How can I predict logistic model? [closed]

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The data is like this.(of course, I have a more data)

week name reponse x1 x2
 5 a 1 1.9 0.5
 6 a 1 1.4 0.4 
 7 a 1 1.62 0.2 
 8 a 1 1.2 0.1
 4 b 0 1 0.2
 5 b 0 1.3 0.3
 7 b 0 1.4 0.4
 8 b 0 0.95 0.3
 1 c 1 -0.6 0.2
 8 c 1 0.2 0.1

I want to know whether whose response is 1 or 0.

model <- glm(response ~ week + x1 + x2, data = df)
predict(model, new_data, type = "response")

but the output is

 1 2 3 4 5 
0.3266134 0.3967968 0.4260996 0.4576125 0.5436105 
 6 7 8 9 10 
0.5428340 0.5596906 0.6288423 0.7670795 0.4516189 

I want to print the "a" is (predictions) "b" is (predictions), ... etc, not 1 is 0.3266134, 2 is 0.3967968 .. etc.

How can I fix it?

If the code is right, how can I interpret it?

r regression time-series logistic mathematical-statistics

share|cite|improve this question

asked Aug 11 at 0:12

0 Hong

213

closed as off-topic by mdewey, Peter Flom♦ Aug 11 at 13:47

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – mdewey, Peter Flom

If this question can be reworded to fit the rules in the help center, please edit the question.

add a comment | 

up vote
2
down vote

favorite

1

The data is like this.(of course, I have a more data)

week name reponse x1 x2
 5 a 1 1.9 0.5
 6 a 1 1.4 0.4 
 7 a 1 1.62 0.2 
 8 a 1 1.2 0.1
 4 b 0 1 0.2
 5 b 0 1.3 0.3
 7 b 0 1.4 0.4
 8 b 0 0.95 0.3
 1 c 1 -0.6 0.2
 8 c 1 0.2 0.1

I want to know whether whose response is 1 or 0.

model <- glm(response ~ week + x1 + x2, data = df)
predict(model, new_data, type = "response")

but the output is

 1 2 3 4 5 
0.3266134 0.3967968 0.4260996 0.4576125 0.5436105 
 6 7 8 9 10 
0.5428340 0.5596906 0.6288423 0.7670795 0.4516189 

I want to print the "a" is (predictions) "b" is (predictions), ... etc, not 1 is 0.3266134, 2 is 0.3967968 .. etc.

How can I fix it?

If the code is right, how can I interpret it?

r regression time-series logistic mathematical-statistics

share|cite|improve this question

asked Aug 11 at 0:12

0 Hong

213

closed as off-topic by mdewey, Peter Flom♦ Aug 11 at 13:47

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – mdewey, Peter Flom

If this question can be reworded to fit the rules in the help center, please edit the question.

add a comment | 

up vote
2
down vote

favorite

1

up vote
2
down vote

favorite

1

1

The data is like this.(of course, I have a more data)

week name reponse x1 x2
 5 a 1 1.9 0.5
 6 a 1 1.4 0.4 
 7 a 1 1.62 0.2 
 8 a 1 1.2 0.1
 4 b 0 1 0.2
 5 b 0 1.3 0.3
 7 b 0 1.4 0.4
 8 b 0 0.95 0.3
 1 c 1 -0.6 0.2
 8 c 1 0.2 0.1

I want to know whether whose response is 1 or 0.

model <- glm(response ~ week + x1 + x2, data = df)
predict(model, new_data, type = "response")

but the output is

 1 2 3 4 5 
0.3266134 0.3967968 0.4260996 0.4576125 0.5436105 
 6 7 8 9 10 
0.5428340 0.5596906 0.6288423 0.7670795 0.4516189 

I want to print the "a" is (predictions) "b" is (predictions), ... etc, not 1 is 0.3266134, 2 is 0.3967968 .. etc.

How can I fix it?

If the code is right, how can I interpret it?

r regression time-series logistic mathematical-statistics

share|cite|improve this question

asked Aug 11 at 0:12

0 Hong

213

The data is like this.(of course, I have a more data)

week name reponse x1 x2
 5 a 1 1.9 0.5
 6 a 1 1.4 0.4 
 7 a 1 1.62 0.2 
 8 a 1 1.2 0.1
 4 b 0 1 0.2
 5 b 0 1.3 0.3
 7 b 0 1.4 0.4
 8 b 0 0.95 0.3
 1 c 1 -0.6 0.2
 8 c 1 0.2 0.1

I want to know whether whose response is 1 or 0.

model <- glm(response ~ week + x1 + x2, data = df)
predict(model, new_data, type = "response")

but the output is

 1 2 3 4 5 
0.3266134 0.3967968 0.4260996 0.4576125 0.5436105 
 6 7 8 9 10 
0.5428340 0.5596906 0.6288423 0.7670795 0.4516189 

I want to print the "a" is (predictions) "b" is (predictions), ... etc, not 1 is 0.3266134, 2 is 0.3967968 .. etc.

How can I fix it?

If the code is right, how can I interpret it?

r regression time-series logistic mathematical-statistics

share|cite|improve this question

asked Aug 11 at 0:12

0 Hong

213

share|cite|improve this question

share|cite|improve this question

asked Aug 11 at 0:12

0 Hong

213

asked Aug 11 at 0:12

0 Hong

213

asked Aug 11 at 0:12

0 Hong

213

213

closed as off-topic by mdewey, Peter Flom♦ Aug 11 at 13:47

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – mdewey, Peter Flom

If this question can be reworded to fit the rules in the help center, please edit the question.

closed as off-topic by mdewey, Peter Flom♦ Aug 11 at 13:47

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – mdewey, Peter Flom

If this question can be reworded to fit the rules in the help center, please edit the question.

add a comment | 

add a comment | 

1 Answer
1

active

oldest

votes

up vote
3
down vote



accepted

First, your code ignores the binary nature of the response, and the fact that you have multiple observations with the same name. Without knowing details of the variables and the data collection, it is impossible to be sure what is the appropriate model to use.

But assuming name refers to the subjects, and you are measuring the same subjects in different weeks, this is longitudinal data, and you should probably use a mixed effects model.

For example,

library(lme4)
model <- glmer(response ~ week + x1 + x2 + (1 | name), 
 family=binomial, data=df)

The family=binomial indicates that response is a binary variable. The (1 | name) indicates that the data are repeated measures on the same subjects. The model has a random effect for subjects.

Now to answer your question, it depends what you mean by a prediction. You have different values of the covariates x1 and x2 for each week/subject combination, so you naturally have a different prediction for each row of your data. Just use predict() to get these values. The order of the resulting output is the same as the order of the rows in your data frame.

If you are expecting one value per name, then I assume you want to estimate effects due to the subject rather than predictions. You can get that with ranef(model). However, these will be conditional on the week, x1 and x2 being zero. You can estimate subject effects conditional on other values of week, x1 and x2; a common approach is to set them to their median values.

share|cite|improve this answer

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

• 
  
  
  

  
  

  
  
  
  
  
  

  
  I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
  – 0 Hong
  Aug 11 at 2:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  What kind of analysis I do for it?
  – 0 Hong
  Aug 11 at 2:43
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Just use predict in that case. The order of the results is the same as the order of your rows.
  – Rob Hyndman
  Aug 11 at 3:54
  
  

  
  
  
  

add a comment | 

1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

active

oldest

votes

active

oldest

votes

up vote
3
down vote



accepted

First, your code ignores the binary nature of the response, and the fact that you have multiple observations with the same name. Without knowing details of the variables and the data collection, it is impossible to be sure what is the appropriate model to use.

But assuming name refers to the subjects, and you are measuring the same subjects in different weeks, this is longitudinal data, and you should probably use a mixed effects model.

For example,

library(lme4)
model <- glmer(response ~ week + x1 + x2 + (1 | name), 
 family=binomial, data=df)

The family=binomial indicates that response is a binary variable. The (1 | name) indicates that the data are repeated measures on the same subjects. The model has a random effect for subjects.

Now to answer your question, it depends what you mean by a prediction. You have different values of the covariates x1 and x2 for each week/subject combination, so you naturally have a different prediction for each row of your data. Just use predict() to get these values. The order of the resulting output is the same as the order of the rows in your data frame.

If you are expecting one value per name, then I assume you want to estimate effects due to the subject rather than predictions. You can get that with ranef(model). However, these will be conditional on the week, x1 and x2 being zero. You can estimate subject effects conditional on other values of week, x1 and x2; a common approach is to set them to their median values.

share|cite|improve this answer

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

• 
  
  
  

  
  

  
  
  
  
  
  

  
  I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
  – 0 Hong
  Aug 11 at 2:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  What kind of analysis I do for it?
  – 0 Hong
  Aug 11 at 2:43
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Just use predict in that case. The order of the results is the same as the order of your rows.
  – Rob Hyndman
  Aug 11 at 3:54
  
  

  
  
  
  

add a comment | 

up vote
3
down vote



accepted

First, your code ignores the binary nature of the response, and the fact that you have multiple observations with the same name. Without knowing details of the variables and the data collection, it is impossible to be sure what is the appropriate model to use.

But assuming name refers to the subjects, and you are measuring the same subjects in different weeks, this is longitudinal data, and you should probably use a mixed effects model.

For example,

library(lme4)
model <- glmer(response ~ week + x1 + x2 + (1 | name), 
 family=binomial, data=df)

The family=binomial indicates that response is a binary variable. The (1 | name) indicates that the data are repeated measures on the same subjects. The model has a random effect for subjects.

Now to answer your question, it depends what you mean by a prediction. You have different values of the covariates x1 and x2 for each week/subject combination, so you naturally have a different prediction for each row of your data. Just use predict() to get these values. The order of the resulting output is the same as the order of the rows in your data frame.

If you are expecting one value per name, then I assume you want to estimate effects due to the subject rather than predictions. You can get that with ranef(model). However, these will be conditional on the week, x1 and x2 being zero. You can estimate subject effects conditional on other values of week, x1 and x2; a common approach is to set them to their median values.

share|cite|improve this answer

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

• 
  
  
  

  
  

  
  
  
  
  
  

  
  I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
  – 0 Hong
  Aug 11 at 2:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  What kind of analysis I do for it?
  – 0 Hong
  Aug 11 at 2:43
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Just use predict in that case. The order of the results is the same as the order of your rows.
  – Rob Hyndman
  Aug 11 at 3:54
  
  

  
  
  
  

add a comment | 

up vote
3
down vote



accepted

up vote
3
down vote



accepted

First, your code ignores the binary nature of the response, and the fact that you have multiple observations with the same name. Without knowing details of the variables and the data collection, it is impossible to be sure what is the appropriate model to use.

But assuming name refers to the subjects, and you are measuring the same subjects in different weeks, this is longitudinal data, and you should probably use a mixed effects model.

For example,

library(lme4)
model <- glmer(response ~ week + x1 + x2 + (1 | name), 
 family=binomial, data=df)

The family=binomial indicates that response is a binary variable. The (1 | name) indicates that the data are repeated measures on the same subjects. The model has a random effect for subjects.

Now to answer your question, it depends what you mean by a prediction. You have different values of the covariates x1 and x2 for each week/subject combination, so you naturally have a different prediction for each row of your data. Just use predict() to get these values. The order of the resulting output is the same as the order of the rows in your data frame.

If you are expecting one value per name, then I assume you want to estimate effects due to the subject rather than predictions. You can get that with ranef(model). However, these will be conditional on the week, x1 and x2 being zero. You can estimate subject effects conditional on other values of week, x1 and x2; a common approach is to set them to their median values.

share|cite|improve this answer

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

First, your code ignores the binary nature of the response, and the fact that you have multiple observations with the same name. Without knowing details of the variables and the data collection, it is impossible to be sure what is the appropriate model to use.

But assuming name refers to the subjects, and you are measuring the same subjects in different weeks, this is longitudinal data, and you should probably use a mixed effects model.

For example,

library(lme4)
model <- glmer(response ~ week + x1 + x2 + (1 | name), 
 family=binomial, data=df)

The family=binomial indicates that response is a binary variable. The (1 | name) indicates that the data are repeated measures on the same subjects. The model has a random effect for subjects.

Now to answer your question, it depends what you mean by a prediction. You have different values of the covariates x1 and x2 for each week/subject combination, so you naturally have a different prediction for each row of your data. Just use predict() to get these values. The order of the resulting output is the same as the order of the rows in your data frame.

If you are expecting one value per name, then I assume you want to estimate effects due to the subject rather than predictions. You can get that with ranef(model). However, these will be conditional on the week, x1 and x2 being zero. You can estimate subject effects conditional on other values of week, x1 and x2; a common approach is to set them to their median values.

share|cite|improve this answer

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

share|cite|improve this answer

share|cite|improve this answer

edited Aug 11 at 3:55

edited Aug 11 at 3:55

edited Aug 11 at 3:55

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

answered Aug 11 at 1:40

Rob Hyndman

33.9k15101149

33.9k15101149

• 
  
  
  

  
  

  
  
  
  
  
  

  
  I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
  – 0 Hong
  Aug 11 at 2:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  What kind of analysis I do for it?
  – 0 Hong
  Aug 11 at 2:43
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Just use predict in that case. The order of the results is the same as the order of your rows.
  – Rob Hyndman
  Aug 11 at 3:54
  
  

  
  
  
  

add a comment | 

• 
  
  
  

  
  

  
  
  
  
  
  

  
  I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
  – 0 Hong
  Aug 11 at 2:35
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  What kind of analysis I do for it?
  – 0 Hong
  Aug 11 at 2:43
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Just use predict in that case. The order of the results is the same as the order of your rows.
  – Rob Hyndman
  Aug 11 at 3:54
  
  

  
  
  
  

I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
– 0 Hong
Aug 11 at 2:35

I'm sorry. My purpose is to predict name's response(i.e, state) on week9, week 10, ... etc.
– 0 Hong
Aug 11 at 2:35

What kind of analysis I do for it?
– 0 Hong
Aug 11 at 2:43

What kind of analysis I do for it?
– 0 Hong
Aug 11 at 2:43

Just use predict in that case. The order of the results is the same as the order of your rows.
– Rob Hyndman
Aug 11 at 3:54

Just use predict in that case. The order of the results is the same as the order of your rows.
– Rob Hyndman
Aug 11 at 3:54

add a comment |