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This is different, follow up question to someone else's question here:

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm() for the data on each side of the changepoint. To make sure the lines connected I was finding the intercept of my two best fit lies and clipping them up to and from their intersection so it was a smooth break point in what looked like a continuous "checkmark" line. At first it worked really well with one study and the intersections were very close to the changepoints cpt.mean() found. Now we are on a study that has data that isn't as clean and the intersection of the two lines is very far from the changepoint. So I thought it would be better to find linear regression lines that anchor to the changepoint. I have read and understand the basic solution to this question linked above but I cant seem to figure out how to limit which data it looks at for the linear regression. Normally I do something like this if my changepoint was the 26th data point and I needed to perform a linear regression for only the first 26 data points (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables:

lm(I(MAP-y0)~I(PP-x0) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-y0)~I(PP[1:26]-x0) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

My data has a "baseline" and then there is a clear increase in the slope of the data. I performed a change point analysis to find where the data started increasing rapidly. Normally I take all of the data to the left of the changepoint and do linear regression. Then I take all the data to the right of the changepoint and do another regression. I then take the two equations and plot them separately but I clip the lines so that it appears to have one line that has a "corner" or "elbow". Both options Glen_b listed seem appealing. I am mostly concerned with the x value.

This is different, follow up question to someone else's question here:

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm(). One for the first half of my data and another for my second half of my data. I determine where to divide up the data after cpt.mean() tells me my changepoint. The first line would normally have a slope around 0 and the other could be anywhere from 0.2 to 2. I would then add those lines separately to my graph and clip them so that it appeared that I only had only line graphed but that that line had a "corner." At first it worked really well but the point where the two lines joined together was often further from the changepoint found with cpt.mean because what I graphed was determined by where the two equations mathematically intersected. 

So I thought it would be better to find linear regression lines that anchor to the changepoint. That way when I graph two seperate lines, they connect at the changepoint that cpt.mean. I have read and understand the basic solution to this question linked above but I cant seem to figure out how to limit the regression so that it only fits half of the data.

In a made up scenario my changepoint could be my 26th data point (10, 20)

Normally I do something like this for the first half of the data: (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables and changepoint:

lm(I(MAP-20)~I(PP-10) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-20)~I(PP[1:26]-10) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

Here is an example of a what my graphs normally look like:

Best, Ian

This is different, follow up question to someone else's question here:

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm() for the data on each side of the changepoint. To make sure the lines connected I was finding the intercept of my two best fit lies and clipping them up to and from their intersection so it was a smooth break point in what looked like a continuous "checkmark" line. At first it worked really well with one study and the intersections were very close to the changepoints cpt.mean() found. Now we are on a study that has data that isn't as clean and the intersection of the two lines is very far from the changepoint. So I thought it would be better to find linear regression lines that anchor to the changepoint. I have read and understand the basic solution to this question linked above but I cant seem to figure out how to limit which data it looks at for the linear regression. Normally I do something like this if my changepoint was the 26th data point and I needed to perform a linear regression for only the first 26 data points (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables:

lm(I(MAP-y0)~I(PP-x0) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-y0)~I(PP[1:26]-x0) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

Best, Ian

This is different, follow up question to someone else's question here:

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm() for the data on each side of the changepoint. To make sure the lines connected I was finding the intercept of my two best fit lies and clipping them up to and from their intersection so it was a smooth break point in what looked like a continuous "checkmark" line. At first it worked really well with one study and the intersections were very close to the changepoints cpt.mean() found. Now we are on a study that has data that isn't as clean and the intersection of the two lines is very far from the changepoint. So I thought it would be better to find linear regression lines that anchor to the changepoint. I have read and understand the basic solution to this question linked above but I cant seem to figure out how to limit which data it looks at for the linear regression. Normally I do something like this if my changepoint was the 26th data point and I needed to perform a linear regression for only the first 26 data points (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables:

lm(I(MAP-y0)~I(PP-x0) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-y0)~I(PP[1:26]-x0) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

My data has a "baseline" and then there is a clear increase in the slope of the data. I performed a change point analysis to find where the data started increasing rapidly. Normally I take all of the data to the left of the changepoint and do linear regression. Then I take all the data to the right of the changepoint and do another regression. I then take the two equations and plot them separately but I clip the lines so that it appears to have one line that has a "corner" or "elbow". Both options Glen_b listed seem appealing. I am mostly concerned with the x value.

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm() for the data on each side of the changepoint. To make sure the lines connected I was finding the intercept of my two best fit lies and clipping them up to and from their intersection so it was a smooth break point in what looked like a continuous "checkmark" line. At first it worked really well with one study and the intersections were very close to the changepoints cpt.mean() found. Now we are on a study that has data that isn't as clean and the intersection of the two lines is very far from the changepoint. So I thought it would be better to find linear regression lines that anchor to the changepoint. I have read and understand the basic solution to this question here: but I cant seem to figure out how to limit which data it looks at for the linear regression. Normally I do something like this if my changepoint was the 26th data point and I needed to perform a linear regression for only the first 26 data points (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables:

lm(I(MAP-y0)~I(PP-x0) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-y0)~I(PP[1:26]-x0) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

Best, Ian

This is different, follow up question to someone else's question here:

I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my lab. I am detecting changepoints in physiological data using cpt.mean() and until now I have been doing two separate linear regressions with lm() for the data on each side of the changepoint. To make sure the lines connected I was finding the intercept of my two best fit lies and clipping them up to and from their intersection so it was a smooth break point in what looked like a continuous "checkmark" line. At first it worked really well with one study and the intersections were very close to the changepoints cpt.mean() found. Now we are on a study that has data that isn't as clean and the intersection of the two lines is very far from the changepoint. So I thought it would be better to find linear regression lines that anchor to the changepoint. I have read and understand the basic solution to this question linked above but I cant seem to figure out how to limit which data it looks at for the linear regression. Normally I do something like this if my changepoint was the 26th data point and I needed to perform a linear regression for only the first 26 data points (PP is my X and MAP is my Y variable):

lm(MAP[1:26]~PP[1:26])

The simple solution to anchoring the best fit line to a specific point,(X0, Y0) was this

lm(I(y-y0)~I(x-x0) + 0)

with my variables:

lm(I(MAP-y0)~I(PP-x0) + 0)

I figured I could just combine the two as follows:

lm(I(MAP[1:26]-y0)~I(PP[1:26]-x0) +0)

No error came up but nothing showed up on my scatter plot. Not sure whats happening.

Best, Ian