I would like to fit a nonlinear model just with the fixed structure specification using nlme R package.
model <- nlme(y ~ Asym/(1+exp((xmid-x)/scal)),
data = data,
fixed = list(Asym + xmid + scal ~ treatment))
#random = Asym ~ 1|subject)
However I am getting the following error:
Error in parse(text = paste("~", paste(nVal, collapse = "/"))) :
<text>:2:0: unexpected end of input
1: ~
^
Is there a way to circunvent this issue? Any advice is more than welcome.
I believe you want the gnls() function (also from the nlme package) with the params= argument rather than fixed=. Try this:
model <- gnls(y ~ Asym/(1+exp((xmid-x)/scal)),
data = data,
params = list(Asym + xmid + scal ~ treatment),
start= ...)
FWIW, if you're really fitting a logistic (and this isn't just a simplified example of what you want to do), fitting might be faster/more robust with the SSlogis() self-starting function in place of your explicit formula ...
Related
I am running a linear mixed effects models using the "nlme" package looking at stress and lifestyle as predictors of change in cognition over 4 years in a longitudinal dataset. All variables in the model are continuous variables.
I am able to create the model and get the summary statistics using this code:
mod1 <- lme(MS ~ age + sex + edu + GDST1*Time + HLI*Time + GDST1*HLI*Time, random= ~ 1|ID, data=NuAge_long, na.action=na.omit)
summary(mod1)
I am trying to use the "interactions" package to probe the 3-way interaction:
sim_slopes(model = mod1, pred = Time, modx = GDST1, mod2 = HLI, data = NuAge_long)
but am receiving this error:
Error in if (tcol == "df") tcol <- "t val." : argument is of length zero
I am also trying to plot the interaction using the same "interactions" package:
interact_plot(model = mod1, pred = Time, modx = GDST1, mod2 = HLI, data = NuAge_long)
and am receiving this error:
Error in UseMethod("family") : no applicable method for 'family' applied to an object of class "lme"
I can't seem to find what these errors mean and why I'm getting them. Any help would be appreciated!
From ?interactions::sim_slopes:
The function is tested with ‘lm’, ‘glm’,
‘svyglm’, ‘merMod’, ‘rq’, ‘brmsfit’, ‘stanreg’ models. Models
from other classes may work as well but are not officially
supported. The model should include the interaction of
interest.
Note this does not include lme models. On the other hand, merMod models are those generated by lme4::[g]lmer(), and as far as I can tell you should be able to fit this model equally well with lmer():
library(lme4)
mod1 <- lmer(MS ~ age + sex + edu + GDST1*Time + HLI*Time + GDST1*HLI*Time
+ (1|ID), data=NuAge_long)
(things will get harder if you want to specify correlation structures, e.g. correlation = corAR1(), which works for lme() but not lmer() ...)
I would like to use the gamlss package for fitting a model benefiting from more available distributions in that package. However, I am struggling to correctly specify my random effects or at least I think there is a mistake because if I compare the output of a lmer model with Gaussian distribution and the gamlss model with Gaussian distribution output differs. If comparing a lm model without the random effects and a gamlss model with Gaussian distribution and without random effects output is similar.
I unfortunately cannot share my data to reproduce it.
Here my code:
df <- subset.data.frame(GFW_food_agg, GFW_food_agg$fourC_area_perc < 200, select = c("ISO3", "Year", "Forest_loss_annual_perc_boxcox", "fourC_area_perc", "Pop_Dens_km2", "Pop_Growth_perc", "GDP_Capita_current_USD", "GDP_Capita_growth_perc",
"GDP_AgrForFis_percGDP", "Gini_2008_2018", "Arable_land_perc", "Forest_loss_annual_perc_previous_year", "Forest_extent_2000_perc"))
fourC <- lmer(Forest_loss_annual_perc_boxcox ~ fourC_area_perc + Pop_Dens_km2 + Pop_Growth_perc + GDP_Capita_current_USD +
GDP_Capita_growth_perc + GDP_AgrForFis_percGDP + Gini_2008_2018 + Arable_land_perc + Forest_extent_2000_perc + (1|ISO3) + (1|Year),
data = df)
summary(fourC)
resid_panel(fourC)
df <- subset.data.frame(GFW_food_agg, GFW_food_agg$fourC_area_perc < 200, select = c("ISO3", "Year", "Forest_loss_annual_perc_boxcox", "fourC_area_perc", "Pop_Dens_km2", "Pop_Growth_perc", "GDP_Capita_current_USD", "GDP_Capita_growth_perc",
"GDP_AgrForFis_percGDP", "Gini_2008_2018", "Arable_land_perc", "Forest_loss_annual_perc_previous_year", "Forest_extent_2000_perc"))
df <- na.omit(df)
df$ISO3 <- as.factor(df$ISO3)
df$Year <- as.factor(df$Year)
fourC <- gamlss(Forest_loss_annual_perc_boxcox ~ fourC_area_perc + Pop_Dens_km2 + Pop_Growth_perc + GDP_Capita_current_USD +
GDP_Capita_growth_perc + GDP_AgrForFis_percGDP + Gini_2008_2018 + Arable_land_perc + Forest_extent_2000_perc + random(ISO3) + random(Year),
data = df, family = NO, control = gamlss.control(n.cyc = 200))
summary(fourC)
plot(fourC)
How do the random effects need to be specified in gamlss to be similar to the random effects in lmer?
If I specify the random effects instead using
re(random = ~1|ISO3) + re(random = ~1|Year)
I get the following error:
Error in model.frame.default(formula = Forest_loss_annual_perc_boxcox ~ :
variable lengths differ (found for 're(random = ~1 | ISO3)')
I found the +re(random=~1|x) specification to work fairly well with my GAMLSS. Have you double check that the NA's are being removed from your dataset? Sometimes na.omit does not work properly.
Have a look at this thread that has the same error than yours, but in a GAM. You can try that code to remove your NA's
Error in model.frame.default: variable lengths differ
I have estimated a Tobit model using the censReg package, along with the censReg function. Alternatively, the same Tobit model is estimated using the tobit function in the AER package.
Now, I really like to have some goodness of fit statistic, such as the Pseudo-R2. However, whenever I try to estimate this, the output returns as NA. For example:
Tobit <- censReg(Listing$occupancy_rate ~ ., left = -Inf, right = 1, data = Listing)
PseudoR2(Tobit, which = "McFadden")
[1] NA
So far, I have only seen reported Pseudo-R2's when people use Stata. Does anyone know how to estimate it in R?
Alternatively, Tobit estimates the (log)Sigma, which is basically the standard deviation of the residuals. Could I use this to calculate the R2?
All help is really appreciated.
You can use DescTools package to calculate PseudoR2. You have not provided any sample data. So, it is hard for me to run your model. I am using a default dataset like
library(DescTools)
r.glm <- glm(Survived ~ ., data=Untable(Titanic), family=binomial)
PseudoR2(r.glm, c("McFadden"))
For your model, you can use something like
library(AER)
data("Affairs", package = "AER")
fm.tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
data = Affairs)
#Create a function for pseudoR2 calculation
pseudoR2 <- function(obj) 1 - as.vector(logLik(obj)/logLik(update(obj, . ~ 1)))
pseudoR2(fm.tobit)
#>[1] 0.05258401
Or using censReg as you have used
library(censReg)
data("Affairs", package = "AER")
estResult <- censReg(affairs ~ age + yearsmarried + religiousness +
occupation + rating, data = Affairs)
summary(estResult)
pseudoR2(estResult)
#>[1] 0.05258401
You can find the details about pseudoR2 in the following link
R squared in logistic regression
Using glmer, I can run a logistic regression mixed model just fine. But when I try to do the same using glmulti, I get errors (described below). I think the problem is with the function I am specifying for use in glmulti. I want a function that specifies a logistic regression model for data containing continuous fixed covariates and categorical random effects, using a logit link. The response variable is a binary 0/1.
Sample data:
library(lme4)
library(rJava)
library(glmulti)
set.seed(666)
x1 = rnorm(1000) # some continuous variables
x2 = rnorm(1000)
x3 = rnorm(1000)
r1 = rep(c("red", "blue"), times = 500) #categorical random effects
r2 = rep(c("big", "small"), times = 500)
z = 1 + 2*x1 + 3*x2 +2*x3
pr = 1/(1+exp(-z))
y = rbinom(1000,1,pr) # bernoulli response variable
df = data.frame(y=y,x1=x1,x2=x2, x3=x3, r1=r1, r2=r2)
A single glmer logistic regression works just fine:
model1<-glmer(y~x1+x2+x3+(1|r1)+(1|r2),data=df,family="binomial")
But errors occur when I try to use the same model structure through glmulti:
# create a function - I think this is where my problem is
glmer.glmulti<-function(formula, data, family=binomial(link ="logit"), random="", ...){
glmer(paste(deparse(formula),random),data=data,...)
}
# run glmulti models
glmulti.logregmixed <-
glmulti(formula(glmer(y~x1+x2+x3+(1|r1)+(1|r2), data=df), fixed.only=TRUE), #error w/o fixed.only=TRUE
data=df,
level = 2,
method = "g",
crit = "aicc",
confsetsize = 128,
plotty = F, report = F,
fitfunc = glmer.glmulti,
family = binomial(link ="logit"),
random="+(1|r1)","+(1|r2)", # possibly this line is incorrect?
intercept=TRUE)
#Errors returned:
singular fit
Error in glmulti(formula(glmer(y ~ x1 + x2 + x3 + (1 | r1) + (1 | r2), :
Improper call of glmulti.
In addition: Warning message:
In glmer(y ~ x1 + x2 + x3 + (1 | r1) + (1 | r2), data = df) :
calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directly
I've tried various changes to the function, and within the formula and fitfunc portion of the glmulti code. I've tried substituting lmer for glmer and I guess I don't understand the error. I'm also afraid that calling lmer may change the model structure, as during one of my attempts the summary() of the model stated "Linear mixed model fit by REML ['lmerMod']." I need the glmulti models to be the same as what I'm obtaining with model1 using glmer (ie summary(model1) gives "Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']"
Many similar questions remain unanswered. Thanks in advance!
Credit:
sample data set created with help from here:
https://stats.stackexchange.com/questions/46523/how-to-simulate-artificial-data-for-logistic-regression
glmulti code adapted from here:
Model selection using glmulti
I followed this example for running a piecewise mixed model using lmer, and it works very well. However, I am having trouble translating the model to lme because I need to deal with heteroscedasticity, and lmer doesn’t have that ability.
Code to reproduce the problem is here. I included details about the experimental design in the code if you think it’s necessary to answer the question.
Here is the model without the breakpoint:
linear <- lmer(mass ~ lat + (1 | pop/line), data = df)
And here is how I run it with the breakpoint:
bp = 30
b1 <- function(x, bp) ifelse(x < bp, x, 0)
b2 <- function(x, bp) ifelse(x < bp, 0, x)
breakpoint <- lmer(mass ~ b1(lat, bp) + b2(lat, bp) + (1 | pop/line), data = df)
The problem is that I have pretty severe heteroscedasticity. As far as I understand, that means I should be using lme from the nlme package. Here is the linear model in lme:
ctrl <- lmeControl(opt='optim')
linear2 <- lme(mass ~ lat , random=~1|pop/line, na.action = na.exclude, data=df, control = ctrl, weights=varIdent(form=~1|pop))
And this is the breakpoint model that is, well, breaking:
breakpoint2 <- lme(mass ~ b1(lat, bp) + b2(lat, bp), random=~1|pop/line, na.action = na.exclude, data=df, control = ctrl, weights=varIdent(form=~1|pop))
Here is the error message:
Error in model.frame.default(formula = ~pop + mass + lat + bp + line, : variable lengths differ (found for 'bp')
How can I translate this lovely breakpoint model from lmer to lme? Thank you!
Looks like lme doesn't like it when you use variables in your formula that aren't in the data.frame you are fitting your model on. One option would be to build your formula first then pass it to lme. For example
myform <- eval(substitute(mass ~ b1(lat, bp) + b2(lat, bp), list(bp=bp)))
breakpoint2 <- lme(myform, random=~1|pop/line, na.action = na.exclude, data=df, control = ctrl, weights=varIdent(form=~1|pop))
The eval()/substitute() is just to swap out the bp in your formula with the value of the variable bp
Or if bp were always 30, you would just put that directly in the formula
breakpoint2 <- lme(mass ~ b1(lat, 30) + b2(lat, 30), random=~1|pop/line, na.action = na.exclude, data=df, control = ctrl, weights=varIdent(form=~1|pop))
and that would work as well.