Re: [R] R vs SPSS - simple effects analysis in mixed 2x2 ANOVA scheme - same data, different results
The only people who will be able to help you are people who use both R
and SPSS as you do not show the result from either. So even though they
can re-run your R commands they cannot compare them with SPSS.
On 22/05/2016 09:49, Michu Kom wrote:
down votefavoriteHell
<http://stats.stackexchange.com/questions/213592/r-vs-spss-simple-effects-analysis-in-mixed-2x2-anova-scheme-same-data-diffe#>
Hello,
I prepared a mixed 2x2 ANOVA design analysis both in SPSS and in R. The
SPSS script is correct, but in R script there is a mistake somewhere. To
test that I generated artificial data from a normal distribution to
simulate the interaction between two independent variables. There were no
difference between the results in main effects, but results of simple
effects analysis do not match when comparing between levels of variable
which introduced repeated measures (GROUP A: PRE vs POST ; GROUP B: PRE vs
POST).
I would be very thankful if you can help me. The code below will do
everything for you.
*Here is the code in R which:* - generates the data - calculates mixed
ANOVA - prepares data to csv format to import to SPSS - performs simple
effect analysis (there is probably a mistake)
N <- 100
absMean <- 1
sdCustom <- 5
grA_pre <- data.frame(ID = seq(N), lvl=rnorm(N, mean=absMean,
sd=sdCustom), group=factor('A'), stage = factor('pre'))
grA_post <- data.frame(ID = seq(N), lvl=rnorm(N, mean=-absMean,
sd=sdCustom), group=factor('A'), stage = factor('post'))
grB_pre <- data.frame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=-absMean,
sd=sdCustom), group=factor('B'), stage = factor('pre'))
grB_post <- data.frame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=absMean,
sd=sdCustom), group=factor('B'), stage = factor('post'))
gr <- rbind(grA_pre, grA_post, grB_pre, grB_post)
names(gr)
head(gr)
# save set to .csv to import to SPSS
grSPSS <- reshape(data = gr, timevar = "stage", idvar = c("ID",
"group"), direction = "wide")
write.csv2(grSPSS, file = "sample2.csv")
library(ggplot2)
library(plyr)
library(ez)
print("Omnibus mixed ANOVA - main effects and interactions")
ezPlot(data = gr, wid = ID, dv = lvl, between = group, within = stage,
type = "III", x = group, split = stage, x_lab = "Group", y_lab =
"Level of experience")
ezANOVA(data = gr, wid = ID, dv = lvl, between = group, within =
stage, detailed = TRUE, type = "III")#ezStats(data = gr, wid = ID, dv
= lvl, between = group, within = stage, type = "III")
print("Simple main effects analysis")
dataA <- subset(gr, group == "A" )
dataB <- subset(gr, group == "B" )
dataPRE <- subset(gr, stage == "pre" )
dataPOST <- subset(gr, stage == "post" )
print("GROUP = A: PRE vs POST")
simpleEffControlANOVA <- ezANOVA(data = dataA, dv = lvl, wid = ID,
within = stage, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("GROUP = B: PRE vs POST")
simpleEffControlANOVA <- ezANOVA(data = dataB, dv = lvl, wid = ID,
within = stage, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("STAGE = PRE: A vs B")
simpleEffControlANOVA <- ezANOVA(data = dataPRE, dv = lvl, wid = ID,
between = group, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("STAGE = POST: A vs B")
simpleEffControlANOVA <- ezANOVA(data = dataPOST, dv = lvl, wid = ID,
between = group, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
*Here is the code for SPSS Syntax which:* - calculates everything on
imported data, generated by R
DATASET ACTIVATE DataSet1.
GLM lvl.pre lvl.post BY group
/WSFACTOR=stage 2 Polynomial
/METHOD=SSTYPE(3)
/POSTHOC=group(TUKEY T3)
/EMMEANS=TABLES(group) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(stage) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(group*stage) COMPARE(group)
/EMMEANS=TABLES(group*stage) COMPARE(stage)
/PLOT=PROFILE(group*stage)
/PRINT=DESCRIPTIVE ETASQ OPOWER HOMOGENEITY
/CRITERIA=ALPHA(.05)
/WSDESIGN=stage
/DESIGN=group.
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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[R] R vs SPSS - simple effects analysis in mixed 2x2 ANOVA scheme - same data, different results
down votefavoriteHell
<http://stats.stackexchange.com/questions/213592/r-vs-spss-simple-effects-analysis-in-mixed-2x2-anova-scheme-same-data-diffe#>
Hello,
I prepared a mixed 2x2 ANOVA design analysis both in SPSS and in R. The
SPSS script is correct, but in R script there is a mistake somewhere. To
test that I generated artificial data from a normal distribution to
simulate the interaction between two independent variables. There were no
difference between the results in main effects, but results of simple
effects analysis do not match when comparing between levels of variable
which introduced repeated measures (GROUP A: PRE vs POST ; GROUP B: PRE vs
POST).
I would be very thankful if you can help me. The code below will do
everything for you.
*Here is the code in R which:* - generates the data - calculates mixed
ANOVA - prepares data to csv format to import to SPSS - performs simple
effect analysis (there is probably a mistake)
N <- 100
absMean <- 1
sdCustom <- 5
grA_pre <- data.frame(ID = seq(N), lvl=rnorm(N, mean=absMean,
sd=sdCustom), group=factor('A'), stage = factor('pre'))
grA_post <- data.frame(ID = seq(N), lvl=rnorm(N, mean=-absMean,
sd=sdCustom), group=factor('A'), stage = factor('post'))
grB_pre <- data.frame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=-absMean,
sd=sdCustom), group=factor('B'), stage = factor('pre'))
grB_post <- data.frame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=absMean,
sd=sdCustom), group=factor('B'), stage = factor('post'))
gr <- rbind(grA_pre, grA_post, grB_pre, grB_post)
names(gr)
head(gr)
# save set to .csv to import to SPSS
grSPSS <- reshape(data = gr, timevar = "stage", idvar = c("ID",
"group"), direction = "wide")
write.csv2(grSPSS, file = "sample2.csv")
library(ggplot2)
library(plyr)
library(ez)
print("Omnibus mixed ANOVA - main effects and interactions")
ezPlot(data = gr, wid = ID, dv = lvl, between = group, within = stage,
type = "III", x = group, split = stage, x_lab = "Group", y_lab =
"Level of experience")
ezANOVA(data = gr, wid = ID, dv = lvl, between = group, within =
stage, detailed = TRUE, type = "III")#ezStats(data = gr, wid = ID, dv
= lvl, between = group, within = stage, type = "III")
print("Simple main effects analysis")
dataA <- subset(gr, group == "A" )
dataB <- subset(gr, group == "B" )
dataPRE <- subset(gr, stage == "pre" )
dataPOST <- subset(gr, stage == "post" )
print("GROUP = A: PRE vs POST")
simpleEffControlANOVA <- ezANOVA(data = dataA, dv = lvl, wid = ID,
within = stage, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("GROUP = B: PRE vs POST")
simpleEffControlANOVA <- ezANOVA(data = dataB, dv = lvl, wid = ID,
within = stage, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("STAGE = PRE: A vs B")
simpleEffControlANOVA <- ezANOVA(data = dataPRE, dv = lvl, wid = ID,
between = group, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
print("STAGE = POST: A vs B")
simpleEffControlANOVA <- ezANOVA(data = dataPOST, dv = lvl, wid = ID,
between = group, detailed = TRUE, type = "III"
)print(simpleEffControlANOVA)
*Here is the code for SPSS Syntax which:* - calculates everything on
imported data, generated by R
DATASET ACTIVATE DataSet1.
GLM lvl.pre lvl.post BY group
/WSFACTOR=stage 2 Polynomial
/METHOD=SSTYPE(3)
/POSTHOC=group(TUKEY T3)
/EMMEANS=TABLES(group) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(stage) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(group*stage) COMPARE(group)
/EMMEANS=TABLES(group*stage) COMPARE(stage)
/PLOT=PROFILE(group*stage)
/PRINT=DESCRIPTIVE ETASQ OPOWER HOMOGENEITY
/CRITERIA=ALPHA(.05)
/WSDESIGN=stage
/DESIGN=group.
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and provide commented, minimal, self-contained, reproducible code.
[R] R and SPSS
Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) Thanks. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
I think we'll need some output to know so we can see the differences. (And data and code would be useful too, if you could provide a small example). One thought is that the programs might remove a variable that is completely collinear, but the different programs might remove different variables - so check that the same variables have been removed. Jeremy On 6 November 2012 13:39, Hui Du hui...@dataventures.com wrote: Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) Thanks. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
Jeremy Miles jeremy.miles at gmail.com writes: I think we'll need some output to know so we can see the differences. (And data and code would be useful too, if you could provide a small example). Definitely. One thought is that the programs might remove a variable that is completely collinear, but the different programs might remove different variables - so check that the same variables have been removed. Jeremy Furthermore, with severely collinear models, small numerical differences can make a big difference, either in which variables you choose to remove (e.g. if the variance inflation factors come out as (9.999,10.001) for variables 1 and 2 in one package, but (10.001,9.999) in the other, or in the actual linear algebra (based on technical details of linear algebra and floating-point computations) On 6 November 2012 13:39, Hui Du Hui.Du at dataventures.com wrote: Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
Hi, There exists a R plug-in for SPSS. You can find it on the SPSS website. Hope it helps. Alain Liviu Andronic wrote: Hello, On Wed, Nov 26, 2008 at 9:25 PM, Applejus [EMAIL PROTECTED] wrote: I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? I would doubt you could do this, but for the least provide commented, minimal, self-contained, reproducible code. It would help if you were more specific. Liviu -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau d.126 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
There exists a R plug-in for SPSS. You can find it on the SPSS website. ... and there is a page on the R wiki: http://wiki.r-project.org/rwiki/doku.php?id=tips:callingr:spss HTH, Tobias I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? I would doubt you could do this, but for the least provide commented, minimal, self-contained, reproducible code. It would help if you were more specific. Liviu -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau d.126 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
Thanks all! Tobias Verbeke wrote: There exists a R plug-in for SPSS. You can find it on the SPSS website. ... and there is a page on the R wiki: http://wiki.r-project.org/rwiki/doku.php?id=tips:callingr:spss HTH, Tobias I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? I would doubt you could do this, but for the least provide commented, minimal, self-contained, reproducible code. It would help if you were more specific. Liviu -- Alain Guillet Statistician and Computer Scientist SMCS - Institut de statistique - Université catholique de Louvain Bureau d.126 Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve Belgium tel: +32 10 47 30 50 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- View this message in context: http://www.nabble.com/R-and-SPSS-tp20708367p20723360.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R and SPSS
Hi, I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? Thanks! -- View this message in context: http://www.nabble.com/R-and-SPSS-tp20708367p20708367.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
Hello, On Wed, Nov 26, 2008 at 9:25 PM, Applejus [EMAIL PROTECTED] wrote: I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? I would doubt you could do this, but for the least provide commented, minimal, self-contained, reproducible code. It would help if you were more specific. Liviu -- Do you know how to read? http://www.alienetworks.com/srtest.cfm Do you know how to write? http://garbl.home.comcast.net/~garbl/stylemanual/e.htm#e-mail __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
On Wed, 2008-11-26 at 12:25 -0800, Applejus wrote: Hi, I have a code in R. Could anyone give me the best possible way (or just ways!) to integrate it in SPSS? Thanks! You will need a SPSS registration, but go here and get the SPSS r plugin. http://www.spss.com/devcentral/ It lets you access R from within SPSS. Best of both worlds. -- Insert something humorous here. :-) __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R and SPSS
On Nov 26, 2008, at 7:57 PM, Andrew Choens wrote: It lets you access R from within SPSS. Best of both worlds. -- Insert something humorous here. :-) OK, I'll bite. It lets you access R from within SPSS. Best of both worlds. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R vs SPSS contrasts
On 10/11/2008 3:31 PM, Ted Harding wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. Hi Ted: Here are two links with the same content giving a brief description of SPSS simple contrasts: http://www.ats.ucla.edu/stat/spss/library/contrast.htm http://support.spss.com/productsext/spss/documentation/statistics/articles/contrast.htm These pages explain how simple contrasts differ from indicator (contr.treatment) and deviation (contr.sum) contrasts. For a factor with 3 levels, I believe you can reproduce SPSS simple contrasts (with the first category as reference) like this: C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), ncol=2)) ... attr(,contrasts) [,1] [,2] L -0.333 -0.333 M 0.667 -0.333 H -0.333 0.667 Levels: L M H For a factor with 2 levels, like this: C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) ... attr(,contrasts) [,1] A -0.5 B 0.5 Levels: A B Your description of the effect of SPSS simple contrasts - intercept coefficient of contr.sum and non-intercept coefficients of contr.treatment - sounds accurate to me. hope this helps, Chuck E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R vs SPSS contrasts
Looks like the contrast matrix for indicator is contr.SAS(n), for deviation is contr.sum(n) and for simple is: (diag(n) - 1/n)[, -1] That works at least for the n = 3 example in the link. Perhaps the others could be checked against SPSS for a variety of values of n to be sure. On Sun, Oct 12, 2008 at 12:32 PM, Chuck Cleland [EMAIL PROTECTED] wrote: On 10/11/2008 3:31 PM, Ted Harding wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. Hi Ted: Here are two links with the same content giving a brief description of SPSS simple contrasts: http://www.ats.ucla.edu/stat/spss/library/contrast.htm http://support.spss.com/productsext/spss/documentation/statistics/articles/contrast.htm These pages explain how simple contrasts differ from indicator (contr.treatment) and deviation (contr.sum) contrasts. For a factor with 3 levels, I believe you can reproduce SPSS simple contrasts (with the first category as reference) like this: C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), ncol=2)) ... attr(,contrasts) [,1] [,2] L -0.333 -0.333 M 0.667 -0.333 H -0.333 0.667 Levels: L M H For a factor with 2 levels, like this: C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) ... attr(,contrasts) [,1] A -0.5 B 0.5 Levels: A B Your description of the effect of SPSS simple contrasts - intercept coefficient of contr.sum and non-intercept coefficients of contr.treatment - sounds accurate to me. hope this helps, Chuck E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R vs SPSS contrasts
The formula should be (diag(n) - 1/n)[, -n] On Sun, Oct 12, 2008 at 1:36 PM, Gabor Grothendieck [EMAIL PROTECTED] wrote: Looks like the contrast matrix for indicator is contr.SAS(n), for deviation is contr.sum(n) and for simple is: (diag(n) - 1/n)[, -1] That works at least for the n = 3 example in the link. Perhaps the others could be checked against SPSS for a variety of values of n to be sure. On Sun, Oct 12, 2008 at 12:32 PM, Chuck Cleland [EMAIL PROTECTED] wrote: On 10/11/2008 3:31 PM, Ted Harding wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. Hi Ted: Here are two links with the same content giving a brief description of SPSS simple contrasts: http://www.ats.ucla.edu/stat/spss/library/contrast.htm http://support.spss.com/productsext/spss/documentation/statistics/articles/contrast.htm These pages explain how simple contrasts differ from indicator (contr.treatment) and deviation (contr.sum) contrasts. For a factor with 3 levels, I believe you can reproduce SPSS simple contrasts (with the first category as reference) like this: C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), ncol=2)) ... attr(,contrasts) [,1] [,2] L -0.333 -0.333 M 0.667 -0.333 H -0.333 0.667 Levels: L M H For a factor with 2 levels, like this: C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) ... attr(,contrasts) [,1] A -0.5 B 0.5 Levels: A B Your description of the effect of SPSS simple contrasts - intercept coefficient of contr.sum and non-intercept coefficients of contr.treatment - sounds accurate to me. hope this helps, Chuck E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R vs SPSS contrasts
I found this link: http://webs.edinboro.edu/EDocs/SPSS/SPSS%20Regression%20Models%2013.0.pdf which indicates that the contrast in SPSS that is used depends not only on the contrast selected but also on the reference category selected and the two can be chosen independently. Thus one could have simple/first, simple/last, deviation/first, deviation/last, etc. An R contr.SPSS function would have to specify both the deviation type and the first/last in order to handle all SPSS variations. On Sun, Oct 12, 2008 at 1:48 PM, Gabor Grothendieck [EMAIL PROTECTED] wrote: The formula should be (diag(n) - 1/n)[, -n] On Sun, Oct 12, 2008 at 1:36 PM, Gabor Grothendieck [EMAIL PROTECTED] wrote: Looks like the contrast matrix for indicator is contr.SAS(n), for deviation is contr.sum(n) and for simple is: (diag(n) - 1/n)[, -1] That works at least for the n = 3 example in the link. Perhaps the others could be checked against SPSS for a variety of values of n to be sure. On Sun, Oct 12, 2008 at 12:32 PM, Chuck Cleland [EMAIL PROTECTED] wrote: On 10/11/2008 3:31 PM, Ted Harding wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. Hi Ted: Here are two links with the same content giving a brief description of SPSS simple contrasts: http://www.ats.ucla.edu/stat/spss/library/contrast.htm http://support.spss.com/productsext/spss/documentation/statistics/articles/contrast.htm These pages explain how simple contrasts differ from indicator (contr.treatment) and deviation (contr.sum) contrasts. For a factor with 3 levels, I believe you can reproduce SPSS simple contrasts (with the first category as reference) like this: C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), ncol=2)) ... attr(,contrasts) [,1] [,2] L -0.333 -0.333 M 0.667 -0.333 H -0.333 0.667 Levels: L M H For a factor with 2 levels, like this: C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) ... attr(,contrasts) [,1] A -0.5 B 0.5 Levels: A B Your description of the effect of SPSS simple contrasts - intercept coefficient of contr.sum and non-intercept coefficients of contr.treatment - sounds accurate to me. hope this helps, Chuck E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R vs SPSS contrasts
Very many thanks, Chuck and Gabor, for the comments and the references to on-line explanations. It is beginning to become clear! Most grateful. Ted. On 12-Oct-08 18:03:53, Gabor Grothendieck wrote: I found this link: http://webs.edinboro.edu/EDocs/SPSS/SPSS%20Regression%20Models%2013.0.pd f which indicates that the contrast in SPSS that is used depends not only on the contrast selected but also on the reference category selected and the two can be chosen independently. Thus one could have simple/first, simple/last, deviation/first, deviation/last, etc. An R contr.SPSS function would have to specify both the deviation type and the first/last in order to handle all SPSS variations. On Sun, Oct 12, 2008 at 1:48 PM, Gabor Grothendieck [EMAIL PROTECTED] wrote: The formula should be (diag(n) - 1/n)[, -n] On Sun, Oct 12, 2008 at 1:36 PM, Gabor Grothendieck [EMAIL PROTECTED] wrote: Looks like the contrast matrix for indicator is contr.SAS(n), for deviation is contr.sum(n) and for simple is: (diag(n) - 1/n)[, -1] That works at least for the n = 3 example in the link. Perhaps the others could be checked against SPSS for a variety of values of n to be sure. On Sun, Oct 12, 2008 at 12:32 PM, Chuck Cleland [EMAIL PROTECTED] wrote: On 10/11/2008 3:31 PM, Ted Harding wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. Hi Ted: Here are two links with the same content giving a brief description of SPSS simple contrasts: http://www.ats.ucla.edu/stat/spss/library/contrast.htm http://support.spss.com/productsext/spss/documentation/statistics/art icles/contrast.htm These pages explain how simple contrasts differ from indicator (contr.treatment) and deviation (contr.sum) contrasts. For a factor with 3 levels, I believe you can reproduce SPSS simple contrasts (with the first category as reference) like this: C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), ncol=2)) ... attr(,contrasts) [,1] [,2] L -0.333 -0.333 M 0.667 -0.333 H -0.333 0.667 Levels: L M H For a factor with 2 levels, like this: C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) ... attr(,contrasts) [,1] A -0.5 B 0.5 Levels: A B Your description of the effect of SPSS simple contrasts - intercept coefficient of contr.sum and non-intercept coefficients of contr.treatment - sounds accurate to me. hope this helps, Chuck E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 12-Oct-08 Time: 21:04:48 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained
Re: [R] R vs SPSS contrasts
Don't know but perhaps you could just use each of: contr.helmert, contr.poly, contr.sum, contr.treatment, contr.SAS in turn on the R side until you get one that matches. Once you find out adding a contr.SPSS to R might be nice. On Sat, Oct 11, 2008 at 3:31 PM, Ted Harding [EMAIL PROTECTED] wrote: Hi Folks, I'm comparing some output from R with output from SPSS. The coefficients of the independent variables (which are all factors, each at 2 levels) are identical. However, R's Intercept (using default contr.treatment) differs from SPSS's 'constant'. It seems that the contrasts were set in SPSS using /CONTRAST (varname)=Simple(1) I can get R's Intercept to match SPSS's 'constant' if I use contr.sum in R. Can someone please confirm that that is a correct match for the SPSS Simple(1), with identical effect? And is there a convenient on-line reference where I can look up what SPSS's /CONTRAST statements exactly mean? I've done a lot of googling, withbout coming up with anything satisfactory. With thanks, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-08 Time: 20:31:53 -- XFMail -- __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
