I want to be able to input the variable name that I'll be using in the "weights" option in the lmer function. So then I can change the dataset, and cycle through the "weights" and pull the correct variable.
I want to pull the correct column for weights within the for loop.
So for y, the equation would be:
lmer(y~x+(1|study), weights = weight.var)
And y1:
lmer(y1~x+(1|study),weights = weight.var1)
So I named the weighting variables (weight.opt), then want to use them in the formula within the for loop. I can use "as.formula" to get the formula working and connected to the dataset, but I'm not sure how to do something similar with the weights.
x <- rnorm(300,0,1)
y <- x*rnorm(300,2,0.5)
y1 <- x*rnorm(300,0.1,0.1)
study <- rep(c("a","b","c"),each = 100)
weight.var <- rep(c(0.5,2,4),each = 100)
weight.var1 <- rep(c(0.1,.2,.15),each = 100)
library(lme4)
dataset <- data.frame(x,y,y1,study,weight.var,weight.var1)
resp1 <- c("y","y1")
weight.opt <- c("weight.var","weight.var1")
for(i in 1:2){
lmer(as.formula(paste(resp1[i],"~x+(1|study)")),weights = weight.opt[i],data = dataset)
}
This seems to work fine:
res_list <- list()
for(i in 1:2){
res_list[[i]] <- lmer(as.formula(paste(resp1[i],"~x+(1|study)")),
weights = dataset[[weight.opt[i]]],data = dataset)
}
Related
I want to use mixture copula for reliability analysis， now ，with the help of a friend ，I've already finished it ‘RVMs_fitted’ 。now i want to perform the probability integral transformation （PIT），but the function of RVINEPIT can’t use，because RVINEPIT(data，RVM)，this RVM not RVINEMATRIX Here is my code：
library(vineclust)
data1 <- read.csv("D:/ASTUDY/Rlanguage/Mix copula/data.csv", header = FALSE)
fit <- vcmm(data = data1, total_comp=3,is_cvine = 0)
print(fit)
summary(fit)
RVMs_fitted <- list()
RVMs_fitted[[1]] <- VineCopula::RVineMatrix(Matrix=fit$output$vine_structure[,,1],
family=fit$output$bicop_familyset[,,1],
par=fit$output$bicop_param[,,1],
par2=fit$output$bicop_param2[,,1])
RVMs_fitted[[2]] <- VineCopula::RVineMatrix(Matrix=fit$output$vine_structure[,,2],
family=fit$output$bicop_familyset[,,2],
par=fit$output$bicop_param[,,2],
par2=fit$output$bicop_param2[,,2])
RVMs_fitted[[3]] <- VineCopula::RVineMatrix(Matrix=fit$output$vine_structure[,,3],
family=fit$output$bicop_familyset[,,3],
par=fit$output$bicop_param[,,3],
par2=fit$output$bicop_param2[,,3])
RVM<-RVMs_fitted
meanx <- c(0.47,0.508,0.45,0.52,0.48)
sigmax <- c(0.318,0.322,0.296,0.29,0.279)
ux1<-pnorm(x[1],meanx[1],sigmax[1])
ux2<-pnorm(x[2],meanx[2],sigmax[2])
ux3<-pnorm(x[3],meanx[3],sigmax[3])
ux4<-pnorm(x[4],meanx[4],sigmax[4])
ux5<-pnorm(x[5],meanx[5],sigmax[5])
data <- c(ux1,ux2,ux3,ux4,ux5)
du=RVinePIT(data, RVM)
y=t(qnorm(t(du)))
Error:
In RVinePIT: RVM has to be an RVineMatrix object.
You have multiple problems here:
RVM is a list. However, you tried to fit RVinePIT to a list, while it works for one data at a time.
The same holds for the y.
I do not have your data, but try it with other data.
Here is the code (it should work):
library(vineclust)
library(VineCopula)
data1 <- read.csv("D:/ASTUDY/Rlanguage/Mix copula/data.csv", header = FALSE)
fit <- vcmm(data = data, total_comp=3,is_cvine = 0)
print(fit)
summary(fit)
RVMs_fitted <- list()
RVMs_fitted[[1]] <- RVineMatrix(Matrix=fit$output$vine_structure[,,1],
family=fit$output$bicop_familyset[,,1],
par=fit$output$bicop_param[,,1],
par2=fit$output$bicop_param2[,,1])
RVMs_fitted[[2]] <- RVineMatrix(Matrix=fit$output$vine_structure[,,2],
family=fit$output$bicop_familyset[,,2],
par=fit$output$bicop_param[,,2],
par2=fit$output$bicop_param2[,,2])
RVMs_fitted[[3]] <- RVineMatrix(Matrix=fit$output$vine_structure[,,3],
family=fit$output$bicop_familyset[,,3],
par=fit$output$bicop_param[,,3],
par2=fit$output$bicop_param2[,,3])
RVM<-RVMs_fitted
meanx <- c(0.47,0.508,0.45,0.52,0.48)
sigmax <- c(0.318,0.322,0.296,0.29,0.279)
ux1<-pnorm(x[1],meanx[1],sigmax[1])
ux2<-pnorm(x[2],meanx[2],sigmax[2])
ux3<-pnorm(x[3],meanx[3],sigmax[3])
ux4<-pnorm(x[4],meanx[4],sigmax[4])
ux5<-pnorm(x[5],meanx[5],sigmax[5])
data <- c(ux1,ux2,ux3,ux4,ux5)### This must be a matrix to work with RVinePIT
du=lapply(1:3, function(i) RVinePIT(data, RVM[[i]]))
y <-lapply(1:3, function(i) t(qnorm(t(du[[i]]))))
I have a big data set and I want to choose randomly subsets (randomly_live) from it and then run a model (logistic regression) in R. So I want to run 100 logistic regressions to count how many times coefficients were with positive sign, haw many times they were significant and show the best model by Hosmer-Lemeshow criteria.
I think it's possible to make it by loop, but I feel really confused with that.
This is a piece of code that I have for one iteration
randomRows = function(df,n){
return(df[sample(nrow(df),n),])
}
set.seed(567)
df.split <- split(full_data, full_data$ID)
df.sample <- lapply(df.split, randomRows, 1)
df.final <- do.call("rbind", df.sample)
randomly_live <- randomRows(df.final, nrow(default))
data1 <- rbind(default, randomly_live)
model = glm(default ~ log(assets)+…+H1, data = data1,
family = 'binomial')
library(ResourceSelection)
hl <- hoslem.test(model$y, fitted(model), g=10)
Can anyone please help?
Here is something that could work
myResults <- list()
for(i in 1:100){
model <- glm(vs ~ . , data = mtcars)
hl <- hoslem.test(model$y, fitted(model), g=10)
pos <- length(which(coef(model)>0))
pvals <- summary(model)$coefficients[,4]
hl_pval <- hl$p.value
myResults[[i]] <- list(pos = pos, pvals = pvals,hl_pval=hl_pval)
}
# lowest pvalue
which.min(unlist(lapply(myResults, FUN = function(x) x[[3]])))
I am new to R and programming, I want to store values from loop to a data frame in R. I want ker, cValues, accuracyValues values to be stored a data frame from bellow code. I am not able to achieve this, Data Frame is only saving last value not all the values.
Can you please help me with this please.
# Define a vector which has different kernel methods
kerna <- c("rbfdot","polydot","vanilladot","tanhdot","laplacedot",
"besseldot","anovadot","splinedot")
# Define a for loop to calculate accuracy for different values of C and kernel
for (ker in kerna){
cValues <- c()
accuracyValues <- c()
for (c in 1:100) {
model <- ksvm(V11~V1+V2+V3+V4+V5+V6+V7+V8+V9+V10,
data = credit_card_data,
type ="C-svc",
kernel = ker,
C=c,
scaled =TRUE)
pred <- predict(model,credit_card_data[,1:10])
#pred
accuracy <- sum(pred== credit_card_data$V11)/nrow(credit_card_data)
cValues[c] <- c;
accuracyValues[c] <- accuracy;
}
for(i in 1:100) {
print(paste("kernal:",ker, "c=",cValues[i],"accuracy=",accuracyValues[i]))
}
}
Starting from your base code, set up the structure of the output data frame. Then, loop through and fill in the accuracy values on each iteration. This method also "flattens" the nested loop and gets rid of your c variable which conflicts with the built-in c() function.
kerna <- c("rbfdot","polydot","vanilladot","tanhdot","laplacedot",
"besseldot","anovadot","splinedot")
# Create dataframe to store output data
df <- data.frame(kerna = rep(kerna, each = 100),
cValues = rep(1:100, times = length(kerna)),
accuracyValues = NA,
stringsAsFactors = F)
# Define a for loop to calculate accuracy for different values of C and kernel
for (i in 1:nrow(df)){
ker <- df$kerna[i]
j <- df$cValues[i]
model <- ksvm(V11~V1+V2+V3+V4+V5+V6+V7+V8+V9+V10,
data = credit_card_data,
type ="C-svc",
kernel = ker,
C=j,
scaled =TRUE)
pred <- predict(model,credit_card_data[,1:10])
accuracy <- sum(pred== credit_card_data$V11)/nrow(credit_card_data)
# Insert accuracy into df$accuracyValues
df$accuracyValues[i] <- accuracy;
}
Consider Map to build a list of data frames from each pairing of ker and cValues (1:100) generated from expand.grid and row bind all elements together.
k_c_pairs_df <- expand.grid(kerna=kerna, c_value=1:100, stringsAsFactors = FALSE)
model_fct <- function(ker, c) {
model <- ksvm(V11~V1+V2+V3+V4+V5+V6+V7+V8+V9+V10,
data = credit_card_data,
type ="C-svc",
kernel = ker,
C=c,
scaled =TRUE)
pred <- predict(model,credit_card_data[,1:10])
accuracy <- sum(pred== credit_card_data$V11)/nrow(credit_card_data)
print(paste("kernal:",ker, "c=",cValues[i],"accuracy=",accuracyValues[i]))
return(data.frame(kernel = ker, cValues = c, accuracyValues = accuracy))
}
df_list <- Map(model_fct, k_c_pairs_df$ker, k_c_pairs_df$c_value)
final_df <- do.call(rbind, df_list)
while using Regsubsets from package leaps on data with linear dependencies, I found that results given by coef() and by summary()$which differs. It seems that, when linear dependencies are found, reordering changes position of coefficients and coef() returns wrong values.
I use mtcars just to "simulate" the problem I had with other data. In first example there is no issue of lin. dependencies and best given model by BIC is mpg~wt+cyl and both coef(),summary()$which gives the same result. In second example I add dummy variable so there is possibility of perfect multicollinearity, but variables in this order (dummy in last column) don't cause the problem. In last example after changing order of variables in dataset, the problem finally appears and coef(),summary()$which gives different models. Is there anything incorrect in this approach? Is there any other way to get coefficients from regsubsets?
require("leaps") #install.packages("leaps")
###Example1
dta <- mtcars[,c("mpg","cyl","am","wt","hp") ]
bestSubset.cars <- regsubsets(mpg~., data=dta)
(best.sum <- summary(bestSubset.cars))
#
w <- which.min(best.sum$bic)
best.sum$which[w,]
#
best.sum$outmat
coef(bestSubset.cars, w)
#
###Example2
dta2 <- cbind(dta, manual=as.numeric(!dta$am))
bestSubset.cars2 <- regsubsets(mpg~., data=dta)
(best.sum2 <- summary(bestSubset.cars2))
#
w <- which.min(best.sum2$bic)
best.sum2$which[w,]
#
coef(bestSubset.cars2, w)
#
###Example3
bestSubset.cars3 <- regsubsets(mpg~., data=dta2[,c("mpg","manual","am","cyl","wt","hp")])
(best.sum3 <- summary(bestSubset.cars3))
#
w <- which.min(best.sum3$bic)
best.sum3$which[w,]
#
coef(bestSubset.cars3, w)
#
best.sum2$which
coef(bestSubset.cars2,1:4)
best.sum3$which
coef(bestSubset.cars3,1:4)
The order of vars by summary.regsubsets and regsubsets are different. The generic function coef() of regsubsets calls those two in one function, and the results are in mess if you are trying to force.in or using formula with fixed order. Changing some lines in the coef() function might help. Try codes below, see if it works!
coef.regsubsets <- function (object, id, vcov = FALSE, ...)
{
s <- summary(object)
invars <- s$which[id, , drop = FALSE]
betas <- vector("list", length(id))
for (i in 1:length(id)) {
# added
var.name <- names(which(invars[i, ]))
thismodel <- which(object$xnames %in% var.name)
names(thismodel) <- var.name
# deleted
#thismodel <- which(invars[i, ])
qr <- .Fortran("REORDR", np = as.integer(object$np),
nrbar = as.integer(object$nrbar), vorder = as.integer(object$vorder),
d = as.double(object$d), rbar = as.double(object$rbar),
thetab = as.double(object$thetab), rss = as.double(object$rss),
tol = as.double(object$tol), list = as.integer(thismodel),
n = as.integer(length(thismodel)), pos1 = 1L, ier = integer(1))
beta <- .Fortran("REGCF", np = as.integer(qr$np), nrbar = as.integer(qr$nrbar),
d = as.double(qr$d), rbar = as.double(qr$rbar), thetab = as.double(qr$thetab),
tol = as.double(qr$tol), beta = numeric(length(thismodel)),
nreq = as.integer(length(thismodel)), ier = numeric(1))$beta
names(beta) <- object$xnames[qr$vorder[1:qr$n]]
reorder <- order(qr$vorder[1:qr$n])
beta <- beta[reorder]
if (vcov) {
p <- length(thismodel)
R <- diag(qr$np)
R[row(R) > col(R)] <- qr$rbar
R <- t(R)
R <- sqrt(qr$d) * R
R <- R[1:p, 1:p, drop = FALSE]
R <- chol2inv(R)
dimnames(R) <- list(object$xnames[qr$vorder[1:p]],
object$xnames[qr$vorder[1:p]])
V <- R * s$rss[id[i]]/(object$nn - p)
V <- V[reorder, reorder]
attr(beta, "vcov") <- V
}
betas[[i]] <- beta
}
if (length(id) == 1)
beta
else betas
}
Another solution that works for me is to randomize the order of the column(independent variables) in your dataset before running the regsubsets. The idea is that after reorder hopefully the highly correlated columns will be far apart from each other and will not trigger the reorder behavior in the regsubsets algorithm.
I'm new to loops and I have a problem with calling variable from i'th data frame.
I'm able to call each data frame correctly, but when I should call a specified variable inside each data frame problems come:
Example:
for (i in 1:15) {
assign(
paste("model", i, sep = ""),
(lm(response ~ variable, data = eval(parse(text = paste("data", i, sep = "")))))
)
plot(data[i]$response, predict.lm(eval(parse(text = paste("model", i, sep = ""))))) #plot obs vs preds
}
Here I'm doing a simple one variable linear model 15 times, which works just fine. Problems come when I try to plot the results. How should I call data[i] response?
Let's say there are multiple dataframes with names: data1 ...data15 and that there are no other data-objects that begin with the letters: d,a,t,a. Lets also assume that in each of those dataframes are columns named 'response' and 'variable'. The this would gather the dataframes into a list and draw separate plots for the linear regression lines.
dlist <- lapply ( ls(patt='^data'), get)
lapply(dlist, function(df)
plot(NA, xlim=range(df$variable), ylim=range(df$response)
abline( coef( lm(response ~ variable, data=df) ) )
)
If you wanted to name the dataframes in that list, you could use your paste code to supply names:
names(dlist) <- paste("data", i, sep = "")
There are many other assignments you could make in the context of this loop, but you would need to describe the desired results better than with failed efforts.
Here's modified code that should work. It does one variable lm-model and calculates correlation of predicted and observed values and stores it into an empty matrix. It also plots these values.
Thanks Thomas for help.
par(mfrow=c(4,5))
results.matrix <- matrix(NA, nrow = 20, ncol = 2)
colnames(results.matrix) <- c("Subset","Correlation")
for (i in 1:length(datalist)) {
model <- lm(response ~ variable, data = datalist[[i]])
pred <- predict.lm(model)
cor <- (cor.test(pred, datalist[[i]]$response))
plot(pred, datalist[[i]]$response, xlab="pred", ylab="obs")
results.matrix[i, 1] <- i
results.matrix[i, 2] <- cor$estimate
}