Displaying 4 results from an estimated 4 matches for "000mm".
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000mp
2004 Apr 20
0
strange result with contrasts
...For the moment I'm just looking at the dose effects of the complete model:
> summary(lm(value ~ dose * time * batch, data = d))$coefficients[1:5,]
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.80211741 0.01505426 451.8399839 1.962247e-101
dose010mM-000mM -0.03454211 0.04113846 -0.8396549 4.046723e-01
dose025mM-000mM -0.01972550 0.04288981 -0.4599111 6.473607e-01
dose050mM-000mM -0.12015983 0.05356935 -2.2430704 2.886726e-02 <- significant
dose100mM-000mM 0.01252061 0.04113846 0.3043529 7.619872e-01
A collegue of mine has run the same...
2004 Apr 27
2
paste dimnames problem
Hello,
I've the following list n:
> n
[[1]]
[1] "NEW" "OLD" "PRG"
[[2]]
[1] "04h" "24h"
[[3]]
[1] "000mM" "010mM" "025mM" "050mM" "100mM"
where
n <- dimnames(some.multidim.array)
I'm trying to define a generic function that generates meaningful names from this list, e.g. NEW.04h.000mM would be the first name, then NEW.04h.010mM, ... . Overall this...
2004 May 14
1
help with memory greedy storage
...ent factors, but not just for one row but for several rows, depending what which(rows == g),] returns, and a new factor ('probe') is generated. This results in a 1344 by 6 data frame.
Example data frame returned by probeDf:
Value batch time dose array probe
1 2.317804 NEW 24h 000mM 1 1
2 2.495390 NEW 24h 000mM 2 1
3 2.412247 NEW 24h 000mM 3 1
...
144 8.851469 OLD 04h 100mM 60 2
145 8.801430 PRG 24h 000mM 61 2
146 8.308224 PRG 24h 000mM 62 2
...
This data frame is not the problem since, it gets generated on-the-f...
2004 May 10
5
R versus SAS: lm performance
...39;contr.poly'))
The 1st colum is the value to be modeled, and the others are factors.
> names(df.gene1data) <- c("Va", "Ba", "Ti", "Do", "Ar", "Pr")
> df[c(1:2,1343:1344),]
Va Do Ti Ba Ar Pr
1 2.317804 000mM 24h NEW 1 1
2 2.495390 000mM 24h NEW 2 1
8315 2.979641 025mM 04h PRG 83 16
8415 4.505787 000mM 04h PRG 84 16
this is a dataframe with 1344 rows.
x <- Sys.time();
wlm <- lm(Va ~
Ba+Ti+Do+Pr+Ba:Ti+Ba:Do+Ba:Pr+Ti:Do+Ti:Pr+Do:Pr+Ba:Ti:Do+Ba:Ti:Pr+Ba:Do:Pr+Ti:Do:Pr+Ba:Ti:Do:Pr...