I am currently trying to conduct logistic regression where one of the variables is a vector of 32 dummy variables. Each dummy represents a type of crime. For example:
narcotics <- ifelse(train$PRIMARY.DESCRIPTION == "NARCOTICS", 1,0)
Then the vector is created:
crime.type <- c(narcotics, theft, other.offense, burglary, motor.vehicle.theft, battery, robbery, assault, criminal.damage, deceptive.practice, kidnapping, etc.)
The logistic model is as follows:
logit.mod.train <- lm(street1 ~ BEAT+WARD+X.COORDINATE+Y.COORDINATE+LATITUDE+LONGITUDE+crime.type, data = train, family = "binomial")
It's important to note that street1 is actually a dummy variable for the location of the crime being on the street. So the column is LOCATION.DESCRIPTION and the element is street.
street1 <- ifelse(train$LOCATION.DESCRIPTION == "STREET", 1,0).
It yields this error:
Error in model.frame.default(formula = street1 ~ BEAT + WARD + X.COORDINATE + :
variable lengths differ (found for 'crime.type')
I thought this would work because they are derived from the same data set and the dummies represent each unique element of one of the columns. When I input each dummy variable separately it's successful but I want to condense the regression and make it more efficient.
Thank you in advance
If you intend for each type of crime to be its own predictor, you'll need to bind them to train, and then specify the variables in your lm formula. (Actually for logit it should be glm().)
For a more compact formula, subset train in the data= argument of glm() to include only your response variable and your intended design matrix. Then use street1 ~ . as your formula.
train <- cbind(train, narcotics, theft)I
model.vars <- c("narcotics", "theft", "street1")
logit.mod.train <- glm(street1 ~ ., data = train[,model.vars], family = "binomial")
More explanation:
Using ifelse as you've done produces a 1 or 0 for every element in train.
When you define crime.type as narcotics (which has the length of train) plus any additional elements, crime.type is longer than the number of rows in train.
Then you're asking lm() to process a lopsided design matrix, where one predictor (crime.type) has more elements in it than the other predictors. That's why you're getting the error.
Here's a replication of the issue:
N <- 100
train <- data.frame(PRIMARY.DESCRIPTION=sample(c("A","B"), replace = T, size = N),
response = rbinom(n=N, prob=0.7, size=1))
dim(train) # 100 2
narcotics <- ifelse(train$PRIMARY.DESCRIPTION == "A", 1, 0)
length(narcotics) # 100
theft <- ifelse(train$PRIMARY.DESCRIPTION == "B", 1, 0)
length(theft) # 100
crime.type <- c(desc.A, desc.B)
length(crime.type) # 200
logit.mod.train <- glm(response ~ PRIMARY.DESCRIPTION+crime.type, data = train, family = "binomial")
Error in model.frame.default(formula = response ~ PRIMARY.DESCRIPTION + :
variable lengths differ (found for 'crime.type')
Related
I'm trying to fit a multilevel (random effects) ordered logit model using the ordinal package, but I keep running into this error:
Error in region:country1 : NA/NaN argument
Here's my simplified model. I'm regressing an indicator of happiness on a number of variables, including class, gender, age, etc. There are two nested levels: regions within countries.
library(ordinal)
# Set as factor
data$happiness <- as.factor(data$happiness)
# Remove NA
missing_country1 <- is.na(data$country1)
data <- data[!missing_country1, ]
missing_region <- is.na(data$region)
data <- data[!missing_region, ]
# Model
model1 <- clmm(happiness ~ age + gender + class + (1 | country1 / region),
data = data,
na.action = na.omit
)
I have removed all NA and NaN from both country1 and region.
Thanks,
Figured it out: it was because ordinal doesn't automatically convert the grouping variables to factor, so you need to do it manually.
EDITED:
I'm trying to assess the effect of variables (e.g. presence of severe trauma) on a continous variable (here energy expenditure (=REE) in calories) over time (Day). The dataframe is called my_data. Amongst the variables
Following I would like to display the results using the mixed linear model for each assessed variable in one large file.
General concept:
REE ~ Time*predictor + (1 + Time | Case identifier)
(1) Starting creating the lmer model:
library(tidyverse)
library(ggpmisc)
library(sjPlot)
library(lme4)
mixed.modelloop <- function(x) {
lmer(REE ~ Day*(x) + (1 + Day | Studynumber),
data=my_data,
REML=FALSE,
na.action=na.omit,
control = lmerControl(check.nobs.vs.nRE = "ignore"))
}
(2) Then creating the predictors (x)
cols <- c(colnames(my_data))
(3) And then generating the overall purrr function:
output <- purrr::map(cols, ~ mixed.modelloop(.x) %>% tab_model)
(4) generating the file which should include all separate univariate mixed model analyses:
pdf(file="mixed linear models.pdf" )
output
dev.off()
Unfortunately currently after step (3) I'm getting the following error message:
Error in model.frame.default(data = my_data, na.action = na.omit, drop.unused.levels = TRUE, :
variable lengths differ (found for 'x')
Any idea on how to adapt the function to resolve this issue?
Thanks!
Formulas have special rules, you can't insert a string into them and expect them to work.
This should work, although you haven't given a reproducible example to test with ...
mixed.modelloop <- function(x) {
form <- reformulate(c(sprintf("Day*%s", x), "(1 + Day | Studynumber)"),
response = "REE")
lmer(form,
data=my_data,
REML=FALSE,
na.action=na.omit,
control = lmerControl(check.nobs.vs.nRE = "ignore"))
}
I have an R coding question.
This is my first time asking a question here, so apologies if I am unclear or do something wrong.
I am trying to use a Generalized Linear Mixed Model (GLMM) with Poisson error family to test for any significant effect on a count response variable by three separate dichotomous variables (AGE = ADULT or JUVENILE, SEX = MALE or FEMALE and MEDICATION = NEW or OLD) and an interaction between AGE and MEDICATION (AGE:MEDICATION).
There is some dependency in my data in that the data was collected from a total of 22 different sites (coded as SITE vector with 33 distinct levels), and the data was collected over a total of 21 different years (coded as YEAR vector with 21 distinct levels, and treated as a categorical variable). Unfortunately, every SITE was not sampled for each YEAR, with some being sampled for a greater number of years than others.
The data is also quite sparse, in that I do not have a great number of measurements of the response variable (coded as COUNT and an integer vector) per SITE per YEAR.
My Poisson GLMM is constructed using the following code:
model <- glmer(data = mydata,
family = poisson(link = "log"),
formula = COUNT ~ SEX + SEX:MEDICATION + AGE + AGE:SEX + MEDICATION + AGE:MEDICATION + (1|SITE/YEAR),
offset = log(COUNT.SAMPLE.SIZE),
nAGQ = 0)
In order to try and obtain more reliable estimates for the fixed effect coefficients (particularly given the sparse nature of my data), I am trying to obtain 95% confidence intervals for the fixed effect coefficients through non-parametric bootstrapping.
I have come across the "glmmboot" package which can be used to conduct non-parametric bootstrapping of GLMMs, however when I try to run the non-parametric bootstrapping using the following code:
library(glmmboot)
bootstrap_model(base_model = model,
base_data = mydata,
resamples = 1000)
When I run this code, I receive the following message:
Performing case resampling (no random effects)
Naturally, though, my model does have random effects, namely (1|SITE/YEAR).
If I try to tell the function to resample from a specific block, by adding in the "reample_specific_blocks" argument, i.e.:
library(glmmboot)
bootstrap_model(base_model = model,
base_data = mydata,
resamples = 1000,
resample_specific_blocks = "YEAR")
Then I get the following error message:
Performing block resampling, over SITE
Error: Invalid grouping factor specification, YEAR:SITE
I get a similar error message if I try set 'resample_specific_blocks' to "SITE".
If I then try to set 'resample_specific_blocks' to "SITE:YEAR" or "SITE/YEAR" I get the following error message:
Error in bootstrap_model(base_model = model, base_data = mydata, resamples = 1000, :
No random columns from formula found in resample_specific_blocks
I have tried explicitly nesting YEAR within SITE and then adapting the model accordingly using the code:
mydata <- within(mydata, SAMPLE <- factor(SITE:YEAR))
model.refit <- glmer(data = mydata,
family = poisson(link = "log"),
formula = COUNT ~ SEX + AGE + MEDICATION + AGE:MEDICATION + (1|SITE) + (1|SAMPLE),
offset = log(COUNT.SAMPLE.SIZE),
nAGQ = 0)
bootstrap_model(base_model = model.refit,
base_data = mydata,
resamples = 1000,
resample_specific_blocks = "SAMPLE")
But unfortunately I just get this error message:
Error: Invalid grouping factor specification, SITE
The same error message comes up if I set resample_specific_blocks argument to SITE, or if I just remove the resample_specific_blocks argument.
I believe that the case_bootstrap() function found in the lmeresampler package could potentially be another option, but when I look into the help for it it looks like I would need to create a function and I unfortunately have no experience with creating my own functions within R.
If anyone has any suggestions on how I can get the bootstrap_model() function in the glmmboot package to recognise the random effects in my model/dataframe, or any suggestions for alternative methods on conducting non-parametric bootstrapping to create 95% confidence intervals for the coefficients of the fixed effects in my model, it would be greatly appreciated! Many thanks in advance, and for reading such a lengthy question!
For reference, I include links to the RDocumentation and GitHub for the glmmboot package:
https://www.rdocumentation.org/packages/glmmboot/versions/0.6.0
https://github.com/ColmanHumphrey/glmmboot
The following is code that will allow for creation of a reproducible example using the data set from lme4::grouseticks
#Load in required packages
library(tidyverse)
library(lme4)
library(glmmboot)
library(psych)
#Load in the grouseticks dataframe
data("grouseticks")
tibble(grouseticks)
#Create dummy vectors for SEX, AGE and MEDICATION
set.seed(1)
SEX <-sample(1:2, size = 403, replace = TRUE)
SEX <- as.factor(ifelse(SEX == 1, "MALE", "FEMALE"))
set.seed(2)
AGE <- sample(1:2, size = 403, replace = TRUE)
AGE <- as.factor(ifelse(AGE == 1, "ADULT", "JUVENILE"))
set.seed(3)
MEDICATION <- sample(1:2, size = 403, replace = TRUE)
MEDICATION <- as.factor(ifelse(MEDICATION == 1, "OLD", "NEW"))
grouseticks$SEX <- SEX
grouseticks$AGE <- AGE
grouseticks$MEDICATION <- MEDICATION
#Use the INDEX vector to create a vector of sample sizes per LOCATION
#per YEAR
grouseticks$INDEX <- 1
sample.sizes <- grouseticks %>%
group_by(LOCATION, YEAR) %>%
summarise(SAMPLE.SIZE = sum(INDEX))
#Combine the dataframes together into the dataframe to be used in the
#model
mydata$SAMPLE.SIZE <- as.integer(mydata$SAMPLE.SIZE)
#Create the Poisson GLMM model
model <- glmer(data = mydata,
family = poisson(link = "log"),
formula = TICKS ~ SEX + SEX + AGE + MEDICATION + AGE:MEDICATION + (1|LOCATION/YEAR),
nAGQ = 0)
#Attempt non-parametric bootstrapping on the model to get 95%
#confidence intervals for the coefficients of the fixed effects
set.seed(1)
Model.bootstrap <- bootstrap_model(base_model = model,
base_data = mydata,
resamples = 1000)
Model.bootstrap
http://www.statsci.org/data/oz/snails.txt
You can get data from here.
My data is 4*3*3*2 completely randomized design experiment data. I want to model the probability of survival in terms of the stimulus variables.
I tried ANOVA, but I'm not sure whether it's right or not.
Because I want to model the "probability", should I use logistic model??
(I also tried logistic model. But the data shows the sum of 0(Survived) and 1(Deaths). Even though it is not 0 and 1, can I use logistic??)
I want to put "probability" as Y variable.
So I used logit but it's not working.
The program says that y is Inf.
How can I use logit as Y variable in aov?
glm_a <- glm(Deaths ~ Exposure + Rel.Hum + Temp + Species, data = data,
family = binomial)
prob <- Deaths / 20
logitt <- log(prob / (1 - prob))
logmodel <- lm(logitt ~ data$Species + data$Exposure + data$Rel.Hum + data$Temp)
summary(logmodel)
A <- factor(data$Species, levels = c("A", "B"), labels = c(-1, 1))
glm_a <- glm(Y ~ data$Species * data$Exposure * data$Rel.Hum * data$Temp,
data=data, family = binomial)
summary(glm_a)
help("glm") should direct you to help("family"), which reveals the following
For the binomial and quasibinomial families the response can be specified in one of three ways:
As a factor: ‘success’ is interpreted as the factor not having the first level (and hence usually of having the second level).
As a numerical vector with values between 0 and 1, interpreted as the proportion of successful cases (with the total number of cases given by the weights).
As a two-column integer matrix: the first column gives the number of successes and the second the number of failures.
So for the question "How can I make logistic model with this data?", we can go with route #3 quite easily:
data <- read.table("http://www.statsci.org/data/oz/snails.txt", header = TRUE)
glm_a <- glm(cbind(Deaths, N - Deaths) ~ Species * Exposure * Rel.Hum * Temp,
data = data, family = binomial)
summary(glm_a)
# [output omitted]
As for the question "I tried ANOVA, but I'm not sure whether it's right or not. Because I want to model the "probability", should I use logistic model?", it's better to ask on Cross Validated
I am trying to perform logistic regression using R in a dataset provided here : http://archive.ics.uci.edu/ml/machine-learning-databases/00451/
It is about breast cancer. This dataset contains a column Classification which contains only 1 (if patient doesn't have cancer) or 2 (if patient has cancer)
library(ISLR)
dataCancer <- read.csv("~/Desktop/Isep/Machine
Leaning/TD/Project_Cancer/dataR2.csv")
attach(dataCancer)
#Step : Split data into training and testing data
training = (BMI>25)
testing = !training
training_data = dataCancer[training,]
testing_data = dataCancer[testing,]
Classification_testing = Classification[testing]
#Step : Fit a logistic regression model using training data
classification_model = glm(Classification ~ ., data =
training_data,family = binomial )
When running my script I get :
> classification_model = glm(Classification ~ ., data = training_data,family = binomial )
Error in eval(family$initialize) : y values must be 0 <= y <= 1
> summary(classification_model)
Error in summary(classification_model) : object 'classification_model' not found .
I added as.factor(dataCancer$Classification) as seen in other posts but it has not solved my problem.
Can you suggest me a way to have a classification's value between 0 and 1 if it is the content of this predictor?
You added the as.factor(dataCancer$Classification) in the script, but even if the dataset dataCancer is attached, a command like the one above does not transform the dataset variable Classification into a factor. It only returns a factor on the console.
Since you want to fit the model on the training dataset, you either specify
training_data$Classification <- as.factor(training_data$Classification)
classification_model <- glm(Classification ~ ., data =
training_data, family = binomial)
or use the as.factor function in the glm line code
classification_model <- glm(as.factor(Classification) ~ ., data =
training_data, family = binomial)
classification_model = glm(Classification ~ ., data = training_data,family = binomial )
Error in eval(family$initialize) : y values must be 0 <= y <= 1
This is because your data contains numeric values, not factor values. I hope you did
dataCancer$Classification <- as.factor(dataCancer$Classification)
Ideally, 1,0 or 1,2 will not matter as long as it's a factor. But, if doing the above also doesn't help, then you can try converting 1,2 to 1,0 and then trying the same code.
Of course the second error is because logistic regression variable was not created at all.
You need to recode the Dependent variable as 0,1 so use the below code.
library(car)
dataCancer$Classification <- recode(dataCancer$Classification, "1=0; 2=1")