se=TRUE)
See help("plot.gam") for other options.
Related
I have climatic data of different weather stations. I want to interpolate this data using regression instead of kriging or IDW. Can anyone tell how to do this in R?
Some example data (you should have included that with your question)
library(raster)
r <- raster(system.file("external/test.grd", package="raster"))
ra <- aggregate(r, 10)
d <- na.omit(data.frame(v=values(ra), xyFromCell(ra, 1:ncell(ra))))
Fit a model
m <- glm(v ~ ., data=d)
Predict that to a raster
p <- raster(r)
p <- interpolate(p, m)
Remove unwanted areas
p <- mask(p, r)
I am building a quadratic model with lm in R:
y <- data[[1]]
x <- data[[2]]
x2 <- x^2
quadratic.model = lm(y ~ x + x2)
Now I want to display both the predicted values and the actual values on a plot. I tried this:
par(las=1,bty="l")
plot(y~x)
P <- predict(quadratic.model)
lines(x, P)
but the line comes up all squiggely. Maybe it has to do with the fact that it's quadratic? Thanks for any help.
You need order():
P <- predict(quadratic.model)
plot(y~x)
reorder <- order(x)
lines(x[reorder], P[reorder])
My answer here is related: Problems displaying LOESS regression line and confidence interval
I am building a quadratic model with lm in R:
y <- data[[1]]
x <- data[[2]]
x2 <- x^2
quadratic.model = lm(y ~ x + x2)
Now I want to display both the predicted values and the actual values on a plot. I tried this:
par(las=1,bty="l")
plot(y~x)
P <- predict(quadratic.model)
lines(x, P)
but the line comes up all squiggely. Maybe it has to do with the fact that it's quadratic? Thanks for any help.
You need order():
P <- predict(quadratic.model)
plot(y~x)
reorder <- order(x)
lines(x[reorder], P[reorder])
My answer here is related: Problems displaying LOESS regression line and confidence interval
i'm trying to get the residual plots for a non-linear model i built with bbmle, but have no idea how to approach this task. The bble package has some notes on residuals but no way of plotting something like a histogram. Any help would be greatly appreciated
The residuals() function seems to work. And you can then do whatever you want - histogram, qqplot, scatterplot of residuals against predicted values (predict() also has a method). For example:
set.seed(1002)
lymax <- c(0,2)
lhalf <- 0
x <- runif(200)
g <- factor(rep(c("a","b"),each=100))
y <- rnbinom(200,mu=exp(lymax[g])/(1+x/exp(lhalf)),size=2)
dat <- data.frame(y,g,x)
fit3 <- mle2(y~dnbinom(mu=exp(lymax)/(1+x/exp(lhalf)),size=exp(logk)),
parameters=list(lymax~g),
start=list(lymax=0,lhalf=0,logk=0),
data=dat)
par(mfrow=c(2,2))
hist(residuals(fit3))
qqnorm(residuals(fit3))
hist(residuals(fit3, type="response"))
qqnorm(residuals(fit3, type="response"))
Or have I missed the point?
I run a nonparametric regression using the np package (npreg) and try to plot my results for the variable of interest x1 holding all other variables at their means/modes.
library("np")
y <- rnorm(100)
x1 <- rnorm(100,10,30)
x2 <- rbinom(100,1,0.5)
x3 <- rbinom(100,1,0.5)
model.np <- npreg(y ~ x1 + x2 + x3)
plot(model.np)
The plots are exactly what I want but I cannot figure out how to generate them separately "by hand". In particular, I only want the first (of the three) output plots.
Apparantly, a detailed answer can be found in the help file for the npplot-routine with plot.behavior being the crucial argument.
For my example, plotting only the x1-graph could be done via:
nlmodel.plot <- plot(model.np, plot.behavior="data")
y.eval <- fitted(nlmodel.plot$r1) #fit partial regression model for r1=airnoise
y.se <- se(nlmodel.plot$r1) #grab SE from botstrap
y.lower.ci <- y.eval + logp.se[,1] #lower CI
y.upper.ci <- y.eval + logp.se[,2] #upper CI
x1.eval <- nlmodel.plot$r1$eval[,1] #grab x1 values saved in plot$r1
plot(x1,y)
lines(x1.eval,y.eval)
lines(x1.eval,y.lower.ci,lty=3)
lines(x1.eval,y.upper.ci,lty=3)