I have already follow your step, it still not work
when I merge groupA and groupB , the error message was shown
Error in rbind(deparse.level, ...) : replacement has length zero
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First, I have "predict_SO2_a" which is contained 24 data. I want to insert
"NA" in 11th row. Then, "predict_SO2_a" becomes 25 data.
After insert the row, I want to use "with" function to combine the
data.frame
/>groupA$predict_SO2<-with(groupA, predict_SO2_a).
/
/>dput(predict_SO2_a)
c(39.793230812
i'm not success to insert row in specific location and merge another file
I have a question and I feel confused about the usage of "rbind", "cbind"
and "data.frame)
data 1:
> predict_SO2_a
predict_SO2_a
1 39.793231
2 30.252578
3 32.467584
4 31.941509
5 27.908320
Hi again!
I have a question about R.
I have done gam in previous version of R with "mgcv" package and saved the
workspace. This workspace contains different models and I will do prediction
by these GAMs.
However, I install new version of R. and use the same workspace. when I type
summary(models),
Hi everyone! I have a simple question.
my data is
predict_SO2_a
1 39.793231
2 30.252578
3 32.467584
4 31.941509
5 27.908320
6 11.594137
79.368125
8 12.319093
9 11.558811
10 7.937192
11 11.211306
12 12.400342
13 12.39314
I really want to know how to calculate the predicted valued when I use
gam.predict .
my model is:
newSO2 ~ s(pressure, bs = "cr") + s(maxtemp, bs = "cr") + mintemp +
RH + solar + s(windspeed, bs = "cr") + s(transport, bs = "cr")
> my new dataset is :
pressure: -7.056847826
maxtemp: 2.737507
Thank, it OK now
but I don't understand what is the meaning of y <- y[!is.na(y[5]),]
Thank in advance!
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Dear all,
I have a lot of problems on R-programming.
for example
my csv. file is ..
Date wrfRH wrfsolar wrfwindspeed wrfrain wrftd wrfta
21/10/2010 92.97 22.11 53.27 0 1546.337861 61.00852664
22/10/2010 87.35 21.99 40.89 0 1300.408288 62.85352227
23/10/2010 88.38 21.71 28.04 0.01 1
Dear all,
I find some troubles about how to extact the row from csv. file by using
if-statement condition.
I want to extract the row if the rainfall is greater than the mean of
rainfall and using the wrfta divided into 3 groups
that's
rainfall greater than mean -> group A ( create file group A_rai
pre<-predict(ozonea,groupA,type="terms",terms=NULL,newdata.guaranteed=FALSE,na.action=na.pass)
yeah!
but I don't know how to only show the value of s(ratio,bs="cr")
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I'm confused...
I type ..
predict.gam(ozonea,type=s(ratio,bs="cr"))
pressure maxtemp s(avetemp) s(ratio)
1 -0.0459102290 -0.185178463 0.263358446 -0.164558673
2 -0.0286464652 -0.194731320 0.199315027 0.727823293
30.0478073459 -0.013227033 0.002228896 0.342373202
4 -0.00
hi! I hope all of you can help me this question
for example GAMs:
ozonea<-gam(newozone~
pressure+maxtemp+s(avetemp,bs="cr")+s(ratio,bs="cr"),family=gaussian
(link=log),groupA,methods=REML)
formula(ozonea)
newozone ~ pressure + maxtemp + s(avetemp, bs = "cr") + s(ratio,bs = "cr")
#formula of gam
thank you all of u
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R-help@r-project.org mailing list
https://stat.et
x11()
what does it mean?
if my data has missing value, can I plot the graph?
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i want to plot probability density function,predictvalue has missing value
and observevalue has not missing value, I tried ..
attach(test) #test is the name of the data file
names(test)
plot(density(predictvalue,na.rm=TRUE))
lines(density(observevalue))
is it correct?
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however, if i have an excel file, but there have 6 variables, a,b,c,d,e,f.
how to plot the probability density function of a and d in one graph, b and
e in another graph?
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I have 2 series of variables, I want to plot the probability density function
of these 2 variabels (i.e. two curves in one graph), I just want to compare
these two variable distribution.
what should I do?
can I use ggplot2 package?
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chris,
I'm not using lmer, i just use gam mixed with smoothing function and linear
function
and summary of the model, it shows
Family: gaussian
Link function: log
Formula:
newNO2 ~ pressure + s(maxtemp, bs = "cr") + s(avetemp, bs = "cr") +
s(mintemp, bs = "cr") + RH + s(solar, bs = "cr") +
using AIC/BIC, I'm not know too much about this. I just know using p-value to
perform stepwise regression
if I used p-value to perform multimodel stepwise regression, is it correct
in the first message box?
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If I used mixed model ( linear + smoothing function)
How can I used p-value to perform stepwise regression???
Is it similar of Pr(<|t|) of linear term and P-value of smoothing term but T
value of linear and F of smoothing term?
please help!
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First of all, I have GAMs
noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian
(link=log),groupD,methods=REML)
Then I type " summary(noxd)". and show
Family: gaussian
Link function: log
For example:
GAMs and after stepwise regression:
cod<-gam(newCO~RH+s(solar,bs="cr")+windspeed+s(transport,bs="cr"),family=gaussian
(link=log),groupD,methods=REML)
I used 10 year meterorology data (2000-2010) to form equation of
concentration of carbon monoxide.
NOW, I have 2011 meteorology data,
I have 5 GAMs ( model1, model2, model3, model4 and model5)
Before I use some data X(predictor -January to June data) to form a equation
and calculate the expected value of Y (predictand -January to June). After
variable selection, GAMs (Model 1)were bulit up! R-square :0.40
NOW, I want to use new
y<-gam(a~s(b),family=gaussian(link=log),data)
y<-gam(loga~s(b), family =gaussian (link=identity),data)
y<-gam(loga~s(b),family=gaussian(link=log),data)
what are the diffent these 3 equations?
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i use "mgcv" package
First, what is the different between partial response and partial residual
plot?
actually, is it partial response is as same as partial regression?
second, if I use "log-link fuction", how to plot partial response and
partial residual plot by R?
Finally, If I want to plot p
b<-rep(0,n-11)
for(i in 0:(11-10))
b<-b+a[(i+1):(n-11+i)]
i don't know what does "rep" mean and for-loop!
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i read a book from WOOD, there's an example which is talking about the
pollutant.
library(gamair)
library(mgcv)
y<-gam(death~s(time,bs="cr",k=200)+s(pm10median,bs="cr")+s(so2median,bs="cr")+s(o3median,bs="cr")+s(tmpd,bs="cr"),data=chicago,family=Possion)
lag.sum<-function(a,10,11)
{n<-length(a)
b
it is from Generalized Additive Models an introduction with R by Simon
N.Wood . Page 251
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Actually, I 'm using "mgcv" package.
But I learnt basic linear regression in my school, it is really hard for me
to handle these problem!
by the way, i need to work harder!
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Sent f
I'm beginner in R! I have a lot of problems on R.
I have three questions about GAM
1. What is the function of Gaussian distribution in GAM?(if I choose family
is Gaussian)
Is it used in the predictand value (Y)?
2. How to plot a graph the gam function?
For example: y<-gam(a~s(b),family=gauss
I'm new R-programming user, I need to use gam function.
y<-gam(a~s(b),family=gaussian(link=log),data)
y<-gam(loga~s(b), family =gaussian (link=identity),data)
why these two command results are different?
I guess these two command results are same, but actally these two command
results are differen
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