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gung - Reinstate Monica
gung - Reinstate Monica
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I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

    >library(MASS)
    >boxcox(residuals)
    Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the Box-Cox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the Box-Cox transformation normalize the residuals? Is there another way to implement the Box-Cox transformation so that it only needs to look at the distribution of the data?

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

    >library(MASS)
    >boxcox(residuals)
    Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the Box-Cox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the Box-Cox transformation normalize the residuals? Is there another way to implement the Box-Cox transformation so that it only needs to look at the distribution of the data?

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

>library(MASS)
>boxcox(residuals)
 Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the Box-Cox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the Box-Cox transformation normalize the residuals? Is there another way to implement the Box-Cox transformation so that it only needs to look at the distribution of the data?

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edit approved Aug 13, 2013 at 18:29
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edit approved Aug 13, 2013 at 18:29
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deleted 5 characters in body; edited title
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Nick Cox
Nick Cox
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Box Cox Transformation-Cox transformation for Residualsresiduals in R

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

    >library(MASS)
    >boxcox(residuals)
    Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the boxcoxBox-Cox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the box coxBox-Cox transformation normalize the residuals? Is there another way to implement the box coxBox-Cox transformation so that it only needs to look at the distribution of the data?

Thanks

Box Cox Transformation for Residuals in R

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

    >library(MASS)
    >boxcox(residuals)
    Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the boxcox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the box cox transformation normalize the residuals? Is there another way to implement the box cox transformation so that it only needs to look at the distribution of the data?

Thanks

Box-Cox transformation for residuals in R

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can obtain confidence intervals that are representative of the distribution. I have tried doing the following:

    >library(MASS)
    >boxcox(residuals)
    Error: $ operator is invalid for atomic vectors

However, I get an error. After looking at the Box-Cox more closely I have to input a formula or fitted object. If I input the measured vs. predicted fit, will the Box-Cox transformation normalize the residuals? Is there another way to implement the Box-Cox transformation so that it only needs to look at the distribution of the data?

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GK89
GK89
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