I m using R lm() function to make a multiple linear regression
lmfit <- lm(formula = `Var1` ~
`Var2`
+ `Var3`
+ `Var4`,
data=df)
Then the leveragePlots function from car library
library(car)
leveragePlots(lmfit)
This gives me plots with linear regression for each Var but I haven't find a way to display the confidence interval. Can you please help?
This will probably seem like a very round about way of doing what you want as I don't know how to do it in leveragePlots() but here I used ggplot2 which provides a lot of flexibility. You will need all of these packages installed which you can do with install.packages(c('ggplot2', 'magrittr', 'gridExtra', 'purrr')). I use the mtcars dataset in this example because it comes built in with R. So you can run this code as is and see what is happening. Just replace the mtcars and my variables with yours, and you should get what you want.
# Load packages
library(ggplot2)
library(magrittr)
library(gridExtra)
library(purrr)
# provide the data, x variable, y variable and this function will
# create a scatterplot with a linear model fit
create_plots <- function(df, xvar, yvar) {
if (!is.character(xvar) | !is.character(yvar)) {
stop('xvar and yvar must but characters/strings')
}
plot <- df %>%
ggplot(aes_string(x = xvar, y = yvar)) +
geom_point() +
geom_smooth(method = 'lm', se = T)
plot
}
# map over all the variables for which you would like to create plots
graphs <- purrr::map(c('disp', 'wt'), create_plots, df = mtcars,
yvar = 'hp')
first_plot <- graphs[[1]] # save the results in variables
second_plot <- graphs[[2]]
grid.arrange(first_plot, second_plot) # combine the plots
Related
I'm using imputed data to test a series of regression models, including some moderation models.
Imputation
imp_data <- mice(data,m=20,maxit=20,meth='cart',seed=12345)
I then convert this to long format so I can recode / sum variables as needed, beore turning back to mids format
impdatlong_mids<-as.mids(impdat_long)
Example model:
model1 <- with(impdatlong_mids,
lm(Outcome ~ p1_sex + p2 + p3 + p4
+ p5+ p6+ p7+ p8+ p9+ p10
+ p11+ p1_sex*p12+ p1_sex*p13 + p14)
in non-imputed data, to create a graphic representation of the significant ineraction, I'd use (e.g.)
interact_plot (model=model1, pred = p1_sex, modx = p12)
This doesn't work with imputed data / mids objects.
Has anyone plotted an interaction using imputed data, and able to help or share examples?
Thanks
EDIT: Reproducible example
library(tidyverse)
library(interactions)
library(mice)
# library(reprex) does not work with this
set.seed(42)
options(warn=-1)
#---------------------------------------#
# Data preparations
# loading an editing data
d <- mtcars
d <- d %>% mutate_at(c('cyl','am'),factor)
# create missing data and impute it
mi_d <- d
nr_of_NAs <- 30
for (i in 1:nr_of_NAs) {
mi_d[sample(nrow(mi_d),1),sample(ncol(mi_d),1)] <- NA
}
mi_d <- mice(mi_d, m=2, maxit=2)
#---------------------------------------#
# regressions
#not imputed
lm_d <- lm(qsec ~ cyl*am + mpg*disp, data=d)
#imputed dataset
lm_mi <- with(mi_d,lm(qsec ~ cyl*am + mpg*disp))
lm_mi_pool <- pool(lm_mi)
#---------------------------------------#
# interaction plots
# not imputed
#continuous
interactions::interact_plot(lm_d, pred=mpg,modx=disp, interval=T,int.width=0.3)
#categorical
interactions::cat_plot(lm_d, pred = cyl, modx = am)
#---------------------------------------#
# interaction plots
# imputed
#continuous
interactions::interact_plot(lm_mi_pool, pred=mpg,modx=disp, interval=T,int.width=0.3)
# Error in model.frame.default(model) : object is not a matrix
#categorical
interactions::cat_plot(lm_mi_pool, pred = cyl, modx = am)
# Error in model.frame.default(model) : object is not a matrix
The problem seems to be that neither interact_plot, cat_plot or any other available package allows for (at least categorical) interaction plotting with objects of class mipo or pooled regression outputs.
I am using the walking data from the mice package as an example. One way to get the interaction plot (well version of one type of interaction plot) is to use the gtsummary package. Under the hood it will take the model1 use pool() from mice to average over the models and then use a combo of tbl_regression() and plot() to output a plot of the coefficients in the model. The tbl_regression() function is what is calling the pool() function.
library(mice)
library(dplyr)
library(gtsummary)
imp_data <- mice(mice::walking,m=20,maxit=20,meth='cart',seed=12345)
model1 <- with(imp_data,
lm(age ~ sex*YA))
model1 %>%
tbl_regression() %>%
plot()
The package emmeans allows you to extract interaction effects from a mira object. Here is a gentle introduction. After that, the interactions can be plotted with appropriate ggplot. This example is for the categorical variables but could be extended to the continous case - after the emmeans part things get relatively straighforward.
library(ggplot2)
library(ggstance)
library(emmeans)
library(khroma)
library(jtools)
lm_mi <- with(mi_d,lm(qsec ~ gear*carb))
#extracting interaction effects
emcatcat <- emmeans(lm_mi, ~gear*carb)
tidy <- as_tibble(emcatcat)
#plotting
pd <- position_dodge(0.5)
ggplot(tidy, aes(y=gear, x=emmean, colour=carb)) +
geom_linerangeh(aes(xmin=lower.CL, xmax=upper.CL), position=pd,size = 2) +
geom_point(position=pd,size = 4)+
ggtitle('Interactions') +
labs (x = "aggreageted interaction effect") +
scale_color_bright() +
theme_nice()
this can be extended to a three-way interaction plot with facet_grid as long as you have a third categorical interaction term.
I want to build confidence intervals around a large set of fitted values using predictNLS from the propogate package in R. As an example, I will use the data set they reference in the function description (https://rdrr.io/github/anspiess/propagate/man/predictNLS.html), DNase, and building a model that takes the values conc and density as features:
library(propogate)
library(dplyr)
library(modelr)
DNase <- DNase
modeldna <- DNase %>% group_by(Run) %>%
do(run_model = nls(density ~ a * exp(b * conc),
start = list(a = 1 , b = 0.5),
data = .)) %>% ungroup()
I then want to give each row the model that it is assigned to so that predictions can be added:
DNApredict <- full_join(as_tibble(DNase), modeldna, by = "Run")
Add in the predictions:
DNApredict <- DNApredict %>%
group_by(Run) %>%
do(add_predictions(., var = "predicted_density", first(.$run_model)))
And then, I want to add the confidence interval data that predictNLS seems to provide, by giving it that same data and asking it to give a confidence interval for each fitted point in the predicted_density column:
confidence_interval <- predictNLS(model = modeldna, newdata = DNApredict$predicted_density, interval = "confidence")
However, the following error arises:
Error in as.list(object$call$formula) :
argument "object" is missing, with no default
Does anyone know what might be causing this? I know that it will likely seem obvious to some of you what the object it is calling is, so I apologize if this is a ridiculous question. I am really hoping to be able to use this function to create confidence intervals around a series of fitted values. Thank you very much in advance.
Since you are running an nls on each Run in the sample data set, it is easy to get a list of nls models by splitting each run into its own data frame, and running nls on each data frame using lapply
library(propagate)
DNase <- DNase
modeldna <- DNase %>% split(DNase$Run)
models <- lapply(modeldna, function(d) nls(density ~ a * exp(b * conc),
start = list(a = 1 , b = 0.5),
data = d))
Now we can get predictions for each point in each model just as easily by running predictNLS on each model (again inside lapply)
results <- lapply(seq_along(modeldna), function(i) {
predictNLS(models[[i]], newdata = data.frame(conc = modeldna[[i]]$conc))
})
Because of the output structure of predictNLS, we need to extract the predictions for each row and coerce them into a data frame:
predictions <- lapply(results, function(x) {
as.data.frame(do.call(rbind, lapply(x$prop, function(y) y$prop)))})
Finally, we can stick our predictions (including confidence intervals) back onto the original data frame:
all_results <- do.call(rbind, lapply(seq_along(modeldna),
function(i) cbind(modeldna[[i]], predictions[[i]])))
This now gives us a complete data frame of original data points, and the relevant predictions with confidence intervals.
To show this, we can plot the results in ggplot. Here we show one plot for each run, including its original data, the predicted value as a dotted line, and the 95% confidence limit as a pale blue ribbon:
library(ggplot2)
ggplot(all_results, aes(x = conc, y = density)) +
geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`),
fill = "deepskyblue4", alpha = 0.2) +
geom_point() +
geom_line(aes(y = Mean.1), linetype = 2) +
facet_wrap(.~factor(Run, levels = 1:11)) +
theme_bw()
Say I've fit a model as follows fit = lm(Y ~ X + Dummy1 + Dummy2)
How can I extract the regression for a specific dummy variable?
I'm hoping to do something like the following to plot all the regressions:
plot(...)
abline(extracted.lm.dummy1)
abline(extracted.lm.dummy2)
I would look into the sjPlot package. Here is the documentation for sjp.lm, which can be used to visualize linear models in various ways. The package also has some nice tools for tabular summaries of models.
An example:
library(sjPlot)
library(dplyr)
# add a second categorical variable to the iris dataset
# then generate a linear model
set.seed(123)
fit <- iris %>%
mutate(Category = factor(sample(c("A", "B"), 150, replace = TRUE))) %>%
lm(Sepal.Length ~ Sepal.Width + Species + Category, data = .)
Different kinds of plot include:
Marginal effects plot, probably closest to what you want
sjp.lm(fit, type = "eff", vars = c("Category", "Species"))
"Forest plot" (beta coefficients + confidence interval)
sjp.lm(fit)
Let's say that I have some data and I have created a linear model to fit the data. Then I plot the data using ggplot2 and I want to add the linear model to the plot. As far as I know, this is the standard way of doing it (using the built-in cars dataset):
library(ggplot2)
fit <- lm(dist ~ speed, data = cars)
summary(fit)
p <- ggplot(cars, aes(speed, dist))
p <- p + geom_point()
p <- p + geom_smooth(method='lm')
p
However, the above violates the DRY principle ('don't repeat yourself'): it involves creating the linear model in the call to lm and then recreating it in the call to geom_smooth. This seems inelegant to me, and it also introduces a space for bugs. For example, if I change the model that is created with lm but forget to change the model that is created with geom_smooth, then the summary and the plot won't be of the same model.
Is there a way of using ggplot2 to plot an already existing linear model, e.g. by passing the lm object itself to the geom_smooth function?
What one needs to do is to create a new data frame with the observations from the old one plus the predicted values from the model, then plot that dataframe using ggplot2.
library(ggplot2)
# create and summarise model
cars.model <- lm(dist ~ speed, data = cars)
summary(cars.model)
# add 'fit', 'lwr', and 'upr' columns to dataframe (generated by predict)
cars.predict <- cbind(cars, predict(cars.model, interval = 'confidence'))
# plot the points (actual observations), regression line, and confidence interval
p <- ggplot(cars.predict, aes(speed,dist))
p <- p + geom_point()
p <- p + geom_line(aes(speed, fit))
p <- p + geom_ribbon(aes(ymin=lwr,ymax=upr), alpha=0.3)
p
The great advantage of doing this is that if one changes the model (e.g. cars.model <- lm(dist ~ poly(speed, 2), data = cars)) then the plot and the summary will both change.
Thanks to Plamen Petrov for making me realise what was needed here. As he points out, this approach will only work if predict is defined for the model in question; if not, one has to define it oneself.
I believe you want to do something along the lines of :
library(ggplot2)
# install.packages('dplyr')
library(dplyr)
fit <- lm(dist ~ speed, data = cars)
cars %>%
mutate( my_model = predict(fit) ) %>%
ggplot() +
geom_point( aes(speed, dist) ) +
geom_line( aes(speed, my_model) )
This will also work for more complex models as long as the corresponding predict method is defined. Otherwise you will need to define it yourself.
In the case of linear model you can add the confidence/prediction bands with slightly more work and reproduce your plot.
I have the following model
require(effects)
fit<-lme(x ~ y, data, random= ~1|item)
plot(allEffects(fit)
fit2<-lme(x ~ y, data2, random = ~1|item)
plot(allEffects(fit2)
How can I plot fit and fit2 overlaying? I have tried the par(new=T), but it does not work. The graphs plot fine individually.
I'm not sure there's a very nice way to do this. I usually extract the information from the effects structure and plot it with ggplot (lattice would be possible too).
Here's an example:
library(effects)
library(nlme)
library(plyr) ## utilities
Fit a model to the first and second half of one of the standard example data sets:
fm1 <- lme(distance ~ age, random = ~1|Subject,
data = Orthodont[1:54,])
fm2 <- update(fm1, data = Orthodont[55:108,])
a1 <- allEffects(fm1)
a2 <- allEffects(fm2)
Extract the information from the efflist object. This is the part that isn't completely general ... the hard part is getting out the predictor variable.
as.data.frame.efflist <- function(x) {
ldply(x,
function(z) {
r <- with(z,data.frame(fit,
var=variables[[1]]$levels,
lower,upper))
return(plyr::rename(r,setNames(z$variables[[1]]$name,"var")))
})
}
For convenience, use ldply to put the results of both models together:
comb <- ldply(list(fm1=a1,fm2=a2),as.data.frame,.id="model")
Now plot:
library(ggplot2); theme_set(theme_bw())
ggplot(comb,aes(age,fit,
ymin=lower,ymax=upper,
colour=model,fill=model))+
geom_line()+
geom_ribbon(alpha=0.2,colour=NA)+
geom_rug(sides="b")
The rug plot component is a little silly here.