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Categorical numerical variable into continuous form for regression problem
Quickly reading very large tables as dataframesConvert data.frame columns from factors to charactersHow to convert a factor to integernumeric without loss of information?How to assign colors to categorical variables in ggplot2 that have stable mapping?Create an empty data.frameBuild Regression Model with Categorical VariableLinear regression analysis with string/categorical features (variables)?Convert different categorical variables to dummy variablesDoes linear regression work with a categorical independent variable & continuous dependent variable?categorical variables into numeric in R
I have a dataset in which all columns are numerical.Some of the columns have categories in numerical form having levels >= 2.
Do i need to convert that categorical numerical column into factor for regression analysis or not ?
Please suggest any better approach in R.
r machine-learning regression data-analysis regression-testing
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
add a comment |
I have a dataset in which all columns are numerical.Some of the columns have categories in numerical form having levels >= 2.
Do i need to convert that categorical numerical column into factor for regression analysis or not ?
Please suggest any better approach in R.
r machine-learning regression data-analysis regression-testing
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
add a comment |
I have a dataset in which all columns are numerical.Some of the columns have categories in numerical form having levels >= 2.
Do i need to convert that categorical numerical column into factor for regression analysis or not ?
Please suggest any better approach in R.
r machine-learning regression data-analysis regression-testing
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
I have a dataset in which all columns are numerical.Some of the columns have categories in numerical form having levels >= 2.
Do i need to convert that categorical numerical column into factor for regression analysis or not ?
Please suggest any better approach in R.
r machine-learning regression data-analysis regression-testing
r machine-learning regression data-analysis regression-testing
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
asked Mar 7 at 20:54
Sameer Kumar PandeySameer Kumar Pandey
83
83
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Yes you do. You can prove it to yourself...
x <- rep(1:5, 20)
y <- rnorm(100)
# not converting to factors
m1 <- lm (y ~ x)
# converting to factors
m2 <- lm(y ~ as.factor(x) )
summary(m1) # one fitted coefficent
summary(m2) # five fitted coefficients
answered Mar 8 at 0:19
user3357177user3357177
1694
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Yes you do. You can prove it to yourself...
x <- rep(1:5, 20)
y <- rnorm(100)
# not converting to factors
m1 <- lm (y ~ x)
# converting to factors
m2 <- lm(y ~ as.factor(x) )
summary(m1) # one fitted coefficent
summary(m2) # five fitted coefficients
answered Mar 8 at 0:19
user3357177user3357177
1694
add a comment |
Yes you do. You can prove it to yourself...
x <- rep(1:5, 20)
y <- rnorm(100)
# not converting to factors
m1 <- lm (y ~ x)
# converting to factors
m2 <- lm(y ~ as.factor(x) )
summary(m1) # one fitted coefficent
summary(m2) # five fitted coefficients
answered Mar 8 at 0:19
user3357177user3357177
1694
add a comment |
Yes you do. You can prove it to yourself...
x <- rep(1:5, 20)
y <- rnorm(100)
# not converting to factors
m1 <- lm (y ~ x)
# converting to factors
m2 <- lm(y ~ as.factor(x) )
summary(m1) # one fitted coefficent
summary(m2) # five fitted coefficients
answered Mar 8 at 0:19
user3357177user3357177
1694
Yes you do. You can prove it to yourself...
x <- rep(1:5, 20)
y <- rnorm(100)
# not converting to factors
m1 <- lm (y ~ x)
# converting to factors
m2 <- lm(y ~ as.factor(x) )
summary(m1) # one fitted coefficent
summary(m2) # five fitted coefficients
answered Mar 8 at 0:19
user3357177user3357177
1694
answered Mar 8 at 0:19
user3357177user3357177
1694
answered Mar 8 at 0:19
user3357177user3357177
1694
answered Mar 8 at 0:19
user3357177user3357177
1694
1694
add a comment |
add a comment |
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