I'm new with R and I have fit 3 models for my data as follows:
Model 1: y = a(x) + b
lm1 = lm(data$CBI ~ data$dNDVI)
Model 2: y = a(x)2 + b(x) + c
lm2 <- lm(CBI ~ dNDVI + I(dNDVI^2), data=data)
Model 3: y = x(a|x| + b)–1
lm3 = nls(CBI ~ dNDVI*(a*abs(dNDVI) + b) - 1, start = c(a = 1.5, b = 2.7), data = data)
Now I would like to plot all these three models in R but I could not find the way to do it, can you please help me? I have tried with the first two models as follow and it work but I don't know how to add the Model 3 on it:
ggplot(data = data, aes(x = dNDVI, y = CBI)) +
geom_point() +
geom_smooth(method = lm, formula = y ~ x, size = 1, se = FALSE) +
geom_smooth(method = lm, formula = y ~ x + I(x^2), size = 1, se = FALSE ) +
theme_bw()
I also would like to add a legend which show 3 different colours or types of lines/curves for the 3 models as well. Can you please guide me how to make it in the figure?
Using iris as a dummy set to represent the three models:
new.dat <- data.frame(Sepal.Length=seq(min(iris$Sepal.Length),
max(iris$Sepal.Length), length.out=50)) #new data.frame to predict the fitted values for each model
m1 <- lm(Petal.Length ~ Sepal.Length, iris)
m2 <- lm(Petal.Length ~ Sepal.Length + I(Sepal.Length^2), data=iris)
m3 <- nls(Petal.Length ~ Sepal.Length*(a*abs(Sepal.Length) + b) - 1,
start = c(a = 1.5, b = 2.7), data = iris)
new.dat$m1.fitted <- predict(m1, new.dat)
new.dat$m2.fitted <- predict(m2, new.dat)
new.dat$m3.fitted <- predict(m3, new.dat)
new.dat <- new.dat %>% gather(var, val, m1.fitted:m3.fitted) #stacked format of fitted data of three models (to automatically generate the legend in ggplot)
ggplot(new.dat, aes(Sepal.Length, val, colour=var)) +
geom_line()
Related
#linear regression
fit1 <- lm(temp ~ usage ,data= electemp)
#polynomial regression
fit2 <- lm(temp ~ poly(electemp$usage,degree), data = electemp)
ggplot(data=electemp, aes(x=temp,y=usage))+geom_point()+
stat_smooth(method="lm",col="red"). #linear regression
ggplot(electemp, aes(usage, temp) ) +
geom_point() +
stat_smooth(method = lm, formula=temp~ poly(electemp$usage, 3, raw=TRUE))
I am using the same ggplot for my polynomial regression but getting "Error: Aesthetics must be either length 1 or the same as the data (55): x".
You need to use x and y in the formula you pass to geom_smooth, not the variable names in your data frame.
Here's an example using some dummy data (though the structure and names are the same, so it should work on your own data):
library(ggplot2)
fit1 <- lm(temp ~ usage ,data= electemp)
fit2 <- lm(temp ~ poly(usage, 3), data = electemp)
ggplot(electemp, aes(usage, temp)) +
geom_point() +
stat_smooth(method = "lm", col = "red")
ggplot(electemp, aes(usage, temp) ) +
geom_point() +
stat_smooth(method = lm, formula= y ~ poly(x, 3))
Data
set.seed(1)
electemp <- data.frame(usage = 1:60,
temp = 20 + .2 * 1:60 - 0.02*(1:60)^2 +
0.0005 * (1:60)^3 + rnorm(60, 0, 5))
Created on 2020-11-24 by the reprex package (v0.3.0)
I have fitted 6 lm() models and 1 gam() model on the same dataset.
Now I want to plot them all in one plot on top of each other. Can I do this without defining the models again in ggplot?
My case is this
I have
model1 <- lm(y~1, data = data) %>% coef()
model2 <- lm(y~x, data = data) %>% coef()
model3 <- lm(y~abs(x), data = data) %>% coef()
...
model7 <- gam(y~s(x), data = data) %>% coef()
can I feed the stored coefficients of my models to ggplot?
ggplot(data, mapping = aes(x = x, y = y)) +
geom_point() +
geom_abline(model1) +
geom_abline(model2) +
....
Or do Is the only way to plot the model prediction lines to manualy fill out the parameters like this:
ggplot(data, mapping = aes(x = x, y = y)) +
geom_point() +
geom_abline(intercept = model1[1]) +
geom_abline(slope = model2[2], intercept = model2[1]) +
geom_abline(slope = model3[2], intercept = model3[1]) +
...
Example code
set.seed(123)
x <- rnorm(50)
y <- rweibull(50,1)
d <- as.data.frame(cbind(x,y))
model1 <- coef(lm(y~1, data = d))
model2 <- coef(lm(y~x, data = d))
model3 <- coef(lm(y~abs(x), data = d))
Including the SE for each line/model and a legend would be welcome as well.
In order for this to work, you really need to save the whole model. So if we assume you have the entire model
# set.seed(101) used for sample data
model1 <- lm(y~1, data = d)
model2 <- lm(y~x, data = d)
model3 <- lm(y~abs(x), data = d)
We can write a helper function to predict new values from these models over a the given range of x values. Here's such a function
newvalsforx <- function(x) {
xrng <- seq(min(x), max(x), length.out=100)
function(m) data.frame(x=xrng, y=predict(m, data.frame(x=xrng)))
}
pred <- newvals(d$x)
This pred() will make predictions from the models over the observed range of x. We can then use these as new data to pass to geom_lines that we can add to a plot. For example
ggplot(d, aes(x,y)) +
geom_point() +
geom_line(data=pred(model1), color="red") +
geom_line(data=pred(model2), color="blue") +
geom_line(data=pred(model3), color="green")
This gives me
So I have 2 models for the data set that I am using:
> Bears1Fit1 <- lm(Weight ~ Neck.G)
>
> Bears2Fit2 <- lm(Weight ~ Neck.G + I(Neck.G)^2)
I want to plot these two models on the same scatterplot. I have this so far:
> plot(Neck.G, Weight, pch = c(1), main = "Black Bears Data: Weight Vs Neck Girth", xlab = "Neck Girth (inches) ", ylab = "Weight (pounds)")
> abline(Bears1Fit1)
However, I am unsure of how I should put the quadratic model on the same graph as well. I want to be able to have both lines on the same graph.
Here is an example with cars data set:
data(cars)
make models:
model_lm <- lm(speed ~ dist, data = cars)
model_lm2 <- lm(speed ~ dist + I(dist^2), data = cars)
make new data:
new.data <- data.frame(dist = seq(from = min(cars$dist),
to = max(cars$dist), length.out = 200))
predict:
pred_lm <- predict(model_lm, newdata = new.data)
pred_lm2 <- predict(model_lm2, newdata = new.data)
plot:
plot(speed ~ dist, data = cars)
lines(pred_lm ~ new.data$dist, col = "red")
lines(pred_lm2 ~ new.data$dist, col = "blue")
legend("topleft", c("linear", "quadratic"), col = c("red", "blue"), lty = 1)
with ggplot2
library(ggplot2)
put all data in one data frame and convert to long format using melt from reshape2
preds <- data.frame(new.data,
linear = pred_lm,
quadratic = pred_lm2)
preds <- reshape2::melt(preds,
id.vars = 1)
plot
ggplot(data = preds)+
geom_line(aes(x = dist, y = value, color = variable ))+
geom_point(data = cars, aes(x = dist, y = speed))+
theme_bw()
EDIT: another way using just ggplot2 using two geom_smooth layers, one with the default formula y ~ x (so it need not be specified) and one with a quadratic model formula = y ~ x + I(x^2). In order to get a legend we can specify color within the aes call naming the desired entry as we want it to show in the legend.
ggplot(cars,
aes(x = dist, y = speed)) +
geom_point() +
geom_smooth(method = "lm",
aes(color = "linear"),
se = FALSE) +
geom_smooth(method = "lm",
formula = y ~ x + I(x^2),
aes(color = "quadratic"),
se = FALSE) +
theme_bw()
I'm running a set of models with the same independent variables but different dependent variables and would like to create a set of coefficient plots in one figures in which each model gets its own panel. The following code provides intuition but in this all of the models are integrated into one figure rather than have 3 unique panels side-by-side in one figure:
require("coefplot")
set.seed(123)
dat <- data.frame(x = rnorm(100), z = rnorm(100), y1 = rnorm(100), y2 = rnorm(100), y3 = rnorm(100))
mod1 <- lm(y1 ~ x + z, data = dat)
mod2 <- lm(y2 ~ x + z, data = dat)
mod3 <- lm(y3 ~ x + z, data = dat)
multiplot(mod1,mod2, mod3)
Which generates this plot:
Any thoughts on how to get them to panel next to each other in one figure? Thanks!
I haven't used the coefplot package before, but you can create a coefficient plot directly in ggplot2.
set.seed(123)
dat <- data.frame(x = rnorm(100), z = rnorm(100), y1 = rnorm(100), y2 = rnorm(100), y3 = rnorm(100))
mod1 <- lm(y1 ~ x + z, data = dat)
mod2 <- lm(y2 ~ x + z, data = dat)
mod3 <- lm(y3 ~ x + z, data = dat)
## Create data frame of model coefficients and standard errors
# Function to extract what we need
ce = function(model.obj) {
extract = summary(get(model.obj))$coefficients[ ,1:2]
return(data.frame(extract, vars=row.names(extract), model=model.obj))
}
# Run function on the three models and bind into single data frame
coefs = do.call(rbind, sapply(paste0("mod",1:3), ce, simplify=FALSE))
names(coefs)[2] = "se"
# Faceted coefficient plot
ggplot(coefs, aes(vars, Estimate)) +
geom_hline(yintercept=0, lty=2, lwd=1, colour="grey50") +
geom_errorbar(aes(ymin=Estimate - se, ymax=Estimate + se, colour=vars),
lwd=1, width=0) +
geom_point(size=3, aes(colour=vars)) +
facet_grid(. ~ model) +
coord_flip() +
guides(colour=FALSE) +
labs(x="Coefficient", y="Value") +
theme_grey(base_size=15)
I'm in the process of putting some incidence data together for a proposal. I know that the data takes on a sigmoid shape overall so I fit it using NLS in R. I was trying to get some confidence intervals to plot as well so I used bootstrapping for the parameters, made three lines and here's where I'm having my problem. The bootstrapped CIs give me three sets of values, but because of equation the lines they are crossing.
Picture of Current Plot with "Ideal" Lines in Black
NLS is not my strong suit so perhaps I'm not going about this the right way. I've used mainly a self start function to this point just to get something down on the plot. The second NLS equation will give the same output, but I've put it down now so that I can alter later if needed.
Here is my code thus far:
data <- readRDS(file = "Incidence.RDS")
inc <- nls(y ~ SSlogis(x, beta1, beta2, beta3),
data = data,
control = list(maxiter = 100))
b1 <- summary(inc)$coefficients[1,1]
b2 <- summary(inc)$coefficients[2,1]
b3 <- summary(inc)$coefficients[3,1]
inc2 <- nls(y ~ phi1 / (1 + exp(-(x - phi2) / phi3)),
data = data,
start = list(phi1 = b1, phi2 = b2, phi3 = b3),
control = list(maxiter = 100))
inc2.boot <- nlsBoot(inc2, niter = 1000)
phi1 <- summary(inc2)$coefficients[1,1]
phi2 <- summary(inc2)$coefficients[2,1]
phi3 <- summary(inc2)$coefficients[3,1]
phi1_L <- inc2.boot$bootCI[1,2]
phi2_L <- inc2.boot$bootCI[2,2]
phi3_L <- inc2.boot$bootCI[3,2]
phi1_U <- inc2.boot$bootCI[1,3]
phi2_U <- inc2.boot$bootCI[2,3]
phi3_U <- inc2.boot$bootCI[3,3]
#plot lines
age <- c(20:95)
mean_incidence <- phi1 / (1 + exp(-(age - phi2) / phi3))
lower_incidence <- phi1_L / (1 + exp(-(age - phi2_L) / phi3_L))
upper_incidence <- phi1_U / (1 + exp(-(age - phi2_U) / phi3_U))
inc_line <- data.frame(age, mean_incidence, lower_incidence, upper_incidence)
p <- ggplot()
p <- (p
+ geom_point(data = data, aes(x = x, y = y), color = "darkgreen")
+ geom_line(data = inc_line,
aes(x = age, y = mean_incidence),
color = "blue",
linetype = "solid")
+ geom_line(data = inc_line,
aes(x = age, y = lower_incidence),
color = "blue",
linetype = "dashed")
+ geom_line(data = inc_line,
aes(x = age, y = upper_incidence),
color = "blue",
linetype = "dashed")
+ geom_ribbon(data = inc_line,
aes(x = age, ymin = lower_incidence, ymax = upper_incidence),
fill = "blue", alpha = 0.20)
+ labs(x = "\nAge", y = "Incidence (per 1,000 person years)\n")
)
print(p)
Here's a link to the data.
Any help on what to do next or if this is even possible given my current set up would be appreciated.
Thanks
Try plot.drc in the drc package.
library(drc)
fm <- drm(y ~ x, data = data, fct = LL.3())
plot(fm, type = "bars")
P.S. Please include the library calls in your questions so that the code is self contained and complete. In the case of the question here: library(ggplot2); library(nlstools) .