Stacking lapply results











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I am using the following code to generate data, and i am estimating regression models across a list of variables (covar1 and covar2). I have also created confidence intervals for the coefficients and merged them together.



I have been examining all sorts of examples here and on other sites, but i can't seem to accomplish what i want. I want to stack the results for each covar into a single data frame, labeling each cluster of results by the covar it is attributable to (i.e., "covar1" and "covar2"). Here is the code for generating data and results using lapply:



##creating a fake dataset (N=1000, 500 at treated, 500 at control group)
#outcome variable
outcome <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 70, sd = 10))

#running variable
running.var <- seq(0, 1, by = .0001)
running.var <- sample(running.var, size = 1000, replace = T)

##Put negative values for the running variable in the control group
running.var[1:500] <- -running.var[1:500]

#treatment indicator (just a binary variable indicating treated and control groups)
treat.ind <- c(rep(0,500), rep(1,500))

#create covariates
set.seed(123)
covar1 <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 50, sd = 20))
covar2 <- c(rnorm(500, mean = 10, sd = 20), rnorm(500, mean = 10, sd = 30))
data <- data.frame(cbind(outcome, running.var, treat.ind, covar1, covar2))
data$treat.ind <- as.factor(data$treat.ind)

#Bundle the covariates names together
covars <- c("covar1", "covar2")

#loop over them using a convenient feature of the "as.formula" function
models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = d)
ci <-confint(regres, level=0.95)
regres_ci <- cbind(summary(regres)$coefficient, ci)
})
names(models) <- covars
print(models)


Any nudge in the right direction, or link to a post i just haven't come across, is greatly appreciated.










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  • What is d in the code?
    – m0nhawk
    Nov 21 at 19:32










  • In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
    – 12b345b6b78
    Nov 21 at 19:33










  • good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
    – Nate
    Nov 21 at 19:35















up vote
0
down vote

favorite












I am using the following code to generate data, and i am estimating regression models across a list of variables (covar1 and covar2). I have also created confidence intervals for the coefficients and merged them together.



I have been examining all sorts of examples here and on other sites, but i can't seem to accomplish what i want. I want to stack the results for each covar into a single data frame, labeling each cluster of results by the covar it is attributable to (i.e., "covar1" and "covar2"). Here is the code for generating data and results using lapply:



##creating a fake dataset (N=1000, 500 at treated, 500 at control group)
#outcome variable
outcome <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 70, sd = 10))

#running variable
running.var <- seq(0, 1, by = .0001)
running.var <- sample(running.var, size = 1000, replace = T)

##Put negative values for the running variable in the control group
running.var[1:500] <- -running.var[1:500]

#treatment indicator (just a binary variable indicating treated and control groups)
treat.ind <- c(rep(0,500), rep(1,500))

#create covariates
set.seed(123)
covar1 <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 50, sd = 20))
covar2 <- c(rnorm(500, mean = 10, sd = 20), rnorm(500, mean = 10, sd = 30))
data <- data.frame(cbind(outcome, running.var, treat.ind, covar1, covar2))
data$treat.ind <- as.factor(data$treat.ind)

#Bundle the covariates names together
covars <- c("covar1", "covar2")

#loop over them using a convenient feature of the "as.formula" function
models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = d)
ci <-confint(regres, level=0.95)
regres_ci <- cbind(summary(regres)$coefficient, ci)
})
names(models) <- covars
print(models)


Any nudge in the right direction, or link to a post i just haven't come across, is greatly appreciated.










share|improve this question






















  • What is d in the code?
    – m0nhawk
    Nov 21 at 19:32










  • In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
    – 12b345b6b78
    Nov 21 at 19:33










  • good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
    – Nate
    Nov 21 at 19:35













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I am using the following code to generate data, and i am estimating regression models across a list of variables (covar1 and covar2). I have also created confidence intervals for the coefficients and merged them together.



I have been examining all sorts of examples here and on other sites, but i can't seem to accomplish what i want. I want to stack the results for each covar into a single data frame, labeling each cluster of results by the covar it is attributable to (i.e., "covar1" and "covar2"). Here is the code for generating data and results using lapply:



##creating a fake dataset (N=1000, 500 at treated, 500 at control group)
#outcome variable
outcome <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 70, sd = 10))

#running variable
running.var <- seq(0, 1, by = .0001)
running.var <- sample(running.var, size = 1000, replace = T)

##Put negative values for the running variable in the control group
running.var[1:500] <- -running.var[1:500]

#treatment indicator (just a binary variable indicating treated and control groups)
treat.ind <- c(rep(0,500), rep(1,500))

#create covariates
set.seed(123)
covar1 <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 50, sd = 20))
covar2 <- c(rnorm(500, mean = 10, sd = 20), rnorm(500, mean = 10, sd = 30))
data <- data.frame(cbind(outcome, running.var, treat.ind, covar1, covar2))
data$treat.ind <- as.factor(data$treat.ind)

#Bundle the covariates names together
covars <- c("covar1", "covar2")

#loop over them using a convenient feature of the "as.formula" function
models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = d)
ci <-confint(regres, level=0.95)
regres_ci <- cbind(summary(regres)$coefficient, ci)
})
names(models) <- covars
print(models)


Any nudge in the right direction, or link to a post i just haven't come across, is greatly appreciated.










share|improve this question













I am using the following code to generate data, and i am estimating regression models across a list of variables (covar1 and covar2). I have also created confidence intervals for the coefficients and merged them together.



I have been examining all sorts of examples here and on other sites, but i can't seem to accomplish what i want. I want to stack the results for each covar into a single data frame, labeling each cluster of results by the covar it is attributable to (i.e., "covar1" and "covar2"). Here is the code for generating data and results using lapply:



##creating a fake dataset (N=1000, 500 at treated, 500 at control group)
#outcome variable
outcome <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 70, sd = 10))

#running variable
running.var <- seq(0, 1, by = .0001)
running.var <- sample(running.var, size = 1000, replace = T)

##Put negative values for the running variable in the control group
running.var[1:500] <- -running.var[1:500]

#treatment indicator (just a binary variable indicating treated and control groups)
treat.ind <- c(rep(0,500), rep(1,500))

#create covariates
set.seed(123)
covar1 <- c(rnorm(500, mean = 50, sd = 10), rnorm(500, mean = 50, sd = 20))
covar2 <- c(rnorm(500, mean = 10, sd = 20), rnorm(500, mean = 10, sd = 30))
data <- data.frame(cbind(outcome, running.var, treat.ind, covar1, covar2))
data$treat.ind <- as.factor(data$treat.ind)

#Bundle the covariates names together
covars <- c("covar1", "covar2")

#loop over them using a convenient feature of the "as.formula" function
models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = d)
ci <-confint(regres, level=0.95)
regres_ci <- cbind(summary(regres)$coefficient, ci)
})
names(models) <- covars
print(models)


Any nudge in the right direction, or link to a post i just haven't come across, is greatly appreciated.







r






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share|improve this question




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asked Nov 21 at 19:30









Jason Schoeneberger

32




32












  • What is d in the code?
    – m0nhawk
    Nov 21 at 19:32










  • In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
    – 12b345b6b78
    Nov 21 at 19:33










  • good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
    – Nate
    Nov 21 at 19:35


















  • What is d in the code?
    – m0nhawk
    Nov 21 at 19:32










  • In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
    – 12b345b6b78
    Nov 21 at 19:33










  • good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
    – Nate
    Nov 21 at 19:35
















What is d in the code?
– m0nhawk
Nov 21 at 19:32




What is d in the code?
– m0nhawk
Nov 21 at 19:32












In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
– 12b345b6b78
Nov 21 at 19:33




In the lm() call within the lapply(), is d meant to be data? Also, it would help if you could outline the expected output (dimensions and colnames of the expected dataframe)
– 12b345b6b78
Nov 21 at 19:33












good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
– Nate
Nov 21 at 19:35




good points above, I'm guessing something like models %>% purrr::map_df(broom::tidy, .id = "covar_id") will get close to what you want
– Nate
Nov 21 at 19:35












1 Answer
1






active

oldest

votes

















up vote
1
down vote













You can use do.call were de second argument is a list (like in here):



do.call(rbind, models)


I made a (possible) improve to your lapply function. This way you can save the estimated parameters and the variables in a data.frame:



models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = data)
ci <-confint(regres, level=0.95)
regres_ci <- data.frame(covar=x,param=rownames(summary(regres)$coefficient),
summary(regres)$coefficient, ci)
})

do.call(rbind,models)





share|improve this answer





















  • Thanks! This is concise and gives me what i need. Much appreciated!
    – Jason Schoeneberger
    Nov 21 at 22:38











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1 Answer
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active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes








up vote
1
down vote













You can use do.call were de second argument is a list (like in here):



do.call(rbind, models)


I made a (possible) improve to your lapply function. This way you can save the estimated parameters and the variables in a data.frame:



models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = data)
ci <-confint(regres, level=0.95)
regres_ci <- data.frame(covar=x,param=rownames(summary(regres)$coefficient),
summary(regres)$coefficient, ci)
})

do.call(rbind,models)





share|improve this answer





















  • Thanks! This is concise and gives me what i need. Much appreciated!
    – Jason Schoeneberger
    Nov 21 at 22:38















up vote
1
down vote













You can use do.call were de second argument is a list (like in here):



do.call(rbind, models)


I made a (possible) improve to your lapply function. This way you can save the estimated parameters and the variables in a data.frame:



models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = data)
ci <-confint(regres, level=0.95)
regres_ci <- data.frame(covar=x,param=rownames(summary(regres)$coefficient),
summary(regres)$coefficient, ci)
})

do.call(rbind,models)





share|improve this answer





















  • Thanks! This is concise and gives me what i need. Much appreciated!
    – Jason Schoeneberger
    Nov 21 at 22:38













up vote
1
down vote










up vote
1
down vote









You can use do.call were de second argument is a list (like in here):



do.call(rbind, models)


I made a (possible) improve to your lapply function. This way you can save the estimated parameters and the variables in a data.frame:



models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = data)
ci <-confint(regres, level=0.95)
regres_ci <- data.frame(covar=x,param=rownames(summary(regres)$coefficient),
summary(regres)$coefficient, ci)
})

do.call(rbind,models)





share|improve this answer












You can use do.call were de second argument is a list (like in here):



do.call(rbind, models)


I made a (possible) improve to your lapply function. This way you can save the estimated parameters and the variables in a data.frame:



models <- lapply(covars, function(x){
regres <- lm(as.formula(paste(x," ~ running.var + treat.ind",sep = "")), data = data)
ci <-confint(regres, level=0.95)
regres_ci <- data.frame(covar=x,param=rownames(summary(regres)$coefficient),
summary(regres)$coefficient, ci)
})

do.call(rbind,models)






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 21 at 19:46









P. Paccioretti

1155




1155












  • Thanks! This is concise and gives me what i need. Much appreciated!
    – Jason Schoeneberger
    Nov 21 at 22:38


















  • Thanks! This is concise and gives me what i need. Much appreciated!
    – Jason Schoeneberger
    Nov 21 at 22:38
















Thanks! This is concise and gives me what i need. Much appreciated!
– Jason Schoeneberger
Nov 21 at 22:38




Thanks! This is concise and gives me what i need. Much appreciated!
– Jason Schoeneberger
Nov 21 at 22:38


















 

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