Displaying 20 results from an estimated 7000 matches similar to: "formatting data for predict()"
2012 Jun 06
3
Sobel's test for mediation and lme4/nlme
Hello,
Any advice or pointers for implementing Sobel's test for mediation in
2-level model setting? For fitting the hierarchical models, I am using
"lme4" but could also revert to "nlme" since it is a relatively simple
varying intercept model and they yield identical estimates. I apologize for
this is an R question with an embedded statistical question.
I noticed that a
2023 Nov 30
1
back tick names with predict function
?s 17:38 de 30/11/2023, Robert Baer escreveu:
> I am having trouble using back ticks with the R extractor function
> 'predict' and an lm() model.? I'm trying too construct some nice vectors
> that can be used for plotting the two types of regression intervals.? I
> think it works with normal column heading names but it fails when I have
> "special"
2011 Mar 19
1
strange PREDICTIONS from a PIECEWISE LINEAR (mixed) MODEL
Hi Dears,
When I introduce an interaciton in a piecewise model I obtain some quite
unusual results.
If that would't take u such a problem I'd really appreciate an advise from
you.
I've reproduced an example below...
Many thanks
x<-rnorm(1000)
y<-exp(-x)+rnorm(1000)
plot(x,y)
abline(v=-1,col=2,lty=2)
mod<-lm(y~x+x*(x>-1))
summary(mod)
yy<-predict(mod)
2023 Nov 30
1
back tick names with predict function
I am having trouble using back ticks with the R extractor function
'predict' and an lm() model.? I'm trying too construct some nice vectors
that can be used for plotting the two types of regression intervals.? I
think it works with normal column heading names but it fails when I have
"special" back-tick names.? Can anyone help with how I would reference
these?? Short of
2011 Jun 24
2
mgcv:gamm: predict to reflect random s() effects?
Dear useRs,
I am using the gamm function in the mgcv package to model a smooth relationship between a covariate and my dependent variable, while allowing for quantification of the subjectwise variability in the smooths. What I would like to do is to make subjectwise predictions for plotting purposes which account for the random smooth components of the fit.
An example. (sessionInfo() is at
2013 Dec 16
2
vectorizaciones
Hola a tod en s,
tengo que hacer una operación con matrices que lleva un doble bucle. He
intentado vectorizarlo pero sin mucho éxito con la función "Vectorize".
¿sabríais de alguna manera de evitar los bucles o de que funcionase Vectorize?
Adjunto un ejemplo
#...................
vec1a <- as.vector(1:3)
n1a <- 3
n2a <- 3
P1a <- matrix(rnorm(30),nrow=3)
P2a <-
2010 Sep 11
3
confidence bands for a quasipoisson glm
Dear all,
I have a quasipoisson glm for which I need confidence bands in a graphic:
gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva)
summary(gm6)
library('VIM')
b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")).
My first steps for the solution are following:
range(b_dist_min_new)
2023 Dec 01
1
back tick names with predict function
Also, and possibly more constructively, when you get an error like
> CI.c = predict(mod2, data.frame( `plant-density` = x), interval = 'c') # fail
Error in eval(predvars, data, env) : object 'plant-density' not found
you should check your assumptions. Does "newdata" actually contain a columnn called "plant-density":
> head(data.frame( `plant-density`
2005 Jun 15
3
Error using newdata argument in survfit
Dear R-helpers,
To get curves for a pseudo cohort other than the one centered at the mean of
the covariates, I have been trying to use the newdata argument to survfit
with no success. Here is my model statement, the newdata and the ensuing
error. What am I doing wrong?
> summary(fit)
Call:
coxph(formula = Surv(Start, Stop, Event, type = "counting") ~
Week + LagAOO + Prior.f +
2013 Feb 12
0
error message from predict.coxph
In one particular situation predict.coxph gives an error message. Namely: stratified data, predict='expected', new data, se=TRUE. I think I found the error but I'll leave that to you to decide.
Thanks,
Chris
######## CODE
library(survival)
set.seed(20121221)
nn <- 10 # sample size in each group
lambda0 <- 0.1 # event rate in group 0
lambda1 <- 0.2 # event rate in group 1
2008 Jul 03
1
lines() warning message
I have data that looks like
Year,Recruit,Spawner,Mtempcv
1958,4532,775,0.24125
1959,22996,2310,0.16319
1960,628,2990,0.46056
1961,879,1400,0.33028
1962,14747,1130,0.22618
1963,13205,790,0.20596
1964,31793,1195,0.19229
1965,10621,981,0.20363
1966,22271,870,0.3452
1967,8736,1104,0.27511
1968,8761,883,0.10884
1969,18885,1421,0.17799
1970,10098,1198,0.2106
1971,3394,760,0.22098
1972,1697,1354,0.39461
2011 Apr 04
1
Clarks 2Dt function in R
Dear Ben,
you answerd to Nancy Shackelford about Clarks 2Dt function.
Since the thread ended just after your reply,
I would like to ask, if you have an idea how to use this function in R
I defined it the following way:
function(x , p, u) {
(p/(pi*u))*(1+(x^2/u))^(p+1)
}
and would like to fit this one to my obeservational data (count)
[,1] [,2]
[1,] 15 12
[2,] 45 13
[3,]
2010 Sep 27
1
Variation of predictor of linear model
Hi folks,
I use lm to run regression and I don't know how to predict dependent
variable based on the model.
I used predict.lm(model, newdata=80), but it gave me warnings.
Also, how can I get the variance of dependent variable based on model.
Thanks.
[[alternative HTML version deleted]]
2007 Nov 13
2
plotting coxph results using survfit() function
i want to make survival plots for a coxph object using survfit
function. mod.phm is an object of coxph class which calculated results
using columns X and Y from the DataFrame. Both X and Y are
categorical. I want survival plots which shows a single line for each
of the categories of X i.e. '4' and 'C'. I am getting the following
error:
> attach(DataFrame)
>
2017 Jun 12
0
plotting gamm results in lattice
Hi Maria
If you have problems just start with a small model with predictions and then plot with xyplot
the same applies to xyplot
Try
library(gamm4)
spring <- dget(file = "G:/1/example.txt")
str(spring)
'data.frame': 11744 obs. of 11 variables:
$ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ...
$ SITE_ID
2017 Jun 12
2
plotting gamm results in lattice
Dear all,?
I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution.
I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model:
model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,
2010 Jan 28
2
Data.frame manipulation
Hi All,
I'm conducting a meta-analysis and have taken a data.frame with multiple
rows per
study (for each effect size) and performed a weighted average of effect size
for
each study. This results in a reduced # of rows. I am particularly
interested in
simply reducing the additional variables in the data.frame to the first row
of the
corresponding id variable. For example:
2012 Jan 30
1
Linear Mixed Model set-up
Hello,
I have some data covering contaminant concentrations in fish over a time
period of ~35 years. Each year, multiple samples of fish were taken (with
varying sample sizes each year). Ultimately, I want an estimation of the
variance between years, and the variance within years + random effects. I
used a linear mixed model to estimate these variances, but after reading a
number of different
2017 Aug 09
3
Plotting log transformed predicted values from lme
Hi,
I am performing meta-regression using linear mixed-effect model with the
lme() function that has two fixed effect variables;one as a log
transformed variable (x) and one as factor (y) variable, and two nested
random intercept terms.
I want to save the predicted values from that model and show the log curve
in a plot ; predicted~log(x)
mod<-lme(B~log(x)+as.factor(y),
2012 Nov 27
4
Fitting and plotting a coxph with survfit, package(surv)
Hi Dear R-users
I have a database with 18000 observations and 20 variables. I am running
cox regression on five variables and trying to use survfit to plot the
survival based on a specific variable without success.
Lets say I have the following coxph:
>library(survival)
>fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian)
>fit
what I am trying to do is plot a survival