Effect plot in R

 library(effects)x1 <- predictorEffect("x6", model)plot(x1)x2 <- predictorEffect("x2", model) plot(x2, main="model COVID 19",xlab="X2", ylab="x")  #single plotplot(allEffects(model)) #all p...

model summary in R

 # check that model is good fit or notwith(model, cbind(res.deviance = deviance, df = df.residual,                   p = pchisq(deviance, df.residual, lower.tail=FALSE)))## odds ratios and 95% CI ***********exp(cbind(OR = coef(model), confint(model)))# MORE SUMMARIES ###########################################anova(model)       ...

Poisson lognormal regression model in R

 library(PLNmodels)library(ggplot2)library(corrplot)Y=read.csv(choose.files())X=read.csv(choose.files())c=list(Y,X)names(c) <- c("output", "input")d <- prepare_data(c$output, c$input)model <- PLN(Abundance ~              LANE_WIDTH     ...

p value for any statistical model using R

 #2 tailed z testz<- coef(model)/standard_error(model)p<- (1-pnorm(abs(z),0,1))*2p        #pva...

Beta Regression Model

 library(betareg)a <- read.csv(choose.files())# beta regression modelsummary(betareg(P_SPEEDING_ADJ ~ log(AVG_AADT), data=...

Poisson Model

 # Step 1 - understand data# Load datap <- read.csv(file.choose())head(p)# STEP 2- EFA(exploratory factor analysis)#summary of variablessummary(p)#variancevar(p)# Dependent variable plothist(p$SPEEDING_CRASH,     main = "Histogram of Speeding Crash",     xlab = "Speeding Crash Number",     ylab = "Frequency")#STEP 3- Poisson regression modelmodel...

Probit Model

 require(aod)require(ggplot2)mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")## convert rank to a factor (categorical variable)mydata$rank <- factor(mydata$rank)## view first few rowshead(mydata)summary(mydata)xtabs(~rank + admit, data = mydata) #myprobit <- glm(admit ~ gre + gpa + rank, family = binomial(link = "probit"),          ...

Logistic Model

 #Coded by Tawkir Ahmed library(ggplot2) # USed for plotting datalibrary(dplyr) # Used to extract columns in the datalibrary(rms) # Used to extract p-value from logistic modellibrary(aod)theme_set(theme_gray() ) # the default# logistic modelcovid <- read.csv(choose.files())labs <- attributes(covid)$labelssummary(covid)# collapse all missing values to NAcovid$x <- factor(covid$x,...

Negative Binomial Model

 library(betareg)a <- read.csv(choose.files())# beta regression modelsummary(betareg(P_SPEEDING_ADJ ~ log(AVG_AADT), data=a))#negative binomial modelrequire(foreign)require(ggplot2)require(MASS)b<- a$P_SPEEDING_ADJc<- a$AVG_AADTd<- log(c)SPEEDING_AADT<- b*dmodel1= glm.nb(formula=SPEEDING_CRASH ~SPEEDING_AADT  ,                data...

Neural Network in R

 library(neuralnet)# creating training data setTAWKIR=c(20,10,30,20,80,30)AHMED=c(90,20,40,50,50,80)Placed=c(1,0,0,0,1,1)# Here, you will combine multiple columns or features into a single set of datadf=data.frame(TAWKIR,AHMED,Placed)# load libraryrequire(neuralnet)# fit neural networknn=neuralnet(Placed~TAWKIR+AHMED,data=df,...

Bar plot

 plot(rf,$a xlib= " ", ylib=" ", main = "Error in different tree...

Random Forest plot

 library(randomForest)# Load the dataset and exploredata1 <- read.csv(file.choose(), header = TRUE)head(data1)str(data1)summary(data1)# Split into Train and Validation sets# Training Set : Validation Set = 70 : 30 (random)set.seed(1000)train <- sample(nrow(data1), 0.7*nrow(data1), replace...

SHAP plot

 #Part 1: library inputsuppressPackageStartupMessages({  library(SHAPforxgboost)  library(xgboost)  library(data.table)  library(ggplot2)})#part 2:#file load and shap value calculationa <- read.csv(file.choose())X1 = as.matrix(a[,-1])mod1 = xgboost::xgboost(  data =...

Text plot

 ## A blank plot to set up a coordinate system## Final result will be Figure 3-42> plot(0:10, 0:10, type = "n")## Some regular text as a baselinetext(2,10, "Regular text", pos = 4)## Set text larger and use serif familypar(list(cex = 2, family = "serif"))## Add some texttext(2,8, "Serif Family",...

Bi Plot for PCA

 ## Use datasets:USArrestsdata(USArrests) # Get datafilenames(USArrests) # View variable names## Scaled PCA using entire data.framepca1 = prcomp(USArrests, scale = TRUE)## Both following commands produce same PCA as previouspca2 = prcomp(~., data = USArrests, scale = TRUE)pca3 = prcomp(~ Murder...

Scree plot from PCA

 ## Use datasets:USArrestsdata(USArrests) # Get datafilenames(USArrests) # View variable names## Scaled PCA using entire data.framepca1 = prcomp(USArrests, scale = TRUE)## Both following commands produce same PCA as previouspca2 = prcomp(~., data = USArrests, scale = TRUE)pca3 = prcomp(~ Murder...

Pic chart

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Dot chart

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Bar chart

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Line plot

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Scatter plot

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