R help - Feb 2014 - plot out the predicted values and the upper and lower limits

Hi,
I have realized a multiple linear regression. 
To know how well my model does in terms of prediction, I can compute prediction
intervals bands and decide if they are narrow enough to be of use. If they are
too wide, then they probably are not useful.

So what I am trying to do is :
Theoretically I know that I can use the "predict" command in R to
generate the prediction interval for a set of points. The idea is to find the
linear regression using the lm command. Then I can use the predict command to
get the prediction interval for a set of points in the domain. Then I plot out
the predicted values as well as the upper and lower limits of the
prediction intervals for those values.
My problem is to practice what I theoretically know, especially using R.

My linear model is the following :
LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score +
Quality.score, data=Dataset)
summary(LinearModel.1)
predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval =
c("none", "confidence", "prediction"),level =
0.95, type = c("response", "terms"),terms = NULL)

Could you please help me with my R codes ?

Thanks for your precious help,
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On 26/02/14 11:57, varin sacha wrote:
> Hi,
> I have realized a multiple linear regression.
> To know how well my model does in terms of prediction, I can compute
prediction intervals bands and decide if they are narrow enough to be of use. If
they are too wide, then they probably are not useful.
>
> So what I am trying to do is :
> Theoretically I know that I can use the "predict" command in R to
generate the prediction interval for a set of points. The idea is to find the
linear regression using the lm command. Then I can use the predict command to
get the prediction interval for a set of points in the domain. Then I plot out
the predicted values as well as the upper and lower limits of the prediction
intervals for those values.
> My problem is to practice what I theoretically know, especially using R.
>
> My linear model is the following :
> LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score + Quality.score,
data=Dataset)
> summary(LinearModel.1)
> predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval =
c("none", "confidence", "prediction"),level =
0.95, type = c("response", "terms"),terms = NULL)
>
> Could you please help me with my R codes ?
If you want prediction intervals, ask for them!!!  There is no point at 
all in simply repeating the default for the argument "interval".  If
you
actually want the default, then don't say anything at all.  I.e. omit 
the "interval" argument from your call and don't clutter things up
with
irrelevancies.

But here you *don't* want the default (which is "none").  You want
"prediction".  So set interval="prediction" in your call. 
Actually you
can just set interval="p" because of partial argument matching.

cheers,

Rolf Turner

Many thanks Rolf,

These codes below are ok :
LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score +
Quality.score, data=Dataset)
summary(LinearModel.1)

predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval =
c("prediction"),level = 0.95, type = c("response"))

One problem remains : If I want to plot out (in a same graph) the predicted
values as well as the upper and lower limits of the prediction intervals for
those values. How can I do, many thanks once more.

Best,




Le Mercredi 26 février 2014 4h59, varin sacha <varinsacha@yahoo.fr> a
écrit :
 
Hi,
I have realized a multiple linear regression. 
To know how well my model does in terms of prediction, I can compute prediction
intervals bands and decide if they are narrow enough to be of use. If they are
too wide, then they probably are not useful.

So what I am trying to do is :
Theoretically I know that I can use the "predict" command in R to
generate the prediction interval for a set of points. The idea is to find the
linear regression using the lm command. Then I can use the predict command to
get the prediction interval for a set of points in the domain. Then I plot out
the predicted values as well as the upper and lower limits of the
prediction intervals for those values.
My problem is to practice what I theoretically know, especially using R.

My linear model is the following :
LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score +
Quality.score, data=Dataset)
summary(LinearModel.1)
predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval =
c("none", "confidence", "prediction"),level =
0.95, type = c("response", "terms"),terms = NULL)

Could you please help me with my R codes ?

Thanks for your precious help,
    [[alternative HTML version deleted]]

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On 27/02/14 05:43, varin sacha wrote:
> Many thanks Rolf,
>
> These codes below are ok :
> LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score +
> Quality.score, data=Dataset)
> summary(LinearModel.1)
> predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval >
c("prediction"),level = 0.95, type = c("response"))
>
> One problem remains : If I want to plot out (in a same
> graph) the predicted values as well as the upper and lower limits of the
> prediction intervals for those values. How can I do, many thanks once more.
This is very a very basic R plotting problem.  If you are going to use 
R, you should learn how to use R.  Your emails indicate that you haven't 
really got a clue. Read some of the excellent introductory material 
available from the R web page.  Start with a thorough read of "An 
Introduction to R".  Then have a look at some of the material available 
under "contributed documentation".  It will take a while but will be 
worth the effort if you wish to become anything like competent at using R.

I could tell you how to do the plot you want, in a few lines, but I am 
not going to.  You need to do some learning.

cheers,

Rolf

P. S. An indication that you haven't a clue is the fact that you use

	interval = c("prediction")

Just what on earth do you think that "c()" is doing for you here? 
Learn
what the c() function means and what its purpose is.  Don't just slap 
down bits of code in a higgledy-piggledy manner and hope that something 
will work.  Wrapping "prediction" in c() is harmless in this instance,
but is unnecessary, redundant and confusing.  In general using 
inappropriate code that you don't understand will result in errors.

R.

When in doubt, first consult the included online help.

   ?predict.lm

Offers this example, which seems to meet the request

x <- rnorm(15)
y <- x + rnorm(15)
predict(lm(y ~ x))
new <- data.frame(x = seq(-3, 3, 0.5))
predict(lm(y ~ x), new, se.fit = TRUE)
pred.w.plim <- predict(lm(y ~ x), new, interval = "prediction")
pred.w.clim <- predict(lm(y ~ x), new, interval = "confidence")
matplot(new$x, cbind(pred.w.clim, pred.w.plim[,-1]),
        lty = c(1,2,2,3,3), type = "l", ylab = "predicted
y")


(note that all the functions come with R and are loaded by default)


And predict.lm can be found in the "See also" section of ?predict.


-Don

-- 
Don MacQueen

Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062





On 2/25/14 2:57 PM, "varin sacha" <varinsacha at yahoo.fr>
wrote:
>Hi,
>I have realized a multiple linear regression.
>To know how well my model does in terms of prediction, I can compute
>prediction intervals bands and decide if they are narrow enough to be of
>use. If they are too wide, then they probably are not useful.
>
>So what I am trying to do is :
>Theoretically I know that I can use the "predict" command in R to
>generate the prediction interval for a set of points. The idea is to find
>the linear regression using the lm command. Then I can use the predict
>command to get the prediction interval for a set of points in the domain.
>Then I plot out the predicted values as well as the upper and lower
>limits of the prediction intervals for those values.
>My problem is to practice what I theoretically know, especially using R.
>
>My linear model is the following :
>LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score + Quality.score,
>data=Dataset)
>summary(LinearModel.1)
>predict(LinearModel.1, se.fit = FALSE, scale = NULL, df = Inf,interval
>c("none", "confidence", "prediction"),level =
0.95, type = c("response",
>"terms"),terms = NULL)
>
>Could you please help me with my R codes ?
>
>Thanks for your precious help,
>	[[alternative HTML version deleted]]
>