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Uniformly quantize the dependent variable into 4, 8, and 16 levels / R Programming
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat is the best Data Mining algorithm for prediction based on a single variable?How to extract features and classify alert emails coming from monitoring tools into proper category?How to get the inertia at the begining when using sklearn.cluster.KMeans and MiniBatchKMeanshow to sum a variable by group in r and display in a tabular form?Cluster a categorical variable without breaking the existing categoriesConceptual Question about finding relation between one categorical variable and one numeric variableWhat's the difference between finding the average Euclidean distance and using inertia_ in KMeans in sklearn?What's the difference between finding the average Euclidean distance and using inertia_ in KMeans in sklearn?Hierarchical Clustering and Variable SelectionHow to validate clusters after calculating Gower distances and Ward's clustering in R
$begingroup$
I have a question pertaining to the quantizing my dependent variable into 4, 8, and 16 levels. I will attempt to use kmeans to do this. Here is where I am lost. I need to perform these steps after I break my dependent variable into the 3 levels. Predict the numerical variable "wife_age" using the independent variables: numerical "num_child" and categorical "cmc". Compute the MSE or mean-square error, confusion table, and probability of misclassification. Here is my data set.
Here is what I have thus far:
# install packages Note# tidyverse installs packages for dplyr
install.packages("tidyverse")
install.packages("factoextra")
install.packages("cluster")
#open the library
library(tidyverse)
library(cluster)
library(factoextra)
library(MASS)
# Load dataset
A <- read.table("H5.txt", header = TRUE)
# list variable names
names(A)
# view the data
head(A)
output
> names(A)
[1] "wife_age" "wife_edu" "hus_edu" "num_child" "wife_rel" "wife_work" "hus_occu" "sli" "media_exp"
[10] "cmc"
> # view the data
> head(A)
wife_age wife_edu hus_edu num_child wife_rel wife_work hus_occu sli media_exp cmc
1 24 2 3 3 1 1 2 3 0 1
2 45 1 3 10 1 1 3 4 0 1
3 43 2 3 7 1 1 3 4 0 1
4 42 3 2 9 1 1 3 3 0 1
5 36 3 3 8 1 1 3 2 0 1
6 19 4 4 0 1 1 3 3 0 1
# table broken into 4 clusters
L4 <- kmeans(A$wife_age, 4)
## Total Within cluster sum of square
L4$tot.withinss
L4
B=L4$centers[L4$cluster]
head(B, 4)
hist(B)
mean(B)
sd(B)
B<-factor(B)
A$cmc <- factor(A$cmc)
C = polr(B ~ num_child + cmc, data = A, Hess = TRUE)
summary(C)
predict(C)
Output
> summary(C)
Call:
polr(formula = B ~ num_child + cmc, data = A, Hess = TRUE)
Coefficients:
Value Std. Error t value
num_child 0.5272 0.02618 20.140
cmc2 -0.1603 0.12776 -1.254
cmc3 -1.0119 0.11673 -8.669
Intercepts:
Value Std. Error t value
23.5144628099174|30.8827930174564 0.3663 0.1040 3.5218
30.8827930174564|37.5348101265823 1.8538 0.1167 15.8816
37.5348101265823|45.2316176470588 3.2798 0.1377 23.8142
Residual Deviance: 3446.081
AIC: 3458.081
> predict(C)
[1] 30.8827930174564 45.2316176470588 45.2316176470588 45.2316176470588 45.2316176470588 23.5144628099174
[985] 23.5144628099174 30.8827930174564 23.5144628099174 23.5144628099174 30.8827930174564 30.8827930174564
[991] 23.5144628099174 23.5144628099174 23.5144628099174 45.2316176470588 23.5144628099174 23.5144628099174
[997] 30.8827930174564 37.5348101265823 45.2316176470588 45.2316176470588
[ reached getOption("max.print") -- omitted 473 entries ]
Levels: 23.5144628099174 30.8827930174564 37.5348101265823 45.2316176470588
What I am wanting to do is a prediction. From the predict C. am I actually predicting the wife's age? Is there a better way to do this?
clustering k-means rstudio
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
$endgroup$
add a comment |
$begingroup$
I have a question pertaining to the quantizing my dependent variable into 4, 8, and 16 levels. I will attempt to use kmeans to do this. Here is where I am lost. I need to perform these steps after I break my dependent variable into the 3 levels. Predict the numerical variable "wife_age" using the independent variables: numerical "num_child" and categorical "cmc". Compute the MSE or mean-square error, confusion table, and probability of misclassification. Here is my data set.
Here is what I have thus far:
# install packages Note# tidyverse installs packages for dplyr
install.packages("tidyverse")
install.packages("factoextra")
install.packages("cluster")
#open the library
library(tidyverse)
library(cluster)
library(factoextra)
library(MASS)
# Load dataset
A <- read.table("H5.txt", header = TRUE)
# list variable names
names(A)
# view the data
head(A)
output
> names(A)
[1] "wife_age" "wife_edu" "hus_edu" "num_child" "wife_rel" "wife_work" "hus_occu" "sli" "media_exp"
[10] "cmc"
> # view the data
> head(A)
wife_age wife_edu hus_edu num_child wife_rel wife_work hus_occu sli media_exp cmc
1 24 2 3 3 1 1 2 3 0 1
2 45 1 3 10 1 1 3 4 0 1
3 43 2 3 7 1 1 3 4 0 1
4 42 3 2 9 1 1 3 3 0 1
5 36 3 3 8 1 1 3 2 0 1
6 19 4 4 0 1 1 3 3 0 1
# table broken into 4 clusters
L4 <- kmeans(A$wife_age, 4)
## Total Within cluster sum of square
L4$tot.withinss
L4
B=L4$centers[L4$cluster]
head(B, 4)
hist(B)
mean(B)
sd(B)
B<-factor(B)
A$cmc <- factor(A$cmc)
C = polr(B ~ num_child + cmc, data = A, Hess = TRUE)
summary(C)
predict(C)
Output
> summary(C)
Call:
polr(formula = B ~ num_child + cmc, data = A, Hess = TRUE)
Coefficients:
Value Std. Error t value
num_child 0.5272 0.02618 20.140
cmc2 -0.1603 0.12776 -1.254
cmc3 -1.0119 0.11673 -8.669
Intercepts:
Value Std. Error t value
23.5144628099174|30.8827930174564 0.3663 0.1040 3.5218
30.8827930174564|37.5348101265823 1.8538 0.1167 15.8816
37.5348101265823|45.2316176470588 3.2798 0.1377 23.8142
Residual Deviance: 3446.081
AIC: 3458.081
> predict(C)
[1] 30.8827930174564 45.2316176470588 45.2316176470588 45.2316176470588 45.2316176470588 23.5144628099174
[985] 23.5144628099174 30.8827930174564 23.5144628099174 23.5144628099174 30.8827930174564 30.8827930174564
[991] 23.5144628099174 23.5144628099174 23.5144628099174 45.2316176470588 23.5144628099174 23.5144628099174
[997] 30.8827930174564 37.5348101265823 45.2316176470588 45.2316176470588
[ reached getOption("max.print") -- omitted 473 entries ]
Levels: 23.5144628099174 30.8827930174564 37.5348101265823 45.2316176470588
What I am wanting to do is a prediction. From the predict C. am I actually predicting the wife's age? Is there a better way to do this?
clustering k-means rstudio
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
$endgroup$
$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46
add a comment |
$begingroup$
I have a question pertaining to the quantizing my dependent variable into 4, 8, and 16 levels. I will attempt to use kmeans to do this. Here is where I am lost. I need to perform these steps after I break my dependent variable into the 3 levels. Predict the numerical variable "wife_age" using the independent variables: numerical "num_child" and categorical "cmc". Compute the MSE or mean-square error, confusion table, and probability of misclassification. Here is my data set.
Here is what I have thus far:
# install packages Note# tidyverse installs packages for dplyr
install.packages("tidyverse")
install.packages("factoextra")
install.packages("cluster")
#open the library
library(tidyverse)
library(cluster)
library(factoextra)
library(MASS)
# Load dataset
A <- read.table("H5.txt", header = TRUE)
# list variable names
names(A)
# view the data
head(A)
output
> names(A)
[1] "wife_age" "wife_edu" "hus_edu" "num_child" "wife_rel" "wife_work" "hus_occu" "sli" "media_exp"
[10] "cmc"
> # view the data
> head(A)
wife_age wife_edu hus_edu num_child wife_rel wife_work hus_occu sli media_exp cmc
1 24 2 3 3 1 1 2 3 0 1
2 45 1 3 10 1 1 3 4 0 1
3 43 2 3 7 1 1 3 4 0 1
4 42 3 2 9 1 1 3 3 0 1
5 36 3 3 8 1 1 3 2 0 1
6 19 4 4 0 1 1 3 3 0 1
# table broken into 4 clusters
L4 <- kmeans(A$wife_age, 4)
## Total Within cluster sum of square
L4$tot.withinss
L4
B=L4$centers[L4$cluster]
head(B, 4)
hist(B)
mean(B)
sd(B)
B<-factor(B)
A$cmc <- factor(A$cmc)
C = polr(B ~ num_child + cmc, data = A, Hess = TRUE)
summary(C)
predict(C)
Output
> summary(C)
Call:
polr(formula = B ~ num_child + cmc, data = A, Hess = TRUE)
Coefficients:
Value Std. Error t value
num_child 0.5272 0.02618 20.140
cmc2 -0.1603 0.12776 -1.254
cmc3 -1.0119 0.11673 -8.669
Intercepts:
Value Std. Error t value
23.5144628099174|30.8827930174564 0.3663 0.1040 3.5218
30.8827930174564|37.5348101265823 1.8538 0.1167 15.8816
37.5348101265823|45.2316176470588 3.2798 0.1377 23.8142
Residual Deviance: 3446.081
AIC: 3458.081
> predict(C)
[1] 30.8827930174564 45.2316176470588 45.2316176470588 45.2316176470588 45.2316176470588 23.5144628099174
[985] 23.5144628099174 30.8827930174564 23.5144628099174 23.5144628099174 30.8827930174564 30.8827930174564
[991] 23.5144628099174 23.5144628099174 23.5144628099174 45.2316176470588 23.5144628099174 23.5144628099174
[997] 30.8827930174564 37.5348101265823 45.2316176470588 45.2316176470588
[ reached getOption("max.print") -- omitted 473 entries ]
Levels: 23.5144628099174 30.8827930174564 37.5348101265823 45.2316176470588
What I am wanting to do is a prediction. From the predict C. am I actually predicting the wife's age? Is there a better way to do this?
clustering k-means rstudio
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
$endgroup$
I have a question pertaining to the quantizing my dependent variable into 4, 8, and 16 levels. I will attempt to use kmeans to do this. Here is where I am lost. I need to perform these steps after I break my dependent variable into the 3 levels. Predict the numerical variable "wife_age" using the independent variables: numerical "num_child" and categorical "cmc". Compute the MSE or mean-square error, confusion table, and probability of misclassification. Here is my data set.
Here is what I have thus far:
# install packages Note# tidyverse installs packages for dplyr
install.packages("tidyverse")
install.packages("factoextra")
install.packages("cluster")
#open the library
library(tidyverse)
library(cluster)
library(factoextra)
library(MASS)
# Load dataset
A <- read.table("H5.txt", header = TRUE)
# list variable names
names(A)
# view the data
head(A)
output
> names(A)
[1] "wife_age" "wife_edu" "hus_edu" "num_child" "wife_rel" "wife_work" "hus_occu" "sli" "media_exp"
[10] "cmc"
> # view the data
> head(A)
wife_age wife_edu hus_edu num_child wife_rel wife_work hus_occu sli media_exp cmc
1 24 2 3 3 1 1 2 3 0 1
2 45 1 3 10 1 1 3 4 0 1
3 43 2 3 7 1 1 3 4 0 1
4 42 3 2 9 1 1 3 3 0 1
5 36 3 3 8 1 1 3 2 0 1
6 19 4 4 0 1 1 3 3 0 1
# table broken into 4 clusters
L4 <- kmeans(A$wife_age, 4)
## Total Within cluster sum of square
L4$tot.withinss
L4
B=L4$centers[L4$cluster]
head(B, 4)
hist(B)
mean(B)
sd(B)
B<-factor(B)
A$cmc <- factor(A$cmc)
C = polr(B ~ num_child + cmc, data = A, Hess = TRUE)
summary(C)
predict(C)
Output
> summary(C)
Call:
polr(formula = B ~ num_child + cmc, data = A, Hess = TRUE)
Coefficients:
Value Std. Error t value
num_child 0.5272 0.02618 20.140
cmc2 -0.1603 0.12776 -1.254
cmc3 -1.0119 0.11673 -8.669
Intercepts:
Value Std. Error t value
23.5144628099174|30.8827930174564 0.3663 0.1040 3.5218
30.8827930174564|37.5348101265823 1.8538 0.1167 15.8816
37.5348101265823|45.2316176470588 3.2798 0.1377 23.8142
Residual Deviance: 3446.081
AIC: 3458.081
> predict(C)
[1] 30.8827930174564 45.2316176470588 45.2316176470588 45.2316176470588 45.2316176470588 23.5144628099174
[985] 23.5144628099174 30.8827930174564 23.5144628099174 23.5144628099174 30.8827930174564 30.8827930174564
[991] 23.5144628099174 23.5144628099174 23.5144628099174 45.2316176470588 23.5144628099174 23.5144628099174
[997] 30.8827930174564 37.5348101265823 45.2316176470588 45.2316176470588
[ reached getOption("max.print") -- omitted 473 entries ]
Levels: 23.5144628099174 30.8827930174564 37.5348101265823 45.2316176470588
What I am wanting to do is a prediction. From the predict C. am I actually predicting the wife's age? Is there a better way to do this?
clustering k-means rstudio
clustering k-means rstudio
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
edited Apr 2 at 23:10
Chris Kehl
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
asked Apr 1 at 13:58
Chris KehlChris Kehl
579
579
$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46
add a comment |
$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46
$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46
add a comment |
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$begingroup$
Define "uniform". I don't see how kmeans could be "uniform" in any standard meaning of the word.
$endgroup$
– Anony-Mousse
Apr 1 at 18:19
$begingroup$
broken into levels, if there is a better way please share.
$endgroup$
– Chris Kehl
Apr 1 at 18:52
$begingroup$
@ChrisKehl please do not post pictures of the data analytical activity. It's difficult to reproduce. Also do not BOLD the sentences.. In netspeak it indicates shouting. Suggest to revise the question, state clearly what you have tried, the problem. add bullet points wherever applicable. See here on how to create a minimum reproducible example
$endgroup$
– mnm
Apr 2 at 7:46