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, int
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
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 re
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
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 :
Theoretica
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