I need to create a table in which i I have the id of the panel data regressions in the rows, and the statistics of the model in the columns. I am trying to extract the results (Statistics, df, p-value) from the Breusch Godfrey test to insert into the table. One remark is that I am running many regressions with the same command. I tried doing
GodfreyallModelsResultsF <- lapply(allModelsList, function(x)
bgtest(plm(x, data=data, model="within")))
GodfreyallModelsResultsF2 <- as.data.frame(
matrix(unlist(GodfreyallModelsResultsF,use.names=TRUE),
nrow=length(unlist(GodfreyallModelsResultsF[1]))))
GodfreyallModelsResultsF <- t(GodfreyallModelsResultsF)
colnames(GodfreyallModelsResultsF) <- c("BPTest","df","name","Pvalue","code")
But it didn't work. One last remark is that I was able to use this code to successfully extract from Breusch Pagan test.
Could someone help me in the B-G issue?
Here is the way of extracting p-value:
model <- plm(x, data = data, model = "within")
as.numeric(pbgtest(model)[4])
Related
I am trying to create a for loop to index thorugh each individual response variable I have and train a model using the train() funciton within the Caret Package. I have about 30 response variable and 43 predictor variables. I can train each model individually but I would like to automate the process and have a for loop run through a model (I would like to eventually upscale to multiple models if possible, i.e. lm, rf, cubist, etc.). I then want to save each model to a dataframe along with R-squared values and RMSE values. The individual models that I currenlty have that will run for me goes as follows, with column 11 being the response variable and column 35-68 being predictor variables.
data_Mg <- subset(data_3, !is.na(Mg))
mg.lm <- train(Mg~., data=data_Mg[,c(11,35:68)], method="lm", trControl=control)
mg.cubist <- train(Mg~., data=data_Mg[,c(11,35:68)], method="cubist", trControl=control)
mg.rf <- train(Mg~., data=data_Mg[,c(11,35:68)], method="rf", trControl=control, na.action = na.roughfix)
max(mg.lm$results$Rsquared)
min(mg.lm$results$RMSE)
max(mg.cubist$results$Rsquared)
min(mg.cubist$results$RMSE)
max(mg.rf$results$Rsquared) #Highest R squared
min(mg.rf$results$RMSE)
This gives me 3 models with everything the relevant information that I need. Now for the for loop. I've only tried the lm model so far for this.
bucket <- list()
for(i in 1:ncol(data_4)) { # for-loop response variables, need to end it at response variables, rn will run through all variables
data_y<-subset(data_4, !is.na(i))#get rid of NA's in the "i" column
predictors_i <- colnames(data_4)[i] # Create vector of predictor names
predictors_1.1 <- noquote(predictors_i)
i.lm <- train(predictors_1.1~., data=data_4[,c(i,35:68)], method="lm", trControl=control)
bucket <- i.lm
#mod_summaries[[i - 1]] <- summary(lm(y ~ ., data_y[ , c("i.lm", predictors_i)]))
#data_y <- data_4
}
Below is the error code that I am getting, with Bulk_Densi being the first variable in predictors_1.1. The error code is that variable lengths differ so I originally thought that my issue was that quotes were being added around "Bulk_Densi" but after trying the NoQuote() function I have not gotten anywehre so I am unsure of where I am going wrong.
Error code that I am getting
Please let me know if I can provide any extra info and thanks in advance for the help! I've already tried the info in How to train several models within a loop for and was struggling with that as well.
I would like to do something with MLFlow but I do not find any solution on Internet. I am working with MLFlow and R, and I want to save a regression model. The thing is that by the time I want to predict the testing data, I want to do some transformation of that data. Then I have:
data <- #some data with numeric regressors and dependent variable called 'y'
# Divide into train and test
ind <- sample(nrow(data), 0.8*nrow(data), replace = FALSE)
dataTrain <- data[ind,]
dataTest <- data[-ind,]
# Run model in the mlflow framework
with(mlflow_start_run(), {
model <- lm(y ~ ., data = dataTrain)
predict_fun <- function(model, data_to_predict){
data_to_predict[,3] <- data_to_predict[,3]/2
data_to_predict[,4] <- data_to_predict[,4] + 1
return(predict(model, data_to_predict))
}
predictor <- crate(~predict_fun(model,dataTest),model)
### Some code to use the predictor to get the predictions and measure the accuracy as a log_metric
##################
##################
##################
mlflow_log_model(predictor,'model')
}
As you can notice, my prediction function not only consists in predict the new data you are evaluating, but it also makes some transformations in the third and fourth columns. All examples I saw on the web use the function predict in the crate as the default function of R.
Once I save this model, when I run it in another notebook with some Test data, I get the error: "predict_fun" doesn't exist. That is because my algorithm has not saved this specific function. Do you know what can I do to save and specific prediction function that I have created instead of the default functions that are in R?
This is not the real example I am working with, but it is an approximation of it. The fact is that I want to save extra functions apart from the model itself.
Thank you very much!
I'm running into some problems while running plm regressions in my panel database. Basically, I have to take out a year from my base and also all observations from some variable that are zero. I tried to make a reproducible example using a dataset from AER package.
require (AER)
library (AER)
require(plm)
library("plm")
data("Grunfeld", package = "AER")
View(Grunfeld)
#Here I randomize some observations of the third variable (capital) as zero, to reproduce my dataset
for (i in 1:220) {
x <- rnorm(10,0,1)
if (mean(x) >=0) {
Grunfeld[i,3] <- 0
}
}
View(Grunfeld)
panel <- Grunfeld
#First Method
#This is how I was originally manipulating my data and running my regression
panel <- Grunfeld
dd <-pdata.frame(panel, index = c('firm', 'year'))
dd <- dd[dd$year!=1935, ]
dd <- dd[dd$capital !=0, ]
ols_model_2 <- plm(log(value) ~ (capital), data=dd)
summary(ols_model_2)
#However, I couuldn't plot the variables of the datasets in graphs, because they weren't vectors. So I tried another way:
#Second Method
panel <- panel[panel$year!= 1935, ]
panel <- panel[panel$capital != 0,]
ols_model <- plm(log(value) ~ log(capital), data=panel, index = c('firm','year'))
summary(ols_model)
#But this gave extremely different results for the ols regression!
In my understanding, both approaches sould have yielded the same outputs in the OLS regression. Now I'm afraid my entire analysis is wrong, because I was doing it like the first way. Could anyone explain me what is happening?
Thanks in advance!
You are a running two different models. I am not sure why you would expect results to be the same.
Your first model is:
ols_model_2 <- plm(log(value) ~ (capital), data=dd)
While the second is:
ols_model <- plm(log(value) ~ log(capital), data=panel, index = c('firm','year'))
As you see from the summary of the models, both are "Oneway (individual) effect Within Model". In the first one you dont specify the index, since dd is a pdata.frame object. In the second you do specify the index, because panel is a simple data.frame. However this makes no difference at all.
The difference is using the log of capital or capital without log.
As a side note, leaving out 0 observations is often very problematic. If you do that, make sure you also try alternative ways of dealing with zero, and see how much your results change. You can get started here https://stats.stackexchange.com/questions/1444/how-should-i-transform-non-negative-data-including-zeros
I have a simple Random Forest model I have created and tested in R. For now I have excluded an internal company ID from my training/testing data frames. Is there a way in R that I could include this column in my data and have the training/execution of my model ignore the field?
I obviously would not want the model to try and incorporate it as a variable, but upon an export of the data with a column added of the predicted outcome, I would need that internal id to tie back in other customer data so I know what customers have been categorized as
I am just using the out of the box random forest function from the randomForest library
#divide data into training and test sets
set.seed(3)
id<-sample(2,nrow(Churn_Model_Data_v2),prob=c(0.7,0.3),replace = TRUE)
churn_train<-Churn_Model_Data_v2[id==1,]
churn_test<-Churn_Model_Data_v2[id==2,]
#changes Churn data 1/2 to a factor for model
Churn_Model_Data_v2$`Churn` <- as.factor(Churn_Model_Data_v2$`Churn`)
churn_train$`Churn` <- as.factor(churn_train$`Churn`)
#churn_test$`Churn` <- as.factor(churn_test$`Churn`)
bestmtry <- tuneRF(churn_train,churn_train$`Churn`, stepFactor = 1.2,
improve =0.01, trace=T, plot=T )
#creates model based on training data, views model
churn_forest <- randomForest(`Churn`~. , data= churn_train )
churn_forest
#shows us what variables are most important
importance(churn_forest)
varImpPlot(churn_forest)
#predicts churn diagnosis on test data
predict_churn <- predict(churn_forest, newdata = churn_test, type="class")
predict_churn
A simple example of excluding a particular column or set of columns is as follows
library(MASS)
temp<-petrol
randomForest(No ~ .,data = temp[, !(colnames(temp) %in% c("SG"))]) # One Way
randomForest(No ~ .-SG,data = temp) #Another way with similar result
This method of exclusion is commonly valid across other fuctions/alogorithms in R too.
I would like to loop a mixed model and Tukey test.
All I want to do is to repeat the fitting and the comparison for 3 columns (each one containing a response) and for 4 subgroups (total = 12).
A similar dataframe is available here: https://drive.google.com/open?id=0Bwrsa11LAnrgTXMzWk1fYXR1MHM. The 3 resposes are the columns "RESP_1","RESP_2" and "RESP_3", the subgroups are the variables of column "layer".
I obtain the model and the adjustament for a single response and unique layer by:
#mixed model
Mlm_RESP_1 <-lme(RESP_1~clay+till, random=~1|strata/point, data=loop_lm_tukey)
#tukey
ls_RSP_1 <- lsmeans(Mlm_RESP_1,pairwise~till,adjust="tukey")
ls_RSP_1$contrasts
cld(ls_RSP_1)
Then, I try to loop the model for each column by:
#loop model
mlm_RESP <- lapply(c("RESP_1", "RESP_2","RESP_3"), function(k) {
lme(eval(substitute(j ~ clay+till, list(j = as.name(k)))), random = ~1|strata/point, data = loop_lm_tukey)})
From now, I am not able to loop the Tukey comparison using lsmeans package because lapply returns a list and this package can't handle this kind of class.
Furthermore, how can I loop this for every layer?
Any help to do loop of Tukey's comparison would be much appreciated.
Try this:
mlm_RESP <- lapply(c("RESP_1", "RESP_2","RESP_3"), function(k) {
df=cbind(resp=loop_lm_tukey[,k],loop_lm_tukey[,-c(1:3)])
lme(resp~clay+till, random = ~1|strata/point, data = df)})
res1=lapply(mlm_RESP,function(rm)lsmeans(rm,pairwise~till,adjust="tukey"))
or:
res2=list()
for (i in 1:3) res2[[i]]=lsmeans(mlm_RESP[[i]],pairwise~till,adjust="tukey")
The results is the same.