I use plot_summs to plot my regression coefficients. below is a reproducible sample. I want to do two things. First, I want to remove Frost and Murder from the graph. Second, I want to change the color of Illiteracy and Population to Green and Life Exp and HS Grad to red. I’d appreciate any help I can get here.
fit1 <- lm(Income ~ Frost + Illiteracy + Murder +
Population + Area + Life Exp + HS Grad,
data = states, weights = runif(50, 0.1, 3))
plot_summs(fit1, scale = TRUE)
Without using the jtools package:
If I'm not wrong, plot_summ with scale - TRUE, scales the independent variables and plots the summary of the variable estimates, with mean as point and 2*SE as segments.
states2 <- states
states2[,-1] <- scale(states2[,-1]) # Considering first column is Income
fit2 <- lm(Income ~ ., data = states2, weights = runif(50, 0.1, 3))
df <- as.data.frame(summary(fit2)[["coefficients"]][-1,1:2])
df$variable <- rownames(df)
df <- df[!df$variable %in% c("Frost", "Murder"), ]
library(ggplot2)
ggplot(df) +
geom_point(aes(x = variable, y = Estimate,
color = variable), size = 6) +
geom_segment(aes(x = variable, xend = variable,
y = Estimate - (2 * `Std. Error`),
yend = Estimate + (2 * `Std. Error`),
color = variable), lwd = 2) +
scale_color_manual(values = c("Illiteracy" = "green","Population" = "green",
"Area" = "blue",
"`Life Exp`" = "red", "`HS Grad`" = "red")) +
coord_flip() +
theme_classic()
Related
I am attempting to display a linear model for low x values and a non-linear model for higher x values. To do this, I will use DNase as an example:
library(ggplot2)
#Assinging DNase as a new dataframe:
data_1 <- DNase
#Creating a column that can distinguish low and high range values:
data_1$range <- ifelse(data_1$conc <5, "low", "high")
#Attempting to plot separate lines for low and high range values, and also facet_wrap by run:
ggplot(data_1, aes(x = conc, y = density, colour = range)) +
geom_point(size = 0.5) + stat_smooth(method = "nls",
method.args = list(formula = y ~ a*exp(b*x),
start = list(a = 0.8, b = 0.1)),
data = data_1,
se = FALSE) +
stat_smooth(method = 'lm', formula = 'y~0+x') +
facet_wrap(~Run)
However, as you can see, it seems to plot both the linear model and the non-linear model for both, and I can't quite figure out where to put information that would tell it to only plot one for each. Also, if possible, can I extend these models out to the full range of values on the x axis?
You can provide specific data to each geom. In this case use subset data_1 using range to only provide the relevant data to each stat_smooth() call (and the whole frame to geom_point()
ggplot(NULL, aes(x = conc, y = density, colour = range)) +
geom_point(data = data_1, size = 0.5) +
stat_smooth(data = subset(data_1, range == "high"),
method = "nls",
method.args = list(formula = y ~ a*exp(b*x),
start = list(a = 0.8, b = 0.1)),
se = FALSE) +
stat_smooth(data = subset(data_1, range == "low"), method = 'lm', formula = 'y~0+x') +
facet_wrap(~Run)
If you want to fit both models on all the data, then just calculate those manually in data_1 and plot manually.
I need to create an insightful graphic with a regression line, data points, and confidence intervals. I am not looking for smoothed lines. I have tried multiple codes, but I just can't get it right.
I am looking for something like this:
Some codes I have tried:
p <- scatterplot(df.regsoft$w ~ df.regsoft$b,
data = df.regsoft,
boxplots = FALSE,
regLine = list(method=lm, col="red"),
pch = 16,
cex = 0.7,
xlab = "Fitted Values",
ylab = "Residuals",
legend = TRUE,
smooth = FALSE)
abline(coef = confint.lm(result.rs))
But this doesn't create what I want to create, however it is closest to what I intended. Notice that I took out "smooth" since this is not really what I am looking for.
How can I make this plot interactive?
If you don't mind switch to ggplot and the tidyverse, then this is simply a geom_smooth(method = "lm"):
library(tidyverse)
d <- tibble( #random stuff
x = rnorm(100, 0, 1),
y = 0.25 * x + rnorm(100, 0, 0.25)
)
m <- lm(y ~ x, data = d) #linear model
d %>%
ggplot() +
aes(x, y) + #what to plot
geom_point() +
geom_smooth(method = "lm") +
theme_bw()
without method = "lm" it draws a smoothed line.
As for the Conf. interval (Obs 95%) lines, it seems to me that's simply a quantile regression. In that case, you can use the quantreg package.
If you want to make it interactive, you can use the plotly package:
library(plotly)
p <- d %>%
ggplot() +
aes(x, y) +
geom_point() +
geom_smooth(method = "lm") +
theme_bw()
ggplotly(p)
================================================
P.S.
I am not completely sure this is what the figure you posted is showing (I guess so), but to add the quantile lines, I would just perform two quantile regressions (upper and lower) and then calculate the values of the quantile lines for your data:
library(tidyverse)
library(quantreg)
d <- tibble( #random stuff
x = rnorm(100, 0, 1),
y = 0.25 * x + rnorm(100, 0, 0.25)
)
m <- lm(y ~ x, data = d) #linear model
# 95% quantile, two tailed
rq_low <- rq(y ~ x, data = d, tau = 0.025) #lower quantile
rq_high <- rq(y ~ x, data = d, tau = 0.975) #upper quantile
d %>%
mutate(low = rq_low$coefficients[1] + x * rq_low$coefficients[2],
high = rq_high$coefficients[1] + x * rq_high$coefficients[2]) %>%
ggplot() +
geom_point(aes(x, y)) +
geom_smooth(aes(x, y), method = "lm") +
geom_line(aes(x, low), linetype = "dashed") +
geom_line(aes(x, high), linetype = "dashed") +
theme_bw()
I want to achieve the exact same thing asked in this question:
How to plot the survival curve generated by survreg (package survival of R)?
Except for the fact that I don't want the data to be stratified by a variable (in the question above it was stratified by sex).
I just want the progression free survival for the whole group of treated patients.
So when I copy the code from the other question, here is where I get stuck:
library(survminer)
library(tidyr)
s <- with(lung,Surv(time,status))
fKM <- survfit(s ~ sex,data=lung)
sWei <- survreg(s ~ as.factor(sex),dist='weibull',data=lung) # in my case here I would replace as.factor(sex) by 1
pred.sex1 = predict(sWei, newdata=list(sex=1),type="quantile",p=seq(.01,.99,by=.01)) #Since I don't want to stratify, what do I do with these 2 lines of code?
pred.sex2 = predict(sWei, newdata=list(sex=2),type="quantile",p=seq(.01,.99,by=.01))
df = data.frame(y=seq(.99,.01,by=-.01), sex1=pred.sex1, sex2=pred.sex2)
df_long = gather(df, key= "sex", value="time", -y)
p = ggsurvplot(fKM, data = lung, risk.table = T)
p$plot = p$plot + geom_line(data=df_long, aes(x=time, y=y, group=sex))
I tried replacing as.factor(sex) by 1 and then the rest of the code just does not make sense, can someone help me with this?
Many thanks in advance!
If you just want to plot the overall empirical survival curve, you might do something like this:
library(survival)
library(survminer)
library(tidyr)
s <- with(lung, Surv(time, status))
fKM <- survfit(s ~ 1, data = survival::lung)
ggsurvplot(fKM, ggtheme = theme_bw())
However, if you want to fit a Weibull model with no predictors, then your formula is fine.
sWei <- survreg(s ~ 1, dist = 'weibull', data = lung)
probs <- seq(0.01, 1, by = 0.01)
time <- predict(sWei, type = "quantile", se = TRUE, p = probs)
The only problem is that time is now a named list of two matrices: fit and se.fit. Both have the same number of rows as lung, but all rows are identical, so we just take one from each and calculate the confidence interval in a data frame which we can then use to create a ggplot:
ggplot(data = data.frame(p = 1 - probs,
time = time$fit[1,],
upper = time$fit[1,] + 1.96 * time$se.fit[1,],
lower = time$fit[1,] - 1.96 * time$se.fit[1,])) +
geom_step(aes(p, time, colour = "All"), size = 1) +
geom_ribbon(aes(p, ymin = lower, ymax = upper, fill = "All"), alpha = 0.2) +
coord_flip(ylim = c(0, 1000)) +
scale_fill_discrete(name = "Strata") +
scale_color_discrete(name = "Strata") +
theme_bw() +
theme(legend.position = "top")
Which we can see looks like a pretty good fit.
If you want both in the same plot you can do something like:
df <- data.frame(p = 1 - probs,
time = time$fit[1,],
upper = time$fit[1,] + 1.96 * time$se.fit[1,],
lower = time$fit[1,] - 1.96 * time$se.fit[1,])
ggsurvplot(fKM, ggtheme = theme_bw())$plot +
geom_line(data = df, aes(time, p), linetype = 2, size = 1) +
geom_line(data = df, aes(upper, p), linetype = 2, size = 1) +
geom_line(data = df, aes(lower, p), linetype = 2, size = 1)
Created on 2020-08-18 by the reprex package (v0.3.0)
I am making a scatter plot in R with ggplot2. I am comparing the fraction of votes Hillary and Bernie received in the primary and education level. There is a lot over overlap and way to many points. I tried to use transparency so I could see the overlap but it still looks bad.
Code:
demanalyze <- function(infocode, n = 1){
infoname <- filter(infolookup, column_name == infocode)$description
infocolumn <- as.vector(as.matrix(mydata[infocode]))
ggplot(mydata) +
aes(x = infocolumn) +
ggtitle(infoname) +
xlab(infoname) +
ylab("Fraction of votes each canidate recieved") +
xlab(infoname) +
geom_point(aes(y = sanders_vote_fraction, colour = "Bernie Sanders")) +#, color = alpha("blue",0.02), size=I(1)) +
stat_smooth(aes(y = sanders_vote_fraction), method = "lm", formula = y ~ poly(x, n), size = 1, color = "darkblue", se = F) +
geom_point(aes(y = clinton_vote_fraction, colour = "Hillary Clinton")) +#, color = alpha("red",0.02), size=I(1)) +
stat_smooth(aes(y = clinton_vote_fraction), method = "lm", formula = y ~ poly(x, n), size = 1, color = "darkred", se = F) +
scale_colour_manual("",
values = c("Bernie Sanders" = alpha("blue",0.02), "Hillary Clinton" = alpha("red",0.02))
) +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
}
What could I change to make the overlap areas look less messy?
The standard way to plot a large number of points over 2 dimensions is to use 2D density plots:
With reproducible example:
x1 <- rnorm(1000, mean=10)
x2 <- rnorm(1000, mean=10)
y1 <- rnorm(1000, mean= 5)
y2 <- rnorm(1000, mean = 7)
mydat <- data.frame(xaxis=c(x1, x2), yaxis=c(y1, y2), lab=rep(c("H","B"),each=1000))
head(mydat)
library(ggplot2)
##Dots and density plots (kinda messy, but can play with alpha)
p1 <-ggplot(mydat) + geom_point(aes(x=xaxis, y = yaxis, color=lab),alpha=0.4) +
stat_density2d(aes(x=xaxis, y = yaxis, color=lab))
p1
## just density
p2 <-ggplot(mydat) + stat_density2d(aes(x=xaxis, y = yaxis, color=lab))
p2
There are many parameters to play with, so look here for the full info on the plot type in ggplot2.
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) .