search for: 0.194

- memchr() needs to increment pointer or it's just a busy loop (unless c matches the first character in s) - add memrchr.c --- klibc-0.194/klibc/memchr.c.orig 2005-01-06 23:08:33.703355840 -0700 +++ klibc-0.194/klibc/memchr.c 2005-01-06 23:04:26.436946048 -0700 @@ -12,6 +12,7 @@ while ( n-- ) { if ( *sp == (unsigned char)c ) return (void *)sp; + sp++; } return NULL;

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

A patch for a few more hiccups and trivialities in kinit.c: * The check_path() calls check for "/root" and "/old_root" - I believe that should be "/root" and "/root/old_root". * chdir("/") is recommended after pivot_root() * init_argv[0] isn't set properly to the basename pointed to by char *s - this fix also eliminates six lines of

kinit/nfsmount.c:mount_nfs_root() should use the bootpath specified by bootp/dhcp. If the "nfsroot" option is specified then it overrides the boot server bootpath and a message indicating the override is printed. --- klibc-0.194/kinit/nfsroot.c.orig 2005-01-05 04:13:47.043897880 -0700 +++ klibc-0.194/kinit/nfsroot.c 2005-01-05 04:13:09.316633296 -0700 @@ -66,34 +66,21 @@ const int

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

Hi all, I have this data set: ## Empirical copula ## dt1 = ranking ## dt2 = observed uniform data associated with the ranking   Sample data, > dt1         S_i   R_i  [1,]   7.0  10.0  [2,] 232.5 440.5  [3,] 143.0 141.5  [4,] 272.5 222.0  [5,]  46.0  34.0  [6,] 527.0 483.0  [7,] 420.5 563.5  [8,]  23.5  16.5  [9,]  56.5  68.5 [10,] 341.5 382.5   > dt2       unisk1 unisk2  [1,]  0.008  0.010

Patch to guarantee __optptr initialization and to correctly increment optind for missing arguments. --- klibc-0.194/klibc/getopt.c.orig 2005-01-04 23:18:50.229222640 -0700 +++ klibc-0.194/klibc/getopt.c 2005-01-04 23:17:05.236184008 -0700 @@ -11,7 +11,7 @@ char *optarg; int optind = 1; int opterr, optopt; -static const char *__optptr; +static const char *__optptr = NULL; int getopt(int

There's an "if" statement in kinit.c:get_arg() that is too restrictive and prevents args from having arguments - which causes all get_arg() calls to fail: --- klibc-0.194/kinit/kinit.c.orig 2005-01-05 01:34:50.680645456 -0700 +++ klibc-0.194/kinit/kinit.c 2005-01-05 01:38:30.740191320 -0700 @@ -168,7 +168,7 @@ for (i = 1; i < argc; i++) { if (argv[i] &&

kinit/do_mounts.c:name_to_dev_t() has an inverted logic problem and also doesn't return the correct value for "/dev/nfs". --- klibc-0.194/kinit/do_mounts.c.orig 2005-01-05 01:59:29.250868616 -0700 +++ klibc-0.194/kinit/do_mounts.c 2005-01-05 02:34:30.486432256 -0700 @@ -103,7 +103,7 @@ if ( name[0] == '/' && !stat(name, &st) && S_ISBLK(st.st_mode) )

This patch makes it so that ipconfig only configures devices that are connected: --- klibc-0.194/ipconfig/main.c.orig 2005-01-06 15:20:24.749525888 -0700 +++ klibc-0.194/ipconfig/main.c 2005-01-06 15:24:48.038499880 -0700 @@ -27,6 +27,7 @@ static char do_not_config; static unsigned int default_caps = CAP_DHCP | CAP_BOOTP | CAP_RARP; static int loop_timeout = -1; +static int only_connected = 0;

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Hello, I've been looking through ?phantom and ?expression and this forum for examples of how I might be able to manipulate some of the names that appear on the y-axis of the barplot below. For example, the "gw" in "ECgw" would appear as a subscript...or "qr" would be the theta symbol followed by subscript "r". My attempts haven't even come close

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Dear all R users, I am really struggling to determine the most appropriate lag order of ARIMA model. My understanding is that, as for MA [q] model the auto correlation coeff vanishes after q lag, it says the MA order of a ARIMA model, and for a AR[p] model partial autocorrelation vanishes after p lags it helps to determine the AR lag. And most appropriate model choosed by this argument gives

Hi Folks... Œbeen playing with this for a while, with no luck, so I¹m hoping someone knows it off the top of their head... Difficult to find this nuance in the archives, as so many msgs deal with read.csv! I¹m trying to read a data file with the following structure (a little piece of the actual data, they are actually csv just didn¹t paste with the commas): wavelength SampleA SampleB SampleC

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of