My data frame looks like:
head(bush_status)
distance status count
0 endemic 844
1 exotic 8
5 native 3
10 endemic 5
15 endemic 4
20 endemic 3
The count data is non-normally distributed. I'm trying to fit a generalized additive model to my data in two ways so i can use anova to see if the p-value supports m2.
m1 <- gam(count ~ s(distance) + status, data=bush_status, family="nb")
m2 <- gam(count ~ s(distance, by=status) + status, data=bush_status, family="nb")
m1 works fine, but m2 sends the error message:
"Error in smoothCon(split$smooth.spec[[i]], data, knots, absorb.cons,
scale.penalty = scale.penalty, :
Can't find by variable"
This is pretty beyond me so if anyone could offer any advice that would be much appreciated!
From your comments it became clear that you passed a character variable to by in the smoother. You must pass a factor variable there. This has been a frequent gotcha for me too and I consider it a design flaw (because base R regression functions deal with character variables just fine).
Related
I want to make a mixed anova with the within-subject-factors mzp and cond besides the between-subject-factors cond_order and video_order.
I have 3 timepoints of a repeated measurement, indicated by mzp.
anova.h1 <- aov_car(ee ~ cond_order + video_order + Error(code/mzp*cond), data=dat_long)
Three things I can't find a solution for:
How to separate between within-factors in the error term? A lot of codes I found used *, but I fear it might only be for specific cases? Are there other separating-operators?
mzp has actually 3 levels (i.e. times of measurement) for measuring the dependent variable, cond has only 2 (because there were no baseline measured). So I made for that variable a 3rd timepoint up, by setting values to NA at baseline for cond. But it seems to cause issues now:
Error: Empty cells in within-subjects design (i.e., bad data structure).
table(data[c("mzp", "cond")])
# cond
# mzp s1st t1st s2nd t2nd
# X0 0 0 0 0
# X1 44 43 0 0
# X2 0 0 43 44
I need to examine relations between all 3 times of measuring the dependent variable and its interactions with the independet variables cond, cond_order and video_order. So is there a way of ignoring the NAs in cond, but include every 3 timepoints of the dependent variable for examining the progress of the dependent variable?
Above all, I need this anova to examine the residuals, to test for normality. I tried functions I know and googled (for a model without the cond-variable), but they won't work for this model/this function. I have to examine it graphically. So what works for this anova function?
hist(rstandard(anova.h1))
plot(anova.h1,2)
anova.h1.pr <- proj(anova.h1)
# Error in proj.default(anova.h1) : argument does not contain 'qr' component
res <- anova.h1.pr[["Within"]][ , "Residuals"].
qqnorm(res)
I have some actual data that I am afraid is somewhat nasty.
It's essentially a Positive Negative Binomial distribution (without any zero counts). However, there are some outliers that seem to cause some bad calculations to occur (maybe underflow or NaNs?) The first 8 or so entries are reasonable, but I'm guessing the last few are causing some problems with the fitting.
Here's the data:
> df
counts t
1 1968 1
2 217 2
3 55 3
4 26 4
5 11 5
6 5 6
7 8 7
8 3 8
9 1 10
10 1 11
11 1 12
12 1 13
13 1 15
14 1 18
15 1 26
16 1 59
This command runs for a while and then spits out the error message
> vglm(counts ~ t, data=df, family = posnegbinomial)
Error in if (take.half.step) { : missing value where TRUE/FALSE needed
BUT, if I rerun this cutting off the outliers, I get a solution for posnegbinomial
> vglm(counts ~ t, data=df[1:9,], family = posnegbinomial)
Call:
vglm(formula = counts ~ t, family = posnegbinomial, data = df[1:9,])
Coefficients:
(Intercept):1 (Intercept):2 t
7.7487404 0.7983811 -0.9427189
Degrees of Freedom: 18 Total; 15 Residual
Log-likelihood: -36.21064
If I try the family pospoisson (Positive Poisson: no zero values), I get a similar error "argument is not interpretable as logical".
I do notice that there are a number of similar questions in Stackoverflow about missing values where TRUE/FALSE is needed, but with other R packages. This indicates to me that perhaps the package writers need to better anticipate calculations might fail.
I think your proximal problem is that the predicted means for the negative binomial for your extreme values are so close to zero that they are underflowing to zero, in a way that was not anticipated/protected against by the package authors. (One thing to realize about nonlinear optimization/fitting is that it is always possible to break a fitting method by giving it extreme data ...)
I couldn't get this to work in VGAM, but I'll offer a couple of other suggestions.
plot(log(counts)~t,data=dd)
And eyeballing the data to get an initial estimate of parameter values (at least for the mean model):
m0 <- lm(log(counts)~t,data=subset(dd,t<10))
I thought I might be able to get vglm() to work by setting starting values, but that didn't actually pan out, even when I have fairly good values from other platforms (see below).
glmmADMB
The glmmADMB package can handle positive NB, via family="truncnbinom":
library(glmmADMB)
m1 <- glmmadmb(counts~t, data=dd, family="truncnbinom")
(there are some warning messages ...)
bbmle::mle2()
This requires a little bit more work: it failed with the standard model, but works if I set a floor on the predicted mean ...
library(VGAM) ## for dposnegbin
library(bbmle)
m2 <- mle2(counts~dposnegbin(size=exp(logk),
munb=pmax(exp(logeta),1e-7)),
parameters=list(logeta~t),
data=dd,
start=list(logk=0,logeta=0))
Again warning messages.
Compare glmmADMB, mle2, simple truncated lm fit ...
cc <- cbind(coef(m2),
c(log(m1$alpha),coef(m1)),
c(NA,coef(m0)))
dimnames(cc) <- list(c("log_k","log_int","slope"),
c("mle2","glmmADMB","lm"))
## mle2 glmmADMB lm
## log_k 0.8094678 0.8094625 NA
## log_int 7.7670604 7.7670637 7.1747551
## slope -0.9491796 -0.9491778 -0.8328487
This is in principle also possible with glmmTMB, but it runs into the same kinds of problems as vglm() ...
HI i am doing prediction with my data.if i use data.frame it throws the folloing error.
input(bedrooms="2",bathrooms="2",area="1000") were specified with different types from the fit
here is my program
input <- function(bedrooms,bathrooms,area)
{
delhi <- read.delim("delhi.tsv", na.strings = "")
delhi$lnprice <- log(delhi$price)
heddel <- lm(lnprice ~ bedrooms+ area+ bathrooms,data=delhi)
valuepred = predict (heddel,data.frame(bedrooms=bedrooms,area=area,bathrooms=bathrooms),na.rm = TRUE)
final_prediction = exp(valuepred)
final_prediction
}
if i remove the data.frame it predicts the value for over all data.i got the following output.
1 2 3 4 5 6 7
15480952 11657414 10956873 6011639 6531880 9801468 16157549
9 10 11 14 15 16 17
10698786 5596803 14688143 20339651 22012831 16157618 26644246
but it needs to display one value only.
any idea how to resolve this..any help will be appreciated
Sharon, you want to make a prediction for the specific values of bedroom, bathroom and area, but are putting them in as character rather than numeric values. This is causing the error you are seeing. when you remove the data.frame statement from predict, it will produce predictions based on the data set used to build the model, i.e. delhi.
Try
input(bedrooms=2,bathrooms=2,area=1000)
Too long for a comment.
The other answer should solve your problem, but if you really believe that log(price) is linear in bedrooms + bathrooms + area then you are better off with a generalized linear model (glm) in the poisson family. So something like:
fit <- glm(price~bedrooms+bathrooms+area, dehli, family=poisson)
Then predict using type="response"
pred <- predict(fit, data.frame(bedrooms, bathrooms, area), type="response")
I am using the mlogit package in program R. I have converted my data from its original wide format to long format. Here is a sample of the converted data.frame which I refer to as 'long_perp'. All of the independent variables are individual specific. I have 4258 unique observations in the data-set.
date_id act2 grp.bin pdist ship sea avgknots shore day location chid alt
4.dive 40707_004 TRUE 2 2.250 second light 14.06809 2.30805 12 Lower 4 dive
4.fly 40707_004 FALSE 2 2.250 second light 14.06809 2.30805 12 Lower 4 fly
4.none 40707_004 FALSE 2 2.250 second light 14.06809 2.30805 12 Lower 4 none
5.dive 40707_006 FALSE 2 0.000 second light 15.12650 2.53312 12 Lower 5 dive
5.fly 40707_006 TRUE 2 0.000 second light 15.12650 2.53312 12 Lower 5 fly
5.none 40707_006 FALSE 2 0.000 second light 15.12650 2.53312 12 Lower 5 none
6.dive 40707_007 FALSE 1 1.995 second light 14.02101 2.01680 12 Lower 6 dive
6.fly 40707_007 TRUE 1 1.995 second light 14.02101 2.01680 12 Lower 6 fly
6.none 40707_007 FALSE 1 1.995 second light 14.02101 2.01680 12 Lower 6 none
'act2' is the dependent variable and consists of choices a bird floating on the water could make when approached by a ship; fly, dive, or none. I am interested in how these probabilities relate to the remaining independent variables in the data.frame, i.e. perpendicular distance to the ship path (pdist) sea conditions (sea), speed (avgknots), distance to shore (shore) etc. The independent variables are made of dichotomous, factor and continuous variables.
I ran two multinomial logit models, one including all the choice options and another including only a subset. I then compared these models with the hmftest() function to test for the IIA assumption. The results were confusing the say the least. I will include the codes for the two models and the test output (in case I am miss-specifying the models in the code).
# model including all choice options (fly, dive, none)
mod.1 <- mlogit(act2 ~ 1 | pdist + as.factor(grp.bin) +
as.factor(sea) + avgknots + shore + as.factor(location),long_perp ,
reflevel = 'none')
# model including only a subset of choice options (fly, dive)
mod.alt <- mlogit(act2 ~ 1 | pdist + as.factor(grp.bin) +
as.factor(sea) + avgknots + shore + as.factor(location),long_perp ,
reflevel = 'none', alt.subset = c("fly","dive"))
# IIA test
hmftest(mod.1, mod.alt)
# output
Hausman-McFadden test
data: long_perp
chisq = -968.7303, df = 7, p-value = 1
alternative hypothesis: IIA is rejected
As you can see the chisquare statistic is negative! I assume I am either 1. doing something wrong, or 2. IIA is violated. This result holds true for choice subset (fly, dive), but the IIA assumption is upheld with choice subset (none, dive)? This confuses me.
Next I tried to formulate a nested model as a way to relax the IIA assumption. I nested the choices as nest1 = none, nest2 = fly, dive. This makes sense to me as this seems like a logical break, the bird decides to react or not then decides which reaction to make.
I am unclear on how to run the nested logit models (even after reading the two vignettes for mlogit, Croissant vignette and Train vignette).
When I run my analysis following the example in the Croissant vignette I get the following error.
nested.1 <- mlogit(act2 ~ 0 | pdist + as.factor(grp.bin) + as.factor(ship) +
as.factor(sea) + avgknots + shore + as.factor(location),
long_perp , reflevel="none",nests = list(noact = "none",
react = c("dive","fly")), unscaled = TRUE)
# Error in solve.default(crossprod(attr(x, "gradi")[, !fixed])) :
Lapack routine dgesv: system is exactly singular: U[19,19] = 0
I have read a bit about this error message and it may occur because of complete separation. I have looked at some tables of the data and do not believe this is happening as I have 4,000+ observations and only one factor variable with more than 2 levels (it has 3).
Help on these specific problems is greatly appreciated but I am also open to alternate analyses that I can use to answer my question. I am mainly interested in the probability of flying as a function of perpendicular distance to the ships path.
Thanks, Tim
To get a positive chi-sq, change the code as follows:
alt.subset = c("none", "fly")
that is, the ref level will be in the subset too. It may help, though the P-value may not change much.
I am trying to build a for() loop to manually conduct leave-one-out cross validations for a GLMM fit using the lmer() function from the lme4 pkg. I need to remove an individual, fit the model and use the beta coefficients to predict a response for the individual that was withheld, and repeat the process for all individuals.
I have created some test data to tackle the first step of simply leaving an individual out, fitting the model and repeating for all individuals in a for() loop.
The data have a binary (0,1) Response, an IndID that classifies 4 individuals, a Time variable, and a Binary variable. There are N=100 observations. The IndID is fit as a random effect.
require(lme4)
#Make data
Response <- round(runif(100, 0, 1))
IndID <- as.character(rep(c("AAA", "BBB", "CCC", "DDD"),25))
Time <- round(runif(100, 2,50))
Binary <- round(runif(100, 0, 1))
#Make data.frame
Data <- data.frame(Response, IndID, Time, Binary)
Data <- Data[with(Data, order(IndID)), ] #**Edit**: Added code to sort by IndID
#Look at head()
head(Data)
Response IndID Time Binary
1 0 AAA 31 1
2 1 BBB 34 1
3 1 CCC 6 1
4 0 DDD 48 1
5 1 AAA 36 1
6 0 BBB 46 1
#Build model with all IndID's
fit <- lmer(Response ~ Time + Binary + (1|IndID ), data = Data,
family=binomial)
summary(fit)
As stated above, my hope is to get four model fits – one with each IndID left out in a for() loop. This is a new type of application of the for() command for me and I quickly reached my coding abilities. My attempt is below.
fit <- list()
for (i in Data$IndID){
fit[[i]] <- lmer(Response ~ Time + Binary + (1|IndID), data = Data[-i],
family=binomial)
}
I am not sure storing the model fits as a list is the best option, but I had seen it on a few other help pages. The above attempt results in the error:
Error in -i : invalid argument to unary operator
If I remove the [-i] conditional to the data=Data argument the code runs four fits, but data for each individual is not removed.
Just as an FYI, I will need to further expand the loop to:
1) extract the beta coefs, 2) apply them to the X matrix of the individual that was withheld and lastly, 3) compare the predicted values (after a logit transformation) to the observed values. As all steps are needed for each IndID, I hope to build them into the loop. I am providing the extra details in case my planned future steps inform the more intimidate question of leave-one-out model fits.
Thanks as always!
The problem you are having is because Data[-i] is expecting i to be an integer index. Instead, i is either AAA, BBB, CCC or DDD. To fix the loop, set
data = Data[Data$IndID != i, ]
in you model fit.