Displaying 20 results from an estimated 544 matches for "1q".
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2006 Feb 13
2
?bug? strange factors produced by chron
Hallo all
Please help me. I am lost and do not know what is the problem. I have
a factor called kvartaly.
> attributes(kvartaly)
$levels
[1] "1Q.04" "2Q.04" "3Q.04" "4Q.04" "1Q.05" "2Q.05" "3Q.05" "4Q.05"
$class
[1] "factor"
> mode(kvartaly)
[1] "numeric"
> str(kvartaly)
Factor w/ 8 levels "1Q.04","2Q.04",..: 1 1 1 1 1 1...
2011 Sep 19
1
regression summary results pvalues and coefficients into a excel
...ressionresults,
function(x){x["Pr(>|t|)"][1:2,]}))
6) write.table(t(regpvalues), file = "regression-resultsheadshape.txt",
quote = F, sep ='\t')
> regressionresults
$CUST_54_PI410671829
Call:
lm(formula = morphtrait ~ x, data = m)
Residuals:
Min 1Q Median 3Q Max
-0.23217 -0.08980 -0.04592 -0.00947 1.07688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5985797 0.0364510 16.421 <2e-16 ***
x 0.0005372 0.0011161 0.481 0.632
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05...
2006 Jun 19
3
Bristuff-0.3.0-PRE-1q and & florz patch compile trouble
Again trouble compiling bristuff-0.3.0-PRE-1q with the florz patch on a
x86_64 box (I guess nobody is using x86_64 platform or is able to fix this
themselves?)
First of all when bristuff is downloaded and compile is started it appears
that the bristuff Makefiles are badly broken.
The asterisk Makefiles all do see to find the kernel source...
2009 Mar 17
2
formula question
...cular formulas:
> test.df
y x
1 -0.9261650 1
2 1.5702700 2
3 0.1673920 3
4 0.7893085 4
5 0.3576875 5
6 -1.4620915 6
7 -0.5506215 7
8 -0.3480292 8
9 -1.2344036 9
10 0.8502660 10
> summary(lm(exp(y)~x))
Call:
lm(formula = exp(y) ~ x)
Residuals:
Min 1Q Median 3Q Max
-1.6360 -0.6435 -0.4722 0.4215 2.9127
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1689 0.9782 2.217 0.0574 .
x -0.1368 0.1577 -0.868 0.4108
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1...
2004 Feb 28
2
questions about anova
...Sum Sq Df Mean Sq F value Pr(>F)
Regression 215.7182 9 23.968693976 2686.508 < 2.22e-16
Deviation 480.1218 53814 0.008921876
Total 695.8401 53823
Multiple R-Squared: 0.31, Adjusted R-squared: 0.3099
AIC: (df = 53814) -146390.9
Fitted:
Min 1Q Median 3Q Max
0.007852 0.075619 0.100498 0.139042 0.338186
Residuals:
Min 1Q Median 3Q Max
-0.29758 -0.04418 -0.01411 0.02536 0.51484
>
So, what's the meaning of the "Pr(>F)?
2 - I have six trend surfaces, and I like to make a anova for the...
2011 Nov 20
3
logistic regression by glm
...logistic regression. and treat both response and
predictor as factor
In my first try:
*******************************************************************************
Call:
glm(formula = as.factor(diagnostic) ~ as.factor(7161521) +
as.factor(2281517), family = binomial())
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5370 -1.0431 -0.9416 1.3065 1.4331
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
as.factor(7161521)2 1.39811 0.66618 2.099 0.0358 *
as.factor(7161521)3 0.28192 0.83255 0.339 0.7349
as.factor(2281517)2 -1.11284 0.63692 -1.747...
2012 Jan 09
1
Different lm() Residuals Output
All but one of the summaries of multiple linear regressions in this
analysis set present the residuals by min, 1Q, median, 3Q, and max. Example:
lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4, data = snow.cast)
Residuals:
Min 1Q Median 3Q Max -277.351 -32.551 -2.621
40.812 245.272
The one that doesn't has only a small number of rows (23) and presents the
results as:
lm(f...
2003 Nov 03
2
Odd r-squared
...e
parametrizations of the same model, and should yield the same r-squared.
Any insight much appreciated
Simon.
> set.seed(10,kind=NULL)
> x <- runif(10)
> g <- gl(2,5)
> y <- runif(10)
>
> summary(lm(y ~ g*x))
Call:
lm(formula = y ~ g * x)
Residuals:
Min 1Q Median 3Q Max
-0.35205 -0.14021 0.02486 0.13958 0.39671
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3138 0.2749 1.141 0.297
g2 -0.1568 0.4339 -0.361 0.730
x 0.3556 0.6082 0.585 0.580
g2:x...
2011 Feb 18
3
lm without intercept
Hi,
I am not a statistics expert, so I have this question. A linear model
gives me the following summary:
Call:
lm(formula = N ~ N_alt)
Residuals:
Min 1Q Median 3Q Max
-110.30 -35.80 -22.77 38.07 122.76
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.5177 229.0764 0.059 0.9535
N_alt 0.2832 0.1501 1.886 0.0739 .
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ?...
2004 Aug 26
1
Why terms are dropping out of an lm() model
...2 2 2 2 2 2
[67] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
> summary(d$r)
Min. 1st Qu. Median Mean 3rd Qu. Max.
18.68 19.88 21.94 21.48 22.64 24.36
> summary(d.lm1 <- lm(r ~ p1 + p2, data=d))
Call:
lm(formula = r ~ p1 + p2, data = d)
Residuals:
Min 1Q Median 3Q Max
-0.58107 -0.09248 0.02492 0.26061 0.49617
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.66417 0.06591 283.17 <2e-16 ***
p1 1.96145 0.04036 48.60 <2e-16 ***
p2 0.85801 0.04036 21.26 <2e...
2008 Oct 02
1
missing output in summary() and anova()
...6, 138.04, 140.04, 142.44, 145.47, 144.34, 146.30, 147.54, 147.80)
> x<-c(194.5, 194.3, 197.9, 198.4, 199.4, 199.9, 200.9, 201.1, 201.4, 201.3,
203.6, 204.6, 209.5,208.6, 210.7, 211.9, 212.2)
> fitted.results<-lm(y~x)
> summary(fitted.results)
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-0.32220 -0.14473 -0.06664 0.02184 1.35978
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -42.13778 3.34020 -12.62 2.18e-09 ***
x 0.89549 0.01645 54.43 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' &...
2013 Nov 08
1
Different output from lm() and lmPerm lmp() if categorical variables are included in the analysis
...<- 5*testx1 + 3 + runif(100,-20,20)
test <- data.frame(x1=testx1,x2=
testx2,y=testy)
atest <- lm(y ~ x1*x2,data=test)
aptest <- lmp(y ~ x1*x2,data=test,perm = "", seqs = TRUE, center = FALSE)
summary(atest)
Call:
lm(formula = y ~ x1 * x2, data = test)
Residuals:
Min 1Q Median 3Q Max
-17.1777 -9.5306 -0.9733 7.6840 22.2728
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.0036 3.2488 -0.617 0.539
x1 5.3346 0.2861 18.646 <2e-16 ***
x2b 2.4952 5.2160 0.478 0.633
x1:x2b...
2008 Jan 05
2
Behavior of ordered factors in glm
...I was taught to called a "test
of trend"), at least as a starting point.
> age.mdl<-glm(actual~issuecat,data=xxx,family="poisson")
> summary(age.mdl)
Call:
glm(formula = actual ~ issuecat, family = "poisson", data = xxx)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3190 -0.2262 -0.1649 -0.1221 5.4776
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.31321 0.04865 -88.665 <2e-16 ***
issuecat.L 2.12717 0.13328 15.960 <2e-16 ***
issuecat.Q -0.06568 0.11842 -0.555...
2007 Dec 07
1
Adding a subset to a glm messes up factors?
...tinuous variable). If I dont use subsets then all the factors are shown. I have copied the output from summary for both cases.
Thanks for the help,
Muri
> model<-glm(log(cpue)~year,family=gaussian)
Call:
glm(formula = log(cpue) ~ year, family = gaussian)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0962 -0.5851 -0.1241 0.4805 3.9236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8899 0.1844 4.825 1.42e-06 ***
year1990 -0.6107 0.1925 -3.173 0.00152 **
year1991 -1.7466 0.1902 -9.184 < 2...
2006 Sep 29
2
scatter3d() model.summary coefficients?
...tter3d(samples$x1, samples$y, samples$x2, fit="linear",
residuals=TRUE, bg="white", axis.scales=TRUE, grid=TRUE,
ellipsoid=FALSE, xlab="x1", ylab="y", zlab="x2", model.summary=TRUE)
$linear
Call:
lm(formula = y ~ x + z)
Residuals:
Min 1Q Median 3Q Max
-0.096984 -0.022303 0.004758 0.029354 0.091188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.708945 0.007005 101.20 <2e-16 ***
x 0.278540 0.011262 24.73 <2e-16 ***
z -0.688175 0.01160...
2007 Sep 21
1
Stats 101 : lm with/without intercept
...Pop33000 which factors a Population variable into 3 levels. I want to
investigate whether that categorical variable has some relation with my
dependent variable, so I go :
lm(Cout.ton ~ ClassePop33000, data=ech2)
Call:
lm(formula = Cout.ton ~ ClassePop33000, data = ech2)
Residuals:
Min 1Q Median 3Q Max
-182.24 -62.91 -22.76 66.38 277.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 231.66 11.50 20.141 < 2e-16 ***
ClassePop33000[T.[3000,25000)] -72.91 16.70 -4.3...
2011 Nov 15
2
Models with ordered and unordered factors
...num 72 25 24 2 18 38 62 30 78 34 ...
# $ Day: Factor w/ 3 levels "Day 1","Day 2",..: 1 1 1 1 1 1 2 2 2 2 ...
summary(lm(y~Day,data=dataf)) #Day 2 is not significantly different from
Day 1, but Day 3 is.
#
#Call:
#lm(formula = y ~ Day, data = dataf)
#
#Residuals:
# Min 1Q Median 3Q Max
#-39.833 -14.458 -3.833 13.958 42.167
#
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 29.833 9.755 3.058 0.00797 **
#DayDay 2 18.833 13.796 1.365 0.19234
#DayDay 3 37.000 13.796 2.682 0.01707 *
#---
#Sig...
2011 Nov 14
3
What is the CADF test criterion="BIC" report?
...ADF test
t-test statistic: -1.389086
p-value: 0.855681
Max lag of the diff. dependent variable: 1.000000
Call:
dynlm(formula = formula(model), start = obs.1, end = obs.T)
Residuals:
Min 1Q Median 3Q Max
-0.79726 -0.20587 -0.03332 0.23840 0.70460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.342321 17.435476 1.396 0.167
trnd 0.009959 0.006941 1.435 0.156
L(y, 1) -0.026068 0.018767 -1.389 0.856
L(d(y),...
2004 Feb 08
1
APE: compar.gee( )
...ormula.q 4.13 98/01/27"
[1] "running glm to get initial regression estimate"
[1] 7.9500000 0.5155172
Call:
formula: alt ~ R
Number of observations: 37
Model:
Link: identity
Variance to Mean Relation: gaussian
Summary of Residuals:
Min 1Q Median 3Q Max
-12.1267954 -9.4267954 -7.4267954 -0.4267954 20.7903982
Coefficients:
Estimate S.E. t Pr(T > |t|)
(Intercept) 9.209602 4.760274 1.934679 0.08798892
R1 3.217194 2.548273 1.262500 0.24130121
Estimated Scale Parameter:...
2010 Dec 15
1
lmList and lapply(... lm) different std. errors
...re are different ways, using the following code: (data
is given at the end of this message)
reg <- lapply(split(TRY, VARIABLE2), function(X){lm(X2 ~ X3, data=X)})
lapply(reg, summary)
Which produces the following:
$`1`
Call:
lm(formula = X2 ~ X3, data = X)
Residuals:
Min 1Q Median 3Q Max
-1.24233 -0.30028 0.03706 0.46170 1.12408
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0705 0.2323 13.215 5.95e-15 ***
X3 0.4744 0.2640 1.797 0.0813 .
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01...