I use the Canada data in the vars package as an example. I want to use the first 48 observations to run the var, predict the next quarter, add the predicted data to the original 48 observations, use the 49 observations to run the var, predict the next quarter, add the new prediction to the 49 observations, re-run the var, reiterate the process until all observations are used. In the end, I hope to generate a dataframe that contain predicted values, which I can use to calculate RMSE. Below is my code:
library(dplyr)
library(stats)
library(vars)
data(Canada)
Canada_df <- as.data.frame(Canada)
prefit2 <- Canada_df[1:48,]
locs <- data.frame()
while (i <= nrow(Canada_df) & i >=48){
varfit <- VAR(y = prefit2, p = 6, type = 'const')
pred <- predict(varfit, n.ahead =1)
locs <- sapply(pred$fcst[1:3], function (k) k[ , 1]) %>% matrix(nrow = 1) %>% data.frame()
colnames(locs) <- colnames(prefit)
prefit2 <- bind_rows(prefit2, locs)
i = i + 1
}
When I run the code, there is no error generated. However, there are still 48 observations in prefit2, meaning no prediction was appended during the while loop. Additionally, i turns out to be 5, so it looks like the loop stops when it becomes 5. Not sure where is the problem.
I tried your code and it actually ran as you expected. The only thing worth reminding is to declare i before the while loop. In this case, for example, you want to declare i <- 48.
Related
I have a dataset with daily bond returns for some unique RIC codes (in total approx. 200.000 observations).
Now I want to calculate the standard deviation of those returns for the combined period t-30 to t-6 and t+6 to t+30. This means for every observation i,t, I need the 24 returns before t in the window t-30 to t-6 and 24 returns in the window t+6 to t+30 and calculate the standard deviation based on those 48 observations.
Here is a small snippet of my dataset:
#My data:
date <- c("2022-05-11", "2022-05-12","2022-05-13","2022-05-16","2022-05-17","2022-05-11", "2022-05-12","2022-05-13","2022-05-16","2022-05-17")
ric <- c("AT0000A1D541=", "AT0000A1D541=", "AT0000A1D541=", "AT0000A1D541=", "AT0000A1D541=", "SE247827293=", "SE247827293=", "SE247827293=", "SE247827293=", "SE247827293=")
return <- c(0.001009681, 0.003925873, 0.000354606, -0.000472641, -0.002935700, 0.003750854, 0.012317347, -0.001314047, 0.001014453, -0.007234452)
df <- data.frame(ric, date, return)
I have tried to use the slider package to generate two lists with the returns of the specific time frame. However, I feel that there is some more efficient way to solve this problem. I hope to find some help here.
This is what I tried before:
x <- slide(df$return, ~.x, .before=30, .after = -6)
y <- slide(df$return, ~.x, .before=-6, .after = 30)
z <- mapply(c, x, y, SIMPLIFY=TRUE)
for (i in 1:length(z))
{
df$sd[i] <- sd(z[[i]])
}
I am trying to write a for loop that will generate a correlation for a fixed column (LPS0) vs. all other columns in the data set. I don't want to use a correlation matrix because I only care about the correlation of LPS0 vs all other columns, not the correlations of the other columns with themselves. I then want to include an if statement to print only the significant correlations (p.value <= 0.05). I ran into some issues where some of the p.values are returned as NA, so I switched to an if_else loop. However, I am now getting an error. My code is as follows:
for(i in 3:ncol(microbiota_lps_0_morm)) {
morm_0 <- cor.test(microbiota_lps_0_morm$LPS0, microbiota_lps_0_morm[[colnames(microbiota_lps_0_morm)[i]]], method = "spearman")
if_else(morm_0$p.value <= 0.05, print(morm_0), print("Not Sig"), print("NA"))
}
The first value is returned, and then the loop stops with the following error:
Error in if_else():
! true must be length 1 (length of condition), not 8.
Backtrace: 1. dplyr::if_else(morm_0$p.value <= 0.05, print(morm_0), print("Not Sig"), print("NA"))
How can I make the loop print morm only when p.value <- 0.05?
Here's a long piece of code which aytomates the whole thing. it might be overkill but you can just take the matrix and use whatever you need. it makes use of the tidyverse.
df <- select_if(mtcars,is.numeric)
glimpse(df)
# keeping real names
dict <- cbind(original=names(df),new=paste0("v",1:ncol(df)))
# but changing names for better data viz
colnames(df) <- paste0("v",1:ncol(df))
# correlating between variables + p values
pvals <- list()
corss <- list()
for (coln in colnames(df)) {
pvals[[coln]] <- map(df, ~ cor.test(df[,coln], .)$p.value)
corss[[coln]] <- map(df, ~ cor(df[,coln], .))
}
# Keeping both matrices in a list
matrices <- list(
pvalues = matrix(data=unlist(pvals),
ncol=length(names(pvals)),
nrow=length(names(pvals))),
correlations = matrix(data=unlist(corss),
ncol=length(names(corss)),
nrow=length(names(corss)))
)
rownames(matrices[[1]]) <- colnames(df)
rownames(matrices[[2]]) <- colnames(df)
# Creating a combined data frame
long_cors <- expand.grid(Var1=names(df),Var2=names(df)) %>%
mutate(cor=unlist(matrices["correlations"]),
pval=unlist(matrices["pvalues"]),
same=Var1==Var2,
significant=pval<0.05,
dpcate=duplicated(cor)) %>%
# Leaving no duplicants, non-significant or self-correlation results
filter(same ==F,significant==T,dpcate==F) %>%
select(-c(same,dpcate,significant))
# Plotting correlations
long_cors %>%mutate(negative=cor<0) %>%
ggplot(aes(x=Var1,y=Var2,
color=negative,size=abs(cor),fill=Var2,
label=round(cor,2)))+
geom_label(show.legend = F,alpha=0.2)+
scale_color_manual(values = c("black","darkred"))+
# Sizing each correlation by it's magnitude
scale_size_area(seq(1,100,length=length(unique(long_cors$Var1))))+ theme_light()+
theme(axis.text = element_text(face = "bold",size=12))+
labs(title="Correlation between variables",
caption = "p < 0.05")+xlab("")+ylab("")
If you want to correlate a column of a matrix with the remaining columns, you can do so with one function call:
mtx <- matrix(rnorm(800), ncol=8)
cor(mtx[,1], mtx[,-1])
However, you will not get p-values. For getting p-values, I would recommend this approach:
library(tidyverse)
significant <- map_dbl(2:ncol(mtx),
~ cor.test(mtx[,1], mtx[,.], use="p", method="s")$p.value)
Whenever you feel like you need a for loop in R, chances are, you should be using another approach. for is a very un-R construct, and R gives many better ways of handling the same issues. map_* family of functions from tidyverse is but one of them. Another approach, in base R, would be to use apply:
significant <- apply(mtx[,-1], 2,
\(x) cor.test(x, mtx[,1], method="s", use="p")$p.value)
I have several models that I would like to compare their choices of important predictors over the same data set, Lasso being one of them. The data set I am using consists of census data with around a thousand variables that have been renamed to "x1", "x2" and so on for convenience sake (The original names are extremely long). I would like to report the top features then rename these variables with a shorter more concise name.
My attempt to solve this is by extracting the top variables in each iterated model, put it into a list, then finding the mean of the top variables in X amount of loops. However, my issue is I still find variability with the top 10 most used predictors and so I cannot manually alter the variable names as each run on the code chunk yields different results. I suspect this is because I have so many variables in my analysis and due to CV causing the creation of new models every bootstrap.
For the sake of a simple example I used mtcars and will look for the top 3 most common predictors due to only having 10 variables in this data set.
library(glmnet)
data("mtcars") # Base R Dataset
df <- mtcars
topvar <- list()
for (i in 1:100) {
# CV and Splitting
ind <- sample(nrow(df), nrow(df), replace = TRUE)
ind <- unique(ind)
train <- df[ind, ]
xtrain <- model.matrix(mpg~., train)[,-1]
ytrain <- df[ind, 1]
test <- df[-ind, ]
xtest <- model.matrix(mpg~., test)[,-1]
ytest <- df[-ind, 1]
# Create Model per Loop
model <- glmnet(xtrain, ytrain, alpha = 1, lambda = 0.2)
# Store Coeffecients per loop
coef_las <- coef(model, s = 0.2)[-1, ] # Remove intercept
# Store all nonzero Coefficients
topvar[[i]] <- coef_las[which(coef_las != 0)]
}
# Unlist
varimp <- unlist(topvar)
# Count all predictors
novar <- table(names(varimp))
# Find the mean of all variables
meanvar <- tapply(varimp, names(varimp), mean)
# Return top 3 repeated Coefs
repvar <- novar[order(novar, decreasing = TRUE)][1:3]
# Return mean of repeated Coefs
repvar.mean <- meanvar[names(repvar)]
repvar
Now if you were to rerun the code chunk above you would notice that the top 3 variables change and so if I had to rename these variables it would be difficult to do if they are not constant and changing every run. Any suggestions on how I could approach this?
You can use function set.seed() to ensure your sample will return the same sample each time. For example
set.seed(123)
When I add this to above code and then run twice, the following is returned both times:
wt carb hp
98 89 86
I am performing a PLS-DA analysis in R using the mixOmics package. I have one binary Y variable (presence or absence of wetland) and 21 continuous predictor variables (X) with values ranging from 1 to 100.
I have made the model with the data_training dataset and want to predict new outcomes with the data_validation dataset. These datasets have exactly the same structure.
My code looks like:
library(mixOmics)
model.plsda<-plsda(X,Y, ncomp = 10)
myPredictions <- predict(model.plsda, newdata = data_validation[,-1], dist = "max.dist")
I want to predict the outcome based on 10, 9, 8, ... to 2 principal components. By using the get.confusion_matrix function, I want to estimate the error rate for every number of principal components.
prediction <- myPredictions$class$max.dist[,10] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
I can do this seperately for 10 times, but I want do that a little faster. Therefore I was thinking of making a list with the results of prediction for every number of components...
library(BBmisc)
prediction_test <- myPredictions$class$max.dist
predictions_components <- convertColsToList(prediction_test, name.list = T, name.vector = T, factors.as.char = T)
...and then using lapply with the get.confusion_matrix and get.BER function. But then I don't know how to do that. I have searched on the internet, but I can't find a solution that works. How can I do this?
Many thanks for your help!
Without reproducible there is no way to test this but you need to convert the code you want to run each time into a function. Something like this:
confmat <- function(x) {
prediction <- myPredictions$class$max.dist[,x] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
}
Now lapply:
results <- lapply(10:2, confmat)
That will return a list with the get.BER results for each number of PCs so results[[1]] will be the results for 10 PCs. You will not get values for prediction or confusionmat unless they are included in the results returned by get.BER. If you want all of that, you need to replace the last line to the function with return(list(prediction, confusionmat, get.BER(confusion.mat)). This will produce a list of the lists so that results[[1]][[1]] will be the results of prediction for 10 PCs and results[[1]][[2]] and results[[1]][[3]] will be confusionmat and get.BER(confusion.mat) respectively.
I am trying to calculate a regression variable based on a range of variables in my data set. I would like the regression variable (ei: Threshold 1) to be calculated using a different variable set in each iteration of running the regression.
Aim to collected SSR values for each threshold range, and thus identify the ideal threshold based on the data.
Data (df) variables: Yield, Prec, Price, 0C, 1C, 2C, 3C, 4C, 5C, 6C, 7C, 8C, 9C, 10C
Each loop calculates "thresholds" by selecting a different "b" each time.
a <- df$0C
b <- df$1C
Threshold1 <- (a-b)
Threshold2 <- (b)
Where "b" would be changing in each loop, ranging from 1C to 9C.
Each individual threshold set (1 and 2) should be used to run a regression, and save the SSR for comparison with the subsequent regression utilizing thresholds based on a new "b" value (ranging from 1C TO 9C)
Regression:
reg <- lm(log(Yield)~Threshold1+Threshold2+log(Price)+prec+I(prec^2),data=df)
for each loop of the Regression, I vary the components of calculating thresholds in the following manner:
Current approach is centered around the following code:
df <- read.csv("Data.csv",header=TRUE)
names(df)
0C-9Cvarlist <- names(df)[9:19]
ssr.vec <- matrix(,21,1)
for(i in 1:length(varlist)){
a <- df$0C
b <- df$[i]
Threshold1 <- (a-b)
Threshold2 <- (b)
reg <- lm(log(Yield)~Threshold1+Threshold2+log(Price)+prec+I(prec^2),data=df)
r2 <- summary(reg)$r.squared
ssr.vec[i,] <- c(varlist,r2)
}
colnames(ssr.vec) <- c("varlist","r2")
I am failing to achieve the desired result with the above approach.
Thank you.
I can spot quite a few mistakes...
You need to add variables of interest (Threshold1 anf Threshold2) to the data in the regression. Also, I think that you need to select varlist[i] and not varlist to create your ssr.vec. You need 2 columns to your ssr.vec which is a matrix, so you should call it matrix. You also cannot use something like df$[i] to extract a column! Why is the matrix of length 21 ?! Change the column name to C0,..,C9 and not 0C,..,9C.
For future reference, solve the simple errors before asking question... and include error messages in your post!
This should do the job:
df <- read.csv("Data.csv",header=TRUE)
names(df)[8:19] = paste0("C",0:10)
varlist <- names(df)[9:19]
ssr.vec <- matrix(,21,2)
for(i in 1:length(varlist)){
a <- df$C0
b <- df[,i+9]
df$Threshold1 <- (a-b)
df$Threshold2 <- (b)
reg <- lm(log(Yield)~Threshold1+Threshold2+log(Price)+prec+I(prec^2),data=df)
r2 <- summary(reg)$r.squared
ssr.vec[i,] <- c(varlist[i],r2)
}
colnames(ssr.vec) <- c("varlist","r2")