Displaying 20 results from an estimated 30 matches for "ttabl".
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ttable
2013 Feb 04
3
Modifying Package Data
The bio.infer package contains a data frame
/usr/lib/R/library/bio.infer/data/itis.ttable.rda that needs to be modified.
After loading the bio.infer package and attaching the data frame with the
data() function, I wrote the data frame to a text file.
After adding another row to the data frame I applied read.table() to
create a data frame, but it's in my pwd, not the R library d...
2008 May 24
2
Wine and ALSA
...uot;hw:1,0"
rate 48000
channels 8
period_time 0
period_size 1024
buffer_time 0
buffer_size 8192
}
}
pcm.ch71dup {
type route
slave.pcm dmix8
slave.channels 8
ttable.0.0 1
ttable.1.1 1
ttable.0.2 1
ttable.1.3 1
ttable.0.4 1
ttable.0.5 1
ttable.0.6 1
ttable.1.7 1
}
pcm.duplex {
type asym
playback.pcm "ch71dup"
capture.pcm "hw:1,0"
}
pcm.!default {
type...
2012 Jun 06
3
Sobel's test for mediation and lme4/nlme
...med, out = out, grpid=grpid)
NEWDAT <- na.exclude(NEWDAT)
model1 <- lme(out ~ pred, random=~1|grpid,data = NEWDAT)
model2 <- lme(out ~ pred + med, random=~1|grpid, data = NEWDAT)
model3 <- lme(med ~ pred, random=~1|grpid, data = NEWDAT)
mod1.out <- summary(model1)$tTable
mod2.out <- summary(model2)$tTable
mod3.out <- summary(model3)$tTable
indir <- mod3.out[2, 1] * mod2.out[3, 1]
effvar <- (mod3.out[2, 1])^2 * (mod2.out[3, 2])^2 + (mod2.out[3,
1])^2 * (mod3.out[2, 2])^2
serr <- sqrt(effvar)
zvalue = indir/serr
out...
2013 Jan 31
2
rbind Missing Something: Need New Eyes
...chr
$ INFRACLASS : chr
$ SUPERORDER : chr
$ ORDER : chr
$ SUBORDER : chr
$ INFRAORDER : chr
$ SUPERFAMILY: chr
$ FAMILY : chr
$ SUBFAMILY : chr
$ TRIBE : chr
$ SUBTRIBE : chr
$ GENUS : chr
$ TAXON : chr
One command (all on one line) is:
itis.ttable <- rbind(itis.ttable, data.frame(PHYLUM = "ARTHROPODA",
SUBPHYLUM = "MANDIBULATA", SUPERCLASS = NA, CLASS = "INSECTA", SUBCLASS =
"DICONDYLIA", INFRACLASS = "PTERYGOTA", SUPERORDER = "PANORPIDA", ORDER =
"TRICHOPTERA", SUBORD...
2012 Jan 21
1
Function for multiple t tests
...iated.
I have a working example, as required by the posting guide:
my_swiss = swiss[-1,]
my_swiss$facto = rep(1:2,nrow(my_swiss)/2)
t.test(Fertility~facto,data=my_swiss)
by(my_swiss$Fertility,my_swiss$facto, sd)
t.test(Agriculture~facto,data=my_swiss)
by(my_swiss$Agriculture,my_swiss$facto, sd)
ttable <- function(formula, data) { ???? }
ttable(Fertility + Agriculture ~ facto, data=my_swiss)
facto ? ? ? ? ? ? ?1 ? ? ? ? ? ? ? ? ? ? ? ? ?2
? ? ? ? ? ? ?Mean ?s.d. ? ? ? ? ? ? ? ? ?Mean s.d. ?t-test p-value
Fertility ? ? ?69.19 10.66 ? ? ? ? ? ? ? 70.66 14.38 -0.39 ?0.70
Agriculture ? ?51.65 2...
2006 Apr 10
3
SE estimates for treatment groups from nlme
...model of the following general form:
nlme1<-nlme(y~ SasympOrig(x, Asym, lrc), data=df, fixed=list(Asym~A*B*C, lrc~A*B*C),
start=c(fixef(ETR.nlme)[1], rep(0,17), fixef(ETR.nlme)[2], rep(0,17)))
I am using the default ("contr.treatment" and "contr.poly"). The summary table (tTable) gives me the baseline coefficients and a list of differences and I have no trouble calculating the coefficients for any of my treatment groups by forming the correct linear combination of coefficients. However, I don't understand how to obtain the standard errors for these linear combinations...
2013 Feb 17
2
nested random factor using lme produces errors
...e variable is gut parasites and the factors are moose families which
is nested within treatment. My data is balanced.
To answer this question, I used the lme function like this :
model=lme(parasite~drug,random=~1|drug/family)
But doing a summary on this model gives me warning message :
In pt(-abs(tTable[, "t-value"]), tTable[, "DF"]) : NaNs produced
I don't understand why ?! I noticed that the p-values are not computed and
have NAs values for drug2 and drug3 (from the summary of this model)
Moreover, in the summary, I noticed that in the random effects line I have
standa...
2010 Apr 08
1
formatting a result table (number of digits)
...c <- lme(Ind_Cntrbn ~
D_Clb_Pbl+D_Club+Round+Ind_cntr_1+GR_cntr_1+D_Success_1+IndEarngs_1+D_Unfair_cntr_1+D_1st10rnds+D_Female+D_econ_gov+D_mjr_social+D_frshmn+D_junior+D_senior+UrbanArea+ParentEducation+Empathy+Lcs_Cntrl_Intn+Trust,
random=~1|Group, data=Dataset)
> s=summary(ic)
> t=s$tTable
> t
Value Std.Error DF t-value p-value
(Intercept) 5.683020304 0.78867419 2237 7.20578963 7.853134e-13
D_Clb_Pbl 0.193187682 0.25494452 22 0.75776363 4.566332e-01
D_Club 0.112745107 0.09636280 2237 1.17000650 2.421230e-01
Round...
2007 Jul 30
1
Extract random part of summary nlme
Dear helpers,
I'm estimating multilevel regression models, using the lme-function
from the nlme-package. Let's say that I estimated a model and stored
it inside the object named 'model'. The summary of that model is
shown below:
Using summary(model)$tTable , I receive the following output:
> summary(model)$tTable
Value Std.Error DF t-value p-value
(Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02
sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06
standLRT 0.38593558 0.01...
2006 Apr 25
1
summary.lme: argument "adjustSigma"
...erting it to a REML-like estimate."
Having a look into the code I found:
stdFixed <- sqrt(diag(as.matrix(object$varFix)))
if (object$method == "ML" && adjustSigma == TRUE) {
stdFixed <- sqrt(object$dims$N/(object$dims$N - length(stdFixed))) *
stdFixed
}
tTable <- data.frame(fixed, stdFixed, object$fixDF[["X"]],
fixed/stdFixed, fixed)
To my understanding, only the standard error for the fixed
coefficients is adapted and not the residual standard error.
Therefore only the tTable of the output is affected by the
argument "adjustS...
2007 Jul 31
1
Extracting random parameters from summary lme and lmer
LS,
I'm estimating multilevel regression models, using the lme-function
from the nlme-package. Let's say that I estimated a model and stored
it inside the object named 'model'. The summary of that model is
shown below:
Using summary(model)$tTable , I receive the following output:
> summary(model)$tTable
Value Std.Error DF t-value p-value
(Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02
sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06
standLRT 0.38593558 0.01...
2010 May 15
1
conditional calculations per row (loop versus apply)
...e
Record Type.code Column3 Column4
1 1 0.4
2 3 0.25
3 4 100
4 20 150
5 5 0.4
6 4532 NA
I have no problem writing a foor loop to do this
for (i in 1:nrow(dataframe) ) {
if (Type.code[i]%in%Table.A) Reading[i]<-function 1
else
if (Type.code[i]%in%tTable.B) Reading[i]<-function 2
else
if (Type.code[i]%in%Table.C) Reading[i]<-function 3
}
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Dr Nick Bond
Research Fellow
Monash University
Victoria, Australia, 38000
Email: Nick.Bond@sci.monash.edu.au
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...
2006 Jan 10
1
extracting coefficients from lmer
...I could do this if I could extract the coefficients and standard errors
from the summaries of the lmer models. This is easy to do for the glmmPQL
summaries, using
> glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df,
family = binomial), TRUE)
> summary(glmmPQL.fit)$tTable
Linear mixed-effects model fit by maximum likelihood
Data: df
AIC BIC logLik
1800.477 1840.391 -890.2384
Random effects:
Formula: ~1 | subject
(Intercept) Residual
StdDev: 0.6355517 0.9650671
Variance function:
Structure: fixed weights
Formula: ~invwt
Fixed effec...
2007 Jul 30
0
Extracting random parameters from summary lme
LS,
I'm estimating multilevel regression models, using the lme-function
from the nlme-package. Let's say that I estimated a model and stored
it inside the object named 'model'. The summary of that model is
shown below:
Using summary(model)$tTable , I receive the following output:
> summary(model)$tTable
Value Std.Error DF t-value p-value
(Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02
sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06
standLRT 0.38593558 0.01...
2010 Jun 24
1
Question on WLS (gls vs lm)
...is is not the case. See:
library(nlme)
f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights
= varIdent(form = ~ 1 | Species))
aa <- attributes(summary(f1)$modelStruct$varStruct)$weights
f2 <- lm(Petal.Width ~ Species / Petal.Length, data = iris, weights = aa)
summary(f1)$tTable; summary(f2)
So, the two models with the very same weights do differ (in terms of
standard errors). Could you please explain why? Are these different
types of weights?
Many thanks in advance,
Stats Wolf
2017 Nov 27
0
How to extract coefficients from sequential (type 1) ANOVAs using lmer and lme
...hort.psme.bulk.d13c.3, type ="marginal")
???????????? numDF denDF? F-value p-value
(Intercept)????? 1????3 629.4711? 0.0001
narea???? ???????1????2?? 2.7831? 0.2372
sampleheight???? 1????2? 11.6159? 0.0764
>
>summary(model.short.psme.bulk.d13c.3, type ="sequential")$tTable
?????????????????? Value? Std.Error DF??t-value????? p-value
(Intercept)?? 1.76871397 0.07049685? 3 25.089262 0.0001388446
narea??????? -0.09295561 0.05571991? 2 -1.668266 0.2372012082
sampleheight? 0.13232709 0.03882599? 2?3.408209 0.0763589527
?(Intercept)??1.76871397 0.07049685? 3 25.08926...
2008 Feb 05
2
How to generate table output of t-test
Hi,
Given
test <- matrix(c(1, 1,2,2), 2,2)
t <- apply(test, 1, t.test)
How can I obtain a table of p-values, confidence interval etc, instead of
[[1]]
One Sample t-test
data: newX[, i]
t = 3, df = 1, p-value = 0.2048
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-4.853102 7.853102
sample estimates:
mean of x
1.5
[[2]]
2008 Aug 19
1
R vs Stata on generalized linear mixed models: glmer and xtmelogit
...git' uses 7 points. In order to compare them, I tried also to change the corresponding parameters.
This is the code for R:
rm(list=ls())
library(faraway); library(lme4); library(MASS)
data <- ohio
pql <- glmmPQL(resp~smoke+factor(age), random=~1|id, family=binomial,data)
summary(pql)$tTable["smoke",1:2]
lap <- glmer(resp~smoke+factor(age)+(1|id), family=binomial,data)
attributes(summary(lap))$coefs["smoke",1:2]
agq7 <- glmer(resp~smoke+factor(age)+(1|id),nAGQ=7,family=binomial,data)
write.csv(data,file="data.csv")
This is the code for Stata:
cl...
2006 Jun 15
1
Repost: Estimation when interaction is present: How do I get get the parameters from nlme?
Gday,
This is a repost since I only had one direct reply and I remain
mystified- This
may be stupidity on my part but it may not be so simple.
In brief, my problem is I'm not sure how to extract parameter
values/effect sizes from a nonlinear
regression model with a significant interaction term.
My data sets are dose response curves (force and dose) for muscle that
also have two
2003 Jun 25
2
within group variance of the coeficients in LME
Dear listers,
I can't find the variance or se of the coefficients in a multilevel model
using lme.
I want to calculate a Chi square test statistics for the variability of the
coefficients across levels. I have a simple 2-level problem, where I want to
check weather a certain covariate varies across level 2 units. Pinheiro
Bates suggest just looking at the intervals or doing a rather