Get fitted effects from a model with an rcs term
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I would like to get fitted values from a linear model that includes a restricted cubic spline term fit via rms::rcs(), to pass into an effects plot. The issue is that the package I typically use to get fitted values, effects, throws an error when I try to pass a model with an rcs term.
Here's a minimal reprex:
library(rms)
library(effects)
mod <- lm(Sepal.Length ~ rcs(Sepal.Width, 3), iris)
Effect("Sepal.Width", mod)
##Error in rcspline.eval(x, nk = nknots, inclx = TRUE, pc = pc, fractied = fractied) : knots not specified, and < 6 non-missing observations
I have tried debugging this error, but I can't arrive at how rcspline.eval() got 6+ NAs in x and non-specified nknots. How do I deal with this error? Alternatively, is there another package out there that can get fitted effects from a model with an rcs term?
r modeling effects rcs
asked Nov 10 at 22:35
Dan Villarreal
606
add a comment |
up vote
0
down vote
favorite
I would like to get fitted values from a linear model that includes a restricted cubic spline term fit via rms::rcs(), to pass into an effects plot. The issue is that the package I typically use to get fitted values, effects, throws an error when I try to pass a model with an rcs term.
Here's a minimal reprex:
library(rms)
library(effects)
mod <- lm(Sepal.Length ~ rcs(Sepal.Width, 3), iris)
Effect("Sepal.Width", mod)
##Error in rcspline.eval(x, nk = nknots, inclx = TRUE, pc = pc, fractied = fractied) : knots not specified, and < 6 non-missing observations
I have tried debugging this error, but I can't arrive at how rcspline.eval() got 6+ NAs in x and non-specified nknots. How do I deal with this error? Alternatively, is there another package out there that can get fitted effects from a model with an rcs term?
r modeling effects rcs
asked Nov 10 at 22:35
Dan Villarreal
606
I would have expected that Predict or predict would give you what you expect. Thermsplanet is not in orbit around theeffectscenter of gravity.
– 42-
Nov 10 at 23:28
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I would like to get fitted values from a linear model that includes a restricted cubic spline term fit via rms::rcs(), to pass into an effects plot. The issue is that the package I typically use to get fitted values, effects, throws an error when I try to pass a model with an rcs term.
Here's a minimal reprex:
library(rms)
library(effects)
mod <- lm(Sepal.Length ~ rcs(Sepal.Width, 3), iris)
Effect("Sepal.Width", mod)
##Error in rcspline.eval(x, nk = nknots, inclx = TRUE, pc = pc, fractied = fractied) : knots not specified, and < 6 non-missing observations
I have tried debugging this error, but I can't arrive at how rcspline.eval() got 6+ NAs in x and non-specified nknots. How do I deal with this error? Alternatively, is there another package out there that can get fitted effects from a model with an rcs term?
r modeling effects rcs
asked Nov 10 at 22:35
Dan Villarreal
606
I would like to get fitted values from a linear model that includes a restricted cubic spline term fit via rms::rcs(), to pass into an effects plot. The issue is that the package I typically use to get fitted values, effects, throws an error when I try to pass a model with an rcs term.
Here's a minimal reprex:
library(rms)
library(effects)
mod <- lm(Sepal.Length ~ rcs(Sepal.Width, 3), iris)
Effect("Sepal.Width", mod)
##Error in rcspline.eval(x, nk = nknots, inclx = TRUE, pc = pc, fractied = fractied) : knots not specified, and < 6 non-missing observations
I have tried debugging this error, but I can't arrive at how rcspline.eval() got 6+ NAs in x and non-specified nknots. How do I deal with this error? Alternatively, is there another package out there that can get fitted effects from a model with an rcs term?
r modeling effects rcs
r modeling effects rcs
asked Nov 10 at 22:35
Dan Villarreal
606
asked Nov 10 at 22:35
Dan Villarreal
606
asked Nov 10 at 22:35
Dan Villarreal
606
asked Nov 10 at 22:35
Dan Villarreal
606
asked Nov 10 at 22:35
Dan Villarreal
606
606
I would have expected that Predict or predict would give you what you expect. Thermsplanet is not in orbit around theeffectscenter of gravity.
– 42-
Nov 10 at 23:28
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30
add a comment |
I would have expected that Predict or predict would give you what you expect. Thermsplanet is not in orbit around theeffectscenter of gravity.
– 42-
Nov 10 at 23:28
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30
I would have expected that Predict or predict would give you what you expect. The
rms planet is not in orbit around the effects center of gravity.– 42-
Nov 10 at 23:28
I would have expected that Predict or predict would give you what you expect. The
rms planet is not in orbit around the effects center of gravity.– 42-
Nov 10 at 23:28
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
Users of the rms environment will need to use the specialized functions that support its activities:
library(rms)
ddist <- datadist(iris) # need both datadist and options
options(datadist='ddist')
mod <- ols(Sepal.Length ~ rcs(Sepal.Width, 3), iris) # need ols rather than lm
plot( Predict(mod, Sepal.Width)) # Predict can be done in 2 or 3 dimension
# gives a lattice output
answered Nov 10 at 23:44
42-
210k14248391
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
My understanding is that the mgcv package provides cubic splines via thes()function and supports mixed effects.
– 42-
Nov 11 at 0:29
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Users of the rms environment will need to use the specialized functions that support its activities:
library(rms)
ddist <- datadist(iris) # need both datadist and options
options(datadist='ddist')
mod <- ols(Sepal.Length ~ rcs(Sepal.Width, 3), iris) # need ols rather than lm
plot( Predict(mod, Sepal.Width)) # Predict can be done in 2 or 3 dimension
# gives a lattice output
answered Nov 10 at 23:44
42-
210k14248391
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
My understanding is that the mgcv package provides cubic splines via thes()function and supports mixed effects.
– 42-
Nov 11 at 0:29
add a comment |
up vote
1
down vote
accepted
Users of the rms environment will need to use the specialized functions that support its activities:
library(rms)
ddist <- datadist(iris) # need both datadist and options
options(datadist='ddist')
mod <- ols(Sepal.Length ~ rcs(Sepal.Width, 3), iris) # need ols rather than lm
plot( Predict(mod, Sepal.Width)) # Predict can be done in 2 or 3 dimension
# gives a lattice output
answered Nov 10 at 23:44
42-
210k14248391
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
My understanding is that the mgcv package provides cubic splines via thes()function and supports mixed effects.
– 42-
Nov 11 at 0:29
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Users of the rms environment will need to use the specialized functions that support its activities:
library(rms)
ddist <- datadist(iris) # need both datadist and options
options(datadist='ddist')
mod <- ols(Sepal.Length ~ rcs(Sepal.Width, 3), iris) # need ols rather than lm
plot( Predict(mod, Sepal.Width)) # Predict can be done in 2 or 3 dimension
# gives a lattice output
answered Nov 10 at 23:44
42-
210k14248391
Users of the rms environment will need to use the specialized functions that support its activities:
library(rms)
ddist <- datadist(iris) # need both datadist and options
options(datadist='ddist')
mod <- ols(Sepal.Length ~ rcs(Sepal.Width, 3), iris) # need ols rather than lm
plot( Predict(mod, Sepal.Width)) # Predict can be done in 2 or 3 dimension
# gives a lattice output
answered Nov 10 at 23:44
42-
210k14248391
answered Nov 10 at 23:44
42-
210k14248391
answered Nov 10 at 23:44
42-
210k14248391
answered Nov 10 at 23:44
42-
210k14248391
210k14248391
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
My understanding is that the mgcv package provides cubic splines via thes()function and supports mixed effects.
– 42-
Nov 11 at 0:29
add a comment |
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
My understanding is that the mgcv package provides cubic splines via thes()function and supports mixed effects.
– 42-
Nov 11 at 0:29
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
I see. I'm not sure if this solution will work for me since I ultimately want to apply this to a mixed-effects model, and AFAIK rms's builtin modeling functions don't allow random effects
– Dan Villarreal
Nov 10 at 23:55
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
So you posed a question that didn't include the essential criteria.
– 42-
Nov 10 at 23:58
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
I didn't know where the sticking point would be. You did answer the question, so I'll accept.
– Dan Villarreal
Nov 11 at 0:00
1
1
My understanding is that the mgcv package provides cubic splines via the
s() function and supports mixed effects.– 42-
Nov 11 at 0:29
My understanding is that the mgcv package provides cubic splines via the
s() function and supports mixed effects.– 42-
Nov 11 at 0:29
add a comment |
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I would have expected that Predict or predict would give you what you expect. The
rmsplanet is not in orbit around theeffectscenter of gravity.– 42-
Nov 10 at 23:28
Can you give an example as an answer? I looked into predict but couldn't wrap my head around how to make it work.
– Dan Villarreal
Nov 10 at 23:30